diff --git a/faces.sh b/faces.sh
new file mode 100644
index 0000000..2ac1f5a
--- /dev/null
+++ b/faces.sh
@@ -0,0 +1,83 @@
+#!/bin/bash
+
+python main.py scripts/gridsearch_configs/faces/faces_000.json
+python main.py scripts/gridsearch_configs/faces/faces_001.json
+python main.py scripts/gridsearch_configs/faces/faces_002.json
+python main.py scripts/gridsearch_configs/faces/faces_003.json
+python main.py scripts/gridsearch_configs/faces/faces_004.json
+python main.py scripts/gridsearch_configs/faces/faces_005.json
+python main.py scripts/gridsearch_configs/faces/faces_006.json
+python main.py scripts/gridsearch_configs/faces/faces_007.json
+python main.py scripts/gridsearch_configs/faces/faces_008.json
+python main.py scripts/gridsearch_configs/faces/faces_009.json
+python main.py scripts/gridsearch_configs/faces/faces_010.json
+python main.py scripts/gridsearch_configs/faces/faces_011.json
+python main.py scripts/gridsearch_configs/faces/faces_012.json
+python main.py scripts/gridsearch_configs/faces/faces_013.json
+python main.py scripts/gridsearch_configs/faces/faces_014.json
+python main.py scripts/gridsearch_configs/faces/faces_015.json
+python main.py scripts/gridsearch_configs/faces/faces_016.json
+python main.py scripts/gridsearch_configs/faces/faces_017.json
+python main.py scripts/gridsearch_configs/faces/faces_018.json
+python main.py scripts/gridsearch_configs/faces/faces_019.json
+python main.py scripts/gridsearch_configs/faces/faces_020.json
+python main.py scripts/gridsearch_configs/faces/faces_021.json
+python main.py scripts/gridsearch_configs/faces/faces_022.json
+python main.py scripts/gridsearch_configs/faces/faces_023.json
+python main.py scripts/gridsearch_configs/faces/faces_024.json
+python main.py scripts/gridsearch_configs/faces/faces_025.json
+python main.py scripts/gridsearch_configs/faces/faces_026.json
+python main.py scripts/gridsearch_configs/faces/faces_027.json
+python main.py scripts/gridsearch_configs/faces/faces_028.json
+python main.py scripts/gridsearch_configs/faces/faces_029.json
+python main.py scripts/gridsearch_configs/faces/faces_030.json
+python main.py scripts/gridsearch_configs/faces/faces_031.json
+python main.py scripts/gridsearch_configs/faces/faces_032.json
+python main.py scripts/gridsearch_configs/faces/faces_033.json
+python main.py scripts/gridsearch_configs/faces/faces_034.json
+python main.py scripts/gridsearch_configs/faces/faces_035.json
+python main.py scripts/gridsearch_configs/faces/faces_036.json
+python main.py scripts/gridsearch_configs/faces/faces_037.json
+python main.py scripts/gridsearch_configs/faces/faces_038.json
+python main.py scripts/gridsearch_configs/faces/faces_039.json
+python main.py scripts/gridsearch_configs/faces/faces_040.json
+python main.py scripts/gridsearch_configs/faces/faces_041.json
+python main.py scripts/gridsearch_configs/faces/faces_042.json
+python main.py scripts/gridsearch_configs/faces/faces_043.json
+python main.py scripts/gridsearch_configs/faces/faces_044.json
+python main.py scripts/gridsearch_configs/faces/faces_045.json
+python main.py scripts/gridsearch_configs/faces/faces_046.json
+python main.py scripts/gridsearch_configs/faces/faces_047.json
+python main.py scripts/gridsearch_configs/faces/faces_048.json
+python main.py scripts/gridsearch_configs/faces/faces_049.json
+python main.py scripts/gridsearch_configs/faces/faces_050.json
+python main.py scripts/gridsearch_configs/faces/faces_051.json
+python main.py scripts/gridsearch_configs/faces/faces_052.json
+python main.py scripts/gridsearch_configs/faces/faces_053.json
+python main.py scripts/gridsearch_configs/faces/faces_054.json
+python main.py scripts/gridsearch_configs/faces/faces_055.json
+python main.py scripts/gridsearch_configs/faces/faces_056.json
+python main.py scripts/gridsearch_configs/faces/faces_057.json
+python main.py scripts/gridsearch_configs/faces/faces_058.json
+python main.py scripts/gridsearch_configs/faces/faces_059.json
+python main.py scripts/gridsearch_configs/faces/faces_060.json
+python main.py scripts/gridsearch_configs/faces/faces_061.json
+python main.py scripts/gridsearch_configs/faces/faces_062.json
+python main.py scripts/gridsearch_configs/faces/faces_063.json
+python main.py scripts/gridsearch_configs/faces/faces_064.json
+python main.py scripts/gridsearch_configs/faces/faces_065.json
+python main.py scripts/gridsearch_configs/faces/faces_066.json
+python main.py scripts/gridsearch_configs/faces/faces_067.json
+python main.py scripts/gridsearch_configs/faces/faces_068.json
+python main.py scripts/gridsearch_configs/faces/faces_069.json
+python main.py scripts/gridsearch_configs/faces/faces_070.json
+python main.py scripts/gridsearch_configs/faces/faces_071.json
+python main.py scripts/gridsearch_configs/faces/faces_072.json
+python main.py scripts/gridsearch_configs/faces/faces_073.json
+python main.py scripts/gridsearch_configs/faces/faces_074.json
+python main.py scripts/gridsearch_configs/faces/faces_075.json
+python main.py scripts/gridsearch_configs/faces/faces_076.json
+python main.py scripts/gridsearch_configs/faces/faces_077.json
+python main.py scripts/gridsearch_configs/faces/faces_078.json
+python main.py scripts/gridsearch_configs/faces/faces_079.json
+python main.py scripts/gridsearch_configs/faces/faces_080.json
diff --git a/faces3.sh b/faces3.sh
new file mode 100644
index 0000000..64f9981
--- /dev/null
+++ b/faces3.sh
@@ -0,0 +1,62 @@
+#!/bin/bash
+
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_000.json
+ > scripts/gridsearch_configs/faces3/faces3_000.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_001.json
+ > scripts/gridsearch_configs/faces3/faces3_001.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_002.json
+ > scripts/gridsearch_configs/faces3/faces3_002.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_003.json
+ > scripts/gridsearch_configs/faces3/faces3_003.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_004.json
+ > scripts/gridsearch_configs/faces3/faces3_004.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_005.json
+ > scripts/gridsearch_configs/faces3/faces3_005.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_006.json
+ > scripts/gridsearch_configs/faces3/faces3_006.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_007.json
+ > scripts/gridsearch_configs/faces3/faces3_007.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_008.json
+ > scripts/gridsearch_configs/faces3/faces3_008.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_009.json
+ > scripts/gridsearch_configs/faces3/faces3_009.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_010.json
+ > scripts/gridsearch_configs/faces3/faces3_010.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_011.json
+ > scripts/gridsearch_configs/faces3/faces3_011.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_012.json
+ > scripts/gridsearch_configs/faces3/faces3_012.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_013.json
+ > scripts/gridsearch_configs/faces3/faces3_013.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_014.json
+ > scripts/gridsearch_configs/faces3/faces3_014.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_015.json
+ > scripts/gridsearch_configs/faces3/faces3_015.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_016.json
+ > scripts/gridsearch_configs/faces3/faces3_016.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_017.json
+ > scripts/gridsearch_configs/faces3/faces3_017.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_018.json
+ > scripts/gridsearch_configs/faces3/faces3_018.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_019.json
+ > scripts/gridsearch_configs/faces3/faces3_019.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_020.json
+ > scripts/gridsearch_configs/faces3/faces3_020.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_021.json
+ > scripts/gridsearch_configs/faces3/faces3_021.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_022.json
+ > scripts/gridsearch_configs/faces3/faces3_022.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_023.json
+ > scripts/gridsearch_configs/faces3/faces3_023.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_024.json
+ > scripts/gridsearch_configs/faces3/faces3_024.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_025.json
+ > scripts/gridsearch_configs/faces3/faces3_025.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_026.json
+ > scripts/gridsearch_configs/faces3/faces3_026.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_027.json
+ > scripts/gridsearch_configs/faces3/faces3_027.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_028.json
+ > scripts/gridsearch_configs/faces3/faces3_028.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/faces3/faces3_029.json
+ > scripts/gridsearch_configs/faces3/faces3_029.log 2>&1 &
diff --git a/node.py b/node.py
index 2b7c870..0afd2c4 100644
--- a/node.py
+++ b/node.py
@@ -69,6 +69,7 @@ class Eden_LoRa_trainer:
 
         config = TrainingConfig(
             name=lora_name,
+            output_dir="output",
             lora_training_urls=training_images_folder_path,
             concept_mode=mode,
             ckpt_path=ckpt_path,
diff --git a/nohup.out b/nohup.out
new file mode 100644
index 0000000..143ed87
--- /dev/null
+++ b/nohup.out
@@ -0,0 +1,30874 @@
+2024-08-13 01:41:36.782580: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 01:41:36.876284: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 01:41:36.876315: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 01:41:36.895613: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 01:41:37.298206: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 01:41:37.298289: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 01:41:37.298299: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23100 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/good_styles/eden_crystals' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.75 freeze_unet_before_completion_f=0.0 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='eden_crystals' output_dir='lora_models/styles_final/eden_crystals_13_01-41-39-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723506099.1358397 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
Fetching 17 files:   0%|          | 0/17 [00:00<?, ?it/s]
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
Loading pipeline components...:  29%|██▊       | 2/7 [00:00<00:00, 16.57it/s]
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/eden_crystals_13_01-41-39-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.242
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants on a black background.
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants in the dark.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Futuristic natural elements blending with technology
+
+- In the style of TOK, a potted plant sits on a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves is on a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is on a black background.
+- In the style of TOK, a potted plant sits on a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves is on a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is in the dark.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Futuristic natural elements blending with technology
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 7134.99it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a potted plant s...
+1     in the style of <s0><s1><s2>, a futuristic cit...
+2     in the style of <s0><s1><s2>, a flower with gr...
+3     in the style of <s0><s1><s2>, a plant is growi...
+4     in the style of <s0><s1><s2>, a group of green...
+5     in the style of <s0><s1><s2>, a potted plant s...
+6     in the style of <s0><s1><s2>, a futuristic cit...
+7     in the style of <s0><s1><s2>, a flower with gr...
+8     in the style of <s0><s1><s2>, a plant is growi...
+9     in the style of <s0><s1><s2>, a group of green...
+10    in the style of <s0><s1><s2>, a potted plant s...
+11    in the style of <s0><s1><s2>, a futuristic cit...
+12    in the style of <s0><s1><s2>, a flower with gr...
+13    in the style of <s0><s1><s2>, a plant is growi...
+14    in the style of <s0><s1><s2>, a group of green...
+15    in the style of <s0><s1><s2>, a potted plant s...
+16    in the style of <s0><s1><s2>, a futuristic cit...
+17    in the style of <s0><s1><s2>, a flower with gr...
+18    in the style of <s0><s1><s2>, a plant is growi...
+19    in the style of <s0><s1><s2>, a group of green...
+20    in the style of <s0><s1><s2>, a potted plant s...
+21    in the style of <s0><s1><s2>, a futuristic cit...
+22    in the style of <s0><s1><s2>, a flower with gr...
+23    in the style of <s0><s1><s2>, a plant is growi...
+24    in the style of <s0><s1><s2>, a group of green...
+25    in the style of <s0><s1><s2>, a potted plant s...
+26    in the style of <s0><s1><s2>, a futuristic cit...
+27    in the style of <s0><s1><s2>, a flower with gr...
+28    in the style of <s0><s1><s2>, a plant is growi...
+29    in the style of <s0><s1><s2>, a group of green...
+30    in the style of <s0><s1><s2>, a potted plant s...
+31    in the style of <s0><s1><s2>, a futuristic cit...
+32    in the style of <s0><s1><s2>, a flower with gr...
+33    in the style of <s0><s1><s2>, a plant is growi...
+34    in the style of <s0><s1><s2>, a group of green...
+35    in the style of <s0><s1><s2>, a potted plant s...
+36    in the style of <s0><s1><s2>, a futuristic cit...
+37    in the style of <s0><s1><s2>, a flower with gr...
+38    in the style of <s0><s1><s2>, a plant is growi...
+39    in the style of <s0><s1><s2>, a group of green...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
  0%|          | 0/300 [00:00<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:03<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:03<15:18,  3.07s/it]
# Trainer step: 0, epoch: 0:   1%|          | 2/300 [00:06<17:25,  3.51s/it]
# Trainer step: 0, epoch: 0:   1%|          | 3/300 [00:07<10:28,  2.12s/it]
# Trainer step: 0, epoch: 0:   1%|▏         | 4/300 [00:07<07:11,  1.46s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:08<05:23,  1.10s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:08<04:18,  1.14it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.62

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:09<03:36,  1.35it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:09<03:09,  1.54it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 9/300 [00:10<02:51,  1.69it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 10/300 [00:10<02:39,  1.82it/s]
# Trainer step: 10, epoch: 1:   3%|▎         | 10/300 [00:10<02:39,  1.82it/s]
# Trainer step: 10, epoch: 1:   4%|▎         | 11/300 [00:10<02:30,  1.92it/s]
# Trainer step: 10, epoch: 1:   4%|▍         | 12/300 [00:11<02:24,  1.99it/s]Progress: 7.00%
+---- avg training fps: 4.03

# Trainer step: 10, epoch: 1:   4%|▍         | 13/300 [00:11<02:20,  2.04it/s]
# Trainer step: 10, epoch: 1:   5%|▍         | 14/300 [00:12<02:17,  2.08it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 15/300 [00:12<02:14,  2.11it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 16/300 [00:13<02:13,  2.12it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 17/300 [00:13<02:12,  2.14it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 18/300 [00:14<02:10,  2.15it/s]Progress: 9.00%
+---- avg training fps: 4.91

# Trainer step: 10, epoch: 1:   6%|▋         | 19/300 [00:14<02:10,  2.16it/s]
# Trainer step: 10, epoch: 1:   7%|▋         | 20/300 [00:15<02:09,  2.16it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 20/300 [00:15<02:09,  2.16it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 21/300 [00:15<02:08,  2.16it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 22/300 [00:16<02:08,  2.17it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 23/300 [00:16<02:07,  2.17it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 24/300 [00:16<02:07,  2.17it/s]Progress: 11.00%
+---- avg training fps: 5.51

# Trainer step: 20, epoch: 2:   8%|▊         | 25/300 [00:17<02:06,  2.18it/s]
# Trainer step: 20, epoch: 2:   9%|▊         | 26/300 [00:17<02:05,  2.18it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 27/300 [00:18<02:05,  2.17it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 28/300 [00:18<02:04,  2.18it/s]
# Trainer step: 20, epoch: 2:  10%|▉         | 29/300 [00:19<02:03,  2.19it/s]
# Trainer step: 20, epoch: 2:  10%|█         | 30/300 [00:19<02:03,  2.19it/s]Progress: 13.00%
+---- avg training fps: 5.95

# Trainer step: 30, epoch: 3:  10%|█         | 30/300 [00:20<02:03,  2.19it/s]
# Trainer step: 30, epoch: 3:  10%|█         | 31/300 [00:20<02:02,  2.19it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 32/300 [00:20<02:02,  2.19it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 33/300 [00:21<02:16,  1.95it/s]
# Trainer step: 30, epoch: 3:  11%|█▏        | 34/300 [00:21<02:11,  2.02it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 35/300 [00:22<02:08,  2.07it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 36/300 [00:22<02:05,  2.10it/s]Progress: 15.00%
+---- avg training fps: 6.24

# Trainer step: 30, epoch: 3:  12%|█▏        | 37/300 [00:23<02:03,  2.13it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 38/300 [00:23<02:01,  2.15it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 39/300 [00:24<02:00,  2.16it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 40/300 [00:24<02:00,  2.17it/s]
# Trainer step: 40, epoch: 4:  13%|█▎        | 40/300 [00:24<02:00,  2.17it/s]
# Trainer step: 40, epoch: 4:  14%|█▎        | 41/300 [00:24<01:59,  2.17it/s]
# Trainer step: 40, epoch: 4:  14%|█▍        | 42/300 [00:25<01:58,  2.18it/s]Progress: 17.00%
+---- avg training fps: 6.50

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:25<01:58,  2.18it/s]
# Trainer step: 40, epoch: 4:  15%|█▍        | 44/300 [00:26<01:57,  2.17it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 45/300 [00:26<01:57,  2.16it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 46/300 [00:27<01:56,  2.17it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 47/300 [00:27<01:56,  2.17it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:28<01:56,  2.17it/s]Progress: 19.00%
+---- avg training fps: 6.71

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:28<01:55,  2.16it/s]
# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:29<01:55,  2.17it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 50/300 [00:29<01:55,  2.17it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 51/300 [00:29<01:54,  2.18it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 52/300 [00:33<05:59,  1.45s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 53/300 [00:33<04:44,  1.15s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:34<03:51,  1.06it/s]Progress: 21.00%
+---- avg training fps: 6.23

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:34<03:15,  1.25it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:35<02:50,  1.43it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 57/300 [00:35<02:32,  1.60it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 58/300 [00:36<02:19,  1.73it/s]
# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:36<02:11,  1.84it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:36<02:05,  1.92it/s]Progress: 23.00%
+---- avg training fps: 6.41

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:37<02:05,  1.92it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:37<02:00,  1.99it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:37<01:56,  2.04it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:38<01:54,  2.08it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:39<02:06,  1.86it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:39<02:00,  1.95it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:39<01:56,  2.01it/s]Progress: 25.00%
+---- avg training fps: 6.54

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:40<01:53,  2.06it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:40<01:51,  2.09it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:41<01:49,  2.12it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:41<01:47,  2.13it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:42<01:47,  2.13it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:42<01:46,  2.14it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:42<01:46,  2.14it/s]Progress: 27.00%
+---- avg training fps: 6.67

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:43<01:45,  2.16it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:43<01:44,  2.17it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:44<01:43,  2.17it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:44<01:43,  2.16it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:45<01:43,  2.16it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:45<01:42,  2.16it/s]Progress: 29.00%
+---- avg training fps: 6.79

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:45<01:42,  2.16it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:46<01:41,  2.16it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:46<01:41,  2.16it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:46<01:41,  2.16it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:47<01:40,  2.16it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:47<01:40,  2.16it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:48<01:39,  2.16it/s]Progress: 31.00%
+---- avg training fps: 6.90

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:48<01:39,  2.17it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:49<01:38,  2.17it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:49<01:38,  2.16it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:50<01:38,  2.16it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:50<01:37,  2.17it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:51<01:37,  2.16it/s]Progress: 33.00%
+---- avg training fps: 6.99

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:51<01:37,  2.16it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:51<01:36,  2.17it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:51<01:35,  2.18it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:52<01:34,  2.19it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:52<01:34,  2.18it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:53<01:33,  2.18it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:53<01:33,  2.18it/s]Progress: 35.00%
+---- avg training fps: 7.08

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:54<01:32,  2.19it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:54<01:32,  2.19it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:55<01:32,  2.18it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:55<01:31,  2.18it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:56<01:31,  2.18it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:56<01:31,  2.18it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:59<04:20,  1.32s/it]Progress: 37.00%
+---- avg training fps: 6.82

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:59<03:29,  1.06s/it]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [01:00<02:52,  1.13it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [01:00<02:27,  1.32it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [01:01<02:08,  1.51it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:01<01:56,  1.66it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:02<01:47,  1.78it/s]Progress: 39.00%
+---- avg training fps: 6.90

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:02<01:41,  1.89it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:03<01:47,  1.77it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:03<01:47,  1.77it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:03<01:40,  1.88it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:04<01:35,  1.96it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:04<01:32,  2.02it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:05<01:29,  2.07it/s]Progress: 41.00%
+---- avg training fps: 6.96

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:05<01:27,  2.10it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:05<01:26,  2.13it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:06<01:25,  2.14it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:06<01:24,  2.16it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:07<01:23,  2.18it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:07<01:22,  2.18it/s]Progress: 43.00%
+---- avg training fps: 7.03

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:08<01:22,  2.18it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:08<01:22,  2.18it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:08<01:21,  2.18it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:09<01:20,  2.19it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:09<01:20,  2.18it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:10<01:20,  2.19it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:10<01:19,  2.19it/s]Progress: 45.00%
+---- avg training fps: 7.10

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:11<01:19,  2.19it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:11<01:18,  2.19it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:11<01:18,  2.19it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:12<01:17,  2.19it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:12<01:17,  2.19it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:12<01:17,  2.19it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:13<01:16,  2.19it/s]Progress: 47.00%
+---- avg training fps: 7.16

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:13<01:16,  2.19it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:14<01:15,  2.19it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:14<01:15,  2.19it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:15<01:14,  2.19it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:15<01:14,  2.19it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:16<01:13,  2.19it/s]Progress: 49.00%
+---- avg training fps: 7.22

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:16<01:13,  2.19it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:16<01:12,  2.19it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:17<01:12,  2.19it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:17<01:12,  2.20it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:17<01:12,  2.18it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:18<01:11,  2.18it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:18<01:11,  2.18it/s]Progress: 51.00%
+---- avg training fps: 7.27

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:19<01:10,  2.19it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:19<01:10,  2.19it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:20<01:10,  2.18it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:20<01:09,  2.19it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:21<01:09,  2.19it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:21<01:08,  2.19it/s]Progress: 53.00%
+---- avg training fps: 7.32

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:21<01:08,  2.19it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:21<01:08,  2.19it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:26<03:54,  1.59s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:26<03:03,  1.25s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:27<02:27,  1.01s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:27<02:02,  1.18it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:28<01:45,  1.37it/s]Progress: 55.00%
+---- avg training fps: 7.05

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:28<01:33,  1.54it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:28<01:24,  1.68it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:29<01:18,  1.80it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:29<01:13,  1.90it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:30<01:13,  1.90it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:30<01:10,  1.97it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:30<01:08,  2.02it/s]Progress: 57.00%
+---- avg training fps: 7.10

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:31<01:06,  2.06it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:31<01:05,  2.09it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:32<01:04,  2.11it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:32<01:03,  2.12it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:33<01:02,  2.13it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:33<01:01,  2.13it/s]Progress: 59.00%
+---- avg training fps: 7.14

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:34<01:01,  2.14it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:34<01:00,  2.14it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:34<01:00,  2.14it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:34<00:59,  2.15it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:35<00:59,  2.15it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:35<00:58,  2.15it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:36<00:58,  2.16it/s]Progress: 61.00%
+---- avg training fps: 7.19

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:36<00:57,  2.16it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:37<00:57,  2.16it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:37<00:56,  2.16it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:38<00:56,  2.16it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:38<00:56,  2.16it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:39<00:55,  2.16it/s]Progress: 63.00%
+---- avg training fps: 7.23

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:39<00:55,  2.16it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:39<00:55,  2.15it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:40<00:54,  2.15it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:40<00:54,  2.15it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:41<00:53,  2.15it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:41<00:53,  2.15it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:41<00:52,  2.16it/s]Progress: 65.00%
+---- avg training fps: 7.26

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:42<00:52,  2.15it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:42<00:52,  2.15it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:43<00:51,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:43<00:51,  2.15it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:44<00:51,  2.15it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:44<00:50,  2.15it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:44<00:50,  2.15it/s]Progress: 67.00%
+---- avg training fps: 7.30

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:45<00:49,  2.15it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:45<00:49,  2.15it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:46<00:48,  2.15it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:46<00:48,  2.15it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:47<00:47,  2.16it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:47<00:47,  2.15it/s]Progress: 69.00%
+---- avg training fps: 7.33

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:47<00:46,  2.15it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:48<00:46,  2.15it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:48<00:46,  2.15it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:48<00:45,  2.16it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:52<02:31,  1.54s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:53<01:58,  1.22s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:53<01:35,  1.01it/s]Progress: 71.00%
+---- avg training fps: 7.13

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:54<01:19,  1.20it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:54<01:07,  1.38it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:55<01:00,  1.55it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:55<00:54,  1.69it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:56<00:50,  1.80it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:56<00:47,  1.90it/s]Progress: 73.00%
+---- avg training fps: 7.17

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:57<00:47,  1.90it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:57<00:45,  1.95it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:57<00:43,  2.01it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:58<00:42,  2.05it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:58<00:41,  2.09it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [01:59<00:40,  2.10it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [01:59<00:39,  2.11it/s]Progress: 75.00%
+---- avg training fps: 7.20

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [01:59<00:39,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [02:00<00:38,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:00<00:37,  2.14it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:01<00:37,  2.14it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:01<00:37,  2.14it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:01<00:36,  2.15it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:02<00:36,  2.14it/s]Progress: 77.00%
+---- avg training fps: 7.23

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:02<00:35,  2.14it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:03<00:35,  2.15it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:03<00:35,  2.14it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:04<00:34,  2.14it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:04<00:34,  2.14it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:05<00:33,  2.14it/s]Progress: 79.00%
+---- avg training fps: 7.26

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:05<00:33,  2.14it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:06<00:32,  2.15it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:06<00:32,  2.15it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:06<00:32,  2.15it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:06<00:31,  2.16it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:07<00:31,  2.16it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:07<00:30,  2.16it/s]Progress: 81.00%
+---- avg training fps: 7.29

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:08<00:30,  2.16it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:08<00:29,  2.17it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:09<00:29,  2.17it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:09<00:28,  2.16it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:10<00:28,  2.16it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:10<00:27,  2.16it/s]Progress: 83.00%
+---- avg training fps: 7.32

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:11<00:27,  2.16it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:11<00:27,  2.16it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:11<00:26,  2.16it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:12<00:26,  2.16it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:12<00:25,  2.16it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:12<00:25,  2.15it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:13<00:25,  2.15it/s]Progress: 85.00%
+---- avg training fps: 7.35

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:13<00:24,  2.15it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:14<00:24,  2.16it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:14<00:23,  2.16it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:15<00:23,  2.16it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:15<00:23,  2.16it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:15<00:22,  2.17it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:19<01:16,  1.60s/it]Progress: 87.00%
+---- avg training fps: 7.18

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:20<00:59,  1.26s/it]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:20<00:46,  1.02s/it]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:21<00:38,  1.17it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:21<00:32,  1.36it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:22<00:28,  1.53it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:22<00:24,  1.68it/s]Progress: 89.00%
+---- avg training fps: 7.21

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:23<00:22,  1.80it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:23<00:21,  1.90it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:24<00:21,  1.90it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:24<00:19,  1.98it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:24<00:18,  2.02it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:25<00:17,  2.06it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:25<00:17,  2.09it/s]Progress: 91.00%
+---- avg training fps: 7.23

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:26<00:16,  2.11it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:26<00:15,  2.13it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:26<00:15,  2.14it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:27<00:14,  2.14it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:27<00:14,  2.15it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:28<00:13,  2.15it/s]Progress: 93.00%
+---- avg training fps: 7.26

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:28<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:28<00:13,  2.16it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:29<00:12,  2.16it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:29<00:12,  2.16it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:30<00:12,  2.16it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:30<00:11,  2.16it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:31<00:11,  2.16it/s]Progress: 95.00%
+---- avg training fps: 7.28

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:31<00:10,  2.16it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:32<00:10,  2.16it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:32<00:09,  2.16it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:32<00:09,  2.16it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:33<00:09,  2.16it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:33<00:08,  2.16it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:33<00:08,  2.16it/s]Progress: 97.00%
+---- avg training fps: 7.31

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:34<00:07,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:34<00:07,  2.16it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:35<00:06,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:35<00:06,  2.16it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:36<00:06,  2.15it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:36<00:05,  2.16it/s]Progress: 99.00%
+---- avg training fps: 7.33

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:37<00:05,  2.16it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:37<00:04,  2.16it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:38<00:04,  2.16it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:38<00:04,  2.15it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:38<00:03,  2.15it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:38<00:03,  2.15it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:39<00:02,  2.14it/s]Progress: 100.00%
+---- avg training fps: 7.35

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:39<00:02,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:40<00:01,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:40<00:01,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:41<00:00,  2.15it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:41<00:00,  2.16it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:42<00:00,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.38
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [04:02<00:00,  1.24it/s]
+------------------------------------------
+Training done :)
+2024-08-13 01:46:10.828993: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 01:46:10.928181: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 01:46:10.928209: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 01:46:10.946991: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 01:46:11.347657: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 01:46:11.347748: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 01:46:11.347757: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23112 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/good_styles/eden_crystals' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=1.0 freeze_unet_before_completion_f=0.5 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='eden_crystals' output_dir='lora_models/styles_final/eden_crystals_13_01-46-13-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723506373.187718 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/eden_crystals_13_01-46-13-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.242
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants on a black background.
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants in the dark.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Futuristic natural elements blending with technology
+
+- In the style of TOK, a potted plant sits on a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves is on a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is on a black background.
+- In the style of TOK, a potted plant sits on a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves is on a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is in the dark.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Futuristic natural elements blending with technology
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 6975.68it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a potted plant s...
+1     in the style of <s0><s1><s2>, a futuristic cit...
+2     in the style of <s0><s1><s2>, a flower with gr...
+3     in the style of <s0><s1><s2>, a plant is growi...
+4     in the style of <s0><s1><s2>, a group of green...
+5     in the style of <s0><s1><s2>, a potted plant s...
+6     in the style of <s0><s1><s2>, a futuristic cit...
+7     in the style of <s0><s1><s2>, a flower with gr...
+8     in the style of <s0><s1><s2>, a plant is growi...
+9     in the style of <s0><s1><s2>, a group of green...
+10    in the style of <s0><s1><s2>, a potted plant s...
+11    in the style of <s0><s1><s2>, a futuristic cit...
+12    in the style of <s0><s1><s2>, a flower with gr...
+13    in the style of <s0><s1><s2>, a plant is growi...
+14    in the style of <s0><s1><s2>, a group of green...
+15    in the style of <s0><s1><s2>, a potted plant s...
+16    in the style of <s0><s1><s2>, a futuristic cit...
+17    in the style of <s0><s1><s2>, a flower with gr...
+18    in the style of <s0><s1><s2>, a plant is growi...
+19    in the style of <s0><s1><s2>, a group of green...
+20    in the style of <s0><s1><s2>, a potted plant s...
+21    in the style of <s0><s1><s2>, a futuristic cit...
+22    in the style of <s0><s1><s2>, a flower with gr...
+23    in the style of <s0><s1><s2>, a plant is growi...
+24    in the style of <s0><s1><s2>, a group of green...
+25    in the style of <s0><s1><s2>, a potted plant s...
+26    in the style of <s0><s1><s2>, a futuristic cit...
+27    in the style of <s0><s1><s2>, a flower with gr...
+28    in the style of <s0><s1><s2>, a plant is growi...
+29    in the style of <s0><s1><s2>, a group of green...
+30    in the style of <s0><s1><s2>, a potted plant s...
+31    in the style of <s0><s1><s2>, a futuristic cit...
+32    in the style of <s0><s1><s2>, a flower with gr...
+33    in the style of <s0><s1><s2>, a plant is growi...
+34    in the style of <s0><s1><s2>, a group of green...
+35    in the style of <s0><s1><s2>, a potted plant s...
+36    in the style of <s0><s1><s2>, a futuristic cit...
+37    in the style of <s0><s1><s2>, a flower with gr...
+38    in the style of <s0><s1><s2>, a plant is growi...
+39    in the style of <s0><s1><s2>, a group of green...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:08<04:10,  1.17it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.72

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# Trainer step: 0, epoch: 0:   3%|▎         | 10/300 [00:10<02:36,  1.85it/s]
# Trainer step: 10, epoch: 1:   3%|▎         | 10/300 [00:10<02:36,  1.85it/s]
# Trainer step: 10, epoch: 1:   4%|▎         | 11/300 [00:10<02:28,  1.95it/s]
# Trainer step: 10, epoch: 1:   4%|▍         | 12/300 [00:11<02:23,  2.01it/s]Progress: 7.00%
+---- avg training fps: 4.15

# Trainer step: 10, epoch: 1:   4%|▍         | 13/300 [00:11<02:19,  2.05it/s]
# Trainer step: 10, epoch: 1:   5%|▍         | 14/300 [00:12<02:16,  2.09it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 15/300 [00:12<02:14,  2.12it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 16/300 [00:12<02:12,  2.15it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 17/300 [00:13<02:11,  2.15it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 18/300 [00:13<02:10,  2.16it/s]Progress: 9.00%
+---- avg training fps: 5.03

# Trainer step: 10, epoch: 1:   6%|▋         | 19/300 [00:14<02:09,  2.17it/s]
# Trainer step: 10, epoch: 1:   7%|▋         | 20/300 [00:14<02:09,  2.17it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 20/300 [00:15<02:09,  2.17it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 21/300 [00:15<02:08,  2.17it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 22/300 [00:15<02:08,  2.17it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 23/300 [00:16<02:07,  2.18it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 24/300 [00:16<02:06,  2.18it/s]Progress: 11.00%
+---- avg training fps: 5.63

# Trainer step: 20, epoch: 2:   8%|▊         | 25/300 [00:17<02:06,  2.18it/s]
# Trainer step: 20, epoch: 2:   9%|▊         | 26/300 [00:17<02:05,  2.17it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 27/300 [00:17<02:04,  2.18it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 28/300 [00:18<02:04,  2.18it/s]
# Trainer step: 20, epoch: 2:  10%|▉         | 29/300 [00:18<02:04,  2.18it/s]
# Trainer step: 20, epoch: 2:  10%|█         | 30/300 [00:19<02:04,  2.17it/s]Progress: 13.00%
+---- avg training fps: 6.06

# Trainer step: 30, epoch: 3:  10%|█         | 30/300 [00:19<02:04,  2.17it/s]
# Trainer step: 30, epoch: 3:  10%|█         | 31/300 [00:19<02:03,  2.18it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 32/300 [00:20<02:03,  2.17it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 33/300 [00:20<02:17,  1.94it/s]
# Trainer step: 30, epoch: 3:  11%|█▏        | 34/300 [00:21<02:12,  2.00it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 35/300 [00:21<02:09,  2.05it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 36/300 [00:22<02:06,  2.09it/s]Progress: 15.00%
+---- avg training fps: 6.32

# Trainer step: 30, epoch: 3:  12%|█▏        | 37/300 [00:22<02:04,  2.11it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 38/300 [00:23<02:03,  2.12it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 39/300 [00:23<02:02,  2.14it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 40/300 [00:24<02:01,  2.15it/s]
# Trainer step: 40, epoch: 4:  13%|█▎        | 40/300 [00:24<02:01,  2.15it/s]
# Trainer step: 40, epoch: 4:  14%|█▎        | 41/300 [00:24<02:00,  2.15it/s]
# Trainer step: 40, epoch: 4:  14%|█▍        | 42/300 [00:25<01:59,  2.16it/s]Progress: 17.00%
+---- avg training fps: 6.58

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:25<01:58,  2.16it/s]
# Trainer step: 40, epoch: 4:  15%|█▍        | 44/300 [00:25<01:57,  2.17it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 45/300 [00:26<01:57,  2.17it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 46/300 [00:26<01:56,  2.17it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 47/300 [00:27<01:57,  2.16it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:27<01:56,  2.16it/s]Progress: 19.00%
+---- avg training fps: 6.78

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:28<01:56,  2.16it/s]
# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:28<01:55,  2.17it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 50/300 [00:29<01:55,  2.17it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 51/300 [00:29<01:54,  2.17it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 52/300 [00:32<05:55,  1.43s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 53/300 [00:33<04:42,  1.14s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:33<03:50,  1.07it/s]Progress: 21.00%
+---- avg training fps: 6.30

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:34<03:14,  1.26it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:34<02:48,  1.44it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 57/300 [00:35<02:31,  1.60it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 58/300 [00:35<02:19,  1.74it/s]
# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:36<02:10,  1.85it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:36<02:03,  1.94it/s]Progress: 23.00%
+---- avg training fps: 6.48

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:37<02:03,  1.94it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:37<01:59,  2.00it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:37<02:10,  1.83it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:38<02:03,  1.92it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:38<01:58,  1.99it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:39<01:55,  2.04it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:39<01:54,  2.05it/s]Progress: 25.00%
+---- avg training fps: 6.59

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:40<01:52,  2.06it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:40<01:50,  2.09it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:40<01:49,  2.11it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:41<01:48,  2.12it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:41<01:48,  2.12it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:41<01:47,  2.13it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:42<01:46,  2.14it/s]Progress: 27.00%
+---- avg training fps: 6.72

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:42<01:45,  2.14it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:43<01:45,  2.15it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:43<01:44,  2.15it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:44<01:44,  2.15it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:44<01:43,  2.15it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:45<01:43,  2.15it/s]Progress: 29.00%
+---- avg training fps: 6.84

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:45<01:42,  2.16it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:46<01:41,  2.16it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:46<01:41,  2.16it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:46<01:41,  2.16it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:47<01:41,  2.16it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:47<01:40,  2.16it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:47<01:40,  2.16it/s]Progress: 31.00%
+---- avg training fps: 6.94

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:48<01:39,  2.16it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:48<01:39,  2.15it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:49<01:38,  2.15it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:49<01:38,  2.15it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:50<01:38,  2.15it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:50<01:37,  2.16it/s]Progress: 33.00%
+---- avg training fps: 7.03

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:51<01:37,  2.16it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:51<01:37,  2.15it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:51<01:36,  2.15it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:52<01:36,  2.15it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:52<01:35,  2.17it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:53<01:34,  2.16it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:53<01:34,  2.16it/s]Progress: 35.00%
+---- avg training fps: 7.11

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:53<01:33,  2.16it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:54<01:33,  2.17it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:54<01:33,  2.16it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:55<01:32,  2.16it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:55<01:32,  2.16it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:55<01:32,  2.15it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:59<04:38,  1.41s/it]Progress: 37.00%
+---- avg training fps: 6.81

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:59<03:41,  1.13s/it]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [01:00<03:01,  1.08it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [01:00<02:34,  1.26it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [01:01<02:14,  1.44it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:01<02:00,  1.60it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:02<01:50,  1.73it/s]Progress: 39.00%
+---- avg training fps: 6.89

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:02<01:43,  1.84it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:03<01:38,  1.92it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:03<01:38,  1.92it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:03<01:35,  1.98it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:04<01:32,  2.02it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:04<01:30,  2.06it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:05<01:29,  2.08it/s]Progress: 41.00%
+---- avg training fps: 6.96

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:05<01:27,  2.11it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:05<01:26,  2.12it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:06<01:26,  2.12it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:06<01:25,  2.13it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:07<01:25,  2.13it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:07<01:24,  2.13it/s]Progress: 43.00%
+---- avg training fps: 7.03

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:08<01:24,  2.13it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:08<01:23,  2.13it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:08<01:22,  2.15it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:09<01:22,  2.14it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:09<01:22,  2.14it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:10<01:21,  2.14it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:10<01:21,  2.14it/s]Progress: 45.00%
+---- avg training fps: 7.09

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:11<01:21,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:11<01:20,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:12<01:19,  2.15it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:12<01:19,  2.13it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:12<01:19,  2.13it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:12<01:19,  2.13it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:13<01:18,  2.13it/s]Progress: 47.00%
+---- avg training fps: 7.14

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:13<01:18,  2.14it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:14<01:17,  2.13it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:14<01:17,  2.13it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:15<01:16,  2.14it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:15<01:16,  2.14it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:16<01:15,  2.14it/s]Progress: 49.00%
+---- avg training fps: 7.19

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:16<01:15,  2.13it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:17<01:15,  2.13it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:17<01:15,  2.13it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:17<01:14,  2.13it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:18<01:14,  2.13it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:18<01:13,  2.14it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:19<01:13,  2.14it/s]Progress: 51.00%
+---- avg training fps: 7.24

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:19<01:12,  2.13it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:20<01:12,  2.13it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:20<01:11,  2.13it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:20<01:11,  2.13it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:21<01:11,  2.13it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:21<01:10,  2.14it/s]Progress: 53.00%
+---- avg training fps: 7.28

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:22<01:10,  2.14it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:22<01:11,  2.07it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:26<04:02,  1.64s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:27<03:08,  1.29s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:27<02:31,  1.04s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:28<02:06,  1.15it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:28<01:47,  1.33it/s]Progress: 55.00%
+---- avg training fps: 7.00

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:29<01:35,  1.50it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:29<01:26,  1.65it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:30<01:19,  1.77it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:30<01:14,  1.87it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:30<01:14,  1.87it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:30<01:11,  1.93it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:31<01:09,  1.99it/s]Progress: 57.00%
+---- avg training fps: 7.05

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:31<01:07,  2.03it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:32<01:06,  2.06it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:32<01:05,  2.08it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:33<01:04,  2.09it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:33<01:03,  2.10it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:34<01:02,  2.11it/s]Progress: 59.00%
+---- avg training fps: 7.09

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:34<01:01,  2.11it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:35<01:01,  2.13it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:35<01:01,  2.13it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:35<01:00,  2.12it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:36<01:00,  2.13it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:36<00:59,  2.12it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:37<00:59,  2.13it/s]Progress: 61.00%
+---- avg training fps: 7.13

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:37<00:59,  2.12it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:38<00:58,  2.12it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:38<00:57,  2.13it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:38<00:57,  2.13it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:39<00:56,  2.13it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:39<00:56,  2.13it/s]Progress: 63.00%
+---- avg training fps: 7.17

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:40<00:56,  2.13it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:40<00:55,  2.13it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:40<00:55,  2.13it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:41<00:55,  2.13it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:41<00:54,  2.13it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:42<00:54,  2.12it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:42<00:53,  2.12it/s]Progress: 65.00%
+---- avg training fps: 7.21

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:43<00:52,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:43<00:52,  2.13it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:44<00:52,  2.13it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:44<00:51,  2.12it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:45<00:51,  2.12it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:45<00:51,  2.11it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:45<00:51,  2.11it/s]Progress: 67.00%
+---- avg training fps: 7.24

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:46<00:50,  2.11it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:46<00:50,  2.12it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:46<00:49,  2.12it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:47<00:49,  2.12it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:47<00:48,  2.13it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:48<00:48,  2.12it/s]Progress: 69.00%
+---- avg training fps: 7.28

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:48<00:47,  2.12it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:49<00:47,  2.11it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:49<00:47,  2.11it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:49<00:46,  2.11it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:53<02:34,  1.58s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:54<02:00,  1.25s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:54<01:37,  1.01s/it]Progress: 71.00%
+---- avg training fps: 7.07

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:55<01:20,  1.18it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:55<01:09,  1.36it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:56<01:00,  1.53it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:56<00:55,  1.67it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:57<00:51,  1.78it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:57<00:48,  1.87it/s]Progress: 73.00%
+---- avg training fps: 7.11

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:58<00:48,  1.87it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:58<00:45,  1.94it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:58<00:44,  1.99it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:59<00:42,  2.03it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:59<00:41,  2.06it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [02:00<00:40,  2.08it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [02:00<00:40,  2.09it/s]Progress: 75.00%
+---- avg training fps: 7.14

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [02:01<00:39,  2.10it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [02:01<00:38,  2.11it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:01<00:38,  2.11it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:02<00:37,  2.12it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:02<00:37,  2.12it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:02<00:37,  2.12it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:03<00:36,  2.12it/s]Progress: 77.00%
+---- avg training fps: 7.17

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:03<00:36,  2.12it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:04<00:35,  2.12it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:04<00:35,  2.12it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:05<00:34,  2.12it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:05<00:34,  2.13it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:06<00:33,  2.13it/s]Progress: 79.00%
+---- avg training fps: 7.20

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:06<00:33,  2.12it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:07<00:32,  2.12it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:07<00:32,  2.12it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:07<00:32,  2.12it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:08<00:32,  2.11it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:08<00:31,  2.11it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:09<00:31,  2.11it/s]Progress: 81.00%
+---- avg training fps: 7.23

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:09<00:30,  2.11it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:09<00:30,  2.11it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:10<00:29,  2.12it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:10<00:29,  2.12it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:11<00:28,  2.11it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:11<00:28,  2.13it/s]Progress: 83.00%
+---- avg training fps: 7.25

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:12<00:28,  2.13it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:12<00:27,  2.12it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:12<00:27,  2.12it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:13<00:26,  2.11it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:13<00:26,  2.12it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:14<00:25,  2.12it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:14<00:25,  2.12it/s]Progress: 85.00%
+---- avg training fps: 7.28

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:15<00:25,  2.12it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:15<00:24,  2.12it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:16<00:24,  2.10it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:16<00:23,  2.11it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:17<00:23,  2.11it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:17<00:23,  2.11it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:21<01:12,  1.52s/it]Progress: 87.00%
+---- avg training fps: 7.11

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:21<01:00,  1.29s/it]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:22<00:47,  1.04s/it]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:22<00:38,  1.15it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:23<00:32,  1.34it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:23<00:28,  1.52it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:24<00:25,  1.66it/s]Progress: 89.00%
+---- avg training fps: 7.14

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:24<00:22,  1.79it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:25<00:21,  1.87it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:25<00:21,  1.87it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:25<00:20,  1.95it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:25<00:19,  2.00it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:26<00:18,  2.04it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:26<00:17,  2.07it/s]Progress: 91.00%
+---- avg training fps: 7.17

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:27<00:16,  2.09it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:27<00:16,  2.11it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:28<00:15,  2.12it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:28<00:15,  2.13it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:29<00:14,  2.13it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:29<00:13,  2.14it/s]Progress: 93.00%
+---- avg training fps: 7.19

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:30<00:13,  2.14it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:30<00:13,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:30<00:13,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:31<00:12,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:31<00:12,  2.15it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:32<00:11,  2.14it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:32<00:11,  2.14it/s]Progress: 95.00%
+---- avg training fps: 7.22

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:32<00:10,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:33<00:10,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:33<00:09,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:34<00:09,  2.14it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:34<00:09,  2.14it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:34<00:08,  2.15it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:35<00:08,  2.15it/s]Progress: 97.00%
+---- avg training fps: 7.24

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:35<00:07,  2.14it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:36<00:07,  2.14it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:36<00:06,  2.14it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:37<00:06,  2.14it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:37<00:06,  2.14it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:38<00:05,  2.14it/s]Progress: 99.00%
+---- avg training fps: 7.27

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:38<00:05,  2.14it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:39<00:04,  2.13it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:39<00:04,  2.13it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:39<00:04,  2.13it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:39<00:03,  2.14it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:40<00:03,  2.13it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:40<00:02,  2.13it/s]Progress: 100.00%
+---- avg training fps: 7.29

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:41<00:02,  2.14it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:41<00:01,  2.14it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:42<00:01,  2.14it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:42<00:00,  2.14it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:43<00:00,  2.14it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:43<00:00,  2.13it/s]Progress: 100.00%
+---- avg training fps: 7.31
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [04:03<00:00,  1.23it/s]
+------------------------------------------
+Training done :)
+2024-08-13 01:50:46.895412: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 01:50:46.989922: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 01:50:46.989950: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 01:50:47.008611: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 01:50:47.414189: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 01:50:47.414273: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 01:50:47.414283: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23116 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/journey_small' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=1.0 freeze_unet_before_completion_f=0.0 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='journey_small' output_dir='lora_models/styles_final/journey_small_13_01-50-49-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723506649.2649713 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/journey_small_13_01-50-49-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 0.812
+New train_img_size: [448, 576]
+Validation_img_size was set to: [896, 1152]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red cloak standing in front of a building.
+- A person standing on top of a body of water.
+- A person walking in the desert with a camel in front of a mountain.
+- A man standing on top of a sandy beach next to the ocean.
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red dress is walking through an archway.
+- A person standing on top of a body of water.
+- A person walking in the desert with a dog on a leash.
+- A person walking on a beach next to a body of water.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal and dreamlike, bold compositions
+
+- In the style of TOK: A sunset over a desert landscape with a mountain in the background.
+- In the style of TOK: A woman in a red cloak standing in front of a building.
+- In the style of TOK: A person standing on top of a body of water.
+- In the style of TOK: A person walking in the desert with a camel in front of a mountain.
+- In the style of TOK: A man standing on top of a sandy beach next to the ocean.
+- In the style of TOK: A sunset over a desert landscape with a mountain in the background.
+- In the style of TOK: A woman in a red dress walking through an archway.
+- In the style of TOK: A person standing on top of a body of water.
+- In the style of TOK: A person walking in the desert with a dog on a leash.
+- In the style of TOK: A person walking on a beach next to a body of water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Surreal and dreamlike, bold compositions
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 11104.86it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>: a sunset over a ...
+1     in the style of <s0><s1><s2>: a woman in a red...
+2     in the style of <s0><s1><s2>: a person standin...
+3     in the style of <s0><s1><s2>: a person walking...
+4     in the style of <s0><s1><s2>: a man standing o...
+5     in the style of <s0><s1><s2>: a sunset over a ...
+6     in the style of <s0><s1><s2>: a woman in a red...
+7     in the style of <s0><s1><s2>: a person standin...
+8     in the style of <s0><s1><s2>: a person walking...
+9     in the style of <s0><s1><s2>: a person walking...
+10    in the style of <s0><s1><s2>: a sunset over a ...
+11    in the style of <s0><s1><s2>: a woman in a red...
+12    in the style of <s0><s1><s2>: a person standin...
+13    in the style of <s0><s1><s2>: a person walking...
+14    in the style of <s0><s1><s2>: a man standing o...
+15    in the style of <s0><s1><s2>: a sunset over a ...
+16    in the style of <s0><s1><s2>: a woman in a red...
+17    in the style of <s0><s1><s2>: a person standin...
+18    in the style of <s0><s1><s2>: a person walking...
+19    in the style of <s0><s1><s2>: a person walking...
+20    in the style of <s0><s1><s2>: a sunset over a ...
+21    in the style of <s0><s1><s2>: a woman in a red...
+22    in the style of <s0><s1><s2>: a person standin...
+23    in the style of <s0><s1><s2>: a person walking...
+24    in the style of <s0><s1><s2>: a man standing o...
+25    in the style of <s0><s1><s2>: a sunset over a ...
+26    in the style of <s0><s1><s2>: a woman in a red...
+27    in the style of <s0><s1><s2>: a person standin...
+28    in the style of <s0><s1><s2>: a person walking...
+29    in the style of <s0><s1><s2>: a person walking...
+30    in the style of <s0><s1><s2>: a sunset over a ...
+31    in the style of <s0><s1><s2>: a woman in a red...
+32    in the style of <s0><s1><s2>: a person standin...
+33    in the style of <s0><s1><s2>: a person walking...
+34    in the style of <s0><s1><s2>: a man standing o...
+35    in the style of <s0><s1><s2>: a sunset over a ...
+36    in the style of <s0><s1><s2>: a woman in a red...
+37    in the style of <s0><s1><s2>: a person standin...
+38    in the style of <s0><s1><s2>: a person walking...
+39    in the style of <s0><s1><s2>: a person walking...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:09<04:40,  1.05it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.38

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+---- avg training fps: 3.67

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+---- avg training fps: 4.55

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+---- avg training fps: 5.16

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+---- avg training fps: 5.61

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# Trainer step: 30, epoch: 3:  10%|█         | 31/300 [00:21<02:05,  2.15it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 32/300 [00:21<02:03,  2.16it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 33/300 [00:22<02:03,  2.16it/s]
# Trainer step: 30, epoch: 3:  11%|█▏        | 34/300 [00:22<02:03,  2.15it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 35/300 [00:23<02:06,  2.09it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 36/300 [00:23<02:05,  2.10it/s]Progress: 15.00%
+---- avg training fps: 5.94

# Trainer step: 30, epoch: 3:  12%|█▏        | 37/300 [00:24<02:04,  2.11it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 38/300 [00:24<02:03,  2.12it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 39/300 [00:25<02:02,  2.13it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 40/300 [00:25<02:01,  2.13it/s]
# Trainer step: 40, epoch: 4:  13%|█▎        | 40/300 [00:26<02:01,  2.13it/s]
# Trainer step: 40, epoch: 4:  14%|█▎        | 41/300 [00:26<02:01,  2.14it/s]
# Trainer step: 40, epoch: 4:  14%|█▍        | 42/300 [00:26<02:00,  2.13it/s]Progress: 17.00%
+---- avg training fps: 6.21

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:27<02:00,  2.14it/s]
# Trainer step: 40, epoch: 4:  15%|█▍        | 44/300 [00:27<01:59,  2.14it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 45/300 [00:27<01:59,  2.14it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 46/300 [00:28<01:58,  2.15it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 47/300 [00:28<01:58,  2.14it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:29<01:57,  2.14it/s]Progress: 19.00%
+---- avg training fps: 6.43

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:29<01:57,  2.14it/s]
# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:30<01:56,  2.14it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 50/300 [00:30<01:56,  2.14it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 51/300 [00:30<01:56,  2.14it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 52/300 [00:35<06:39,  1.61s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 53/300 [00:35<05:12,  1.27s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:35<04:12,  1.03s/it]Progress: 21.00%
+---- avg training fps: 5.93

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:36<03:30,  1.16it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:36<03:01,  1.35it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 57/300 [00:37<02:40,  1.51it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 58/300 [00:37<02:25,  1.67it/s]
# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:38<02:15,  1.78it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:38<02:08,  1.87it/s]Progress: 23.00%
+---- avg training fps: 6.11

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:39<02:08,  1.87it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:39<02:03,  1.94it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:39<01:59,  1.99it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:40<01:56,  2.03it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:40<01:54,  2.06it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:41<01:52,  2.09it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:41<01:51,  2.10it/s]Progress: 25.00%
+---- avg training fps: 6.27

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:42<01:50,  2.10it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:42<01:49,  2.11it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:43<01:49,  2.11it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:43<01:48,  2.12it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:43<01:48,  2.12it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:43<01:48,  2.12it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:44<01:47,  2.12it/s]Progress: 27.00%
+---- avg training fps: 6.41

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:44<01:46,  2.13it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:45<01:46,  2.13it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:45<01:45,  2.14it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:46<01:44,  2.13it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:46<01:44,  2.13it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:47<01:43,  2.14it/s]Progress: 29.00%
+---- avg training fps: 6.54

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:47<01:43,  2.13it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:48<01:43,  2.12it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:48<01:43,  2.12it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:48<01:43,  2.13it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:49<01:41,  2.14it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:49<01:41,  2.13it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:50<01:41,  2.13it/s]Progress: 31.00%
+---- avg training fps: 6.65

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:50<01:40,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:50<01:40,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:51<01:40,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:51<01:39,  2.13it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:52<01:38,  2.14it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:52<01:38,  2.13it/s]Progress: 33.00%
+---- avg training fps: 6.75

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:53<01:38,  2.13it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:53<01:38,  2.13it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:53<01:37,  2.13it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:54<01:37,  2.13it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:54<01:36,  2.13it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:55<01:36,  2.13it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:55<01:35,  2.14it/s]Progress: 35.00%
+---- avg training fps: 6.84

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:56<01:34,  2.14it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:56<01:34,  2.14it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:57<01:33,  2.14it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:57<01:33,  2.14it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:58<01:33,  2.14it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:58<01:32,  2.14it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [01:02<05:38,  1.71s/it]Progress: 37.00%
+---- avg training fps: 6.47

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [01:03<04:23,  1.34s/it]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [01:03<03:30,  1.08s/it]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [01:04<02:54,  1.12it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [01:04<02:28,  1.30it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:04<02:10,  1.48it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:05<01:58,  1.62it/s]Progress: 39.00%
+---- avg training fps: 6.56

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:05<01:48,  1.75it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:06<01:42,  1.85it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:06<01:42,  1.85it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:06<01:37,  1.93it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:07<01:34,  1.99it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:07<01:32,  2.03it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:08<01:30,  2.06it/s]Progress: 41.00%
+---- avg training fps: 6.64

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:08<01:28,  2.08it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:09<01:27,  2.10it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:09<01:26,  2.12it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:10<01:25,  2.12it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:10<01:25,  2.13it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:11<01:24,  2.13it/s]Progress: 43.00%
+---- avg training fps: 6.71

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:11<01:24,  2.13it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:11<01:23,  2.13it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:11<01:23,  2.13it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:12<01:22,  2.14it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:12<01:22,  2.14it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:13<01:21,  2.14it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:13<01:21,  2.13it/s]Progress: 45.00%
+---- avg training fps: 6.78

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:14<01:21,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:14<01:20,  2.13it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:15<01:20,  2.13it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:15<01:19,  2.14it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:16<01:19,  2.14it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:16<01:18,  2.14it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:16<01:18,  2.14it/s]Progress: 47.00%
+---- avg training fps: 6.85

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:17<01:17,  2.14it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:17<01:17,  2.14it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:18<01:17,  2.14it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:18<01:16,  2.14it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:18<01:16,  2.14it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:19<01:15,  2.14it/s]Progress: 49.00%
+---- avg training fps: 6.91

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:19<01:15,  2.14it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:20<01:15,  2.11it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:20<01:15,  2.11it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:20<01:15,  2.12it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:21<01:14,  2.12it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:21<01:13,  2.13it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:22<01:13,  2.13it/s]Progress: 51.00%
+---- avg training fps: 6.96

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:22<01:12,  2.13it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:23<01:12,  2.13it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:23<01:11,  2.13it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:24<01:11,  2.14it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:24<01:10,  2.14it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:25<01:10,  2.14it/s]Progress: 53.00%
+---- avg training fps: 7.01

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:25<01:10,  2.14it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:25<01:09,  2.15it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:30<04:14,  1.72s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:30<03:17,  1.34s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:31<02:37,  1.08s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:31<02:10,  1.11it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:32<01:50,  1.30it/s]Progress: 55.00%
+---- avg training fps: 6.74

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:32<01:36,  1.48it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:33<01:27,  1.63it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:33<01:20,  1.76it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:33<01:15,  1.85it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:34<01:15,  1.85it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:34<01:12,  1.93it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:34<01:09,  1.99it/s]Progress: 57.00%
+---- avg training fps: 6.80

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:35<01:07,  2.02it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:35<01:06,  2.06it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:36<01:04,  2.08it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:36<01:03,  2.10it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:37<01:03,  2.11it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:37<01:02,  2.12it/s]Progress: 59.00%
+---- avg training fps: 6.85

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:38<01:01,  2.12it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:38<01:01,  2.12it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:39<01:01,  2.12it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:39<01:00,  2.13it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:39<00:59,  2.14it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:40<00:59,  2.14it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:40<00:59,  2.13it/s]Progress: 61.00%
+---- avg training fps: 6.89

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:40<00:58,  2.13it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:41<00:58,  2.13it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:41<00:57,  2.13it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:42<00:57,  2.13it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:42<00:57,  2.12it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:43<00:56,  2.13it/s]Progress: 63.00%
+---- avg training fps: 6.94

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:43<00:56,  2.13it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:43<00:55,  2.13it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:44<00:55,  2.13it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:44<00:54,  2.13it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:45<00:54,  2.13it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:45<00:53,  2.14it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:46<00:53,  2.14it/s]Progress: 65.00%
+---- avg training fps: 6.98

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:46<00:53,  2.13it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:47<00:52,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:47<00:51,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:48<00:51,  2.14it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:48<00:51,  2.14it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:48<00:51,  2.13it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:48<00:50,  2.14it/s]Progress: 67.00%
+---- avg training fps: 7.02

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:49<00:50,  2.13it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:49<00:49,  2.13it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:50<00:49,  2.13it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:50<00:49,  2.12it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:51<00:48,  2.13it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:51<00:47,  2.13it/s]Progress: 69.00%
+---- avg training fps: 7.06

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:52<00:47,  2.13it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:52<00:46,  2.13it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:53<00:46,  2.13it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:53<00:46,  2.13it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:57<02:53,  1.77s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:58<02:14,  1.38s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:58<01:46,  1.11s/it]Progress: 71.00%
+---- avg training fps: 6.83

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:59<01:27,  1.09it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:59<01:13,  1.28it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [02:00<01:03,  1.45it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [02:00<00:56,  1.61it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [02:01<00:52,  1.74it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [02:01<00:48,  1.85it/s]Progress: 73.00%
+---- avg training fps: 6.87

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [02:02<00:48,  1.85it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [02:02<00:46,  1.92it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [02:02<00:44,  1.98it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [02:03<00:43,  2.02it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [02:03<00:41,  2.06it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [02:04<00:40,  2.09it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [02:04<00:39,  2.10it/s]Progress: 75.00%
+---- avg training fps: 6.91

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [02:05<00:39,  2.10it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [02:05<00:38,  2.11it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:05<00:38,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:06<00:37,  2.12it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:06<00:37,  2.12it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:06<00:37,  2.12it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:07<00:36,  2.13it/s]Progress: 77.00%
+---- avg training fps: 6.95

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:07<00:36,  2.13it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:08<00:35,  2.13it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:08<00:35,  2.13it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:09<00:34,  2.13it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:09<00:34,  2.13it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:10<00:33,  2.13it/s]Progress: 79.00%
+---- avg training fps: 6.98

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:10<00:33,  2.14it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:11<00:32,  2.13it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:11<00:32,  2.13it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:11<00:32,  2.13it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:12<00:31,  2.13it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:12<00:31,  2.14it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:12<00:30,  2.14it/s]Progress: 81.00%
+---- avg training fps: 7.01

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:13<00:30,  2.13it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:13<00:29,  2.15it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:14<00:29,  2.14it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:14<00:29,  2.13it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:15<00:28,  2.13it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:15<00:28,  2.13it/s]Progress: 83.00%
+---- avg training fps: 7.05

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:16<00:28,  2.13it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:16<00:27,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:16<00:27,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:17<00:26,  2.14it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:17<00:26,  2.13it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:18<00:26,  2.11it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:18<00:25,  2.13it/s]Progress: 85.00%
+---- avg training fps: 7.08

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:19<00:24,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:19<00:24,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:20<00:24,  2.12it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:20<00:23,  2.13it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:20<00:23,  2.13it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:20<00:23,  2.13it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:25<01:18,  1.63s/it]Progress: 87.00%
+---- avg training fps: 6.90

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:26<01:04,  1.37s/it]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:26<00:50,  1.10s/it]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:26<00:40,  1.10it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:27<00:34,  1.29it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:27<00:29,  1.47it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:28<00:25,  1.62it/s]Progress: 89.00%
+---- avg training fps: 6.93

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:28<00:23,  1.76it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:29<00:21,  1.86it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:29<00:21,  1.86it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:29<00:20,  1.93it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:30<00:19,  2.00it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:30<00:18,  2.05it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:31<00:17,  2.08it/s]Progress: 91.00%
+---- avg training fps: 6.96

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:31<00:16,  2.10it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:32<00:16,  2.12it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:32<00:15,  2.14it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:33<00:14,  2.14it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:33<00:14,  2.14it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:33<00:13,  2.15it/s]Progress: 93.00%
+---- avg training fps: 6.99

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:34<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:34<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:34<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:35<00:12,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:35<00:12,  2.16it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:36<00:11,  2.16it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:36<00:11,  2.16it/s]Progress: 95.00%
+---- avg training fps: 7.02

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:37<00:10,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:37<00:10,  2.16it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:38<00:09,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:38<00:09,  2.15it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:39<00:09,  2.15it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:39<00:08,  2.15it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:39<00:08,  2.16it/s]Progress: 97.00%
+---- avg training fps: 7.05

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:39<00:07,  2.16it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:40<00:07,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:40<00:07,  2.14it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:41<00:06,  2.15it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:41<00:06,  2.15it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:42<00:05,  2.14it/s]Progress: 99.00%
+---- avg training fps: 7.08

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:42<00:05,  2.15it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:43<00:04,  2.14it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:43<00:04,  2.14it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:43<00:04,  2.14it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:44<00:03,  2.14it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:44<00:03,  2.14it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:45<00:02,  2.14it/s]Progress: 100.00%
+---- avg training fps: 7.10

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:45<00:02,  2.14it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:46<00:01,  2.14it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:46<00:01,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:46<00:00,  2.14it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:47<00:00,  2.13it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:47<00:00,  2.13it/s]Progress: 100.00%
+---- avg training fps: 7.13
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [04:08<00:00,  1.21it/s]
+------------------------------------------
+Training done :)
+2024-08-13 01:55:23.328041: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 01:55:23.419523: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 01:55:23.419549: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 01:55:23.438423: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 01:55:23.841399: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 01:55:23.841483: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 01:55:23.841493: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23109 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/journey_small' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=1.0 freeze_unet_before_completion_f=0.5 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='journey_small' output_dir='lora_models/styles_final/journey_small_13_01-55-25-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723506925.6767387 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/journey_small_13_01-55-25-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 0.812
+New train_img_size: [448, 576]
+Validation_img_size was set to: [896, 1152]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red cloak standing in front of a building.
+- A person standing on top of a body of water.
+- A person walking in the desert with a camel in front of a mountain.
+- A man standing on top of a sandy beach next to the ocean.
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red dress is walking through an archway.
+- A person standing on top of a body of water.
+- A person walking in the desert with a dog on a leash.
+- A person walking on a beach next to a body of water.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal and dreamlike, bold contrasts
+
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red cloak standing in front of a building.
+- In the style of TOK, a person standing on top of a body of water.
+- In the style of TOK, a person walking in the desert with a camel in front of a mountain.
+- In the style of TOK, a man standing on top of a sandy beach next to the ocean.
+- In the style of TOK, a woman in a red dress walking through an archway.
+- In the style of TOK, a person walking in the desert with a dog on a leash.
+- In the style of TOK, a person walking on a beach next to a body of water.
+--------------------------
+GPT-4 returned the wrong number of prompts 8 instead of 10, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red cloak standing in front of a building.
+- A person standing on top of a body of water.
+- A person walking in the desert with a camel in front of a mountain.
+- A man standing on top of a sandy beach next to the ocean.
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red dress is walking through an archway.
+- A person standing on top of a body of water.
+- A person walking in the desert with a dog on a leash.
+- A person walking on a beach next to a body of water.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 1...
+----- GPT response: -----
+Style Description: Dreamy, surreal landscape with vivid colors.
+
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red cloak standing in front of a building.
+- In the style of TOK, a person standing on top of a body of water.
+- In the style of TOK, a person walking in the desert with a camel in front of a mountain.
+- In the style of TOK, a man standing on top of a sandy beach next to the ocean.
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red dress walking through an archway.
+- In the style of TOK, a person standing on top of a body of water.
+- In the style of TOK, a person walking in the desert with a dog on a leash.
+- In the style of TOK, a person walking on a beach next to a body of water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Dreamy, surreal landscape with vivid colors.
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 10288.35it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a sunset over a ...
+1     in the style of <s0><s1><s2>, a woman in a red...
+2     in the style of <s0><s1><s2>, a person standin...
+3     in the style of <s0><s1><s2>, a person walking...
+4     in the style of <s0><s1><s2>, a man standing o...
+5     in the style of <s0><s1><s2>, a sunset over a ...
+6     in the style of <s0><s1><s2>, a woman in a red...
+7     in the style of <s0><s1><s2>, a person standin...
+8     in the style of <s0><s1><s2>, a person walking...
+9     in the style of <s0><s1><s2>, a person walking...
+10    in the style of <s0><s1><s2>, a sunset over a ...
+11    in the style of <s0><s1><s2>, a woman in a red...
+12    in the style of <s0><s1><s2>, a person standin...
+13    in the style of <s0><s1><s2>, a person walking...
+14    in the style of <s0><s1><s2>, a man standing o...
+15    in the style of <s0><s1><s2>, a sunset over a ...
+16    in the style of <s0><s1><s2>, a woman in a red...
+17    in the style of <s0><s1><s2>, a person standin...
+18    in the style of <s0><s1><s2>, a person walking...
+19    in the style of <s0><s1><s2>, a person walking...
+20    in the style of <s0><s1><s2>, a sunset over a ...
+21    in the style of <s0><s1><s2>, a woman in a red...
+22    in the style of <s0><s1><s2>, a person standin...
+23    in the style of <s0><s1><s2>, a person walking...
+24    in the style of <s0><s1><s2>, a man standing o...
+25    in the style of <s0><s1><s2>, a sunset over a ...
+26    in the style of <s0><s1><s2>, a woman in a red...
+27    in the style of <s0><s1><s2>, a person standin...
+28    in the style of <s0><s1><s2>, a person walking...
+29    in the style of <s0><s1><s2>, a person walking...
+30    in the style of <s0><s1><s2>, a sunset over a ...
+31    in the style of <s0><s1><s2>, a woman in a red...
+32    in the style of <s0><s1><s2>, a person standin...
+33    in the style of <s0><s1><s2>, a person walking...
+34    in the style of <s0><s1><s2>, a man standing o...
+35    in the style of <s0><s1><s2>, a sunset over a ...
+36    in the style of <s0><s1><s2>, a woman in a red...
+37    in the style of <s0><s1><s2>, a person standin...
+38    in the style of <s0><s1><s2>, a person walking...
+39    in the style of <s0><s1><s2>, a person walking...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:09<04:33,  1.08it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.45

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+---- avg training fps: 3.78

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+---- avg training fps: 4.66

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+---- avg training fps: 5.28

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+---- avg training fps: 5.73

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+---- avg training fps: 6.08

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# Trainer step: 40, epoch: 4:  14%|█▍        | 42/300 [00:25<01:57,  2.19it/s]Progress: 17.00%
+---- avg training fps: 6.37

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:26<01:56,  2.20it/s]
# Trainer step: 40, epoch: 4:  15%|█▍        | 44/300 [00:26<01:56,  2.20it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 45/300 [00:27<01:56,  2.20it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 46/300 [00:27<01:55,  2.19it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 47/300 [00:28<01:55,  2.20it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:28<01:54,  2.19it/s]Progress: 19.00%
+---- avg training fps: 6.59

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:29<01:54,  2.19it/s]
# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:29<01:54,  2.19it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 50/300 [00:30<01:54,  2.19it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 51/300 [00:30<01:53,  2.19it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 52/300 [00:34<06:29,  1.57s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 53/300 [00:34<05:04,  1.23s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:35<04:06,  1.00s/it]Progress: 21.00%
+---- avg training fps: 6.07

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:35<03:25,  1.19it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:36<02:56,  1.38it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 57/300 [00:36<02:36,  1.55it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 58/300 [00:36<02:22,  1.70it/s]
# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:37<02:12,  1.82it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:37<02:04,  1.92it/s]Progress: 23.00%
+---- avg training fps: 6.26

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:38<02:04,  1.92it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:38<02:00,  1.99it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:38<01:56,  2.05it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:39<01:53,  2.09it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:39<01:51,  2.12it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:40<01:50,  2.14it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:40<01:48,  2.15it/s]Progress: 25.00%
+---- avg training fps: 6.43

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:41<01:47,  2.16it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:41<01:47,  2.16it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:41<01:46,  2.17it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:42<01:45,  2.17it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:42<01:45,  2.17it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:42<01:45,  2.17it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:43<01:44,  2.18it/s]Progress: 27.00%
+---- avg training fps: 6.57

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:43<01:44,  2.18it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:44<01:43,  2.18it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:44<01:43,  2.18it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:45<01:42,  2.17it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:45<01:42,  2.18it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:46<01:42,  2.18it/s]Progress: 29.00%
+---- avg training fps: 6.70

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:46<01:41,  2.18it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:47<01:40,  2.18it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:47<01:40,  2.18it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:47<01:40,  2.18it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:47<01:40,  2.18it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:48<01:39,  2.18it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:48<01:38,  2.18it/s]Progress: 31.00%
+---- avg training fps: 6.81

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:49<01:38,  2.19it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:49<01:37,  2.18it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:50<01:37,  2.18it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:50<01:37,  2.18it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:51<01:36,  2.18it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:51<01:36,  2.18it/s]Progress: 33.00%
+---- avg training fps: 6.91

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:52<01:36,  2.18it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:52<01:35,  2.18it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:52<01:35,  2.18it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:52<01:34,  2.18it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:53<01:34,  2.18it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:53<01:34,  2.18it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:54<01:33,  2.18it/s]Progress: 35.00%
+---- avg training fps: 7.00

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:54<01:33,  2.18it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:55<01:32,  2.18it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:55<01:32,  2.18it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:56<01:32,  2.17it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:56<01:32,  2.17it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:56<01:31,  2.18it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:57<01:30,  2.19it/s]Progress: 37.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.09

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:57<01:30,  2.18it/s]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [00:58<01:30,  2.17it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [00:58<01:29,  2.18it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [00:58<01:28,  2.18it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [00:59<01:28,  2.18it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [00:59<01:28,  2.17it/s]Progress: 39.00%
+---- avg training fps: 7.16

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:00<01:28,  2.17it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:00<01:27,  2.17it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:01<01:27,  2.17it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:01<01:27,  2.17it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:01<01:26,  2.17it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:02<01:26,  2.16it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:02<01:25,  2.17it/s]Progress: 41.00%
+---- avg training fps: 7.23

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:03<01:25,  2.17it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:03<01:24,  2.17it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:04<01:24,  2.17it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:04<01:23,  2.17it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:04<01:23,  2.18it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:05<01:23,  2.16it/s]Progress: 43.00%
+---- avg training fps: 7.29

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:05<01:23,  2.16it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:05<01:22,  2.16it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:06<01:21,  2.17it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:06<01:21,  2.17it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:07<01:21,  2.17it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:07<01:20,  2.17it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:08<01:19,  2.18it/s]Progress: 45.00%
+---- avg training fps: 7.34

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:08<01:19,  2.18it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:09<01:19,  2.17it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:09<01:19,  2.16it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:10<01:18,  2.16it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:10<01:18,  2.16it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:10<01:18,  2.16it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:10<01:17,  2.16it/s]Progress: 47.00%
+---- avg training fps: 7.39

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:11<01:17,  2.16it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:11<01:18,  2.13it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:12<01:17,  2.13it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:12<01:18,  2.09it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:13<01:18,  2.08it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:13<01:17,  2.08it/s]Progress: 49.00%
+---- avg training fps: 7.43

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:14<01:16,  2.09it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:14<01:16,  2.10it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:15<01:16,  2.10it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:15<01:15,  2.10it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:15<01:14,  2.11it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:16<01:13,  2.13it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:16<01:13,  2.12it/s]Progress: 51.00%
+---- avg training fps: 7.47

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:17<01:12,  2.13it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:17<01:11,  2.15it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:18<01:11,  2.15it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:18<01:10,  2.15it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:18<01:10,  2.15it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:19<01:09,  2.16it/s]Progress: 53.00%
+---- avg training fps: 7.51

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:19<01:09,  2.16it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:19<01:09,  2.15it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:24<04:11,  1.70s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:24<03:15,  1.33s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:25<02:36,  1.07s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:25<02:08,  1.13it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:26<01:49,  1.32it/s]Progress: 55.00%
+---- avg training fps: 7.19

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:26<01:35,  1.50it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:27<01:25,  1.65it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:27<01:19,  1.78it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:28<01:14,  1.88it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:28<01:14,  1.88it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:28<01:11,  1.96it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:29<01:08,  2.01it/s]Progress: 57.00%
+---- avg training fps: 7.24

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:29<01:06,  2.05it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:30<01:05,  2.08it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:30<01:03,  2.11it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:30<01:03,  2.13it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:31<01:02,  2.14it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:31<01:01,  2.14it/s]Progress: 59.00%
+---- avg training fps: 7.28

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:32<01:00,  2.16it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:32<01:00,  2.16it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:33<01:00,  2.16it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:33<00:59,  2.16it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:33<00:59,  2.16it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:34<00:58,  2.17it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:34<00:58,  2.16it/s]Progress: 61.00%
+---- avg training fps: 7.32

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:35<00:57,  2.16it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:35<00:57,  2.16it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:36<00:56,  2.17it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:36<00:56,  2.16it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:36<00:56,  2.16it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:37<00:55,  2.16it/s]Progress: 63.00%
+---- avg training fps: 7.36

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:37<00:55,  2.16it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:37<00:55,  2.16it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:38<00:54,  2.15it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:38<00:54,  2.15it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:39<00:53,  2.15it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:39<00:53,  2.15it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:40<00:53,  2.15it/s]Progress: 65.00%
+---- avg training fps: 7.39

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:40<00:52,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:41<00:51,  2.16it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:41<00:51,  2.15it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:42<00:51,  2.15it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:42<00:51,  2.15it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:42<00:50,  2.15it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:42<00:50,  2.15it/s]Progress: 67.00%
+---- avg training fps: 7.42

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:43<00:49,  2.15it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:43<00:49,  2.15it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:44<00:49,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:44<00:48,  2.15it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:45<00:48,  2.14it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:45<00:48,  2.12it/s]Progress: 69.00%
+---- avg training fps: 7.45

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:46<00:47,  2.14it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:46<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:47<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:47<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:51<02:51,  1.75s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:52<02:12,  1.36s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:52<01:44,  1.09s/it]Progress: 71.00%
+---- avg training fps: 7.20

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:53<01:25,  1.11it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:53<01:12,  1.30it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:54<01:02,  1.48it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:54<00:56,  1.64it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:55<00:51,  1.77it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:55<00:48,  1.87it/s]Progress: 73.00%
+---- avg training fps: 7.24

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:56<00:48,  1.87it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:56<00:45,  1.95it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:56<00:43,  2.01it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:57<00:42,  2.05it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:57<00:41,  2.08it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [01:57<00:40,  2.10it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [01:58<00:39,  2.12it/s]Progress: 75.00%
+---- avg training fps: 7.27

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [01:58<00:39,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [01:59<00:38,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [01:59<00:37,  2.14it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:00<00:37,  2.15it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:00<00:37,  2.15it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:00<00:36,  2.15it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:01<00:36,  2.15it/s]Progress: 77.00%
+---- avg training fps: 7.30

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:01<00:35,  2.16it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:02<00:35,  2.16it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:02<00:34,  2.15it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:03<00:34,  2.16it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:03<00:33,  2.16it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:03<00:33,  2.16it/s]Progress: 79.00%
+---- avg training fps: 7.33

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:04<00:33,  2.15it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:04<00:32,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:05<00:32,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:05<00:32,  2.15it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:05<00:31,  2.14it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:06<00:31,  2.14it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:06<00:30,  2.14it/s]Progress: 81.00%
+---- avg training fps: 7.36

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:07<00:30,  2.15it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:07<00:29,  2.15it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:08<00:29,  2.16it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:08<00:28,  2.16it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:09<00:28,  2.15it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:09<00:27,  2.15it/s]Progress: 83.00%
+---- avg training fps: 7.38

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:10<00:27,  2.15it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:10<00:27,  2.15it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:10<00:26,  2.15it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:10<00:26,  2.14it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:11<00:26,  2.15it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:11<00:25,  2.15it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:12<00:25,  2.15it/s]Progress: 85.00%
+---- avg training fps: 7.41

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:12<00:24,  2.15it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:13<00:24,  2.16it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:13<00:23,  2.17it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:14<00:23,  2.16it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:14<00:23,  2.16it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:14<00:22,  2.16it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:15<00:22,  2.17it/s]Progress: 87.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.43

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:15<00:21,  2.17it/s]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:16<00:21,  2.16it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:16<00:20,  2.15it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:16<00:20,  2.17it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:17<00:19,  2.16it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:17<00:19,  2.16it/s]Progress: 89.00%
+---- avg training fps: 7.46

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:18<00:19,  2.15it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:18<00:18,  2.17it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:19<00:18,  2.17it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:19<00:18,  2.15it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:19<00:20,  1.90it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:20<00:18,  1.97it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:20<00:17,  2.03it/s]Progress: 91.00%
+---- avg training fps: 7.47

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:21<00:16,  2.06it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:21<00:16,  2.09it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:22<00:15,  2.11it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:22<00:14,  2.13it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:23<00:14,  2.14it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:23<00:13,  2.14it/s]Progress: 93.00%
+---- avg training fps: 7.49

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:24<00:13,  2.14it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:24<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:24<00:12,  2.16it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:25<00:12,  2.16it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:25<00:12,  2.15it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:25<00:11,  2.17it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:26<00:11,  2.17it/s]Progress: 95.00%
+---- avg training fps: 7.52

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:26<00:10,  2.16it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:27<00:10,  2.16it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:27<00:09,  2.17it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:28<00:09,  2.17it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:28<00:09,  2.17it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:28<00:08,  2.17it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:29<00:08,  2.16it/s]Progress: 97.00%
+---- avg training fps: 7.54

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:29<00:07,  2.17it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:30<00:07,  2.16it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:30<00:06,  2.16it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:31<00:06,  2.16it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:31<00:05,  2.17it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:31<00:05,  2.17it/s]Progress: 99.00%
+---- avg training fps: 7.56

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:32<00:05,  2.16it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:32<00:04,  2.16it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:33<00:04,  2.16it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:33<00:04,  2.18it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:33<00:03,  2.17it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:34<00:03,  2.16it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:34<00:02,  2.15it/s]Progress: 100.00%
+---- avg training fps: 7.58

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:35<00:02,  2.16it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:35<00:01,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:36<00:01,  2.14it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:36<00:00,  2.15it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:37<00:00,  2.15it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:37<00:00,  2.15it/s]Progress: 100.00%
+---- avg training fps: 7.59
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+
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+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [03:58<00:00,  1.26it/s]
+------------------------------------------
+Training done :)
+2024-08-13 01:59:50.783651: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 01:59:50.874995: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 01:59:50.875023: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 01:59:50.894102: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 01:59:51.295489: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 01:59:51.295570: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 01:59:51.295580: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23116 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.5 freeze_unet_before_completion_f=0.0 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='clipx.zip' output_dir='lora_models/styles_final/clipx.zip_13_01-59-53-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723507193.1146789 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Downloading training data...
+Downloading https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip to lora_models/styles_final/clipx.zip_13_01-59-53-style_512_florence_300/images_in/clipx.zip...
+Succesfully prepped 54 .jpg images in lora_models/styles_final/clipx.zip_13_01-59-53-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.314
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 27 images, 27 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 27 to 54)
+Generating 54 captions using mode: style...
+
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100%|██████████| 54/54 [00:00<00:00, 534180.23it/s]
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal, whimsical, detailed, and quirky scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene is Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase describes the best and worst of times.
+- In the style of TOK, a blockchain is depicted using bees.
+- In the style of TOK, a gorgon made of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset shows Venus and Earth circling the sun.
+- In the style of TOK, there is an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is portrayed.
+- In the style of TOK, the phrase exemplifies the age of wisdom and the age of foolishness.
+- In the style of TOK, two people discuss quitting their boring corporation jobs together.
+- In the style of TOK, petroglyphs are presented.
+- In the style of TOK, a room and space exchange based on first and second-degree connections is depicted.
+- In the style of TOK, the fear known as trypophobia is illustrated.
+- In the style of TOK, there is a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing and singing beavers is highlighted.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with patterns akin to Hawaiian shirts.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a Tarsem Singh-like flair.
+- In the style of TOK, a brigade of beavers resembles M. C. Escher's style.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dances in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models and egrets seated in the audience.
+- In the style of TOK, there is a hyper-realistic painting of lemon-lime architecture.
+- In the style of TOK, a mermaid made of fishnets, dead fish, packing peanuts, and other sea trash swims up through the sea to take a peek above water.
+
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 1...
+----- GPT response: -----
+Style Description: Surrealistic, intricate, and fantastical scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is embodied in a visual representation.
+- In the style of TOK, a blockchain is composed of bees.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun is depicted.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is presented.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is embodied in a visual representation.
+- In the style of TOK, two individuals decide to quit their boring corporation job together.
+- In the style of TOK, petroglyphs are depicted.
+- In the style of TOK, a room and space exchange is based on first and second-degree connections.
+- In the style of TOK, trypophobia is visually represented.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of dancing and singing beavers is created with a nod to Jim Henson's Muppets.
+- In the style of TOK, a beaver is shown chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a pattern like a Hawaiian shirt.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is designed in the style of M. C. Escher.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models and egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting showcases lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash is swimming up through the sea to take a peak above the water.
+- In the style of TOK, a mermaid made out of fishnets and other sea trash is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is embodied in a visual representation.
+- In the style of TOK, a blockchain is composed of bees.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun is depicted.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is presented.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is embodied in a visual representation.
+- In the style of TOK, two individuals decide to quit their boring corporation job together.
+- In the style of TOK, petroglyphs are depicted.
+- In the style of TOK, a room and space exchange is based on first and second-degree connections.
+- In the style of TOK, trypophobia is visually represented.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of dancing and singing beavers is created with a nod to Jim Henson's Muppets.
+- In the style of TOK, a beaver is shown chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a pattern like a Hawaiian shirt.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is designed in the style of M. C. Escher.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models and egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting showcases lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash is swimming up through the sea to take a peak above the water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Using GPT concept name as CLIP-segmentation prompt: Surrealistic, intricate, and fantastical scenes
+Generating 54 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
54it [00:00, 15327.36it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 54 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a mermaid made o...
+1     in the style of <s0><s1><s2>, a throng of lill...
+2     in the style of <s0><s1><s2>, fort kochi in ke...
+3     in the style of <s0><s1><s2>, the phrase "they...
+4     in the style of <s0><s1><s2>, a blockchain is ...
+5     in the style of <s0><s1><s2>, a gorgon made ou...
+6     in the style of <s0><s1><s2>, a hillside at su...
+7     in the style of <s0><s1><s2>, an ancient templ...
+8     in the style of <s0><s1><s2>, cats are pooping...
+9     in the style of <s0><s1><s2>, a goblin goat is...
+10    in the style of <s0><s1><s2>, the phrase "it w...
+11    in the style of <s0><s1><s2>, two individuals ...
+12    in the style of <s0><s1><s2>, petroglyphs are ...
+13    in the style of <s0><s1><s2>, a room and space...
+14    in the style of <s0><s1><s2>, trypophobia is v...
+15    in the style of <s0><s1><s2>, a six-pack of ni...
+16    in the style of <s0><s1><s2>, a band of dancin...
+17    in the style of <s0><s1><s2>, a beaver is show...
+18    in the style of <s0><s1><s2>, a brigade of bea...
+19    in the style of <s0><s1><s2>, a brigade of bea...
+20    in the style of <s0><s1><s2>, a brigade of bea...
+21    in the style of <s0><s1><s2>, a castle is movi...
+22    in the style of <s0><s1><s2>, a chorus line of...
+23    in the style of <s0><s1><s2>, a dirty 1950s re...
+24    in the style of <s0><s1><s2>, a fashion show f...
+25    in the style of <s0><s1><s2>, a hyper-realisti...
+26    in the style of <s0><s1><s2>, a mermaid made o...
+27    in the style of <s0><s1><s2>, a mermaid made o...
+28    in the style of <s0><s1><s2>, a throng of lill...
+29    in the style of <s0><s1><s2>, fort kochi in ke...
+30    in the style of <s0><s1><s2>, the phrase "they...
+31    in the style of <s0><s1><s2>, a blockchain is ...
+32    in the style of <s0><s1><s2>, a gorgon made ou...
+33    in the style of <s0><s1><s2>, a hillside at su...
+34    in the style of <s0><s1><s2>, an ancient templ...
+35    in the style of <s0><s1><s2>, cats are pooping...
+36    in the style of <s0><s1><s2>, a goblin goat is...
+37    in the style of <s0><s1><s2>, the phrase "it w...
+38    in the style of <s0><s1><s2>, two individuals ...
+39    in the style of <s0><s1><s2>, petroglyphs are ...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 54
+--- Num batches each epoch = 14
+--- Num Epochs = 22
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
  0%|          | 0/300 [00:00<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:02<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:02<14:49,  2.98s/it]
# Trainer step: 0, epoch: 0:   1%|          | 2/300 [00:08<21:49,  4.40s/it]
# Trainer step: 0, epoch: 0:   1%|          | 3/300 [00:08<12:50,  2.59s/it]
# Trainer step: 0, epoch: 0:   1%|▏         | 4/300 [00:09<08:37,  1.75s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:09<06:17,  1.28s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:10<04:53,  1.00it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.26

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:10<04:00,  1.22it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:11<03:25,  1.42it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 9/300 [00:11<03:01,  1.60it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 10/300 [00:11<02:45,  1.75it/s]
# Trainer step: 0, epoch: 0:   4%|▎         | 11/300 [00:12<02:35,  1.86it/s]
# Trainer step: 0, epoch: 0:   4%|▍         | 12/300 [00:12<02:27,  1.96it/s]Progress: 7.00%
+---- avg training fps: 3.60

# Trainer step: 0, epoch: 0:   4%|▍         | 13/300 [00:13<02:21,  2.03it/s]
# Trainer step: 0, epoch: 0:   5%|▍         | 14/300 [00:13<02:17,  2.08it/s]
# Trainer step: 14, epoch: 1:   5%|▍         | 14/300 [00:14<02:17,  2.08it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 15/300 [00:14<02:25,  1.96it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 16/300 [00:14<02:20,  2.02it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 17/300 [00:15<02:16,  2.08it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 18/300 [00:15<02:13,  2.12it/s]Progress: 9.00%
+---- avg training fps: 4.45

# Trainer step: 14, epoch: 1:   6%|▋         | 19/300 [00:16<02:11,  2.14it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 20/300 [00:16<02:09,  2.16it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 21/300 [00:17<02:08,  2.17it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 22/300 [00:17<02:07,  2.18it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 23/300 [00:18<02:06,  2.19it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 24/300 [00:18<02:05,  2.19it/s]Progress: 11.00%
+---- avg training fps: 5.08

# Trainer step: 14, epoch: 1:   8%|▊         | 25/300 [00:18<02:05,  2.19it/s]
# Trainer step: 14, epoch: 1:   9%|▊         | 26/300 [00:19<02:04,  2.20it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 27/300 [00:19<02:03,  2.20it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 28/300 [00:20<02:03,  2.20it/s]
# Trainer step: 28, epoch: 2:   9%|▉         | 28/300 [00:20<02:03,  2.20it/s]
# Trainer step: 28, epoch: 2:  10%|▉         | 29/300 [00:20<01:58,  2.29it/s]
# Trainer step: 28, epoch: 2:  10%|█         | 30/300 [00:21<01:59,  2.26it/s]Progress: 13.00%
+---- avg training fps: 5.56

# Trainer step: 28, epoch: 2:  10%|█         | 31/300 [00:21<02:00,  2.23it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 32/300 [00:22<02:00,  2.22it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 33/300 [00:22<02:00,  2.21it/s]
# Trainer step: 28, epoch: 2:  11%|█▏        | 34/300 [00:22<02:00,  2.20it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 35/300 [00:23<02:00,  2.19it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 36/300 [00:23<02:00,  2.19it/s]Progress: 15.00%
+---- avg training fps: 5.92

# Trainer step: 28, epoch: 2:  12%|█▏        | 37/300 [00:24<02:00,  2.19it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 38/300 [00:24<01:59,  2.18it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 39/300 [00:25<01:59,  2.18it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 40/300 [00:25<01:59,  2.18it/s]
# Trainer step: 28, epoch: 2:  14%|█▎        | 41/300 [00:26<01:59,  2.17it/s]
# Trainer step: 28, epoch: 2:  14%|█▍        | 42/300 [00:26<01:58,  2.18it/s]Progress: 17.00%
+---- avg training fps: 6.21

# Trainer step: 42, epoch: 3:  14%|█▍        | 42/300 [00:27<01:58,  2.18it/s]
# Trainer step: 42, epoch: 3:  14%|█▍        | 43/300 [00:27<01:53,  2.26it/s]
# Trainer step: 42, epoch: 3:  15%|█▍        | 44/300 [00:27<01:54,  2.23it/s]
# Trainer step: 42, epoch: 3:  15%|█▌        | 45/300 [00:27<01:55,  2.21it/s]
# Trainer step: 42, epoch: 3:  15%|█▌        | 46/300 [00:28<01:55,  2.19it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 47/300 [00:28<01:55,  2.20it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 48/300 [00:29<01:54,  2.19it/s]Progress: 19.00%
+---- avg training fps: 6.44

# Trainer step: 42, epoch: 3:  16%|█▋        | 49/300 [00:29<01:55,  2.18it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 50/300 [00:30<01:54,  2.18it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 51/300 [00:30<01:54,  2.18it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 52/300 [00:34<05:39,  1.37s/it]
# Trainer step: 42, epoch: 3:  18%|█▊        | 53/300 [00:34<04:30,  1.09s/it]
# Trainer step: 42, epoch: 3:  18%|█▊        | 54/300 [00:35<03:42,  1.11it/s]Progress: 21.00%
+---- avg training fps: 6.07

# Trainer step: 42, epoch: 3:  18%|█▊        | 55/300 [00:35<03:08,  1.30it/s]
# Trainer step: 42, epoch: 3:  19%|█▊        | 56/300 [00:36<02:45,  1.48it/s]
# Trainer step: 56, epoch: 4:  19%|█▊        | 56/300 [00:36<02:45,  1.48it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 57/300 [00:36<02:24,  1.68it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 58/300 [00:36<02:14,  1.80it/s]
# Trainer step: 56, epoch: 4:  20%|█▉        | 59/300 [00:37<02:07,  1.89it/s]
# Trainer step: 56, epoch: 4:  20%|██        | 60/300 [00:37<02:02,  1.96it/s]Progress: 23.00%
+---- avg training fps: 6.27

# Trainer step: 56, epoch: 4:  20%|██        | 61/300 [00:38<01:58,  2.02it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 62/300 [00:38<01:55,  2.05it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 63/300 [00:39<01:53,  2.08it/s]
# Trainer step: 56, epoch: 4:  21%|██▏       | 64/300 [00:39<02:11,  1.79it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 65/300 [00:40<02:04,  1.89it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 66/300 [00:40<01:58,  1.97it/s]Progress: 25.00%
+---- avg training fps: 6.38

# Trainer step: 56, epoch: 4:  22%|██▏       | 67/300 [00:41<01:54,  2.03it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 68/300 [00:41<01:52,  2.06it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 69/300 [00:42<01:50,  2.09it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 70/300 [00:42<01:49,  2.11it/s]
# Trainer step: 70, epoch: 5:  23%|██▎       | 70/300 [00:43<01:49,  2.11it/s]
# Trainer step: 70, epoch: 5:  24%|██▎       | 71/300 [00:43<01:44,  2.20it/s]
# Trainer step: 70, epoch: 5:  24%|██▍       | 72/300 [00:43<01:44,  2.19it/s]Progress: 27.00%
+---- avg training fps: 6.53

# Trainer step: 70, epoch: 5:  24%|██▍       | 73/300 [00:44<01:44,  2.18it/s]
# Trainer step: 70, epoch: 5:  25%|██▍       | 74/300 [00:44<01:43,  2.18it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 75/300 [00:45<01:43,  2.17it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 76/300 [00:45<01:42,  2.18it/s]
# Trainer step: 70, epoch: 5:  26%|██▌       | 77/300 [00:45<01:42,  2.18it/s]
# Trainer step: 70, epoch: 5:  26%|██▌       | 78/300 [00:46<01:42,  2.17it/s]Progress: 29.00%
+---- avg training fps: 6.66

# Trainer step: 70, epoch: 5:  26%|██▋       | 79/300 [00:46<01:41,  2.17it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 80/300 [00:47<01:40,  2.18it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 81/300 [00:47<01:40,  2.17it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 82/300 [00:48<01:40,  2.17it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 83/300 [00:48<01:40,  2.17it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 84/300 [00:49<01:39,  2.17it/s]Progress: 31.00%
+---- avg training fps: 6.78

# Trainer step: 84, epoch: 6:  28%|██▊       | 84/300 [00:49<01:39,  2.17it/s]
# Trainer step: 84, epoch: 6:  28%|██▊       | 85/300 [00:49<01:35,  2.26it/s]
# Trainer step: 84, epoch: 6:  29%|██▊       | 86/300 [00:50<01:35,  2.23it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 87/300 [00:50<01:36,  2.21it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 88/300 [00:50<01:37,  2.19it/s]
# Trainer step: 84, epoch: 6:  30%|██▉       | 89/300 [00:51<01:36,  2.19it/s]
# Trainer step: 84, epoch: 6:  30%|███       | 90/300 [00:51<01:36,  2.18it/s]Progress: 33.00%
+---- avg training fps: 6.88

# Trainer step: 84, epoch: 6:  30%|███       | 91/300 [00:52<01:36,  2.17it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 92/300 [00:52<01:35,  2.17it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 93/300 [00:53<01:34,  2.18it/s]
# Trainer step: 84, epoch: 6:  31%|███▏      | 94/300 [00:53<01:34,  2.17it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 95/300 [00:54<01:34,  2.17it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 96/300 [00:54<01:33,  2.17it/s]Progress: 35.00%
+---- avg training fps: 6.97

# Trainer step: 84, epoch: 6:  32%|███▏      | 97/300 [00:55<01:33,  2.18it/s]
# Trainer step: 84, epoch: 6:  33%|███▎      | 98/300 [00:55<01:32,  2.17it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 98/300 [00:55<01:32,  2.17it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 99/300 [00:55<01:28,  2.26it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 100/300 [00:56<01:29,  2.23it/s]
# Trainer step: 98, epoch: 7:  34%|███▎      | 101/300 [00:56<01:29,  2.21it/s]
# Trainer step: 98, epoch: 7:  34%|███▍      | 102/300 [00:59<03:55,  1.19s/it]Progress: 37.00%
+---- avg training fps: 6.77

# Trainer step: 98, epoch: 7:  34%|███▍      | 103/300 [01:00<03:11,  1.03it/s]
# Trainer step: 98, epoch: 7:  35%|███▍      | 104/300 [01:00<02:40,  1.22it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 105/300 [01:01<02:18,  1.41it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 106/300 [01:01<02:03,  1.57it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 107/300 [01:02<01:52,  1.71it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 108/300 [01:02<01:45,  1.82it/s]Progress: 39.00%
+---- avg training fps: 6.86

# Trainer step: 98, epoch: 7:  36%|███▋      | 109/300 [01:03<01:39,  1.92it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 110/300 [01:03<01:35,  1.99it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 111/300 [01:03<01:32,  2.03it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 112/300 [01:04<01:30,  2.07it/s]
# Trainer step: 112, epoch: 8:  37%|███▋      | 112/300 [01:04<01:30,  2.07it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 113/300 [01:04<01:25,  2.18it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 114/300 [01:05<01:25,  2.17it/s]Progress: 41.00%
+---- avg training fps: 6.94

# Trainer step: 112, epoch: 8:  38%|███▊      | 115/300 [01:05<01:25,  2.17it/s]
# Trainer step: 112, epoch: 8:  39%|███▊      | 116/300 [01:06<01:25,  2.16it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 117/300 [01:06<01:24,  2.16it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 118/300 [01:07<01:24,  2.16it/s]
# Trainer step: 112, epoch: 8:  40%|███▉      | 119/300 [01:07<01:23,  2.17it/s]
# Trainer step: 112, epoch: 8:  40%|████      | 120/300 [01:08<01:23,  2.16it/s]Progress: 43.00%
+---- avg training fps: 7.01

# Trainer step: 112, epoch: 8:  40%|████      | 121/300 [01:08<01:23,  2.16it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 122/300 [01:08<01:22,  2.16it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 123/300 [01:09<01:31,  1.94it/s]
# Trainer step: 112, epoch: 8:  41%|████▏     | 124/300 [01:10<01:27,  2.00it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 125/300 [01:10<01:25,  2.04it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 126/300 [01:11<01:24,  2.07it/s]Progress: 45.00%
+---- avg training fps: 7.06

# Trainer step: 126, epoch: 9:  42%|████▏     | 126/300 [01:11<01:24,  2.07it/s]
# Trainer step: 126, epoch: 9:  42%|████▏     | 127/300 [01:11<01:19,  2.18it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 128/300 [01:11<01:18,  2.18it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 129/300 [01:12<01:18,  2.17it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 130/300 [01:12<01:18,  2.17it/s]
# Trainer step: 126, epoch: 9:  44%|████▎     | 131/300 [01:13<01:17,  2.17it/s]
# Trainer step: 126, epoch: 9:  44%|████▍     | 132/300 [01:13<01:17,  2.18it/s]Progress: 47.00%
+---- avg training fps: 7.12

# Trainer step: 126, epoch: 9:  44%|████▍     | 133/300 [01:14<01:16,  2.18it/s]
# Trainer step: 126, epoch: 9:  45%|████▍     | 134/300 [01:14<01:16,  2.17it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 135/300 [01:15<01:16,  2.17it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 136/300 [01:15<01:15,  2.18it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 137/300 [01:16<01:15,  2.16it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 138/300 [01:16<01:14,  2.16it/s]Progress: 49.00%
+---- avg training fps: 7.17

# Trainer step: 126, epoch: 9:  46%|████▋     | 139/300 [01:16<01:14,  2.16it/s]
# Trainer step: 126, epoch: 9:  47%|████▋     | 140/300 [01:17<01:13,  2.16it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 140/300 [01:17<01:13,  2.16it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 141/300 [01:17<01:10,  2.25it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 142/300 [01:18<01:11,  2.22it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 143/300 [01:18<01:11,  2.20it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 144/300 [01:19<01:11,  2.18it/s]Progress: 51.00%
+---- avg training fps: 7.23

# Trainer step: 140, epoch: 10:  48%|████▊     | 145/300 [01:19<01:10,  2.19it/s]
# Trainer step: 140, epoch: 10:  49%|████▊     | 146/300 [01:20<01:10,  2.18it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 147/300 [01:20<01:10,  2.17it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 148/300 [01:21<01:10,  2.16it/s]
# Trainer step: 140, epoch: 10:  50%|████▉     | 149/300 [01:21<01:09,  2.16it/s]
# Trainer step: 140, epoch: 10:  50%|█████     | 150/300 [01:21<01:09,  2.16it/s]Progress: 53.00%
+---- avg training fps: 7.28

# Trainer step: 140, epoch: 10:  50%|█████     | 151/300 [01:22<01:09,  2.16it/s]
# Trainer step: 140, epoch: 10:  51%|█████     | 152/300 [01:27<04:30,  1.82s/it]
# Trainer step: 140, epoch: 10:  51%|█████     | 153/300 [01:27<03:28,  1.42s/it]
# Trainer step: 140, epoch: 10:  51%|█████▏    | 154/300 [01:28<02:45,  1.13s/it]
# Trainer step: 154, epoch: 11:  51%|█████▏    | 154/300 [01:28<02:45,  1.13s/it]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 155/300 [01:28<02:12,  1.10it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 156/300 [01:29<01:52,  1.28it/s]Progress: 55.00%
+---- avg training fps: 6.96

# Trainer step: 154, epoch: 11:  52%|█████▏    | 157/300 [01:29<01:37,  1.46it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 158/300 [01:30<01:27,  1.62it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 159/300 [01:30<01:20,  1.75it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 160/300 [01:31<01:15,  1.86it/s]
# Trainer step: 154, epoch: 11:  54%|█████▎    | 161/300 [01:31<01:11,  1.94it/s]
# Trainer step: 154, epoch: 11:  54%|█████▍    | 162/300 [01:32<01:08,  2.00it/s]Progress: 57.00%
+---- avg training fps: 7.01

# Trainer step: 154, epoch: 11:  54%|█████▍    | 163/300 [01:32<01:06,  2.05it/s]
# Trainer step: 154, epoch: 11:  55%|█████▍    | 164/300 [01:32<01:05,  2.09it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 165/300 [01:33<01:03,  2.11it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 166/300 [01:33<01:03,  2.13it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 167/300 [01:34<01:02,  2.14it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 168/300 [01:34<01:01,  2.15it/s]Progress: 59.00%
+---- avg training fps: 7.06

# Trainer step: 168, epoch: 12:  56%|█████▌    | 168/300 [01:35<01:01,  2.15it/s]
# Trainer step: 168, epoch: 12:  56%|█████▋    | 169/300 [01:35<00:58,  2.24it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 170/300 [01:35<00:58,  2.21it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 171/300 [01:36<00:58,  2.19it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 172/300 [01:36<00:58,  2.17it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 173/300 [01:37<00:58,  2.16it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 174/300 [01:37<00:58,  2.15it/s]Progress: 61.00%
+---- avg training fps: 7.10

# Trainer step: 168, epoch: 12:  58%|█████▊    | 175/300 [01:37<00:58,  2.15it/s]
# Trainer step: 168, epoch: 12:  59%|█████▊    | 176/300 [01:38<00:57,  2.15it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 177/300 [01:38<00:56,  2.16it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 178/300 [01:39<00:56,  2.15it/s]
# Trainer step: 168, epoch: 12:  60%|█████▉    | 179/300 [01:39<00:56,  2.15it/s]
# Trainer step: 168, epoch: 12:  60%|██████    | 180/300 [01:40<00:56,  2.14it/s]Progress: 63.00%
+---- avg training fps: 7.14

# Trainer step: 168, epoch: 12:  60%|██████    | 181/300 [01:40<00:55,  2.14it/s]
# Trainer step: 168, epoch: 12:  61%|██████    | 182/300 [01:41<00:55,  2.14it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 182/300 [01:41<00:55,  2.14it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 183/300 [01:41<00:52,  2.23it/s]
# Trainer step: 182, epoch: 13:  61%|██████▏   | 184/300 [01:42<00:52,  2.20it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 185/300 [01:42<00:52,  2.19it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 186/300 [01:43<00:52,  2.18it/s]Progress: 65.00%
+---- avg training fps: 7.19

# Trainer step: 182, epoch: 13:  62%|██████▏   | 187/300 [01:43<00:52,  2.15it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 188/300 [01:43<00:52,  2.15it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 189/300 [01:44<00:51,  2.16it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 190/300 [01:44<00:51,  2.16it/s]
# Trainer step: 182, epoch: 13:  64%|██████▎   | 191/300 [01:45<00:50,  2.15it/s]
# Trainer step: 182, epoch: 13:  64%|██████▍   | 192/300 [01:45<00:50,  2.15it/s]Progress: 67.00%
+---- avg training fps: 7.22

# Trainer step: 182, epoch: 13:  64%|██████▍   | 193/300 [01:46<00:49,  2.14it/s]
# Trainer step: 182, epoch: 13:  65%|██████▍   | 194/300 [01:46<00:49,  2.14it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 195/300 [01:47<00:49,  2.13it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 196/300 [01:47<00:48,  2.15it/s]
# Trainer step: 196, epoch: 14:  65%|██████▌   | 196/300 [01:48<00:48,  2.15it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 197/300 [01:48<00:46,  2.23it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 198/300 [01:48<00:46,  2.20it/s]Progress: 69.00%
+---- avg training fps: 7.26

# Trainer step: 196, epoch: 14:  66%|██████▋   | 199/300 [01:49<00:46,  2.18it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 200/300 [01:49<00:46,  2.17it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 201/300 [01:49<00:45,  2.17it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 202/300 [01:52<01:58,  1.21s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 203/300 [01:53<01:35,  1.01it/s]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 204/300 [01:53<01:19,  1.21it/s]Progress: 71.00%
+---- avg training fps: 7.12

# Trainer step: 196, epoch: 14:  68%|██████▊   | 205/300 [01:54<01:16,  1.24it/s]
# Trainer step: 196, epoch: 14:  69%|██████▊   | 206/300 [01:55<01:05,  1.43it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 207/300 [01:55<00:58,  1.59it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 208/300 [01:56<00:53,  1.73it/s]
# Trainer step: 196, epoch: 14:  70%|██████▉   | 209/300 [01:56<00:49,  1.84it/s]
# Trainer step: 196, epoch: 14:  70%|███████   | 210/300 [01:56<00:46,  1.93it/s]Progress: 73.00%
+---- avg training fps: 7.16

# Trainer step: 210, epoch: 15:  70%|███████   | 210/300 [01:57<00:46,  1.93it/s]
# Trainer step: 210, epoch: 15:  70%|███████   | 211/300 [01:57<00:43,  2.07it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 212/300 [01:57<00:41,  2.10it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 213/300 [01:58<00:41,  2.12it/s]
# Trainer step: 210, epoch: 15:  71%|███████▏  | 214/300 [01:58<00:40,  2.13it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 215/300 [01:59<00:39,  2.15it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 216/300 [01:59<00:39,  2.15it/s]Progress: 75.00%
+---- avg training fps: 7.19

# Trainer step: 210, epoch: 15:  72%|███████▏  | 217/300 [02:00<00:38,  2.16it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 218/300 [02:00<00:38,  2.15it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 219/300 [02:01<00:37,  2.17it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 220/300 [02:01<00:36,  2.16it/s]
# Trainer step: 210, epoch: 15:  74%|███████▎  | 221/300 [02:01<00:36,  2.15it/s]
# Trainer step: 210, epoch: 15:  74%|███████▍  | 222/300 [02:02<00:36,  2.15it/s]Progress: 77.00%
+---- avg training fps: 7.23

# Trainer step: 210, epoch: 15:  74%|███████▍  | 223/300 [02:02<00:35,  2.16it/s]
# Trainer step: 210, epoch: 15:  75%|███████▍  | 224/300 [02:03<00:35,  2.16it/s]
# Trainer step: 224, epoch: 16:  75%|███████▍  | 224/300 [02:03<00:35,  2.16it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 225/300 [02:03<00:33,  2.25it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 226/300 [02:04<00:33,  2.22it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 227/300 [02:04<00:33,  2.19it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 228/300 [02:05<00:32,  2.18it/s]Progress: 79.00%
+---- avg training fps: 7.26

# Trainer step: 224, epoch: 16:  76%|███████▋  | 229/300 [02:05<00:32,  2.17it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 230/300 [02:06<00:32,  2.16it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 231/300 [02:06<00:32,  2.15it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 232/300 [02:06<00:31,  2.16it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 233/300 [02:07<00:31,  2.15it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 234/300 [02:07<00:30,  2.15it/s]Progress: 81.00%
+---- avg training fps: 7.29

# Trainer step: 224, epoch: 16:  78%|███████▊  | 235/300 [02:08<00:30,  2.16it/s]
# Trainer step: 224, epoch: 16:  79%|███████▊  | 236/300 [02:08<00:29,  2.15it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 237/300 [02:09<00:29,  2.15it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 238/300 [02:09<00:28,  2.15it/s]
# Trainer step: 238, epoch: 17:  79%|███████▉  | 238/300 [02:10<00:28,  2.15it/s]
# Trainer step: 238, epoch: 17:  80%|███████▉  | 239/300 [02:10<00:27,  2.24it/s]
# Trainer step: 238, epoch: 17:  80%|████████  | 240/300 [02:10<00:27,  2.21it/s]Progress: 83.00%
+---- avg training fps: 7.32

# Trainer step: 238, epoch: 17:  80%|████████  | 241/300 [02:11<00:26,  2.19it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 242/300 [02:11<00:26,  2.17it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 243/300 [02:12<00:26,  2.16it/s]
# Trainer step: 238, epoch: 17:  81%|████████▏ | 244/300 [02:12<00:25,  2.16it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 245/300 [02:12<00:25,  2.15it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 246/300 [02:13<00:25,  2.15it/s]Progress: 85.00%
+---- avg training fps: 7.35

# Trainer step: 238, epoch: 17:  82%|████████▏ | 247/300 [02:13<00:24,  2.16it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 248/300 [02:14<00:24,  2.16it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 249/300 [02:14<00:23,  2.15it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 250/300 [02:15<00:23,  2.15it/s]
# Trainer step: 238, epoch: 17:  84%|████████▎ | 251/300 [02:15<00:22,  2.15it/s]
# Trainer step: 238, epoch: 17:  84%|████████▍ | 252/300 [02:19<01:09,  1.45s/it]Progress: 87.00%
+---- avg training fps: 7.20

# Trainer step: 252, epoch: 18:  84%|████████▍ | 252/300 [02:19<01:09,  1.45s/it]
# Trainer step: 252, epoch: 18:  84%|████████▍ | 253/300 [02:19<00:53,  1.14s/it]
# Trainer step: 252, epoch: 18:  85%|████████▍ | 254/300 [02:20<00:43,  1.06it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 255/300 [02:20<00:35,  1.25it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 256/300 [02:21<00:30,  1.43it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 257/300 [02:21<00:26,  1.59it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 258/300 [02:22<00:24,  1.73it/s]Progress: 89.00%
+---- avg training fps: 7.23

# Trainer step: 252, epoch: 18:  86%|████████▋ | 259/300 [02:22<00:22,  1.84it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 260/300 [02:23<00:20,  1.92it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 261/300 [02:23<00:19,  1.99it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 262/300 [02:24<00:18,  2.03it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 263/300 [02:24<00:17,  2.07it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 264/300 [02:25<00:17,  2.09it/s]Progress: 91.00%
+---- avg training fps: 7.26

# Trainer step: 252, epoch: 18:  88%|████████▊ | 265/300 [02:25<00:16,  2.11it/s]
# Trainer step: 252, epoch: 18:  89%|████████▊ | 266/300 [02:25<00:16,  2.12it/s]
# Trainer step: 266, epoch: 19:  89%|████████▊ | 266/300 [02:26<00:16,  2.12it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 267/300 [02:26<00:14,  2.21it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 268/300 [02:26<00:14,  2.17it/s]
# Trainer step: 266, epoch: 19:  90%|████████▉ | 269/300 [02:27<00:14,  2.17it/s]
# Trainer step: 266, epoch: 19:  90%|█████████ | 270/300 [02:27<00:13,  2.15it/s]Progress: 93.00%
+---- avg training fps: 7.28

# Trainer step: 266, epoch: 19:  90%|█████████ | 271/300 [02:28<00:13,  2.15it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 272/300 [02:28<00:13,  2.15it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 273/300 [02:29<00:12,  2.14it/s]
# Trainer step: 266, epoch: 19:  91%|█████████▏| 274/300 [02:29<00:12,  2.14it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 275/300 [02:30<00:11,  2.14it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 276/300 [02:30<00:11,  2.14it/s]Progress: 95.00%
+---- avg training fps: 7.31

# Trainer step: 266, epoch: 19:  92%|█████████▏| 277/300 [02:31<00:10,  2.13it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 278/300 [02:31<00:10,  2.14it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 279/300 [02:32<00:09,  2.13it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 280/300 [02:32<00:09,  2.13it/s]
# Trainer step: 280, epoch: 20:  93%|█████████▎| 280/300 [02:32<00:09,  2.13it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▎| 281/300 [02:32<00:08,  2.22it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▍| 282/300 [02:33<00:08,  2.20it/s]Progress: 97.00%
+---- avg training fps: 7.33

# Trainer step: 280, epoch: 20:  94%|█████████▍| 283/300 [02:33<00:07,  2.18it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▍| 284/300 [02:34<00:07,  2.17it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 285/300 [02:34<00:06,  2.16it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 286/300 [02:35<00:06,  2.17it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 287/300 [02:35<00:06,  2.16it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 288/300 [02:36<00:05,  2.15it/s]Progress: 99.00%
+---- avg training fps: 7.35

# Trainer step: 280, epoch: 20:  96%|█████████▋| 289/300 [02:36<00:05,  2.15it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 290/300 [02:37<00:04,  2.15it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 291/300 [02:37<00:04,  2.14it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 292/300 [02:38<00:03,  2.14it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 293/300 [02:38<00:03,  2.15it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 294/300 [02:38<00:02,  2.14it/s]Progress: 100.00%
+---- avg training fps: 7.38

# Trainer step: 294, epoch: 21:  98%|█████████▊| 294/300 [02:39<00:02,  2.14it/s]
# Trainer step: 294, epoch: 21:  98%|█████████▊| 295/300 [02:39<00:02,  2.24it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▊| 296/300 [02:39<00:01,  2.20it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 297/300 [02:40<00:01,  2.17it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 298/300 [02:40<00:00,  2.17it/s]
# Trainer step: 294, epoch: 21: 100%|█████████▉| 299/300 [02:41<00:00,  2.15it/s]
# Trainer step: 294, epoch: 21: 100%|██████████| 300/300 [02:41<00:00,  2.14it/s]Progress: 100.00%
+---- avg training fps: 7.40

# Trainer step: 294, epoch: 21: : 301it [02:42,  2.15it/s]                       Progress: 100.00%
Reached max steps, stopping training!
+Saving checkpoint at step.. 301
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 294, epoch: 21: : 301it [04:05,  1.23it/s]
+------------------------------------------
+Training done :)
+2024-08-13 02:04:51.883372: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 02:04:51.976011: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 02:04:51.976038: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 02:04:51.995071: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 02:04:52.393460: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:04:52.393543: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:04:52.393553: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23112 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/good_styles/eden_crystals' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.5 freeze_unet_before_completion_f=0.5 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='eden_crystals' output_dir='lora_models/styles_final/eden_crystals_13_02-04-54-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723507494.217606 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/eden_crystals_13_02-04-54-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.242
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants on a black background.
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants in the dark.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal blending of nature and technology
+
+- In the style of TOK, a potted plant sitting on top of a table next to a lamp.
+- In the style of TOK, a futuristic city in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves on a dark blue background.
+- In the style of TOK, a plant growing out of a circuit board.
+- In the style of TOK, a group of green and red plants on a black background.
+- In the style of TOK, a potted plant sitting on top of a table next to a lamp.
+- In the style of TOK, a futuristic city in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves on a dark blue background.
+- In the style of TOK, a plant growing out of a circuit board.
+- In the style of TOK, a group of green and red plants in the dark.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Surreal blending of nature and technology
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 7468.49it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a potted plant s...
+1     in the style of <s0><s1><s2>, a futuristic cit...
+2     in the style of <s0><s1><s2>, a flower with gr...
+3     in the style of <s0><s1><s2>, a plant growing ...
+4     in the style of <s0><s1><s2>, a group of green...
+5     in the style of <s0><s1><s2>, a potted plant s...
+6     in the style of <s0><s1><s2>, a futuristic cit...
+7     in the style of <s0><s1><s2>, a flower with gr...
+8     in the style of <s0><s1><s2>, a plant growing ...
+9     in the style of <s0><s1><s2>, a group of green...
+10    in the style of <s0><s1><s2>, a potted plant s...
+11    in the style of <s0><s1><s2>, a futuristic cit...
+12    in the style of <s0><s1><s2>, a flower with gr...
+13    in the style of <s0><s1><s2>, a plant growing ...
+14    in the style of <s0><s1><s2>, a group of green...
+15    in the style of <s0><s1><s2>, a potted plant s...
+16    in the style of <s0><s1><s2>, a futuristic cit...
+17    in the style of <s0><s1><s2>, a flower with gr...
+18    in the style of <s0><s1><s2>, a plant growing ...
+19    in the style of <s0><s1><s2>, a group of green...
+20    in the style of <s0><s1><s2>, a potted plant s...
+21    in the style of <s0><s1><s2>, a futuristic cit...
+22    in the style of <s0><s1><s2>, a flower with gr...
+23    in the style of <s0><s1><s2>, a plant growing ...
+24    in the style of <s0><s1><s2>, a group of green...
+25    in the style of <s0><s1><s2>, a potted plant s...
+26    in the style of <s0><s1><s2>, a futuristic cit...
+27    in the style of <s0><s1><s2>, a flower with gr...
+28    in the style of <s0><s1><s2>, a plant growing ...
+29    in the style of <s0><s1><s2>, a group of green...
+30    in the style of <s0><s1><s2>, a potted plant s...
+31    in the style of <s0><s1><s2>, a futuristic cit...
+32    in the style of <s0><s1><s2>, a flower with gr...
+33    in the style of <s0><s1><s2>, a plant growing ...
+34    in the style of <s0><s1><s2>, a group of green...
+35    in the style of <s0><s1><s2>, a potted plant s...
+36    in the style of <s0><s1><s2>, a futuristic cit...
+37    in the style of <s0><s1><s2>, a flower with gr...
+38    in the style of <s0><s1><s2>, a plant growing ...
+39    in the style of <s0><s1><s2>, a group of green...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:08<04:10,  1.17it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.73

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+---- avg training fps: 4.16

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+---- avg training fps: 5.03

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# Trainer step: 20, epoch: 2:   7%|▋         | 20/300 [00:15<02:10,  2.15it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 21/300 [00:15<02:10,  2.15it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 22/300 [00:15<02:09,  2.15it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 23/300 [00:16<02:08,  2.16it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 24/300 [00:16<02:07,  2.16it/s]Progress: 11.00%
+---- avg training fps: 5.61

# Trainer step: 20, epoch: 2:   8%|▊         | 25/300 [00:17<02:07,  2.16it/s]
# Trainer step: 20, epoch: 2:   9%|▊         | 26/300 [00:17<02:07,  2.16it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 27/300 [00:18<02:07,  2.15it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 28/300 [00:18<02:06,  2.15it/s]
# Trainer step: 20, epoch: 2:  10%|▉         | 29/300 [00:18<02:05,  2.15it/s]
# Trainer step: 20, epoch: 2:  10%|█         | 30/300 [00:19<02:05,  2.16it/s]Progress: 13.00%
+---- avg training fps: 6.03

# Trainer step: 30, epoch: 3:  10%|█         | 30/300 [00:19<02:05,  2.16it/s]
# Trainer step: 30, epoch: 3:  10%|█         | 31/300 [00:19<02:05,  2.15it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 32/300 [00:20<02:04,  2.15it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 33/300 [00:20<02:03,  2.15it/s]
# Trainer step: 30, epoch: 3:  11%|█▏        | 34/300 [00:21<02:03,  2.16it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 35/300 [00:21<02:02,  2.16it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 36/300 [00:22<02:02,  2.15it/s]Progress: 15.00%
+---- avg training fps: 6.30

# Trainer step: 30, epoch: 3:  12%|█▏        | 37/300 [00:22<02:16,  1.92it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 38/300 [00:23<02:10,  2.00it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 39/300 [00:23<02:07,  2.04it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 40/300 [00:24<02:05,  2.07it/s]
# Trainer step: 40, epoch: 4:  13%|█▎        | 40/300 [00:24<02:05,  2.07it/s]
# Trainer step: 40, epoch: 4:  14%|█▎        | 41/300 [00:24<02:03,  2.10it/s]
# Trainer step: 40, epoch: 4:  14%|█▍        | 42/300 [00:25<02:01,  2.13it/s]Progress: 17.00%
+---- avg training fps: 6.55

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:25<02:00,  2.13it/s]
# Trainer step: 40, epoch: 4:  15%|█▍        | 44/300 [00:26<01:59,  2.14it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 45/300 [00:26<01:58,  2.15it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 46/300 [00:27<01:58,  2.15it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 47/300 [00:27<01:57,  2.15it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:27<01:57,  2.15it/s]Progress: 19.00%
+---- avg training fps: 6.76

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:28<01:56,  2.16it/s]
# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:28<01:56,  2.15it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 50/300 [00:29<01:56,  2.15it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 51/300 [00:29<01:55,  2.15it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 52/300 [00:32<05:44,  1.39s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 53/300 [00:33<04:34,  1.11s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:33<03:45,  1.09it/s]Progress: 21.00%
+---- avg training fps: 6.30

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:34<03:11,  1.28it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:34<02:47,  1.45it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 57/300 [00:35<02:30,  1.61it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 58/300 [00:35<02:18,  1.74it/s]
# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:36<02:10,  1.85it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:36<02:04,  1.93it/s]Progress: 23.00%
+---- avg training fps: 6.47

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:37<02:04,  1.93it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:37<01:59,  1.99it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:37<01:56,  2.04it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:38<01:54,  2.08it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:38<01:52,  2.10it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:38<01:51,  2.11it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:39<01:50,  2.12it/s]Progress: 25.00%
+---- avg training fps: 6.62

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:39<01:49,  2.13it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:40<01:48,  2.15it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:40<01:47,  2.14it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:41<01:47,  2.15it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:41<01:47,  2.15it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:41<01:46,  2.14it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:42<01:59,  1.90it/s]Progress: 27.00%
+---- avg training fps: 6.72

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:42<01:55,  1.97it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:43<01:51,  2.02it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:43<01:49,  2.06it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:44<01:47,  2.08it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:44<01:45,  2.11it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:45<01:44,  2.13it/s]Progress: 29.00%
+---- avg training fps: 6.84

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:45<01:43,  2.14it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:46<01:42,  2.15it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:46<01:42,  2.15it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:46<01:41,  2.15it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:47<01:41,  2.15it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:47<01:40,  2.17it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:47<01:39,  2.17it/s]Progress: 31.00%
+---- avg training fps: 6.94

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:48<01:39,  2.17it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:48<01:38,  2.16it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:49<01:38,  2.17it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:49<01:37,  2.16it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:50<01:37,  2.16it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:50<01:37,  2.16it/s]Progress: 33.00%
+---- avg training fps: 7.04

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:51<01:37,  2.16it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:51<01:36,  2.18it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:51<01:35,  2.17it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:52<01:35,  2.17it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:52<01:35,  2.17it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:53<01:34,  2.17it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:53<01:34,  2.17it/s]Progress: 35.00%
+---- avg training fps: 7.12

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:53<01:33,  2.17it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:54<01:33,  2.16it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:54<01:32,  2.17it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:55<01:32,  2.16it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:55<01:32,  2.16it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:55<01:31,  2.17it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:56<01:31,  2.15it/s]Progress: 37.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.19

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:56<01:31,  2.16it/s]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [00:57<01:30,  2.16it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [00:57<01:30,  2.15it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [00:58<01:30,  2.15it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [00:58<01:29,  2.15it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [00:59<01:29,  2.15it/s]Progress: 39.00%
+---- avg training fps: 7.26

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [00:59<01:29,  2.15it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [00:59<01:28,  2.15it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:00<01:28,  2.15it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:00<01:27,  2.15it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:00<01:27,  2.14it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:01<01:27,  2.14it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:01<01:26,  2.15it/s]Progress: 41.00%
+---- avg training fps: 7.32

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:02<01:26,  2.15it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:02<01:25,  2.15it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:03<01:25,  2.14it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:03<01:24,  2.15it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:04<01:24,  2.15it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:04<01:24,  2.13it/s]Progress: 43.00%
+---- avg training fps: 7.37

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:05<01:24,  2.13it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:05<01:23,  2.15it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:05<01:22,  2.15it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:06<01:22,  2.15it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:06<01:21,  2.15it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:06<01:21,  2.15it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:07<01:21,  2.15it/s]Progress: 45.00%
+---- avg training fps: 7.42

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:07<01:20,  2.15it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:08<01:20,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:08<01:19,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:09<01:19,  2.14it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:09<01:19,  2.14it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:09<01:18,  2.14it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:10<01:17,  2.16it/s]Progress: 47.00%
+---- avg training fps: 7.47

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:10<01:17,  2.15it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:11<01:17,  2.15it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:11<01:16,  2.14it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:12<01:16,  2.15it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:12<01:16,  2.14it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:13<01:15,  2.14it/s]Progress: 49.00%
+---- avg training fps: 7.51

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:13<01:15,  2.15it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:13<01:14,  2.14it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:14<01:14,  2.14it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:14<01:14,  2.14it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:14<01:13,  2.14it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:15<01:13,  2.15it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:15<01:12,  2.14it/s]Progress: 51.00%
+---- avg training fps: 7.55

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:16<01:12,  2.14it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:16<01:12,  2.14it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:17<01:11,  2.14it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:17<01:11,  2.14it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:18<01:10,  2.14it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:18<01:09,  2.16it/s]Progress: 53.00%
+---- avg training fps: 7.59

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:19<01:09,  2.16it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:19<01:09,  2.15it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:23<03:48,  1.54s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:23<02:59,  1.22s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:24<02:24,  1.01it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:24<02:00,  1.20it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:24<01:43,  1.39it/s]Progress: 55.00%
+---- avg training fps: 7.30

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:25<01:32,  1.55it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:25<01:23,  1.70it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:26<01:17,  1.81it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:26<01:13,  1.91it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:27<01:13,  1.91it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:27<01:10,  1.97it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:27<01:08,  2.03it/s]Progress: 57.00%
+---- avg training fps: 7.34

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:28<01:06,  2.06it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:28<01:05,  2.08it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:29<01:04,  2.10it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:29<01:03,  2.11it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:30<01:02,  2.13it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:30<01:01,  2.13it/s]Progress: 59.00%
+---- avg training fps: 7.38

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:31<01:01,  2.14it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:31<01:01,  2.13it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:31<01:01,  2.13it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:31<01:00,  2.14it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:32<00:59,  2.14it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:32<00:59,  2.14it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:33<00:58,  2.15it/s]Progress: 61.00%
+---- avg training fps: 7.42

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:33<00:58,  2.15it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:34<00:57,  2.14it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:34<00:57,  2.14it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:35<00:56,  2.14it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:35<00:56,  2.14it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:36<00:56,  2.14it/s]Progress: 63.00%
+---- avg training fps: 7.45

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:36<00:56,  2.14it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:36<00:55,  2.15it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:37<00:54,  2.15it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:37<00:54,  2.15it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:38<00:54,  2.14it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:38<00:53,  2.15it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:38<00:53,  2.14it/s]Progress: 65.00%
+---- avg training fps: 7.48

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:39<00:52,  2.13it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:39<00:52,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:40<00:51,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:40<00:51,  2.14it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:41<00:51,  2.14it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:41<00:51,  2.13it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:41<00:50,  2.15it/s]Progress: 67.00%
+---- avg training fps: 7.51

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:42<00:49,  2.15it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:42<00:49,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:43<00:49,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:43<00:48,  2.15it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:44<00:48,  2.14it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:44<00:47,  2.14it/s]Progress: 69.00%
+---- avg training fps: 7.54

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:45<00:46,  2.15it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:45<00:46,  2.15it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:45<00:46,  2.15it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:45<00:46,  2.15it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:49<02:27,  1.51s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:50<01:55,  1.19s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:50<01:33,  1.03it/s]Progress: 71.00%
+---- avg training fps: 7.33

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:51<01:17,  1.22it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:51<01:06,  1.40it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:52<00:59,  1.58it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:52<00:53,  1.71it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:53<00:49,  1.83it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:53<00:46,  1.92it/s]Progress: 73.00%
+---- avg training fps: 7.37

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:54<00:46,  1.92it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:54<00:44,  1.99it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:54<00:43,  2.03it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:54<00:42,  2.06it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:55<00:41,  2.09it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [01:55<00:40,  2.11it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [01:56<00:39,  2.13it/s]Progress: 75.00%
+---- avg training fps: 7.40

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [01:56<00:38,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [01:57<00:38,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [01:57<00:37,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [01:58<00:37,  2.14it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [01:58<00:37,  2.14it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [01:58<00:36,  2.14it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [01:59<00:36,  2.15it/s]Progress: 77.00%
+---- avg training fps: 7.42

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [01:59<00:35,  2.15it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:00<00:35,  2.15it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:00<00:34,  2.14it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:01<00:34,  2.15it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:01<00:34,  2.14it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:01<00:33,  2.14it/s]Progress: 79.00%
+---- avg training fps: 7.45

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:02<00:33,  2.14it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:02<00:32,  2.14it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:03<00:32,  2.14it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:03<00:32,  2.14it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:03<00:31,  2.14it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:04<00:31,  2.15it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:04<00:30,  2.15it/s]Progress: 81.00%
+---- avg training fps: 7.47

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:05<00:30,  2.14it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:05<00:29,  2.14it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:06<00:29,  2.14it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:06<00:29,  2.13it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:07<00:28,  2.13it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:07<00:27,  2.15it/s]Progress: 83.00%
+---- avg training fps: 7.50

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:08<00:27,  2.15it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:08<00:27,  2.15it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:08<00:27,  2.14it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:08<00:26,  2.14it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:09<00:26,  2.14it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:09<00:25,  2.14it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:10<00:25,  2.13it/s]Progress: 85.00%
+---- avg training fps: 7.52

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:10<00:24,  2.14it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:11<00:24,  2.14it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:11<00:23,  2.14it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:12<00:23,  2.13it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:12<00:23,  2.13it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:12<00:22,  2.14it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:13<00:22,  2.13it/s]Progress: 87.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.54

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:13<00:22,  2.13it/s]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:14<00:21,  2.14it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:14<00:21,  2.14it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:15<00:20,  2.14it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:15<00:20,  2.13it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:15<00:19,  2.14it/s]Progress: 89.00%
+---- avg training fps: 7.56

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:16<00:19,  2.13it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:16<00:18,  2.13it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:17<00:18,  2.13it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:17<00:18,  2.14it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:17<00:17,  2.14it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:18<00:17,  2.14it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:18<00:16,  2.13it/s]Progress: 91.00%
+---- avg training fps: 7.58

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:19<00:16,  2.14it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:19<00:15,  2.13it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:20<00:15,  2.13it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:20<00:14,  2.14it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:21<00:14,  2.14it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:21<00:14,  2.14it/s]Progress: 93.00%
+---- avg training fps: 7.60

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:22<00:14,  2.14it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:22<00:13,  2.13it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:22<00:13,  2.13it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:23<00:12,  2.13it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:23<00:12,  2.12it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:23<00:11,  2.14it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:24<00:11,  2.14it/s]Progress: 95.00%
+---- avg training fps: 7.62

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:24<00:10,  2.13it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:25<00:10,  2.13it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:25<00:09,  2.13it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:26<00:09,  2.13it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:26<00:09,  2.13it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:26<00:08,  2.13it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:27<00:08,  2.14it/s]Progress: 97.00%
+---- avg training fps: 7.64

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:27<00:07,  2.14it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:28<00:07,  2.13it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:28<00:07,  2.13it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:29<00:06,  2.14it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:29<00:06,  2.13it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:30<00:05,  2.13it/s]Progress: 99.00%
+---- avg training fps: 7.65

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:30<00:05,  2.14it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:30<00:04,  2.14it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:31<00:04,  2.14it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:31<00:04,  2.14it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:31<00:03,  2.13it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:32<00:03,  2.14it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:32<00:02,  2.13it/s]Progress: 100.00%
+---- avg training fps: 7.67

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:33<00:02,  2.13it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:33<00:01,  2.13it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:34<00:01,  2.13it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:34<00:00,  2.13it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:35<00:00,  2.14it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:35<00:00,  2.14it/s]Progress: 100.00%
+---- avg training fps: 7.69
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [03:56<00:00,  1.27it/s]
+------------------------------------------
+Training done :)
+2024-08-13 02:09:20.532324: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 02:09:20.626535: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 02:09:20.626572: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 02:09:20.645332: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 02:09:21.052725: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:09:21.052807: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:09:21.052817: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23108 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/journey_small' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=1.0 freeze_unet_before_completion_f=0.25 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='journey_small' output_dir='lora_models/styles_final/journey_small_13_02-09-22-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723507762.8702936 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/journey_small_13_02-09-22-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 0.812
+New train_img_size: [448, 576]
+Validation_img_size was set to: [896, 1152]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red cloak standing in front of a building.
+- A person standing on top of a body of water.
+- A person walking in the desert with a camel in front of a mountain.
+- A man standing on top of a sandy beach next to the ocean.
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red dress is walking through an archway.
+- A person standing on top of a body of water.
+- A person walking in the desert with a dog on a leash.
+- A person walking on a beach next to a body of water.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal and dreamlike, bold compositions.
+
+- In the style of TOK: A sunset over a desert landscape with a mountain in the background.
+- In the style of TOK: A woman in a red cloak standing in front of a building.
+- In the style of TOK: A person standing on top of a body of water.
+- In the style of TOK: A person walking in the desert with a camel in front of a mountain.
+- In the style of TOK: A man standing on top of a sandy beach next to the ocean.
+- In the style of TOK: A sunset over a desert landscape with a mountain in the background.
+- In the style of TOK: A woman in a red dress walking through an archway.
+- In the style of TOK: A person standing on top of a body of water.
+- In the style of TOK: A person walking in the desert with a dog on a leash.
+- In the style of TOK: A person walking on a beach next to a body of water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Surreal and dreamlike, bold compositions.
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 9978.12it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>: a sunset over a ...
+1     in the style of <s0><s1><s2>: a woman in a red...
+2     in the style of <s0><s1><s2>: a person standin...
+3     in the style of <s0><s1><s2>: a person walking...
+4     in the style of <s0><s1><s2>: a man standing o...
+5     in the style of <s0><s1><s2>: a sunset over a ...
+6     in the style of <s0><s1><s2>: a woman in a red...
+7     in the style of <s0><s1><s2>: a person standin...
+8     in the style of <s0><s1><s2>: a person walking...
+9     in the style of <s0><s1><s2>: a person walking...
+10    in the style of <s0><s1><s2>: a sunset over a ...
+11    in the style of <s0><s1><s2>: a woman in a red...
+12    in the style of <s0><s1><s2>: a person standin...
+13    in the style of <s0><s1><s2>: a person walking...
+14    in the style of <s0><s1><s2>: a man standing o...
+15    in the style of <s0><s1><s2>: a sunset over a ...
+16    in the style of <s0><s1><s2>: a woman in a red...
+17    in the style of <s0><s1><s2>: a person standin...
+18    in the style of <s0><s1><s2>: a person walking...
+19    in the style of <s0><s1><s2>: a person walking...
+20    in the style of <s0><s1><s2>: a sunset over a ...
+21    in the style of <s0><s1><s2>: a woman in a red...
+22    in the style of <s0><s1><s2>: a person standin...
+23    in the style of <s0><s1><s2>: a person walking...
+24    in the style of <s0><s1><s2>: a man standing o...
+25    in the style of <s0><s1><s2>: a sunset over a ...
+26    in the style of <s0><s1><s2>: a woman in a red...
+27    in the style of <s0><s1><s2>: a person standin...
+28    in the style of <s0><s1><s2>: a person walking...
+29    in the style of <s0><s1><s2>: a person walking...
+30    in the style of <s0><s1><s2>: a sunset over a ...
+31    in the style of <s0><s1><s2>: a woman in a red...
+32    in the style of <s0><s1><s2>: a person standin...
+33    in the style of <s0><s1><s2>: a person walking...
+34    in the style of <s0><s1><s2>: a man standing o...
+35    in the style of <s0><s1><s2>: a sunset over a ...
+36    in the style of <s0><s1><s2>: a woman in a red...
+37    in the style of <s0><s1><s2>: a person standin...
+38    in the style of <s0><s1><s2>: a person walking...
+39    in the style of <s0><s1><s2>: a person walking...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:09<04:39,  1.05it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.39

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+---- avg training fps: 3.69

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+---- avg training fps: 4.56

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+---- avg training fps: 5.18

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+---- avg training fps: 5.63

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+---- avg training fps: 5.98

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+---- avg training fps: 6.26

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:26<01:58,  2.17it/s]
# Trainer step: 40, epoch: 4:  15%|█▍        | 44/300 [00:27<01:58,  2.17it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 45/300 [00:27<01:57,  2.17it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 46/300 [00:28<01:57,  2.16it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 47/300 [00:28<01:56,  2.16it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:29<01:56,  2.17it/s]Progress: 19.00%
+---- avg training fps: 6.48

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:29<01:55,  2.17it/s]
# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:30<01:55,  2.17it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 50/300 [00:30<01:55,  2.17it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 51/300 [00:30<01:55,  2.17it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 52/300 [00:34<06:33,  1.59s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 53/300 [00:35<05:08,  1.25s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:35<04:09,  1.01s/it]Progress: 21.00%
+---- avg training fps: 5.98

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:36<03:27,  1.18it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:36<02:58,  1.36it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 57/300 [00:37<02:38,  1.53it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 58/300 [00:37<02:24,  1.67it/s]
# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:38<02:14,  1.79it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:38<02:07,  1.89it/s]Progress: 23.00%
+---- avg training fps: 6.16

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:38<02:07,  1.89it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:38<02:02,  1.95it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:39<01:58,  2.01it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:39<01:55,  2.06it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:40<01:53,  2.08it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:40<01:51,  2.11it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:41<01:50,  2.12it/s]Progress: 25.00%
+---- avg training fps: 6.33

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:41<01:49,  2.13it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:42<01:48,  2.13it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:42<01:47,  2.14it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:43<01:47,  2.15it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:43<01:47,  2.15it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:43<01:47,  2.13it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:44<01:46,  2.14it/s]Progress: 27.00%
+---- avg training fps: 6.47

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:44<01:46,  2.14it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:44<01:45,  2.14it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:45<01:45,  2.14it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:45<01:44,  2.15it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:46<01:43,  2.15it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:46<01:43,  2.15it/s]Progress: 29.00%
+---- avg training fps: 6.59

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:47<01:43,  2.14it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:47<01:42,  2.15it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:48<01:42,  2.15it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:48<01:41,  2.15it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:48<01:41,  2.15it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:49<01:41,  2.15it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:49<01:40,  2.15it/s]Progress: 31.00%
+---- avg training fps: 6.71

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:50<01:39,  2.16it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:50<01:39,  2.16it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:51<01:38,  2.16it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:51<01:38,  2.15it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:51<01:37,  2.16it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:52<01:37,  2.16it/s]Progress: 33.00%
+---- avg training fps: 6.81

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:52<01:37,  2.16it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:52<01:37,  2.15it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:53<01:36,  2.15it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:53<01:36,  2.15it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:54<01:35,  2.15it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:54<01:35,  2.15it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:55<01:34,  2.16it/s]Progress: 35.00%
+---- avg training fps: 6.90

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:55<01:34,  2.16it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:56<01:33,  2.15it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:56<01:33,  2.15it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:57<01:32,  2.15it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:57<01:32,  2.15it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:57<01:32,  2.16it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:57<01:31,  2.16it/s]Progress: 37.00%
Failed to plot token attention loss
+
+---- avg training fps: 6.98

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:58<01:31,  2.15it/s]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [00:58<01:31,  2.14it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [00:59<01:30,  2.14it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [00:59<01:30,  2.14it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:00<01:30,  2.14it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:00<01:29,  2.14it/s]Progress: 39.00%
+---- avg training fps: 7.05

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:01<01:29,  2.14it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:01<01:29,  2.13it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:02<01:29,  2.13it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:02<01:28,  2.14it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:02<01:28,  2.13it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:03<01:27,  2.14it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:03<01:26,  2.14it/s]Progress: 41.00%
+---- avg training fps: 7.12

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:04<01:26,  2.15it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:04<01:25,  2.14it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:05<01:25,  2.14it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:05<01:25,  2.14it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:05<01:24,  2.15it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:06<01:24,  2.14it/s]Progress: 43.00%
+---- avg training fps: 7.18

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:06<01:24,  2.14it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:06<01:23,  2.14it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:07<01:23,  2.14it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:07<01:22,  2.14it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:08<01:22,  2.14it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:08<01:21,  2.14it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:09<01:21,  2.14it/s]Progress: 45.00%
+---- avg training fps: 7.23

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:09<01:20,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:10<01:20,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:10<01:19,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:11<01:19,  2.14it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:11<01:19,  2.14it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:11<01:19,  2.14it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:12<01:17,  2.15it/s]Progress: 47.00%
+---- avg training fps: 7.28

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:12<01:17,  2.15it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:12<01:17,  2.14it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:13<01:17,  2.14it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:13<01:16,  2.14it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:14<01:16,  2.14it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:14<01:16,  2.13it/s]Progress: 49.00%
+---- avg training fps: 7.33

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:15<01:15,  2.14it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:15<01:15,  2.13it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:16<01:15,  2.13it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:16<01:14,  2.13it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:16<01:14,  2.13it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:17<01:13,  2.14it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:17<01:13,  2.13it/s]Progress: 51.00%
+---- avg training fps: 7.37

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:18<01:12,  2.13it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:18<01:12,  2.14it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:19<01:11,  2.14it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:19<01:11,  2.14it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:19<01:10,  2.13it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:20<01:10,  2.14it/s]Progress: 53.00%
+---- avg training fps: 7.42

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:20<01:10,  2.14it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:20<01:09,  2.13it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:25<04:14,  1.72s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:26<03:17,  1.34s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:26<02:37,  1.08s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:26<02:09,  1.12it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:27<01:50,  1.31it/s]Progress: 55.00%
+---- avg training fps: 7.10

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:27<01:36,  1.48it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:28<01:26,  1.64it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:28<01:19,  1.77it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:29<01:15,  1.87it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:29<01:15,  1.87it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:29<01:11,  1.94it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:30<01:09,  2.00it/s]Progress: 57.00%
+---- avg training fps: 7.15

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:30<01:06,  2.05it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:31<01:05,  2.07it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:31<01:04,  2.09it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:32<01:03,  2.10it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:32<01:02,  2.12it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:32<01:02,  2.13it/s]Progress: 59.00%
+---- avg training fps: 7.19

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:33<01:01,  2.13it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:33<01:00,  2.14it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:34<01:00,  2.14it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:34<01:00,  2.14it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:34<00:59,  2.14it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:35<00:59,  2.14it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:35<00:58,  2.15it/s]Progress: 61.00%
+---- avg training fps: 7.23

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:36<00:58,  2.13it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:36<00:58,  2.14it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:37<00:57,  2.14it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:37<00:56,  2.14it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:38<00:56,  2.14it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:38<00:56,  2.14it/s]Progress: 63.00%
+---- avg training fps: 7.27

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:39<00:56,  2.14it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:39<00:55,  2.13it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:39<00:55,  2.13it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:40<00:54,  2.13it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:40<00:54,  2.14it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:40<00:53,  2.13it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:41<00:53,  2.14it/s]Progress: 65.00%
+---- avg training fps: 7.30

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:41<00:53,  2.13it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:42<00:52,  2.13it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:42<00:52,  2.13it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:43<00:51,  2.13it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:43<00:51,  2.13it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:43<00:51,  2.14it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:44<00:50,  2.14it/s]Progress: 67.00%
+---- avg training fps: 7.34

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:44<00:50,  2.13it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:45<00:49,  2.13it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:45<00:49,  2.13it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:46<00:48,  2.13it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:46<00:48,  2.13it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:47<00:47,  2.14it/s]Progress: 69.00%
+---- avg training fps: 7.37

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:47<00:47,  2.13it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:47<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:48<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:48<00:46,  2.13it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:53<02:54,  1.78s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:53<02:14,  1.39s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:54<01:46,  1.11s/it]Progress: 71.00%
+---- avg training fps: 7.12

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:54<01:27,  1.09it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:55<01:13,  1.28it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:55<01:03,  1.46it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:56<00:57,  1.61it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:56<00:52,  1.75it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:56<00:48,  1.85it/s]Progress: 73.00%
+---- avg training fps: 7.15

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:57<00:48,  1.85it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:57<00:46,  1.93it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:57<00:44,  1.98it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:58<00:42,  2.03it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:58<00:41,  2.06it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [01:59<00:40,  2.10it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [01:59<00:39,  2.11it/s]Progress: 75.00%
+---- avg training fps: 7.18

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [02:00<00:39,  2.11it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [02:00<00:38,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:01<00:38,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:01<00:37,  2.13it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:02<00:37,  2.13it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:02<00:37,  2.13it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:02<00:36,  2.14it/s]Progress: 77.00%
+---- avg training fps: 7.22

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:03<00:36,  2.13it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:03<00:35,  2.13it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:04<00:35,  2.13it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:04<00:34,  2.14it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:04<00:34,  2.14it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:05<00:33,  2.14it/s]Progress: 79.00%
+---- avg training fps: 7.25

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:05<00:33,  2.15it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:06<00:32,  2.14it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:06<00:32,  2.14it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:06<00:32,  2.14it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:07<00:31,  2.14it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:07<00:31,  2.14it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:08<00:30,  2.13it/s]Progress: 81.00%
+---- avg training fps: 7.27

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:08<00:30,  2.13it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:09<00:29,  2.14it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:09<00:29,  2.13it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:10<00:29,  2.13it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:10<00:28,  2.13it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:11<00:28,  2.13it/s]Progress: 83.00%
+---- avg training fps: 7.30

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:11<00:28,  2.13it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:11<00:27,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:11<00:27,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:12<00:26,  2.14it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:12<00:26,  2.13it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:13<00:25,  2.14it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:13<00:25,  2.12it/s]Progress: 85.00%
+---- avg training fps: 7.33

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:14<00:24,  2.12it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:14<00:24,  2.12it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:15<00:24,  2.12it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:15<00:23,  2.13it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:16<00:23,  2.13it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:16<00:23,  2.13it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:16<00:22,  2.13it/s]Progress: 87.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.35

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:17<00:21,  2.15it/s]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:17<00:21,  2.14it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:18<00:21,  2.13it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:18<00:20,  2.13it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:19<00:20,  2.13it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:19<00:19,  2.13it/s]Progress: 89.00%
+---- avg training fps: 7.37

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:19<00:19,  2.13it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:20<00:18,  2.15it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:20<00:18,  2.15it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:20<00:18,  2.14it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:21<00:17,  2.13it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:21<00:17,  2.13it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:22<00:16,  2.14it/s]Progress: 91.00%
+---- avg training fps: 7.40

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:22<00:16,  2.13it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:23<00:15,  2.13it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:23<00:15,  2.14it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:24<00:14,  2.14it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:24<00:14,  2.13it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:25<00:14,  2.13it/s]Progress: 93.00%
+---- avg training fps: 7.42

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:25<00:14,  2.13it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:25<00:13,  2.13it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:26<00:13,  2.13it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:26<00:12,  2.13it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:26<00:12,  2.13it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:27<00:11,  2.12it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:27<00:11,  2.12it/s]Progress: 95.00%
+---- avg training fps: 7.44

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:28<00:10,  2.13it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:28<00:10,  2.12it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:29<00:09,  2.12it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:29<00:09,  2.12it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:30<00:09,  2.12it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:30<00:08,  2.12it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:30<00:09,  1.86it/s]Progress: 97.00%
+---- avg training fps: 7.45

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:31<00:08,  1.94it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:31<00:08,  1.99it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:32<00:07,  2.03it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:32<00:06,  2.06it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:33<00:06,  2.09it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:33<00:05,  2.10it/s]Progress: 99.00%
+---- avg training fps: 7.47

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:34<00:05,  2.11it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:34<00:04,  2.12it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:35<00:04,  2.12it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:35<00:04,  2.13it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:35<00:03,  2.13it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:36<00:03,  2.13it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:36<00:02,  2.13it/s]Progress: 100.00%
+---- avg training fps: 7.49

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:37<00:02,  2.13it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:37<00:01,  2.13it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:38<00:01,  2.13it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:38<00:00,  2.14it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:38<00:00,  2.13it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:39<00:00,  2.13it/s]Progress: 100.00%
+---- avg training fps: 7.51
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [04:00<00:00,  1.25it/s]
+------------------------------------------
+Training done :)
+2024-08-13 02:13:48.510854: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 02:13:48.602404: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 02:13:48.602431: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 02:13:48.621198: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 02:13:49.024119: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:13:49.024207: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:13:49.024216: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23103 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.75 freeze_unet_before_completion_f=0.0 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='clipx.zip' output_dir='lora_models/styles_final/clipx.zip_13_02-13-50-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723508030.8450646 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
Fetching 17 files:   0%|          | 0/17 [00:00<?, ?it/s]
Fetching 17 files: 100%|██████████| 17/17 [00:00<00:00, 6136.77it/s]
+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
Loading pipeline components...:  29%|██▊       | 2/7 [00:00<00:00, 16.26it/s]
Loading pipeline components...:  57%|█████▋    | 4/7 [00:00<00:00,  7.56it/s]
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Loading pipeline components...: 100%|██████████| 7/7 [00:01<00:00,  5.09it/s]
+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Downloading training data...
+Downloading https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip to lora_models/styles_final/clipx.zip_13_02-13-50-style_512_florence_300/images_in/clipx.zip...
+Succesfully prepped 54 .jpg images in lora_models/styles_final/clipx.zip_13_02-13-50-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.314
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 27 images, 27 of which have captions.
+upscaling imgs..
+
  0%|          | 0/27 [00:00<?, ?it/s]
100%|██████████| 27/27 [00:00<00:00, 469901.28it/s]
+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 27 to 54)
+Generating 54 captions using mode: style...
+
  0%|          | 0/54 [00:00<?, ?it/s]
100%|██████████| 54/54 [00:00<00:00, 791931.52it/s]
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal, whimsical, detailed, and quirky scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene is Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase describes the best and worst of times.
+- In the style of TOK, a blockchain is depicted using bees.
+- In the style of TOK, a gorgon made of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset shows Venus and Earth circling the sun.
+- In the style of TOK, there is an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is portrayed.
+- In the style of TOK, the phrase exemplifies the age of wisdom and the age of foolishness.
+- In the style of TOK, two people discuss quitting their boring corporation jobs together.
+- In the style of TOK, petroglyphs are presented.
+- In the style of TOK, a room and space exchange based on first and second-degree connections is depicted.
+- In the style of TOK, the fear known as trypophobia is illustrated.
+- In the style of TOK, there is a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers is highlighted.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam in a Hawaiian-shirt pattern.
+- In the style of TOK, a brigade of beavers is quickly building a dam as envisioned by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is depicted in M. C. Escher's style.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models and egrets seated in the audience.
+- In the style of TOK, there is a hyper-realistic painting of lemon-lime architecture.
+--------------------------
+GPT-4 returned the wrong number of prompts 26 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 1...
+----- GPT response: -----
+Style Description: Surrealistic, intricate, and fantastical scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is embodied in a visual representation.
+- In the style of TOK, a blockchain is composed of bees.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun is depicted.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is presented.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is embodied in a visual representation.
+- In the style of TOK, two individuals consider quitting their boring corporation job together.
+- In the style of TOK, petroglyphs are illustrated.
+- In the style of TOK, a room and space exchange is depicted based on first and second-degree connections.
+- In the style of TOK, trypophobia is visually expressed.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of dancing and singing beavers is created with a nod to Jim Henson's Muppets.
+- In the style of TOK, a beaver is shown chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a pattern like a Hawaiian shirt.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is designed in the style of M. C. Escher.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models and egrets as the audience.
+- In the style of TOK, a hyper-realistic painting depicts lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash is swimming up through the sea to take a peak above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 2...
+----- GPT response: -----
+Style Description: Surreal, eclectic, and whimsical themes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi, Kerala, India.
+- In the style of TOK, "They were the best of times, they were the worst of times."
+- In the style of TOK, a blockchain of bees.
+- In the style of TOK, a gorgon made out of sharks eating each other swims underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats pooping in zero-gravity.
+- In the style of TOK, a goblin goat.
+- In the style of TOK, "It was the age of wisdom, it was the age of foolishness."
+- In the style of TOK, “Let's quit our boring corporation job together.”
+- In the style of TOK, petroglyphs.
+- In the style of TOK, a room and space exchange based on first and second-degree connections.
+- In the style of TOK, trypophobia.
+- In the style of TOK, a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers.
+- In the style of TOK, a beaver chewing a rifle in half.
+- In the style of TOK, a brigade of beavers quickly building a dam.
+- In the style of TOK, a castle moving along the grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show with elephant seals as runway models and egrets as the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 25 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 3...
+----- GPT response: -----
+Style Description: Surreal and intricate marine or forest scenes
+
+- In the style of TOK, a mermaid crafted from waste materials swims underwater.
+- In the style of TOK, small Lilliputians gather inside a giant sea-sponge.
+- In the style of TOK, a view of Fort Kochi in Kerala, India.
+- In the style of TOK, an ambiguous scenario reflecting the dual nature of times.
+- In the style of TOK, a chain formation made of bees.
+- In the style of TOK, a gorgon made of self-consuming sharks in the ocean.
+- In the style of TOK, Venus and Earth orbit the sun above a sunset hillside.
+- In the style of TOK, an ancient temple located deep within the Amazon jungle.
+- In the style of TOK, cats are seen defecating in zero-gravity conditions.
+- In the style of TOK, an odd-looking goblin goat.
+- In the style of TOK, a contradictory phrase depicting wisdom and foolishness.
+- In the style of TOK, an invitation to resign from a corporate job together.
+- In the style of TOK, ancient rock carvings (petroglyphs).
+- In the style of TOK, room and space exchanged through personal connections.
+- In the style of TOK, a pattern inducing trypophobia.
+- In the style of TOK, six terrifying images.
+- In the style of TOK, a group of beavers performing musically.
+- In the style of TOK, a beaver gnaws a rifle in half.
+- In the style of TOK, beavers rapidly constructing a dam in a Hawaiian shirt pattern.
+- In the style of TOK, beavers rapidly constructing a dam in a Tarsem Singh film style.
+- In the style of TOK, beavers in an M.C. Escher-like composition.
+- In the style of TOK, a moving castle traverses grassy hills.
+- In the style of TOK, a line of beavers performing a dance routine.
+- In the style of TOK, a 1950s kitchen, frozen and discovered during the space age.
+- In the style of TOK, a fashion show with elephant seal models and egret spectators.
+- In the style of TOK, highly detailed lemon-lime-colored architecture.
+- In the style of TOK, a mermaid made from sea trash swims up to the water’s surface.
+- In the style of TOK, a mermaid crafted from waste materials swims underwater.
+- In the style of TOK, small Lilliputians gather inside a giant sea-sponge.
+- In the style of TOK, a view of Fort Kochi in Kerala, India.
+- In the style of TOK, an ambiguous scenario reflecting the dual nature of times.
+- In the style of TOK, a chain formation made of bees.
+- In the style of TOK, a gorgon made of self-consuming sharks in the ocean.
+- In the style of TOK, Venus and Earth orbit the sun above a sunset hillside.
+- In the style of TOK, an ancient temple located deep within the Amazon jungle.
+- In the style of TOK, cats are seen defecating in zero-gravity conditions.
+- In the style of TOK, an odd-looking goblin goat.
+- In the style of TOK, a contradictory phrase depicting wisdom and foolishness.
+- In the style of TOK, an invitation to resign from a corporate job together.
+- In the style of TOK, ancient rock carvings (petroglyphs).
+- In the style of TOK, room and space exchanged through personal connections.
+- In the style of TOK, a pattern inducing trypophobia.
+- In the style of TOK, six terrifying images.
+- In the style of TOK, a group of beavers performing musically.
+- In the style of TOK, a beaver gnaws a rifle in half.
+- In the style of TOK, beavers rapidly constructing a dam in a Hawaiian shirt pattern.
+- In the style of TOK, beavers rapidly constructing a dam in a Tarsem Singh film style.
+- In the style of TOK, beavers in an M.C. Escher-like composition.
+- In the style of TOK, a moving castle traverses grassy hills.
+- In the style of TOK, a line of beavers performing a dance routine.
+- In the style of TOK, a 1950s kitchen, frozen and discovered during the space age.
+- In the style of TOK, a fashion show with elephant seal models and egret spectators.
+- In the style of TOK, highly detailed lemon-lime-colored architecture.
+- In the style of TOK, a mermaid made from sea trash swims up to the water’s surface.
+--------------------------
+Making sure TOK is added to every training prompt...
+Using GPT concept name as CLIP-segmentation prompt: Surreal and intricate marine or forest scenes
+Generating 54 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
54it [00:00, 14114.32it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 54 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a mermaid crafte...
+1     in the style of <s0><s1><s2>, small lilliputia...
+2     in the style of <s0><s1><s2>, a view of fort k...
+3     in the style of <s0><s1><s2>, an ambiguous sce...
+4     in the style of <s0><s1><s2>, a chain formatio...
+5     in the style of <s0><s1><s2>, a gorgon made of...
+6     in the style of <s0><s1><s2>, venus and earth ...
+7     in the style of <s0><s1><s2>, an ancient templ...
+8     in the style of <s0><s1><s2>, cats are seen de...
+9     in the style of <s0><s1><s2>, an odd-looking g...
+10    in the style of <s0><s1><s2>, a contradictory ...
+11    in the style of <s0><s1><s2>, an invitation to...
+12    in the style of <s0><s1><s2>, ancient rock car...
+13    in the style of <s0><s1><s2>, room and space e...
+14    in the style of <s0><s1><s2>, a pattern induci...
+15    in the style of <s0><s1><s2>, six terrifying i...
+16    in the style of <s0><s1><s2>, a group of beave...
+17    in the style of <s0><s1><s2>, a beaver gnaws a...
+18    in the style of <s0><s1><s2>, beavers rapidly ...
+19    in the style of <s0><s1><s2>, beavers rapidly ...
+20    in the style of <s0><s1><s2>, beavers in an m....
+21    in the style of <s0><s1><s2>, a moving castle ...
+22    in the style of <s0><s1><s2>, a line of beaver...
+23    in the style of <s0><s1><s2>, a 1950s kitchen,...
+24    in the style of <s0><s1><s2>, a fashion show w...
+25    in the style of <s0><s1><s2>, highly detailed ...
+26    in the style of <s0><s1><s2>, a mermaid made f...
+27    in the style of <s0><s1><s2>, a mermaid crafte...
+28    in the style of <s0><s1><s2>, small lilliputia...
+29    in the style of <s0><s1><s2>, a view of fort k...
+30    in the style of <s0><s1><s2>, an ambiguous sce...
+31    in the style of <s0><s1><s2>, a chain formatio...
+32    in the style of <s0><s1><s2>, a gorgon made of...
+33    in the style of <s0><s1><s2>, venus and earth ...
+34    in the style of <s0><s1><s2>, an ancient templ...
+35    in the style of <s0><s1><s2>, cats are seen de...
+36    in the style of <s0><s1><s2>, an odd-looking g...
+37    in the style of <s0><s1><s2>, a contradictory ...
+38    in the style of <s0><s1><s2>, an invitation to...
+39    in the style of <s0><s1><s2>, ancient rock car...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 54
+--- Num batches each epoch = 14
+--- Num Epochs = 22
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:08<04:14,  1.15it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.66

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:09<03:33,  1.37it/s]
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# Trainer step: 0, epoch: 0:   4%|▍         | 12/300 [00:11<02:21,  2.03it/s]Progress: 7.00%
+---- avg training fps: 4.10

# Trainer step: 0, epoch: 0:   4%|▍         | 13/300 [00:11<02:17,  2.09it/s]
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# Trainer step: 14, epoch: 1:   6%|▌         | 18/300 [00:14<02:11,  2.15it/s]Progress: 9.00%
+---- avg training fps: 4.96

# Trainer step: 14, epoch: 1:   6%|▋         | 19/300 [00:14<02:09,  2.17it/s]
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# Trainer step: 14, epoch: 1:   8%|▊         | 24/300 [00:16<02:04,  2.22it/s]Progress: 11.00%
+---- avg training fps: 5.58

# Trainer step: 14, epoch: 1:   8%|▊         | 25/300 [00:17<02:03,  2.22it/s]
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# Trainer step: 28, epoch: 2:  10%|█         | 30/300 [00:19<02:06,  2.14it/s]Progress: 13.00%
+---- avg training fps: 5.99

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# Trainer step: 28, epoch: 2:  12%|█▏        | 36/300 [00:22<01:59,  2.21it/s]Progress: 15.00%
+---- avg training fps: 6.33

# Trainer step: 28, epoch: 2:  12%|█▏        | 37/300 [00:22<01:58,  2.21it/s]
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# Trainer step: 28, epoch: 2:  14%|█▍        | 42/300 [00:25<01:57,  2.20it/s]Progress: 17.00%
+---- avg training fps: 6.61

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# Trainer step: 42, epoch: 3:  16%|█▌        | 48/300 [00:27<01:54,  2.19it/s]Progress: 19.00%
+---- avg training fps: 6.82

# Trainer step: 42, epoch: 3:  16%|█▋        | 49/300 [00:28<01:54,  2.20it/s]
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# Trainer step: 42, epoch: 3:  18%|█▊        | 54/300 [00:33<03:34,  1.15it/s]Progress: 21.00%
+---- avg training fps: 6.40

# Trainer step: 42, epoch: 3:  18%|█▊        | 55/300 [00:33<03:02,  1.34it/s]
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# Trainer step: 56, epoch: 4:  20%|██        | 60/300 [00:35<01:59,  2.00it/s]Progress: 23.00%
+---- avg training fps: 6.59

# Trainer step: 56, epoch: 4:  20%|██        | 61/300 [00:36<01:56,  2.05it/s]
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# Trainer step: 56, epoch: 4:  22%|██▏       | 66/300 [00:38<01:48,  2.16it/s]Progress: 25.00%
+---- avg training fps: 6.74

# Trainer step: 56, epoch: 4:  22%|██▏       | 67/300 [00:39<01:47,  2.17it/s]
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# Trainer step: 70, epoch: 5:  24%|██▍       | 72/300 [00:41<01:51,  2.05it/s]Progress: 27.00%
+---- avg training fps: 6.85

# Trainer step: 70, epoch: 5:  24%|██▍       | 73/300 [00:42<01:48,  2.09it/s]
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# Trainer step: 70, epoch: 5:  26%|██▌       | 78/300 [00:44<01:42,  2.17it/s]Progress: 29.00%
+---- avg training fps: 6.97

# Trainer step: 70, epoch: 5:  26%|██▋       | 79/300 [00:44<01:41,  2.18it/s]
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# Trainer step: 70, epoch: 5:  28%|██▊       | 84/300 [00:47<01:38,  2.18it/s]Progress: 31.00%
+---- avg training fps: 7.08

# Trainer step: 84, epoch: 6:  28%|██▊       | 84/300 [00:47<01:38,  2.18it/s]
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# Trainer step: 84, epoch: 6:  30%|███       | 90/300 [00:49<01:35,  2.21it/s]Progress: 33.00%
+---- avg training fps: 7.17

# Trainer step: 84, epoch: 6:  30%|███       | 91/300 [00:50<01:34,  2.20it/s]
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# Trainer step: 84, epoch: 6:  32%|███▏      | 96/300 [00:52<01:32,  2.20it/s]Progress: 35.00%
+---- avg training fps: 7.25

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# Trainer step: 98, epoch: 7:  33%|███▎      | 100/300 [00:54<01:28,  2.25it/s]
# Trainer step: 98, epoch: 7:  34%|███▎      | 101/300 [00:54<01:29,  2.23it/s]
# Trainer step: 98, epoch: 7:  34%|███▍      | 102/300 [00:57<03:52,  1.17s/it]Progress: 37.00%
+---- avg training fps: 7.03

# Trainer step: 98, epoch: 7:  34%|███▍      | 103/300 [00:58<03:08,  1.04it/s]
# Trainer step: 98, epoch: 7:  35%|███▍      | 104/300 [00:58<02:38,  1.24it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 105/300 [00:58<02:17,  1.42it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 106/300 [00:59<02:02,  1.59it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 107/300 [00:59<01:51,  1.73it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 108/300 [01:00<01:44,  1.84it/s]Progress: 39.00%
+---- avg training fps: 7.11

# Trainer step: 98, epoch: 7:  36%|███▋      | 109/300 [01:00<01:38,  1.93it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 110/300 [01:01<01:35,  2.00it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 111/300 [01:01<01:32,  2.05it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 112/300 [01:02<01:29,  2.09it/s]
# Trainer step: 112, epoch: 8:  37%|███▋      | 112/300 [01:02<01:29,  2.09it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 113/300 [01:02<01:25,  2.19it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 114/300 [01:03<01:25,  2.19it/s]Progress: 41.00%
+---- avg training fps: 7.18

# Trainer step: 112, epoch: 8:  38%|███▊      | 115/300 [01:03<01:24,  2.18it/s]
# Trainer step: 112, epoch: 8:  39%|███▊      | 116/300 [01:03<01:24,  2.18it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 117/300 [01:04<01:23,  2.19it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 118/300 [01:04<01:23,  2.18it/s]
# Trainer step: 112, epoch: 8:  40%|███▉      | 119/300 [01:05<01:22,  2.18it/s]
# Trainer step: 112, epoch: 8:  40%|████      | 120/300 [01:05<01:22,  2.18it/s]Progress: 43.00%
+---- avg training fps: 7.25

# Trainer step: 112, epoch: 8:  40%|████      | 121/300 [01:06<01:22,  2.18it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 122/300 [01:06<01:21,  2.18it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 123/300 [01:07<01:21,  2.18it/s]
# Trainer step: 112, epoch: 8:  41%|████▏     | 124/300 [01:07<01:20,  2.18it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 125/300 [01:08<01:20,  2.18it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 126/300 [01:08<01:19,  2.18it/s]Progress: 45.00%
+---- avg training fps: 7.31

# Trainer step: 126, epoch: 9:  42%|████▏     | 126/300 [01:08<01:19,  2.18it/s]
# Trainer step: 126, epoch: 9:  42%|████▏     | 127/300 [01:08<01:16,  2.27it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 128/300 [01:09<01:26,  1.99it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 129/300 [01:10<01:23,  2.05it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 130/300 [01:10<01:21,  2.08it/s]
# Trainer step: 126, epoch: 9:  44%|████▎     | 131/300 [01:10<01:19,  2.12it/s]
# Trainer step: 126, epoch: 9:  44%|████▍     | 132/300 [01:11<01:19,  2.13it/s]Progress: 47.00%
+---- avg training fps: 7.35

# Trainer step: 126, epoch: 9:  44%|████▍     | 133/300 [01:11<01:17,  2.14it/s]
# Trainer step: 126, epoch: 9:  45%|████▍     | 134/300 [01:12<01:17,  2.15it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 135/300 [01:12<01:16,  2.16it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 136/300 [01:13<01:15,  2.17it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 137/300 [01:13<01:15,  2.17it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 138/300 [01:14<01:14,  2.17it/s]Progress: 49.00%
+---- avg training fps: 7.40

# Trainer step: 126, epoch: 9:  46%|████▋     | 139/300 [01:14<01:14,  2.17it/s]
# Trainer step: 126, epoch: 9:  47%|████▋     | 140/300 [01:15<01:13,  2.17it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 140/300 [01:15<01:13,  2.17it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 141/300 [01:15<01:10,  2.26it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 142/300 [01:15<01:10,  2.24it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 143/300 [01:16<01:10,  2.22it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 144/300 [01:16<01:10,  2.20it/s]Progress: 51.00%
+---- avg training fps: 7.45

# Trainer step: 140, epoch: 10:  48%|████▊     | 145/300 [01:17<01:10,  2.19it/s]
# Trainer step: 140, epoch: 10:  49%|████▊     | 146/300 [01:17<01:10,  2.19it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 147/300 [01:18<01:10,  2.18it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 148/300 [01:18<01:09,  2.18it/s]
# Trainer step: 140, epoch: 10:  50%|████▉     | 149/300 [01:19<01:09,  2.18it/s]
# Trainer step: 140, epoch: 10:  50%|█████     | 150/300 [01:19<01:09,  2.17it/s]Progress: 53.00%
+---- avg training fps: 7.49

# Trainer step: 140, epoch: 10:  50%|█████     | 151/300 [01:20<01:08,  2.17it/s]
# Trainer step: 140, epoch: 10:  51%|█████     | 152/300 [01:23<03:35,  1.46s/it]
# Trainer step: 140, epoch: 10:  51%|█████     | 153/300 [01:24<02:58,  1.22s/it]
# Trainer step: 140, epoch: 10:  51%|█████▏    | 154/300 [01:24<02:24,  1.01it/s]
# Trainer step: 154, epoch: 11:  51%|█████▏    | 154/300 [01:25<02:24,  1.01it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 155/300 [01:25<01:57,  1.23it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 156/300 [01:25<01:41,  1.42it/s]Progress: 55.00%
+---- avg training fps: 7.23

# Trainer step: 154, epoch: 11:  52%|█████▏    | 157/300 [01:26<01:30,  1.59it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 158/300 [01:26<01:22,  1.73it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 159/300 [01:27<01:16,  1.84it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 160/300 [01:27<01:12,  1.93it/s]
# Trainer step: 154, epoch: 11:  54%|█████▎    | 161/300 [01:28<01:09,  2.00it/s]
# Trainer step: 154, epoch: 11:  54%|█████▍    | 162/300 [01:28<01:07,  2.06it/s]Progress: 57.00%
+---- avg training fps: 7.28

# Trainer step: 154, epoch: 11:  54%|█████▍    | 163/300 [01:29<01:05,  2.09it/s]
# Trainer step: 154, epoch: 11:  55%|█████▍    | 164/300 [01:29<01:04,  2.12it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 165/300 [01:29<01:03,  2.14it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 166/300 [01:30<01:01,  2.16it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 167/300 [01:30<01:01,  2.17it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 168/300 [01:31<01:00,  2.18it/s]Progress: 59.00%
+---- avg training fps: 7.33

# Trainer step: 168, epoch: 12:  56%|█████▌    | 168/300 [01:31<01:00,  2.18it/s]
# Trainer step: 168, epoch: 12:  56%|█████▋    | 169/300 [01:31<00:57,  2.27it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 170/300 [01:32<00:57,  2.24it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 171/300 [01:32<00:57,  2.23it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 172/300 [01:33<00:57,  2.23it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 173/300 [01:33<00:57,  2.21it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 174/300 [01:34<00:57,  2.20it/s]Progress: 61.00%
+---- avg training fps: 7.37

# Trainer step: 168, epoch: 12:  58%|█████▊    | 175/300 [01:34<00:56,  2.19it/s]
# Trainer step: 168, epoch: 12:  59%|█████▊    | 176/300 [01:34<00:56,  2.20it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 177/300 [01:35<00:56,  2.19it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 178/300 [01:35<00:55,  2.19it/s]
# Trainer step: 168, epoch: 12:  60%|█████▉    | 179/300 [01:36<00:55,  2.18it/s]
# Trainer step: 168, epoch: 12:  60%|██████    | 180/300 [01:36<00:54,  2.19it/s]Progress: 63.00%
+---- avg training fps: 7.41

# Trainer step: 168, epoch: 12:  60%|██████    | 181/300 [01:37<00:54,  2.18it/s]
# Trainer step: 168, epoch: 12:  61%|██████    | 182/300 [01:37<00:54,  2.18it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 182/300 [01:38<00:54,  2.18it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 183/300 [01:38<00:51,  2.27it/s]
# Trainer step: 182, epoch: 13:  61%|██████▏   | 184/300 [01:38<00:51,  2.24it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 185/300 [01:38<00:51,  2.22it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 186/300 [01:39<00:51,  2.20it/s]Progress: 65.00%
+---- avg training fps: 7.45

# Trainer step: 182, epoch: 13:  62%|██████▏   | 187/300 [01:39<00:51,  2.20it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 188/300 [01:40<00:51,  2.19it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 189/300 [01:40<00:50,  2.18it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 190/300 [01:41<00:50,  2.18it/s]
# Trainer step: 182, epoch: 13:  64%|██████▎   | 191/300 [01:41<00:50,  2.18it/s]
# Trainer step: 182, epoch: 13:  64%|██████▍   | 192/300 [01:42<00:49,  2.17it/s]Progress: 67.00%
+---- avg training fps: 7.48

# Trainer step: 182, epoch: 13:  64%|██████▍   | 193/300 [01:42<00:49,  2.17it/s]
# Trainer step: 182, epoch: 13:  65%|██████▍   | 194/300 [01:43<00:48,  2.18it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 195/300 [01:43<00:48,  2.18it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 196/300 [01:44<00:47,  2.17it/s]
# Trainer step: 196, epoch: 14:  65%|██████▌   | 196/300 [01:44<00:47,  2.17it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 197/300 [01:44<00:45,  2.25it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 198/300 [01:44<00:45,  2.23it/s]Progress: 69.00%
+---- avg training fps: 7.52

# Trainer step: 196, epoch: 14:  66%|██████▋   | 199/300 [01:45<00:45,  2.21it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 200/300 [01:45<00:45,  2.20it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 201/300 [01:46<00:45,  2.19it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 202/300 [01:49<02:01,  1.24s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 203/300 [01:49<01:42,  1.06s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 204/300 [01:50<01:24,  1.14it/s]Progress: 71.00%
+---- avg training fps: 7.36

# Trainer step: 196, epoch: 14:  68%|██████▊   | 205/300 [01:50<01:11,  1.34it/s]
# Trainer step: 196, epoch: 14:  69%|██████▊   | 206/300 [01:51<01:02,  1.51it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 207/300 [01:51<00:55,  1.67it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 208/300 [01:52<00:51,  1.79it/s]
# Trainer step: 196, epoch: 14:  70%|██████▉   | 209/300 [01:52<00:47,  1.90it/s]
# Trainer step: 196, epoch: 14:  70%|███████   | 210/300 [01:53<00:45,  1.97it/s]Progress: 73.00%
+---- avg training fps: 7.40

# Trainer step: 210, epoch: 15:  70%|███████   | 210/300 [01:53<00:45,  1.97it/s]
# Trainer step: 210, epoch: 15:  70%|███████   | 211/300 [01:53<00:42,  2.11it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 212/300 [01:54<00:41,  2.12it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 213/300 [01:54<00:40,  2.14it/s]
# Trainer step: 210, epoch: 15:  71%|███████▏  | 214/300 [01:54<00:39,  2.15it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 215/300 [01:55<00:39,  2.16it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 216/300 [01:55<00:38,  2.17it/s]Progress: 75.00%
+---- avg training fps: 7.43

# Trainer step: 210, epoch: 15:  72%|███████▏  | 217/300 [01:56<00:38,  2.17it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 218/300 [01:56<00:37,  2.17it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 219/300 [01:57<00:37,  2.17it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 220/300 [01:57<00:36,  2.17it/s]
# Trainer step: 210, epoch: 15:  74%|███████▎  | 221/300 [01:58<00:36,  2.17it/s]
# Trainer step: 210, epoch: 15:  74%|███████▍  | 222/300 [01:58<00:35,  2.17it/s]Progress: 77.00%
+---- avg training fps: 7.46

# Trainer step: 210, epoch: 15:  74%|███████▍  | 223/300 [01:59<00:35,  2.18it/s]
# Trainer step: 210, epoch: 15:  75%|███████▍  | 224/300 [01:59<00:35,  2.17it/s]
# Trainer step: 224, epoch: 16:  75%|███████▍  | 224/300 [01:59<00:35,  2.17it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 225/300 [01:59<00:33,  2.26it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 226/300 [02:00<00:33,  2.22it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 227/300 [02:00<00:33,  2.21it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 228/300 [02:01<00:32,  2.21it/s]Progress: 79.00%
+---- avg training fps: 7.49

# Trainer step: 224, epoch: 16:  76%|███████▋  | 229/300 [02:01<00:32,  2.20it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 230/300 [02:02<00:32,  2.18it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 231/300 [02:02<00:31,  2.18it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 232/300 [02:03<00:30,  2.20it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 233/300 [02:03<00:30,  2.18it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 234/300 [02:04<00:30,  2.18it/s]Progress: 81.00%
+---- avg training fps: 7.51

# Trainer step: 224, epoch: 16:  78%|███████▊  | 235/300 [02:04<00:29,  2.18it/s]
# Trainer step: 224, epoch: 16:  79%|███████▊  | 236/300 [02:05<00:29,  2.18it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 237/300 [02:05<00:28,  2.18it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 238/300 [02:05<00:28,  2.18it/s]
# Trainer step: 238, epoch: 17:  79%|███████▉  | 238/300 [02:06<00:28,  2.18it/s]
# Trainer step: 238, epoch: 17:  80%|███████▉  | 239/300 [02:06<00:26,  2.30it/s]
# Trainer step: 238, epoch: 17:  80%|████████  | 240/300 [02:06<00:26,  2.26it/s]Progress: 83.00%
+---- avg training fps: 7.55

# Trainer step: 238, epoch: 17:  80%|████████  | 241/300 [02:07<00:26,  2.23it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 242/300 [02:07<00:26,  2.23it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 243/300 [02:08<00:25,  2.21it/s]
# Trainer step: 238, epoch: 17:  81%|████████▏ | 244/300 [02:08<00:25,  2.20it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 245/300 [02:09<00:25,  2.19it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 246/300 [02:09<00:24,  2.20it/s]Progress: 85.00%
+---- avg training fps: 7.57

# Trainer step: 238, epoch: 17:  82%|████████▏ | 247/300 [02:09<00:24,  2.19it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 248/300 [02:10<00:23,  2.17it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 249/300 [02:10<00:23,  2.17it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 250/300 [02:11<00:22,  2.19it/s]
# Trainer step: 238, epoch: 17:  84%|████████▎ | 251/300 [02:11<00:22,  2.18it/s]
# Trainer step: 238, epoch: 17:  84%|████████▍ | 252/300 [02:15<01:06,  1.38s/it]Progress: 87.00%
+---- avg training fps: 7.43

# Trainer step: 252, epoch: 18:  84%|████████▍ | 252/300 [02:15<01:06,  1.38s/it]
# Trainer step: 252, epoch: 18:  84%|████████▍ | 253/300 [02:15<00:50,  1.09s/it]
# Trainer step: 252, epoch: 18:  85%|████████▍ | 254/300 [02:16<00:41,  1.12it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 255/300 [02:16<00:34,  1.31it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 256/300 [02:17<00:29,  1.49it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 257/300 [02:17<00:26,  1.64it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 258/300 [02:18<00:23,  1.78it/s]Progress: 89.00%
+---- avg training fps: 7.45

# Trainer step: 252, epoch: 18:  86%|████████▋ | 259/300 [02:18<00:21,  1.88it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 260/300 [02:18<00:20,  1.97it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 261/300 [02:19<00:19,  2.03it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 262/300 [02:19<00:18,  2.07it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 263/300 [02:20<00:17,  2.10it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 264/300 [02:20<00:16,  2.13it/s]Progress: 91.00%
+---- avg training fps: 7.48

# Trainer step: 252, epoch: 18:  88%|████████▊ | 265/300 [02:21<00:16,  2.14it/s]
# Trainer step: 252, epoch: 18:  89%|████████▊ | 266/300 [02:21<00:15,  2.15it/s]
# Trainer step: 266, epoch: 19:  89%|████████▊ | 266/300 [02:22<00:15,  2.15it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 267/300 [02:22<00:14,  2.25it/s]
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# Trainer step: 266, epoch: 19:  90%|████████▉ | 269/300 [02:23<00:14,  2.21it/s]
# Trainer step: 266, epoch: 19:  90%|█████████ | 270/300 [02:23<00:13,  2.20it/s]Progress: 93.00%
+---- avg training fps: 7.50

# Trainer step: 266, epoch: 19:  90%|█████████ | 271/300 [02:23<00:13,  2.19it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 272/300 [02:24<00:12,  2.18it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 273/300 [02:24<00:12,  2.18it/s]
# Trainer step: 266, epoch: 19:  91%|█████████▏| 274/300 [02:25<00:11,  2.19it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 275/300 [02:25<00:11,  2.18it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 276/300 [02:26<00:11,  2.17it/s]Progress: 95.00%
+---- avg training fps: 7.53

# Trainer step: 266, epoch: 19:  92%|█████████▏| 277/300 [02:26<00:10,  2.17it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 278/300 [02:27<00:10,  2.18it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 279/300 [02:27<00:09,  2.18it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 280/300 [02:28<00:09,  2.17it/s]
# Trainer step: 280, epoch: 20:  93%|█████████▎| 280/300 [02:28<00:09,  2.17it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▎| 281/300 [02:28<00:08,  2.26it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▍| 282/300 [02:28<00:08,  2.23it/s]Progress: 97.00%
+---- avg training fps: 7.55

# Trainer step: 280, epoch: 20:  94%|█████████▍| 283/300 [02:29<00:07,  2.23it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▍| 284/300 [02:29<00:07,  2.21it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 285/300 [02:30<00:06,  2.19it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 286/300 [02:30<00:06,  2.18it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 287/300 [02:31<00:05,  2.19it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 288/300 [02:31<00:05,  2.18it/s]Progress: 99.00%
+---- avg training fps: 7.57

# Trainer step: 280, epoch: 20:  96%|█████████▋| 289/300 [02:32<00:05,  2.17it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 290/300 [02:32<00:04,  2.16it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 291/300 [02:33<00:04,  2.18it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 292/300 [02:33<00:03,  2.17it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 293/300 [02:34<00:03,  2.17it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 294/300 [02:34<00:02,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.59

# Trainer step: 294, epoch: 21:  98%|█████████▊| 294/300 [02:34<00:02,  2.16it/s]
# Trainer step: 294, epoch: 21:  98%|█████████▊| 295/300 [02:34<00:02,  2.25it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▊| 296/300 [02:35<00:01,  2.24it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 297/300 [02:35<00:01,  2.22it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 298/300 [02:36<00:00,  2.20it/s]
# Trainer step: 294, epoch: 21: 100%|█████████▉| 299/300 [02:36<00:00,  2.18it/s]
# Trainer step: 294, epoch: 21: 100%|██████████| 300/300 [02:37<00:00,  2.19it/s]Progress: 100.00%
+---- avg training fps: 7.61

# Trainer step: 294, epoch: 21: : 301it [02:37,  2.18it/s]                       Progress: 100.00%
Failed to plot token attention loss
+Reached max steps, stopping training!
+Saving checkpoint at step.. 301
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 294, epoch: 21: : 301it [03:57,  1.27it/s]
+------------------------------------------
+Training done :)
+2024-08-13 02:18:44.679792: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 02:18:44.772375: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 02:18:44.772406: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 02:18:44.791362: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 02:18:45.193486: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:18:45.193543: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:18:45.193552: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23115 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/good_styles/eden_crystals' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=1.0 freeze_unet_before_completion_f=0.25 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='eden_crystals' output_dir='lora_models/styles_final/eden_crystals_13_02-18-46-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723508327.029796 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/eden_crystals_13_02-18-46-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.242
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants on a black background.
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants in the dark.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Futuristic natural elements blending with technology
+
+- In the style of TOK, a potted plant sits on a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of space filled with stars.
+- In the style of TOK, a flower with green leaves is on a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is on a black background.
+- In the style of TOK, a potted plant sits on a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of space filled with stars.
+- In the style of TOK, a flower with green leaves is on a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is in the dark.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Futuristic natural elements blending with technology
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 7189.72it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a potted plant s...
+1     in the style of <s0><s1><s2>, a futuristic cit...
+2     in the style of <s0><s1><s2>, a flower with gr...
+3     in the style of <s0><s1><s2>, a plant is growi...
+4     in the style of <s0><s1><s2>, a group of green...
+5     in the style of <s0><s1><s2>, a potted plant s...
+6     in the style of <s0><s1><s2>, a futuristic cit...
+7     in the style of <s0><s1><s2>, a flower with gr...
+8     in the style of <s0><s1><s2>, a plant is growi...
+9     in the style of <s0><s1><s2>, a group of green...
+10    in the style of <s0><s1><s2>, a potted plant s...
+11    in the style of <s0><s1><s2>, a futuristic cit...
+12    in the style of <s0><s1><s2>, a flower with gr...
+13    in the style of <s0><s1><s2>, a plant is growi...
+14    in the style of <s0><s1><s2>, a group of green...
+15    in the style of <s0><s1><s2>, a potted plant s...
+16    in the style of <s0><s1><s2>, a futuristic cit...
+17    in the style of <s0><s1><s2>, a flower with gr...
+18    in the style of <s0><s1><s2>, a plant is growi...
+19    in the style of <s0><s1><s2>, a group of green...
+20    in the style of <s0><s1><s2>, a potted plant s...
+21    in the style of <s0><s1><s2>, a futuristic cit...
+22    in the style of <s0><s1><s2>, a flower with gr...
+23    in the style of <s0><s1><s2>, a plant is growi...
+24    in the style of <s0><s1><s2>, a group of green...
+25    in the style of <s0><s1><s2>, a potted plant s...
+26    in the style of <s0><s1><s2>, a futuristic cit...
+27    in the style of <s0><s1><s2>, a flower with gr...
+28    in the style of <s0><s1><s2>, a plant is growi...
+29    in the style of <s0><s1><s2>, a group of green...
+30    in the style of <s0><s1><s2>, a potted plant s...
+31    in the style of <s0><s1><s2>, a futuristic cit...
+32    in the style of <s0><s1><s2>, a flower with gr...
+33    in the style of <s0><s1><s2>, a plant is growi...
+34    in the style of <s0><s1><s2>, a group of green...
+35    in the style of <s0><s1><s2>, a potted plant s...
+36    in the style of <s0><s1><s2>, a futuristic cit...
+37    in the style of <s0><s1><s2>, a flower with gr...
+38    in the style of <s0><s1><s2>, a plant is growi...
+39    in the style of <s0><s1><s2>, a group of green...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:02<?, ?it/s]
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:08<04:09,  1.18it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.74

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:08<03:29,  1.40it/s]
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# Trainer step: 10, epoch: 1:   4%|▍         | 12/300 [00:11<02:22,  2.02it/s]Progress: 7.00%
+---- avg training fps: 4.17

# Trainer step: 10, epoch: 1:   4%|▍         | 13/300 [00:11<02:18,  2.07it/s]
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# Trainer step: 10, epoch: 1:   6%|▌         | 18/300 [00:13<02:09,  2.17it/s]Progress: 9.00%
+---- avg training fps: 5.06

# Trainer step: 10, epoch: 1:   6%|▋         | 19/300 [00:14<02:08,  2.19it/s]
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# Trainer step: 20, epoch: 2:   8%|▊         | 24/300 [00:16<02:05,  2.19it/s]Progress: 11.00%
+---- avg training fps: 5.66

# Trainer step: 20, epoch: 2:   8%|▊         | 25/300 [00:16<02:05,  2.19it/s]
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# Trainer step: 20, epoch: 2:  10%|█         | 30/300 [00:19<02:03,  2.19it/s]Progress: 13.00%
+---- avg training fps: 6.09

# Trainer step: 30, epoch: 3:  10%|█         | 30/300 [00:19<02:03,  2.19it/s]
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# Trainer step: 30, epoch: 3:  12%|█▏        | 36/300 [00:22<02:05,  2.10it/s]Progress: 15.00%
+---- avg training fps: 6.37

# Trainer step: 30, epoch: 3:  12%|█▏        | 37/300 [00:22<02:03,  2.13it/s]
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# Trainer step: 40, epoch: 4:  14%|█▍        | 42/300 [00:24<01:58,  2.18it/s]Progress: 17.00%
+---- avg training fps: 6.63

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:25<01:57,  2.19it/s]
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# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:27<01:55,  2.19it/s]Progress: 19.00%
+---- avg training fps: 6.83

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:28<01:54,  2.19it/s]
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# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:33<03:47,  1.08it/s]Progress: 21.00%
+---- avg training fps: 6.35

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:34<03:12,  1.27it/s]
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# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:36<02:03,  1.95it/s]Progress: 23.00%
+---- avg training fps: 6.53

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:36<02:03,  1.95it/s]
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# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:39<01:54,  2.05it/s]Progress: 25.00%
+---- avg training fps: 6.65

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:39<01:51,  2.09it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:40<01:49,  2.11it/s]
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# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:41<01:44,  2.17it/s]Progress: 27.00%
+---- avg training fps: 6.79

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:42<01:44,  2.18it/s]
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# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:44<01:41,  2.19it/s]Progress: 29.00%
+---- avg training fps: 6.91

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:45<01:40,  2.20it/s]
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# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:47<01:39,  2.18it/s]Progress: 31.00%
+---- avg training fps: 7.01

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:47<01:38,  2.19it/s]
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# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:50<01:35,  2.19it/s]Progress: 33.00%
+---- avg training fps: 7.11

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:50<01:35,  2.19it/s]
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# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:52<01:33,  2.19it/s]Progress: 35.00%
+---- avg training fps: 7.19

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:53<01:32,  2.20it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:53<01:32,  2.19it/s]
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# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:55<01:30,  2.20it/s]Progress: 37.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.27

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:56<01:30,  2.19it/s]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [00:56<01:29,  2.18it/s]
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# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [00:57<01:28,  2.18it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [00:58<01:28,  2.17it/s]Progress: 39.00%
+---- avg training fps: 7.34

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [00:58<01:27,  2.18it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [00:59<01:27,  2.18it/s]
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# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:00<01:26,  2.17it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:00<01:25,  2.18it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:01<01:25,  2.18it/s]Progress: 41.00%
+---- avg training fps: 7.40

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:01<01:24,  2.18it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:02<01:24,  2.18it/s]
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# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:03<01:23,  2.18it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:03<01:22,  2.18it/s]Progress: 43.00%
+---- avg training fps: 7.46

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:04<01:22,  2.18it/s]
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# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:05<01:20,  2.19it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:05<01:20,  2.18it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:06<01:20,  2.17it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:06<01:19,  2.19it/s]Progress: 45.00%
+---- avg training fps: 7.51

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:07<01:19,  2.18it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:07<01:19,  2.18it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:08<01:18,  2.17it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:08<01:17,  2.19it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:08<01:17,  2.19it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:08<01:17,  2.19it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:09<01:17,  2.18it/s]Progress: 47.00%
+---- avg training fps: 7.56

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:09<01:16,  2.18it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:10<01:16,  2.18it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:10<01:15,  2.18it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:11<01:15,  2.17it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:11<01:15,  2.17it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:12<01:14,  2.17it/s]Progress: 49.00%
+---- avg training fps: 7.60

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:12<01:14,  2.17it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:13<01:13,  2.16it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:13<01:13,  2.16it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:13<01:13,  2.16it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:14<01:12,  2.18it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:14<01:11,  2.18it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:14<01:11,  2.17it/s]Progress: 51.00%
+---- avg training fps: 7.64

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:15<01:11,  2.17it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:15<01:10,  2.18it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:16<01:10,  2.18it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:16<01:10,  2.17it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:17<01:09,  2.16it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:17<01:09,  2.17it/s]Progress: 53.00%
+---- avg training fps: 7.68

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:18<01:09,  2.17it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:18<01:08,  2.17it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:22<03:46,  1.53s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:22<02:57,  1.21s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:23<02:23,  1.02it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:23<01:59,  1.21it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:24<01:43,  1.40it/s]Progress: 55.00%
+---- avg training fps: 7.39

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:24<01:30,  1.57it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:24<01:22,  1.72it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:25<01:16,  1.83it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:25<01:12,  1.92it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:26<01:12,  1.92it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:26<01:09,  1.99it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:26<01:07,  2.05it/s]Progress: 57.00%
+---- avg training fps: 7.43

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:27<01:05,  2.08it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:27<01:04,  2.10it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:28<01:03,  2.13it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:28<01:02,  2.15it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:29<01:01,  2.15it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:29<01:01,  2.15it/s]Progress: 59.00%
+---- avg training fps: 7.47

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:30<01:00,  2.16it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:30<01:00,  2.16it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:30<01:00,  2.16it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:30<00:59,  2.16it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:31<00:59,  2.16it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:31<00:58,  2.17it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:32<00:58,  2.17it/s]Progress: 61.00%
+---- avg training fps: 7.50

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:32<00:57,  2.16it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:33<00:57,  2.16it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:33<00:56,  2.17it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:34<00:56,  2.17it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:34<00:55,  2.17it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:35<00:55,  2.16it/s]Progress: 63.00%
+---- avg training fps: 7.54

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:35<00:55,  2.16it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:35<00:54,  2.18it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:35<00:54,  2.18it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:36<00:53,  2.17it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:36<00:53,  2.17it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:37<00:53,  2.17it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:37<00:52,  2.17it/s]Progress: 65.00%
+---- avg training fps: 7.57

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:38<00:52,  2.16it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:38<00:51,  2.15it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:39<00:51,  2.17it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:39<00:50,  2.17it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:40<00:50,  2.17it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:40<00:50,  2.16it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:40<00:50,  2.16it/s]Progress: 67.00%
+---- avg training fps: 7.60

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:41<00:49,  2.17it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:41<00:49,  2.16it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:42<00:48,  2.16it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:42<00:48,  2.14it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:42<00:47,  2.16it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:43<00:47,  2.15it/s]Progress: 69.00%
+---- avg training fps: 7.62

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:43<00:46,  2.15it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:44<00:46,  2.16it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:44<00:46,  2.16it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:44<00:45,  2.16it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:48<02:30,  1.54s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:49<01:57,  1.21s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:49<01:34,  1.01it/s]Progress: 71.00%
+---- avg training fps: 7.40

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:50<01:18,  1.21it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:50<01:07,  1.40it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:51<00:59,  1.56it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:51<00:54,  1.69it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:52<00:50,  1.82it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:52<00:47,  1.91it/s]Progress: 73.00%
+---- avg training fps: 7.43

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:52<00:47,  1.91it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:52<00:44,  1.98it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:53<00:43,  2.03it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:53<00:42,  2.07it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:54<00:40,  2.10it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [01:54<00:40,  2.12it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [01:55<00:39,  2.13it/s]Progress: 75.00%
+---- avg training fps: 7.46

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [01:55<00:38,  2.14it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [01:56<00:38,  2.15it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [01:56<00:37,  2.16it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [01:57<00:37,  2.16it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [01:57<00:37,  2.16it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [01:57<00:36,  2.16it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [01:58<00:35,  2.17it/s]Progress: 77.00%
+---- avg training fps: 7.49

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [01:58<00:35,  2.17it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [01:58<00:35,  2.17it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [01:59<00:34,  2.16it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [01:59<00:34,  2.17it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:00<00:33,  2.17it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:00<00:33,  2.17it/s]Progress: 79.00%
+---- avg training fps: 7.52

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:01<00:32,  2.16it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:01<00:32,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:02<00:32,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:02<00:31,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:02<00:31,  2.16it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:03<00:31,  2.15it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:03<00:30,  2.17it/s]Progress: 81.00%
+---- avg training fps: 7.54

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:04<00:30,  2.16it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:04<00:29,  2.16it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:05<00:29,  2.15it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:05<00:28,  2.15it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:05<00:28,  2.14it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:06<00:27,  2.14it/s]Progress: 83.00%
+---- avg training fps: 7.57

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:06<00:27,  2.14it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:06<00:27,  2.15it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:07<00:26,  2.16it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:07<00:26,  2.15it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:08<00:26,  2.15it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:08<00:25,  2.16it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:09<00:24,  2.16it/s]Progress: 85.00%
+---- avg training fps: 7.59

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:09<00:24,  2.16it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:10<00:24,  2.15it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:10<00:23,  2.17it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:11<00:23,  2.17it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:11<00:23,  2.17it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:11<00:22,  2.16it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:11<00:22,  2.15it/s]Progress: 87.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.61

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:12<00:21,  2.15it/s]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:12<00:21,  2.16it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:13<00:20,  2.15it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:13<00:20,  2.15it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:14<00:19,  2.16it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:14<00:19,  2.15it/s]Progress: 89.00%
+---- avg training fps: 7.63

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:15<00:19,  2.15it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:15<00:18,  2.16it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:16<00:18,  2.16it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:16<00:18,  2.16it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:16<00:17,  2.16it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:17<00:17,  2.16it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:17<00:16,  2.17it/s]Progress: 91.00%
+---- avg training fps: 7.65

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:17<00:16,  2.16it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:18<00:15,  2.16it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:18<00:15,  2.15it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:19<00:14,  2.17it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:20<00:16,  1.92it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:20<00:15,  1.98it/s]Progress: 93.00%
+---- avg training fps: 7.66

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:20<00:15,  1.98it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:20<00:14,  2.03it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:21<00:13,  2.08it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:21<00:12,  2.11it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:22<00:12,  2.12it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:22<00:11,  2.13it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:23<00:11,  2.16it/s]Progress: 95.00%
+---- avg training fps: 7.68

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:23<00:10,  2.16it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:24<00:10,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:24<00:09,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:25<00:09,  2.17it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:25<00:09,  2.17it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:25<00:08,  2.17it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:26<00:08,  2.17it/s]Progress: 97.00%
+---- avg training fps: 7.70

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:26<00:07,  2.16it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:26<00:07,  2.17it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:27<00:06,  2.16it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:27<00:06,  2.17it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:28<00:06,  2.16it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:28<00:05,  2.18it/s]Progress: 99.00%
+---- avg training fps: 7.72

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:29<00:05,  2.17it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:29<00:04,  2.17it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:30<00:04,  2.17it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:30<00:04,  2.16it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:30<00:03,  2.18it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:31<00:03,  2.16it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:31<00:02,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.74

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:32<00:02,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:32<00:01,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:32<00:01,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:33<00:00,  2.16it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:33<00:00,  2.15it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:34<00:00,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.75
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [03:54<00:00,  1.28it/s]
+------------------------------------------
+Training done :)
+2024-08-13 02:23:11.920940: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 02:23:12.013029: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 02:23:12.013057: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 02:23:12.032109: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 02:23:12.433598: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:23:12.433686: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:23:12.433696: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23132 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.75 freeze_unet_before_completion_f=0.25 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='clipx.zip' output_dir='lora_models/styles_final/clipx.zip_13_02-23-14-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723508594.2655654 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Downloading training data...
+Downloading https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip to lora_models/styles_final/clipx.zip_13_02-23-14-style_512_florence_300/images_in/clipx.zip...
+Succesfully prepped 54 .jpg images in lora_models/styles_final/clipx.zip_13_02-23-14-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.314
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 27 images, 27 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 27 to 54)
+Generating 54 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal, whimsical, detailed, and quirky scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene is Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase describes the best and worst of times.
+- In the style of TOK, a blockchain is made up of bees.
+- In the style of TOK, a gorgon made out of sharks eats each other while swimming underwater.
+- In the style of TOK, a hillside at sunset features Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple stands deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin appears alongside a goat.
+- In the style of TOK, the phrase describes the age of wisdom and the age of foolishness.
+- In the style of TOK, two people discuss quitting their boring corporation jobs together.
+- In the style of TOK, petroglyphs are depicted.
+- In the style of TOK, a room and space exchange is based on first and second degree connections.
+- In the style of TOK, trypophobia is represented.
+- In the style of TOK, six-pack containers burst with nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers performs.
+- In the style of TOK, a beaver chews a rifle in half.
+- In the style of TOK, a brigade of beavers quickly builds a dam.
+- In the style of TOK, a brigade of beavers is depicted in an intricate pattern.
+- In the style of TOK, a brigade of beavers appears in a visually complex design.
+- In the style of TOK, a castle moves along the ground amid grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dances in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models and egrets seated in the audience.
+- In the style of TOK, lemon-lime architecture is depicted in hyper-realistic painting.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash swims up through the sea to take a peek above the water.
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene is Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase describes the best and worst of times.
+- In the style of TOK, a blockchain is made up of bees.
+- In the style of TOK, a gorgon made out of sharks eats each other while swimming underwater.
+- In the style of TOK, a hillside at sunset features Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple stands deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin appears alongside a goat.
+- In the style of TOK, the phrase describes the age of wisdom and the age of foolishness.
+- In the style of TOK, two people discuss quitting their boring corporation jobs together.
+- In the style of TOK, petroglyphs are depicted.
+- In the style of TOK, a room and space exchange is based on first and second degree connections.
+- In the style of TOK, trypophobia is represented.
+- In the style of TOK, six-pack containers burst with nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers performs.
+- In the style of TOK, a beaver chews a rifle in half.
+- In the style of TOK, a brigade of beavers quickly builds a dam.
+- In the style of TOK, a brigade of beavers is depicted in an intricate pattern.
+- In the style of TOK, a brigade of beavers appears in a visually complex design.
+- In the style of TOK, a castle moves along the ground amid grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dances in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models and egrets seated in the audience.
+- In the style of TOK, lemon-lime architecture is depicted in hyper-realistic painting.
+--------------------------
+GPT-4 returned the wrong number of prompts 53 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 1...
+----- GPT response: -----
+Style Description: Surrealistic, intricate, and fantastical scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is embodied in a visual representation.
+- In the style of TOK, a blockchain is composed of bees.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, Venus and Earth are circling the sun over a hillside at sunset.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is depicted.
+- In the style of TOK, the phrase "it was the age of wisdom, it was the age of foolishness" is depicted visually.
+- In the style of TOK, two people decide to quit their boring corporation job together.
+- In the style of TOK, petroglyphs are depicted.
+- In the style of TOK, room and space exchange based on first and second-degree connections is visualized.
+- In the style of TOK, trypophobia is depicted.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of beavers is dancing and singing like characters from Jim Henson's Muppets.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam in a pattern reminiscent of Hawaiian shirts.
+- In the style of TOK, a brigade of beavers is quickly building a dam, directed by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is illustrated in a style similar to M. C. Escher.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is frozen solid and preserved, then rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models, with egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting depicts lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash is swimming up through the sea to take a peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 2...
+----- GPT response: -----
+Style Description: Surreal, eclectic, and whimsical themes
+
+- In the style of TOK, a mermaid swimming underwater, made from sea trash.
+- In the style of TOK, Lilliputians cramped inside giant sea-sponge pores.
+- In the style of TOK, Fort Kochi, Kerala, India.
+- In the style of TOK, a reflection on the best and worst times.
+- In the style of TOK, a blockchain formed by bees.
+- In the style of TOK, a gorgon of sharks eating each other underwater.
+- In the style of TOK, Venus and Earth circling the sun at sunset on a hillside.
+- In the style of TOK, an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats experiencing zero-gravity while pooping.
+- In the style of TOK, a goblin goat.
+- In the style of TOK, a reflection on the age of wisdom and foolishness.
+- In the style of TOK, a proposal to quit a boring corporation job together.
+- In the style of TOK, ancient petroglyphs.
+- In the style of TOK, a room and space exchange based on personal connections.
+- In the style of TOK, trypophobia.
+- In the style of TOK, a 6-pack of items seen as nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers.
+- In the style of TOK, a beaver chewing a rifle in half.
+- In the style of TOK, beavers quickly building a dam.
+- In the style of TOK, beavers quickly building a dam.
+- In the style of TOK, a brigade of beavers.
+- In the style of TOK, a moving castle on grassy hills.
+- In the style of TOK, a line of adorable beavers dancing in unison.
+- In the style of TOK, a frozen 1950s restaurant kitchen rediscovered in the space age.
+- In the style of TOK, a fashion show with elephant seals on the runway and egrets in the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture.
+- In the style of TOK, a mermaid made from sea trash, swimming up to peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 3...
+----- GPT response: -----
+Style Description: Surreal and intricate marine or nature scenes
+
+- In the style of TOK, a mermaid crafted from fishnets and sea trash swims underwater in the ocean.
+- In the style of TOK, Lilliputians are cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi, Kerala, India.
+- In the style of TOK, narrating the best and worst of times.
+- In the style of TOK, a blockchain composed of bees.
+- In the style of TOK, a gorgon formed from sharks cannibalizing each other, swimming underwater.
+- In the style of TOK, a sunset over a hillside with Venus and Earth orbiting the sun.
+- In the style of TOK, an ancient temple located deep within the Amazon jungle.
+- In the style of TOK, cats defecate in zero-gravity.
+- In the style of TOK, a goblin goat.
+- In the style of TOK, narrating the age of wisdom and foolishness.
+- In the style of TOK, a scene where two people quit their boring corporate jobs together.
+- In the style of TOK, ancient petroglyphs.
+- In the style of TOK, an exchange room and space based on first and second degree connections.
+- In the style of TOK, a depiction of trypophobia.
+- In the style of TOK, a pack of six frightening images.
+- In the style of TOK, a band of dancing and singing beavers.
+- In the style of TOK, a beaver gnawing a rifle in half.
+- In the style of TOK, a brigade of beavers quickly constructing a dam as a Hawaiian-shirt pattern.
+- In the style of TOK, a brigade of beavers quickly constructing a dam in Tarsem Singh style.
+- In the style of TOK, a brigade of beavers depicted in an M.C. Escher style.
+- In the style of TOK, a moving castle travels along grassy hills.
+- In the style of TOK, a chorus line of cute beavers dances in unison.
+- In the style of TOK, a frozen and preserved 1950s restaurant kitchen, rediscovered in the space age.
+- In the style of TOK, a fashion show featuring elephant seals as runway models and egrets as the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture.
+- In the style of TOK, a mermaid made from fishnets, dead fish, packing peanuts, and sea trash swims to the surface for a peek.
+- In the style of TOK, a mermaid fashioned from fishnets and sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians are cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India.
+- In the style of TOK, it was the best of times and the worst of times.
+- In the style of TOK, a blockchain made up of bees.
+- In the style of TOK, a gorgon comprised of sharks eating each other, swimming underwater.
+- In the style of TOK, a sunset over a hillside with Venus and Earth orbiting the sun.
+- In the style of TOK, an ancient temple buried deep within the Amazon jungle.
+- In the style of TOK, cats defecating in zero-gravity.
+- In the style of TOK, a goblin goat.
+- In the style of TOK, it was the age of wisdom and foolishness.
+- In the style of TOK, a scene where two people decide to quit their boring corporate jobs together.
+- In the style of TOK, representations of petroglyphs.
+- In the style of TOK, a room and space exchange based on first and second degree connections.
+- In the style of TOK, a pattern invoking trypophobia.
+- In the style of TOK, a collection of six terrifying images.
+- In the style of TOK, a troupe of dancing, singing beavers as Muppets.
+- In the style of TOK, a beaver chewing a rifle in half.
+- In the style of TOK, a brigade of beavers quickly constructing a dam in a Hawaiian-shirt pattern.
+- In the style of TOK, a brigade of beavers swiftly building a dam as seen in Tarsem Singh's works.
+- In the style of TOK, a brigade of beavers illustrated in an M.C. Escher style.
+- In the style of TOK, a mobile castle moving along grassy hills.
+- In the style of TOK, a line of adorable beavers dancing in sync.
+- In the style of TOK, a frozen 1950s restaurant kitchen discovered in the space age.
+- In the style of TOK, a fashion show featuring elephant seals as models and egrets in the audience.
+- In the style of TOK, a hyper-realistic painting depicting lemon-lime architecture.
+- In the style of TOK, a mermaid constructed from fishnets, dead fish, packing peanuts, and sea trash swims upward to peek above the water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Using GPT concept name as CLIP-segmentation prompt: Surreal and intricate marine or nature scenes
+Generating 54 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
54it [00:00, 13968.08it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 54 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a mermaid crafte...
+1     in the style of <s0><s1><s2>, lilliputians are...
+2     in the style of <s0><s1><s2>, fort kochi, kera...
+3     in the style of <s0><s1><s2>, narrating the be...
+4     in the style of <s0><s1><s2>, a blockchain com...
+5     in the style of <s0><s1><s2>, a gorgon formed ...
+6     in the style of <s0><s1><s2>, a sunset over a ...
+7     in the style of <s0><s1><s2>, an ancient templ...
+8     in the style of <s0><s1><s2>, cats defecate in...
+9          in the style of <s0><s1><s2>, a goblin goat.
+10    in the style of <s0><s1><s2>, narrating the ag...
+11    in the style of <s0><s1><s2>, a scene where tw...
+12    in the style of <s0><s1><s2>, ancient petrogly...
+13    in the style of <s0><s1><s2>, an exchange room...
+14    in the style of <s0><s1><s2>, a depiction of t...
+15    in the style of <s0><s1><s2>, a pack of six fr...
+16    in the style of <s0><s1><s2>, a band of dancin...
+17    in the style of <s0><s1><s2>, a beaver gnawing...
+18    in the style of <s0><s1><s2>, a brigade of bea...
+19    in the style of <s0><s1><s2>, a brigade of bea...
+20    in the style of <s0><s1><s2>, a brigade of bea...
+21    in the style of <s0><s1><s2>, a moving castle ...
+22    in the style of <s0><s1><s2>, a chorus line of...
+23    in the style of <s0><s1><s2>, a frozen and pre...
+24    in the style of <s0><s1><s2>, a fashion show f...
+25    in the style of <s0><s1><s2>, a hyper-realisti...
+26    in the style of <s0><s1><s2>, a mermaid made f...
+27    in the style of <s0><s1><s2>, a mermaid fashio...
+28    in the style of <s0><s1><s2>, a throng of lill...
+29    in the style of <s0><s1><s2>, fort kochi in ke...
+30    in the style of <s0><s1><s2>, it was the best ...
+31    in the style of <s0><s1><s2>, a blockchain mad...
+32    in the style of <s0><s1><s2>, a gorgon compris...
+33    in the style of <s0><s1><s2>, a sunset over a ...
+34    in the style of <s0><s1><s2>, an ancient templ...
+35    in the style of <s0><s1><s2>, cats defecating ...
+36         in the style of <s0><s1><s2>, a goblin goat.
+37    in the style of <s0><s1><s2>, it was the age o...
+38    in the style of <s0><s1><s2>, a scene where tw...
+39    in the style of <s0><s1><s2>, representations ...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 54
+--- Num batches each epoch = 14
+--- Num Epochs = 22
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
  0%|          | 0/300 [00:00<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:02<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:02<14:41,  2.95s/it]
# Trainer step: 0, epoch: 0:   1%|          | 2/300 [00:07<20:12,  4.07s/it]
# Trainer step: 0, epoch: 0:   1%|          | 3/300 [00:08<11:57,  2.41s/it]
# Trainer step: 0, epoch: 0:   1%|▏         | 4/300 [00:08<08:04,  1.64s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:09<05:56,  1.21s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:09<04:39,  1.05it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.39

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:10<03:50,  1.27it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:10<03:18,  1.47it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 9/300 [00:10<02:56,  1.65it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 10/300 [00:11<02:42,  1.79it/s]
# Trainer step: 0, epoch: 0:   4%|▎         | 11/300 [00:11<02:32,  1.90it/s]
# Trainer step: 0, epoch: 0:   4%|▍         | 12/300 [00:12<02:25,  1.99it/s]Progress: 7.00%
+---- avg training fps: 3.77

# Trainer step: 0, epoch: 0:   4%|▍         | 13/300 [00:12<02:20,  2.05it/s]
# Trainer step: 0, epoch: 0:   5%|▍         | 14/300 [00:13<02:16,  2.10it/s]
# Trainer step: 14, epoch: 1:   5%|▍         | 14/300 [00:13<02:16,  2.10it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 15/300 [00:13<02:22,  2.01it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 16/300 [00:14<02:17,  2.07it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 17/300 [00:14<02:14,  2.11it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 18/300 [00:15<02:11,  2.14it/s]Progress: 9.00%
+---- avg training fps: 4.63

# Trainer step: 14, epoch: 1:   6%|▋         | 19/300 [00:15<02:09,  2.16it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 20/300 [00:16<02:08,  2.18it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 21/300 [00:16<02:07,  2.19it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 22/300 [00:16<02:06,  2.20it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 23/300 [00:17<02:05,  2.21it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 24/300 [00:17<02:05,  2.20it/s]Progress: 11.00%
+---- avg training fps: 5.26

# Trainer step: 14, epoch: 1:   8%|▊         | 25/300 [00:18<02:04,  2.21it/s]
# Trainer step: 14, epoch: 1:   9%|▊         | 26/300 [00:18<02:03,  2.21it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 27/300 [00:19<02:03,  2.21it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 28/300 [00:19<02:03,  2.21it/s]
# Trainer step: 28, epoch: 2:   9%|▉         | 28/300 [00:20<02:03,  2.21it/s]
# Trainer step: 28, epoch: 2:  10%|▉         | 29/300 [00:20<01:57,  2.30it/s]
# Trainer step: 28, epoch: 2:  10%|█         | 30/300 [00:20<01:58,  2.27it/s]Progress: 13.00%
+---- avg training fps: 5.74

# Trainer step: 28, epoch: 2:  10%|█         | 31/300 [00:20<01:59,  2.25it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 32/300 [00:21<01:59,  2.24it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 33/300 [00:21<01:59,  2.23it/s]
# Trainer step: 28, epoch: 2:  11%|█▏        | 34/300 [00:22<01:59,  2.23it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 35/300 [00:22<01:59,  2.22it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 36/300 [00:23<01:58,  2.22it/s]Progress: 15.00%
+---- avg training fps: 6.10

# Trainer step: 28, epoch: 2:  12%|█▏        | 37/300 [00:23<01:58,  2.22it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 38/300 [00:24<01:58,  2.22it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 39/300 [00:24<01:58,  2.21it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 40/300 [00:24<01:57,  2.20it/s]
# Trainer step: 28, epoch: 2:  14%|█▎        | 41/300 [00:25<01:57,  2.21it/s]
# Trainer step: 28, epoch: 2:  14%|█▍        | 42/300 [00:25<01:57,  2.20it/s]Progress: 17.00%
+---- avg training fps: 6.39

# Trainer step: 42, epoch: 3:  14%|█▍        | 42/300 [00:26<01:57,  2.20it/s]
# Trainer step: 42, epoch: 3:  14%|█▍        | 43/300 [00:26<01:52,  2.29it/s]
# Trainer step: 42, epoch: 3:  15%|█▍        | 44/300 [00:26<01:52,  2.27it/s]
# Trainer step: 42, epoch: 3:  15%|█▌        | 45/300 [00:27<01:53,  2.25it/s]
# Trainer step: 42, epoch: 3:  15%|█▌        | 46/300 [00:27<01:54,  2.23it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 47/300 [00:28<01:54,  2.21it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 48/300 [00:28<01:54,  2.20it/s]Progress: 19.00%
+---- avg training fps: 6.62

# Trainer step: 42, epoch: 3:  16%|█▋        | 49/300 [00:29<01:54,  2.20it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 50/300 [00:29<01:54,  2.18it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 51/300 [00:29<01:53,  2.19it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 52/300 [00:34<06:33,  1.59s/it]
# Trainer step: 42, epoch: 3:  18%|█▊        | 53/300 [00:34<05:07,  1.25s/it]
# Trainer step: 42, epoch: 3:  18%|█▊        | 54/300 [00:35<04:07,  1.01s/it]Progress: 21.00%
+---- avg training fps: 6.08

# Trainer step: 42, epoch: 3:  18%|█▊        | 55/300 [00:35<03:26,  1.19it/s]
# Trainer step: 42, epoch: 3:  19%|█▊        | 56/300 [00:35<02:57,  1.37it/s]
# Trainer step: 56, epoch: 4:  19%|█▊        | 56/300 [00:36<02:57,  1.37it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 57/300 [00:36<02:32,  1.59it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 58/300 [00:36<02:19,  1.73it/s]
# Trainer step: 56, epoch: 4:  20%|█▉        | 59/300 [00:37<02:10,  1.85it/s]
# Trainer step: 56, epoch: 4:  20%|██        | 60/300 [00:37<02:03,  1.94it/s]Progress: 23.00%
+---- avg training fps: 6.28

# Trainer step: 56, epoch: 4:  20%|██        | 61/300 [00:38<01:59,  2.01it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 62/300 [00:38<01:56,  2.05it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 63/300 [00:39<01:53,  2.09it/s]
# Trainer step: 56, epoch: 4:  21%|██▏       | 64/300 [00:39<01:51,  2.12it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 65/300 [00:40<01:49,  2.14it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 66/300 [00:40<01:49,  2.15it/s]Progress: 25.00%
+---- avg training fps: 6.45

# Trainer step: 56, epoch: 4:  22%|██▏       | 67/300 [00:40<01:48,  2.16it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 68/300 [00:41<01:47,  2.17it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 69/300 [00:41<01:46,  2.17it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 70/300 [00:42<01:46,  2.17it/s]
# Trainer step: 70, epoch: 5:  23%|██▎       | 70/300 [00:42<01:46,  2.17it/s]
# Trainer step: 70, epoch: 5:  24%|██▎       | 71/300 [00:42<01:41,  2.25it/s]
# Trainer step: 70, epoch: 5:  24%|██▍       | 72/300 [00:43<01:42,  2.23it/s]Progress: 27.00%
+---- avg training fps: 6.60

# Trainer step: 70, epoch: 5:  24%|██▍       | 73/300 [00:43<01:42,  2.21it/s]
# Trainer step: 70, epoch: 5:  25%|██▍       | 74/300 [00:44<01:43,  2.19it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 75/300 [00:44<01:43,  2.18it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 76/300 [00:45<01:42,  2.18it/s]
# Trainer step: 70, epoch: 5:  26%|██▌       | 77/300 [00:45<01:42,  2.18it/s]
# Trainer step: 70, epoch: 5:  26%|██▌       | 78/300 [00:45<01:42,  2.17it/s]Progress: 29.00%
+---- avg training fps: 6.72

# Trainer step: 70, epoch: 5:  26%|██▋       | 79/300 [00:46<01:41,  2.17it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 80/300 [00:46<01:41,  2.17it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 81/300 [00:47<01:40,  2.17it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 82/300 [00:47<01:40,  2.16it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 83/300 [00:48<01:40,  2.15it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 84/300 [00:48<01:40,  2.16it/s]Progress: 31.00%
+---- avg training fps: 6.84

# Trainer step: 84, epoch: 6:  28%|██▊       | 84/300 [00:49<01:40,  2.16it/s]
# Trainer step: 84, epoch: 6:  28%|██▊       | 85/300 [00:49<01:35,  2.25it/s]
# Trainer step: 84, epoch: 6:  29%|██▊       | 86/300 [00:49<01:36,  2.22it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 87/300 [00:50<01:37,  2.20it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 88/300 [00:50<01:37,  2.18it/s]
# Trainer step: 84, epoch: 6:  30%|██▉       | 89/300 [00:51<01:36,  2.18it/s]
# Trainer step: 84, epoch: 6:  30%|███       | 90/300 [00:51<01:36,  2.17it/s]Progress: 33.00%
+---- avg training fps: 6.93

# Trainer step: 84, epoch: 6:  30%|███       | 91/300 [00:51<01:36,  2.16it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 92/300 [00:52<01:36,  2.16it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 93/300 [00:52<01:35,  2.16it/s]
# Trainer step: 84, epoch: 6:  31%|███▏      | 94/300 [00:53<01:35,  2.15it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 95/300 [00:53<01:35,  2.15it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 96/300 [00:54<01:34,  2.15it/s]Progress: 35.00%
+---- avg training fps: 7.02

# Trainer step: 84, epoch: 6:  32%|███▏      | 97/300 [00:54<01:34,  2.15it/s]
# Trainer step: 84, epoch: 6:  33%|███▎      | 98/300 [00:55<01:34,  2.15it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 98/300 [00:55<01:34,  2.15it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 99/300 [00:55<01:29,  2.24it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 100/300 [00:56<01:30,  2.20it/s]
# Trainer step: 98, epoch: 7:  34%|███▎      | 101/300 [00:56<01:31,  2.18it/s]
# Trainer step: 98, epoch: 7:  34%|███▍      | 102/300 [00:59<03:46,  1.14s/it]Progress: 37.00%
+---- avg training fps: 6.83

# Trainer step: 98, epoch: 7:  34%|███▍      | 103/300 [00:59<03:05,  1.06it/s]
# Trainer step: 98, epoch: 7:  35%|███▍      | 104/300 [01:00<02:36,  1.25it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 105/300 [01:00<02:16,  1.43it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 106/300 [01:01<02:01,  1.59it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 107/300 [01:01<01:52,  1.72it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 108/300 [01:02<01:44,  1.84it/s]Progress: 39.00%
+---- avg training fps: 6.91

# Trainer step: 98, epoch: 7:  36%|███▋      | 109/300 [01:02<01:39,  1.92it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 110/300 [01:03<01:36,  1.98it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 111/300 [01:03<01:33,  2.02it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 112/300 [01:03<01:31,  2.06it/s]
# Trainer step: 112, epoch: 8:  37%|███▋      | 112/300 [01:04<01:31,  2.06it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 113/300 [01:04<01:26,  2.17it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 114/300 [01:04<01:26,  2.16it/s]Progress: 41.00%
+---- avg training fps: 6.98

# Trainer step: 112, epoch: 8:  38%|███▊      | 115/300 [01:05<01:26,  2.15it/s]
# Trainer step: 112, epoch: 8:  39%|███▊      | 116/300 [01:05<01:25,  2.15it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 117/300 [01:06<01:25,  2.14it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 118/300 [01:06<01:24,  2.14it/s]
# Trainer step: 112, epoch: 8:  40%|███▉      | 119/300 [01:07<01:24,  2.14it/s]
# Trainer step: 112, epoch: 8:  40%|████      | 120/300 [01:07<01:23,  2.14it/s]Progress: 43.00%
+---- avg training fps: 7.05

# Trainer step: 112, epoch: 8:  40%|████      | 121/300 [01:08<01:23,  2.14it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 122/300 [01:08<01:23,  2.14it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 123/300 [01:09<01:22,  2.14it/s]
# Trainer step: 112, epoch: 8:  41%|████▏     | 124/300 [01:09<01:22,  2.14it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 125/300 [01:09<01:21,  2.14it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 126/300 [01:10<01:21,  2.14it/s]Progress: 45.00%
+---- avg training fps: 7.11

# Trainer step: 126, epoch: 9:  42%|████▏     | 126/300 [01:10<01:21,  2.14it/s]
# Trainer step: 126, epoch: 9:  42%|████▏     | 127/300 [01:10<01:17,  2.23it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 128/300 [01:11<01:17,  2.21it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 129/300 [01:11<01:18,  2.18it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 130/300 [01:12<01:18,  2.17it/s]
# Trainer step: 126, epoch: 9:  44%|████▎     | 131/300 [01:12<01:18,  2.16it/s]
# Trainer step: 126, epoch: 9:  44%|████▍     | 132/300 [01:13<01:18,  2.15it/s]Progress: 47.00%
+---- avg training fps: 7.17

# Trainer step: 126, epoch: 9:  44%|████▍     | 133/300 [01:13<01:17,  2.14it/s]
# Trainer step: 126, epoch: 9:  45%|████▍     | 134/300 [01:14<01:17,  2.14it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 135/300 [01:14<01:16,  2.15it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 136/300 [01:15<01:16,  2.14it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 137/300 [01:15<01:16,  2.14it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 138/300 [01:15<01:15,  2.14it/s]Progress: 49.00%
+---- avg training fps: 7.22

# Trainer step: 126, epoch: 9:  46%|████▋     | 139/300 [01:16<01:15,  2.14it/s]
# Trainer step: 126, epoch: 9:  47%|████▋     | 140/300 [01:16<01:14,  2.13it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 140/300 [01:17<01:14,  2.13it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 141/300 [01:17<01:11,  2.23it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 142/300 [01:18<01:24,  1.87it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 143/300 [01:18<01:20,  1.94it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 144/300 [01:19<01:18,  2.00it/s]Progress: 51.00%
+---- avg training fps: 7.25

# Trainer step: 140, epoch: 10:  48%|████▊     | 145/300 [01:19<01:15,  2.04it/s]
# Trainer step: 140, epoch: 10:  49%|████▊     | 146/300 [01:19<01:14,  2.07it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 147/300 [01:20<01:13,  2.10it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 148/300 [01:20<01:12,  2.11it/s]
# Trainer step: 140, epoch: 10:  50%|████▉     | 149/300 [01:21<01:10,  2.13it/s]
# Trainer step: 140, epoch: 10:  50%|█████     | 150/300 [01:21<01:10,  2.14it/s]Progress: 53.00%
+---- avg training fps: 7.29

# Trainer step: 140, epoch: 10:  50%|█████     | 151/300 [01:22<01:09,  2.14it/s]
# Trainer step: 140, epoch: 10:  51%|█████     | 152/300 [01:26<03:53,  1.58s/it]
# Trainer step: 140, epoch: 10:  51%|█████     | 153/300 [01:26<03:03,  1.25s/it]
# Trainer step: 140, epoch: 10:  51%|█████▏    | 154/300 [01:27<02:27,  1.01s/it]
# Trainer step: 154, epoch: 11:  51%|█████▏    | 154/300 [01:27<02:27,  1.01s/it]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 155/300 [01:27<02:00,  1.21it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 156/300 [01:28<01:43,  1.39it/s]Progress: 55.00%
+---- avg training fps: 7.02

# Trainer step: 154, epoch: 11:  52%|█████▏    | 157/300 [01:28<01:41,  1.41it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 158/300 [01:29<01:30,  1.58it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 159/300 [01:29<01:22,  1.71it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 160/300 [01:30<01:16,  1.83it/s]
# Trainer step: 154, epoch: 11:  54%|█████▎    | 161/300 [01:30<01:12,  1.92it/s]
# Trainer step: 154, epoch: 11:  54%|█████▍    | 162/300 [01:31<01:09,  1.98it/s]Progress: 57.00%
+---- avg training fps: 7.07

# Trainer step: 154, epoch: 11:  54%|█████▍    | 163/300 [01:31<01:07,  2.03it/s]
# Trainer step: 154, epoch: 11:  55%|█████▍    | 164/300 [01:32<01:05,  2.07it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 165/300 [01:32<01:04,  2.10it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 166/300 [01:33<01:03,  2.11it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 167/300 [01:33<01:02,  2.12it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 168/300 [01:34<01:01,  2.13it/s]Progress: 59.00%
+---- avg training fps: 7.12

# Trainer step: 168, epoch: 12:  56%|█████▌    | 168/300 [01:34<01:01,  2.13it/s]
# Trainer step: 168, epoch: 12:  56%|█████▋    | 169/300 [01:34<00:58,  2.23it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 170/300 [01:34<00:58,  2.21it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 171/300 [01:35<00:58,  2.19it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 172/300 [01:35<00:58,  2.18it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 173/300 [01:36<00:58,  2.17it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 174/300 [01:36<00:58,  2.16it/s]Progress: 61.00%
+---- avg training fps: 7.16

# Trainer step: 168, epoch: 12:  58%|█████▊    | 175/300 [01:37<00:57,  2.16it/s]
# Trainer step: 168, epoch: 12:  59%|█████▊    | 176/300 [01:37<00:57,  2.16it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 177/300 [01:38<00:56,  2.16it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 178/300 [01:38<00:56,  2.16it/s]
# Trainer step: 168, epoch: 12:  60%|█████▉    | 179/300 [01:39<00:56,  2.15it/s]
# Trainer step: 168, epoch: 12:  60%|██████    | 180/300 [01:39<00:55,  2.15it/s]Progress: 63.00%
+---- avg training fps: 7.20

# Trainer step: 168, epoch: 12:  60%|██████    | 181/300 [01:39<00:55,  2.16it/s]
# Trainer step: 168, epoch: 12:  61%|██████    | 182/300 [01:40<00:55,  2.14it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 182/300 [01:40<00:55,  2.14it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 183/300 [01:40<00:52,  2.24it/s]
# Trainer step: 182, epoch: 13:  61%|██████▏   | 184/300 [01:41<00:52,  2.21it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 185/300 [01:41<00:52,  2.19it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 186/300 [01:42<00:52,  2.18it/s]Progress: 65.00%
+---- avg training fps: 7.24

# Trainer step: 182, epoch: 13:  62%|██████▏   | 187/300 [01:42<00:52,  2.16it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 188/300 [01:43<00:51,  2.16it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 189/300 [01:43<00:51,  2.15it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 190/300 [01:44<00:51,  2.15it/s]
# Trainer step: 182, epoch: 13:  64%|██████▎   | 191/300 [01:44<00:50,  2.15it/s]
# Trainer step: 182, epoch: 13:  64%|██████▍   | 192/300 [01:45<00:50,  2.15it/s]Progress: 67.00%
+---- avg training fps: 7.28

# Trainer step: 182, epoch: 13:  64%|██████▍   | 193/300 [01:45<00:49,  2.15it/s]
# Trainer step: 182, epoch: 13:  65%|██████▍   | 194/300 [01:45<00:49,  2.15it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 195/300 [01:46<00:48,  2.15it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 196/300 [01:46<00:48,  2.14it/s]
# Trainer step: 196, epoch: 14:  65%|██████▌   | 196/300 [01:47<00:48,  2.14it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 197/300 [01:47<00:46,  2.23it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 198/300 [01:47<00:46,  2.20it/s]Progress: 69.00%
+---- avg training fps: 7.32

# Trainer step: 196, epoch: 14:  66%|██████▋   | 199/300 [01:48<00:46,  2.18it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 200/300 [01:48<00:46,  2.16it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 201/300 [01:49<00:45,  2.17it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 202/300 [01:52<02:02,  1.25s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 203/300 [01:52<01:38,  1.02s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 204/300 [01:53<01:21,  1.17it/s]Progress: 71.00%
+---- avg training fps: 7.18

# Trainer step: 196, epoch: 14:  68%|██████▊   | 205/300 [01:53<01:09,  1.36it/s]
# Trainer step: 196, epoch: 14:  69%|██████▊   | 206/300 [01:54<01:01,  1.53it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 207/300 [01:54<01:01,  1.52it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 208/300 [01:55<00:54,  1.67it/s]
# Trainer step: 196, epoch: 14:  70%|██████▉   | 209/300 [01:55<00:50,  1.80it/s]
# Trainer step: 196, epoch: 14:  70%|███████   | 210/300 [01:56<00:47,  1.89it/s]Progress: 73.00%
+---- avg training fps: 7.20

# Trainer step: 210, epoch: 15:  70%|███████   | 210/300 [01:56<00:47,  1.89it/s]
# Trainer step: 210, epoch: 15:  70%|███████   | 211/300 [01:56<00:43,  2.04it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 212/300 [01:57<00:42,  2.07it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 213/300 [01:57<00:41,  2.09it/s]
# Trainer step: 210, epoch: 15:  71%|███████▏  | 214/300 [01:57<00:40,  2.12it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 215/300 [01:58<00:40,  2.12it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 216/300 [01:58<00:39,  2.13it/s]Progress: 75.00%
+---- avg training fps: 7.24

# Trainer step: 210, epoch: 15:  72%|███████▏  | 217/300 [01:59<00:38,  2.13it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 218/300 [01:59<00:38,  2.14it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 219/300 [02:00<00:37,  2.14it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 220/300 [02:00<00:37,  2.13it/s]
# Trainer step: 210, epoch: 15:  74%|███████▎  | 221/300 [02:01<00:37,  2.13it/s]
# Trainer step: 210, epoch: 15:  74%|███████▍  | 222/300 [02:01<00:36,  2.13it/s]Progress: 77.00%
+---- avg training fps: 7.27

# Trainer step: 210, epoch: 15:  74%|███████▍  | 223/300 [02:02<00:36,  2.13it/s]
# Trainer step: 210, epoch: 15:  75%|███████▍  | 224/300 [02:02<00:35,  2.12it/s]
# Trainer step: 224, epoch: 16:  75%|███████▍  | 224/300 [02:03<00:35,  2.12it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 225/300 [02:03<00:33,  2.22it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 226/300 [02:03<00:33,  2.19it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 227/300 [02:04<00:33,  2.17it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 228/300 [02:04<00:33,  2.15it/s]Progress: 79.00%
+---- avg training fps: 7.30

# Trainer step: 224, epoch: 16:  76%|███████▋  | 229/300 [02:04<00:33,  2.15it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 230/300 [02:05<00:32,  2.14it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 231/300 [02:05<00:32,  2.13it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 232/300 [02:06<00:31,  2.13it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 233/300 [02:06<00:31,  2.12it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 234/300 [02:07<00:31,  2.13it/s]Progress: 81.00%
+---- avg training fps: 7.32

# Trainer step: 224, epoch: 16:  78%|███████▊  | 235/300 [02:07<00:30,  2.12it/s]
# Trainer step: 224, epoch: 16:  79%|███████▊  | 236/300 [02:08<00:30,  2.13it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 237/300 [02:08<00:29,  2.13it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 238/300 [02:09<00:29,  2.14it/s]
# Trainer step: 238, epoch: 17:  79%|███████▉  | 238/300 [02:09<00:29,  2.14it/s]
# Trainer step: 238, epoch: 17:  80%|███████▉  | 239/300 [02:09<00:27,  2.24it/s]
# Trainer step: 238, epoch: 17:  80%|████████  | 240/300 [02:10<00:27,  2.21it/s]Progress: 83.00%
+---- avg training fps: 7.35

# Trainer step: 238, epoch: 17:  80%|████████  | 241/300 [02:10<00:26,  2.19it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 242/300 [02:11<00:26,  2.17it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 243/300 [02:11<00:26,  2.16it/s]
# Trainer step: 238, epoch: 17:  81%|████████▏ | 244/300 [02:11<00:25,  2.16it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 245/300 [02:12<00:25,  2.15it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 246/300 [02:12<00:25,  2.14it/s]Progress: 85.00%
+---- avg training fps: 7.38

# Trainer step: 238, epoch: 17:  82%|████████▏ | 247/300 [02:13<00:24,  2.14it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 248/300 [02:13<00:24,  2.15it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 249/300 [02:14<00:23,  2.14it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 250/300 [02:14<00:23,  2.14it/s]
# Trainer step: 238, epoch: 17:  84%|████████▎ | 251/300 [02:15<00:22,  2.16it/s]
# Trainer step: 238, epoch: 17:  84%|████████▍ | 252/300 [02:19<01:15,  1.57s/it]Progress: 87.00%
+---- avg training fps: 7.21

# Trainer step: 252, epoch: 18:  84%|████████▍ | 252/300 [02:19<01:15,  1.57s/it]
# Trainer step: 252, epoch: 18:  84%|████████▍ | 253/300 [02:19<00:57,  1.22s/it]
# Trainer step: 252, epoch: 18:  85%|████████▍ | 254/300 [02:20<00:45,  1.01it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 255/300 [02:20<00:37,  1.20it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 256/300 [02:21<00:31,  1.38it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 257/300 [02:21<00:27,  1.55it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 258/300 [02:22<00:24,  1.69it/s]Progress: 89.00%
+---- avg training fps: 7.24

# Trainer step: 252, epoch: 18:  86%|████████▋ | 259/300 [02:22<00:22,  1.80it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 260/300 [02:23<00:21,  1.89it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 261/300 [02:23<00:19,  1.95it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 262/300 [02:23<00:18,  2.01it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 263/300 [02:24<00:18,  2.05it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 264/300 [02:24<00:17,  2.07it/s]Progress: 91.00%
+---- avg training fps: 7.27

# Trainer step: 252, epoch: 18:  88%|████████▊ | 265/300 [02:25<00:16,  2.09it/s]
# Trainer step: 252, epoch: 18:  89%|████████▊ | 266/300 [02:25<00:16,  2.10it/s]
# Trainer step: 266, epoch: 19:  89%|████████▊ | 266/300 [02:26<00:16,  2.10it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 267/300 [02:26<00:14,  2.20it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 268/300 [02:26<00:14,  2.19it/s]
# Trainer step: 266, epoch: 19:  90%|████████▉ | 269/300 [02:27<00:14,  2.16it/s]
# Trainer step: 266, epoch: 19:  90%|█████████ | 270/300 [02:27<00:13,  2.15it/s]Progress: 93.00%
+---- avg training fps: 7.29

# Trainer step: 266, epoch: 19:  90%|█████████ | 271/300 [02:28<00:13,  2.15it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 272/300 [02:28<00:13,  2.14it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 273/300 [02:29<00:12,  2.14it/s]
# Trainer step: 266, epoch: 19:  91%|█████████▏| 274/300 [02:29<00:12,  2.13it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 275/300 [02:29<00:11,  2.13it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 276/300 [02:30<00:11,  2.13it/s]Progress: 95.00%
+---- avg training fps: 7.32

# Trainer step: 266, epoch: 19:  92%|█████████▏| 277/300 [02:30<00:10,  2.13it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 278/300 [02:31<00:10,  2.14it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 279/300 [02:31<00:09,  2.13it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 280/300 [02:32<00:09,  2.13it/s]
# Trainer step: 280, epoch: 20:  93%|█████████▎| 280/300 [02:32<00:09,  2.13it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▎| 281/300 [02:32<00:08,  2.22it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▍| 282/300 [02:33<00:08,  2.19it/s]Progress: 97.00%
+---- avg training fps: 7.34

# Trainer step: 280, epoch: 20:  94%|█████████▍| 283/300 [02:33<00:07,  2.17it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▍| 284/300 [02:34<00:07,  2.16it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 285/300 [02:34<00:06,  2.14it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 286/300 [02:35<00:06,  2.14it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 287/300 [02:35<00:06,  2.13it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 288/300 [02:36<00:05,  2.12it/s]Progress: 99.00%
+---- avg training fps: 7.36

# Trainer step: 280, epoch: 20:  96%|█████████▋| 289/300 [02:36<00:05,  2.14it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 290/300 [02:36<00:04,  2.12it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 291/300 [02:37<00:04,  2.12it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 292/300 [02:37<00:03,  2.12it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 293/300 [02:38<00:03,  2.12it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 294/300 [02:38<00:02,  2.12it/s]Progress: 100.00%
+---- avg training fps: 7.38

# Trainer step: 294, epoch: 21:  98%|█████████▊| 294/300 [02:39<00:02,  2.12it/s]
# Trainer step: 294, epoch: 21:  98%|█████████▊| 295/300 [02:39<00:02,  2.22it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▊| 296/300 [02:39<00:01,  2.19it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 297/300 [02:40<00:01,  2.17it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 298/300 [02:40<00:00,  2.14it/s]
# Trainer step: 294, epoch: 21: 100%|█████████▉| 299/300 [02:41<00:00,  2.14it/s]
# Trainer step: 294, epoch: 21: 100%|██████████| 300/300 [02:41<00:00,  2.14it/s]Progress: 100.00%
+---- avg training fps: 7.40

# Trainer step: 294, epoch: 21: : 301it [02:42,  2.12it/s]                       Progress: 100.00%
Reached max steps, stopping training!
+Saving checkpoint at step.. 301
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 294, epoch: 21: : 301it [04:05,  1.22it/s]
+------------------------------------------
+Training done :)
+2024-08-13 02:28:21.267241: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 02:28:21.359036: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 02:28:21.359064: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 02:28:21.378498: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 02:28:21.780788: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:28:21.780871: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:28:21.780881: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23119 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.5 freeze_unet_before_completion_f=0.5 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='clipx.zip' output_dir='lora_models/styles_final/clipx.zip_13_02-28-23-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723508903.5469067 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Downloading training data...
+Downloading https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip to lora_models/styles_final/clipx.zip_13_02-28-23-style_512_florence_300/images_in/clipx.zip...
+Succesfully prepped 54 .jpg images in lora_models/styles_final/clipx.zip_13_02-28-23-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.314
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 27 images, 27 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 27 to 54)
+Generating 54 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal, whimsical, detailed, and quirky scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene depicts Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase describes the best and worst of times.
+- In the style of TOK, a blockchain is visualized using bees.
+- In the style of TOK, a gorgon made out of sharks eats each other while swimming underwater.
+- In the style of TOK, a hillside at sunset features Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple stands deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is portrayed.
+- In the style of TOK, the phrase exemplifies the age of wisdom and foolishness.
+- In the style of TOK, the message suggests quitting a boring corporation job together.
+- In the style of TOK, petroglyphs are depicted in the image.
+- In the style of TOK, a room and space exchange based on first and second-degree connections is shown.
+- In the style of TOK, the fear known as trypophobia is illustrated.
+- In the style of TOK, a 6-pack is referred to as nightmare fuel.
+- In the style of TOK, a band of beavers sings and dances like Jim Henson's Muppets.
+- In the style of TOK, a beaver chews a rifle in half.
+- In the style of TOK, a brigade of beavers quickly builds a dam, depicted as a Hawaiian-shirt pattern.
+- In the style of TOK, a brigade of beavers quickly builds a dam, resembling Tarsem Singh's work.
+- In the style of TOK, a brigade of beavers is illustrated in the manner of M. C. Escher.
+- In the style of TOK, a castle moves along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dances in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is frozen solid and preserved, later rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models and egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting showcases lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash swims up through the sea to take a peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 1...
+----- GPT response: -----
+Style Description: Surrealistic, intricate, and fantastical scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is embodied in a visual representation.
+- In the style of TOK, a blockchain is composed of bees.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun is depicted.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is presented.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is embodied in a visual representation.
+- In the style of TOK, two individuals decide to quit their boring corporation job together.
+- In the style of TOK, petroglyphs are depicted.
+- In the style of TOK, a room and space exchange is based on first and second-degree connections.
+- In the style of TOK, trypophobia is visually represented.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of dancing and singing beavers is created with a nod to Jim Henson's Muppets.
+- In the style of TOK, a beaver is shown chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a pattern like a Hawaiian shirt.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is designed in the style of M. C. Escher.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting showcases lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash is swimming up through the sea to take a peak above the water.
+- In the style of TOK, a mermaid made out of fishnets and other sea trash is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is embodied in a visual representation.
+- In the style of TOK, a blockchain is composed of bees.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun is depicted.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is presented.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is embodied in a visual representation.
+- In the style of TOK, two individuals decide to quit their boring corporation job together.
+- In the style of TOK, petroglyphs are depicted.
+- In the style of TOK, a room and space exchange is based on first and second-degree connections.
+- In the style of TOK, trypophobia is visually represented.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of dancing and singing beavers is created with a nod to Jim Henson's Muppets.
+- In the style of TOK, a beaver is shown chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a pattern like a Hawaiian shirt.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is designed in the style of M. C. Escher.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting showcases lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash is swimming up through the sea to take a peak above the water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Using GPT concept name as CLIP-segmentation prompt: Surrealistic, intricate, and fantastical scenes
+Generating 54 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
54it [00:00, 13279.34it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 54 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a mermaid made o...
+1     in the style of <s0><s1><s2>, a throng of lill...
+2     in the style of <s0><s1><s2>, fort kochi in ke...
+3     in the style of <s0><s1><s2>, the phrase "they...
+4     in the style of <s0><s1><s2>, a blockchain is ...
+5     in the style of <s0><s1><s2>, a gorgon made ou...
+6     in the style of <s0><s1><s2>, a hillside at su...
+7     in the style of <s0><s1><s2>, an ancient templ...
+8     in the style of <s0><s1><s2>, cats are pooping...
+9     in the style of <s0><s1><s2>, a goblin goat is...
+10    in the style of <s0><s1><s2>, the phrase "it w...
+11    in the style of <s0><s1><s2>, two individuals ...
+12    in the style of <s0><s1><s2>, petroglyphs are ...
+13    in the style of <s0><s1><s2>, a room and space...
+14    in the style of <s0><s1><s2>, trypophobia is v...
+15    in the style of <s0><s1><s2>, a six-pack of ni...
+16    in the style of <s0><s1><s2>, a band of dancin...
+17    in the style of <s0><s1><s2>, a beaver is show...
+18    in the style of <s0><s1><s2>, a brigade of bea...
+19    in the style of <s0><s1><s2>, a brigade of bea...
+20    in the style of <s0><s1><s2>, a brigade of bea...
+21    in the style of <s0><s1><s2>, a castle is movi...
+22    in the style of <s0><s1><s2>, a chorus line of...
+23    in the style of <s0><s1><s2>, a dirty 1950s re...
+24    in the style of <s0><s1><s2>, a fashion show f...
+25    in the style of <s0><s1><s2>, a hyper-realisti...
+26    in the style of <s0><s1><s2>, a mermaid made o...
+27    in the style of <s0><s1><s2>, a mermaid made o...
+28    in the style of <s0><s1><s2>, a throng of lill...
+29    in the style of <s0><s1><s2>, fort kochi in ke...
+30    in the style of <s0><s1><s2>, the phrase "they...
+31    in the style of <s0><s1><s2>, a blockchain is ...
+32    in the style of <s0><s1><s2>, a gorgon made ou...
+33    in the style of <s0><s1><s2>, a hillside at su...
+34    in the style of <s0><s1><s2>, an ancient templ...
+35    in the style of <s0><s1><s2>, cats are pooping...
+36    in the style of <s0><s1><s2>, a goblin goat is...
+37    in the style of <s0><s1><s2>, the phrase "it w...
+38    in the style of <s0><s1><s2>, two individuals ...
+39    in the style of <s0><s1><s2>, petroglyphs are ...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 54
+--- Num batches each epoch = 14
+--- Num Epochs = 22
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:03<?, ?it/s]
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:10<05:01,  1.02s/it]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.21

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:10<04:07,  1.18it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:11<03:31,  1.38it/s]
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# Trainer step: 0, epoch: 0:   4%|▍         | 12/300 [00:13<02:32,  1.89it/s]Progress: 7.00%
+---- avg training fps: 3.51

# Trainer step: 0, epoch: 0:   4%|▍         | 13/300 [00:13<02:26,  1.96it/s]
# Trainer step: 0, epoch: 0:   5%|▍         | 14/300 [00:14<02:22,  2.01it/s]
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# Trainer step: 14, epoch: 1:   6%|▌         | 18/300 [00:16<02:17,  2.06it/s]Progress: 9.00%
+---- avg training fps: 4.34

# Trainer step: 14, epoch: 1:   6%|▋         | 19/300 [00:16<02:14,  2.09it/s]
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# Trainer step: 14, epoch: 1:   8%|▊         | 24/300 [00:18<02:10,  2.11it/s]Progress: 11.00%
+---- avg training fps: 4.95

# Trainer step: 14, epoch: 1:   8%|▊         | 25/300 [00:19<02:09,  2.13it/s]
# Trainer step: 14, epoch: 1:   9%|▊         | 26/300 [00:19<02:08,  2.13it/s]
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# Trainer step: 28, epoch: 2:   9%|▉         | 28/300 [00:21<02:07,  2.13it/s]
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# Trainer step: 28, epoch: 2:  10%|█         | 30/300 [00:21<02:03,  2.19it/s]Progress: 13.00%
+---- avg training fps: 5.42

# Trainer step: 28, epoch: 2:  10%|█         | 31/300 [00:22<02:04,  2.17it/s]
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# Trainer step: 28, epoch: 2:  12%|█▏        | 35/300 [00:24<02:04,  2.13it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 36/300 [00:24<02:03,  2.13it/s]Progress: 15.00%
+---- avg training fps: 5.77

# Trainer step: 28, epoch: 2:  12%|█▏        | 37/300 [00:24<02:03,  2.13it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 38/300 [00:25<02:02,  2.14it/s]
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# Trainer step: 28, epoch: 2:  13%|█▎        | 40/300 [00:26<02:01,  2.14it/s]
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# Trainer step: 28, epoch: 2:  14%|█▍        | 42/300 [00:27<02:00,  2.14it/s]Progress: 17.00%
+---- avg training fps: 6.06

# Trainer step: 42, epoch: 3:  14%|█▍        | 42/300 [00:27<02:00,  2.14it/s]
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# Trainer step: 42, epoch: 3:  16%|█▌        | 48/300 [00:30<01:57,  2.14it/s]Progress: 19.00%
+---- avg training fps: 6.29

# Trainer step: 42, epoch: 3:  16%|█▋        | 49/300 [00:30<01:57,  2.14it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 50/300 [00:31<01:57,  2.13it/s]
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# Trainer step: 42, epoch: 3:  18%|█▊        | 54/300 [00:35<03:45,  1.09it/s]Progress: 21.00%
+---- avg training fps: 5.93

# Trainer step: 42, epoch: 3:  18%|█▊        | 55/300 [00:36<03:11,  1.28it/s]
# Trainer step: 42, epoch: 3:  19%|█▊        | 56/300 [00:36<02:48,  1.45it/s]
# Trainer step: 56, epoch: 4:  19%|█▊        | 56/300 [00:37<02:48,  1.45it/s]
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# Trainer step: 56, epoch: 4:  20%|██        | 60/300 [00:38<02:04,  1.93it/s]Progress: 23.00%
+---- avg training fps: 6.13

# Trainer step: 56, epoch: 4:  20%|██        | 61/300 [00:39<02:00,  1.98it/s]
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# Trainer step: 56, epoch: 4:  22%|██▏       | 66/300 [00:41<02:04,  1.88it/s]Progress: 25.00%
+---- avg training fps: 6.25

# Trainer step: 56, epoch: 4:  22%|██▏       | 67/300 [00:42<01:59,  1.95it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 68/300 [00:42<01:55,  2.00it/s]
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# Trainer step: 70, epoch: 5:  23%|██▎       | 70/300 [00:44<01:51,  2.07it/s]
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# Trainer step: 70, epoch: 5:  24%|██▍       | 72/300 [00:44<01:44,  2.17it/s]Progress: 27.00%
+---- avg training fps: 6.40

# Trainer step: 70, epoch: 5:  24%|██▍       | 73/300 [00:45<01:45,  2.16it/s]
# Trainer step: 70, epoch: 5:  25%|██▍       | 74/300 [00:45<01:45,  2.15it/s]
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# Trainer step: 70, epoch: 5:  26%|██▌       | 78/300 [00:47<01:43,  2.14it/s]Progress: 29.00%
+---- avg training fps: 6.52

# Trainer step: 70, epoch: 5:  26%|██▋       | 79/300 [00:47<01:43,  2.13it/s]
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# Trainer step: 70, epoch: 5:  28%|██▊       | 84/300 [00:50<01:41,  2.13it/s]Progress: 31.00%
+---- avg training fps: 6.65

# Trainer step: 84, epoch: 6:  28%|██▊       | 84/300 [00:50<01:41,  2.13it/s]
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# Trainer step: 84, epoch: 6:  30%|███       | 90/300 [00:52<01:37,  2.14it/s]Progress: 33.00%
+---- avg training fps: 6.74

# Trainer step: 84, epoch: 6:  30%|███       | 91/300 [00:53<01:37,  2.14it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 92/300 [00:53<01:37,  2.14it/s]
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# Trainer step: 84, epoch: 6:  32%|███▏      | 96/300 [00:55<01:35,  2.13it/s]Progress: 35.00%
+---- avg training fps: 6.83

# Trainer step: 84, epoch: 6:  32%|███▏      | 97/300 [00:56<01:35,  2.13it/s]
# Trainer step: 84, epoch: 6:  33%|███▎      | 98/300 [00:56<01:34,  2.14it/s]
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# Trainer step: 98, epoch: 7:  34%|███▎      | 101/300 [00:57<01:31,  2.18it/s]
# Trainer step: 98, epoch: 7:  34%|███▍      | 102/300 [01:00<04:00,  1.22s/it]Progress: 37.00%
+---- avg training fps: 6.64

# Trainer step: 98, epoch: 7:  34%|███▍      | 103/300 [01:01<03:15,  1.01it/s]
# Trainer step: 98, epoch: 7:  35%|███▍      | 104/300 [01:01<02:43,  1.20it/s]
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# Trainer step: 98, epoch: 7:  36%|███▌      | 108/300 [01:03<01:47,  1.79it/s]Progress: 39.00%
+---- avg training fps: 6.72

# Trainer step: 98, epoch: 7:  36%|███▋      | 109/300 [01:04<01:42,  1.87it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 110/300 [01:04<01:37,  1.94it/s]
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# Trainer step: 98, epoch: 7:  37%|███▋      | 112/300 [01:05<01:32,  2.03it/s]
# Trainer step: 112, epoch: 8:  37%|███▋      | 112/300 [01:06<01:32,  2.03it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 113/300 [01:06<01:27,  2.14it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 114/300 [01:06<01:27,  2.13it/s]Progress: 41.00%
+---- avg training fps: 6.80

# Trainer step: 112, epoch: 8:  38%|███▊      | 115/300 [01:07<01:26,  2.13it/s]
# Trainer step: 112, epoch: 8:  39%|███▊      | 116/300 [01:07<01:26,  2.12it/s]
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# Trainer step: 112, epoch: 8:  39%|███▉      | 118/300 [01:08<01:25,  2.12it/s]
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# Trainer step: 112, epoch: 8:  40%|████      | 120/300 [01:09<01:24,  2.12it/s]Progress: 43.00%
+---- avg training fps: 6.87

# Trainer step: 112, epoch: 8:  40%|████      | 121/300 [01:09<01:24,  2.12it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 122/300 [01:10<01:23,  2.12it/s]
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# Trainer step: 112, epoch: 8:  41%|████▏     | 124/300 [01:11<01:22,  2.12it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 125/300 [01:11<01:32,  1.90it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 126/300 [01:12<01:28,  1.97it/s]Progress: 45.00%
+---- avg training fps: 6.92

# Trainer step: 126, epoch: 9:  42%|████▏     | 126/300 [01:12<01:28,  1.97it/s]
# Trainer step: 126, epoch: 9:  42%|████▏     | 127/300 [01:12<01:22,  2.10it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 128/300 [01:13<01:21,  2.11it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 129/300 [01:13<01:20,  2.12it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 130/300 [01:14<01:20,  2.12it/s]
# Trainer step: 126, epoch: 9:  44%|████▎     | 131/300 [01:14<01:19,  2.13it/s]
# Trainer step: 126, epoch: 9:  44%|████▍     | 132/300 [01:15<01:19,  2.12it/s]Progress: 47.00%
+---- avg training fps: 6.98

# Trainer step: 126, epoch: 9:  44%|████▍     | 133/300 [01:15<01:18,  2.13it/s]
# Trainer step: 126, epoch: 9:  45%|████▍     | 134/300 [01:16<01:17,  2.13it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 135/300 [01:16<01:17,  2.14it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 136/300 [01:17<01:16,  2.14it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 137/300 [01:17<01:16,  2.14it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 138/300 [01:17<01:15,  2.14it/s]Progress: 49.00%
+---- avg training fps: 7.04

# Trainer step: 126, epoch: 9:  46%|████▋     | 139/300 [01:18<01:15,  2.14it/s]
# Trainer step: 126, epoch: 9:  47%|████▋     | 140/300 [01:18<01:15,  2.13it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 140/300 [01:19<01:15,  2.13it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 141/300 [01:19<01:11,  2.23it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 142/300 [01:19<01:12,  2.19it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 143/300 [01:20<01:12,  2.17it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 144/300 [01:20<01:12,  2.16it/s]Progress: 51.00%
+---- avg training fps: 7.10

# Trainer step: 140, epoch: 10:  48%|████▊     | 145/300 [01:21<01:12,  2.15it/s]
# Trainer step: 140, epoch: 10:  49%|████▊     | 146/300 [01:21<01:11,  2.14it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 147/300 [01:22<01:11,  2.15it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 148/300 [01:22<01:10,  2.15it/s]
# Trainer step: 140, epoch: 10:  50%|████▉     | 149/300 [01:23<01:10,  2.13it/s]
# Trainer step: 140, epoch: 10:  50%|█████     | 150/300 [01:23<01:10,  2.13it/s]Progress: 53.00%
+---- avg training fps: 7.14

# Trainer step: 140, epoch: 10:  50%|█████     | 151/300 [01:23<01:09,  2.13it/s]
# Trainer step: 140, epoch: 10:  51%|█████     | 152/300 [01:29<04:33,  1.84s/it]
# Trainer step: 140, epoch: 10:  51%|█████     | 153/300 [01:29<03:30,  1.43s/it]
# Trainer step: 140, epoch: 10:  51%|█████▏    | 154/300 [01:29<02:47,  1.14s/it]
# Trainer step: 154, epoch: 11:  51%|█████▏    | 154/300 [01:30<02:47,  1.14s/it]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 155/300 [01:30<02:13,  1.09it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 156/300 [01:30<01:52,  1.28it/s]Progress: 55.00%
+---- avg training fps: 6.83

# Trainer step: 154, epoch: 11:  52%|█████▏    | 157/300 [01:31<01:38,  1.45it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 158/300 [01:31<01:28,  1.60it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 159/300 [01:32<01:21,  1.73it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 160/300 [01:32<01:16,  1.83it/s]
# Trainer step: 154, epoch: 11:  54%|█████▎    | 161/300 [01:33<01:12,  1.91it/s]
# Trainer step: 154, epoch: 11:  54%|█████▍    | 162/300 [01:33<01:09,  1.97it/s]Progress: 57.00%
+---- avg training fps: 6.88

# Trainer step: 154, epoch: 11:  54%|█████▍    | 163/300 [01:34<01:07,  2.03it/s]
# Trainer step: 154, epoch: 11:  55%|█████▍    | 164/300 [01:34<01:06,  2.06it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 165/300 [01:35<01:05,  2.07it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 166/300 [01:35<01:04,  2.09it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 167/300 [01:36<01:03,  2.11it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 168/300 [01:36<01:02,  2.12it/s]Progress: 59.00%
+---- avg training fps: 6.94

# Trainer step: 168, epoch: 12:  56%|█████▌    | 168/300 [01:36<01:02,  2.12it/s]
# Trainer step: 168, epoch: 12:  56%|█████▋    | 169/300 [01:36<00:58,  2.23it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 170/300 [01:37<00:59,  2.20it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 171/300 [01:37<00:58,  2.19it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 172/300 [01:38<00:58,  2.18it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 173/300 [01:38<00:58,  2.17it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 174/300 [01:39<00:58,  2.17it/s]Progress: 61.00%
+---- avg training fps: 6.98

# Trainer step: 168, epoch: 12:  58%|█████▊    | 175/300 [01:39<00:57,  2.16it/s]
# Trainer step: 168, epoch: 12:  59%|█████▊    | 176/300 [01:40<00:57,  2.15it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 177/300 [01:40<00:57,  2.15it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 178/300 [01:41<00:56,  2.15it/s]
# Trainer step: 168, epoch: 12:  60%|█████▉    | 179/300 [01:41<00:56,  2.16it/s]
# Trainer step: 168, epoch: 12:  60%|██████    | 180/300 [01:41<00:55,  2.15it/s]Progress: 63.00%
+---- avg training fps: 7.03

# Trainer step: 168, epoch: 12:  60%|██████    | 181/300 [01:42<00:55,  2.15it/s]
# Trainer step: 168, epoch: 12:  61%|██████    | 182/300 [01:42<00:54,  2.15it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 182/300 [01:43<00:54,  2.15it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 183/300 [01:43<00:51,  2.26it/s]
# Trainer step: 182, epoch: 13:  61%|██████▏   | 184/300 [01:43<00:52,  2.22it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 185/300 [01:44<00:52,  2.20it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 186/300 [01:44<00:52,  2.18it/s]Progress: 65.00%
+---- avg training fps: 7.07

# Trainer step: 182, epoch: 13:  62%|██████▏   | 187/300 [01:45<00:51,  2.18it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 188/300 [01:45<00:51,  2.17it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 189/300 [01:46<00:51,  2.16it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 190/300 [01:46<00:50,  2.16it/s]
# Trainer step: 182, epoch: 13:  64%|██████▎   | 191/300 [01:47<00:50,  2.16it/s]
# Trainer step: 182, epoch: 13:  64%|██████▍   | 192/300 [01:47<00:50,  2.15it/s]Progress: 67.00%
+---- avg training fps: 7.11

# Trainer step: 182, epoch: 13:  64%|██████▍   | 193/300 [01:47<00:49,  2.15it/s]
# Trainer step: 182, epoch: 13:  65%|██████▍   | 194/300 [01:48<00:49,  2.15it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 195/300 [01:48<00:48,  2.15it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 196/300 [01:49<00:48,  2.15it/s]
# Trainer step: 196, epoch: 14:  65%|██████▌   | 196/300 [01:49<00:48,  2.15it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 197/300 [01:49<00:45,  2.25it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 198/300 [01:50<00:47,  2.16it/s]Progress: 69.00%
+---- avg training fps: 7.15

# Trainer step: 196, epoch: 14:  66%|██████▋   | 199/300 [01:50<00:46,  2.15it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 200/300 [01:51<00:46,  2.15it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 201/300 [01:51<00:45,  2.16it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 202/300 [01:54<01:59,  1.22s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 203/300 [01:55<01:35,  1.01it/s]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 204/300 [01:55<01:19,  1.20it/s]Progress: 71.00%
+---- avg training fps: 7.03

# Trainer step: 196, epoch: 14:  68%|██████▊   | 205/300 [01:56<01:08,  1.39it/s]
# Trainer step: 196, epoch: 14:  69%|██████▊   | 206/300 [01:56<01:00,  1.56it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 207/300 [01:56<00:54,  1.70it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 208/300 [01:57<00:50,  1.82it/s]
# Trainer step: 196, epoch: 14:  70%|██████▉   | 209/300 [01:57<00:47,  1.91it/s]
# Trainer step: 196, epoch: 14:  70%|███████   | 210/300 [01:58<00:45,  1.99it/s]Progress: 73.00%
+---- avg training fps: 7.08

# Trainer step: 210, epoch: 15:  70%|███████   | 210/300 [01:58<00:45,  1.99it/s]
# Trainer step: 210, epoch: 15:  70%|███████   | 211/300 [01:58<00:42,  2.12it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 212/300 [01:59<00:41,  2.13it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 213/300 [01:59<00:40,  2.15it/s]
# Trainer step: 210, epoch: 15:  71%|███████▏  | 214/300 [02:00<00:39,  2.16it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 215/300 [02:00<00:46,  1.83it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 216/300 [02:01<00:43,  1.93it/s]Progress: 75.00%
+---- avg training fps: 7.10

# Trainer step: 210, epoch: 15:  72%|███████▏  | 217/300 [02:01<00:41,  2.00it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 218/300 [02:02<00:39,  2.06it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 219/300 [02:02<00:38,  2.10it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 220/300 [02:03<00:37,  2.12it/s]
# Trainer step: 210, epoch: 15:  74%|███████▎  | 221/300 [02:03<00:36,  2.15it/s]
# Trainer step: 210, epoch: 15:  74%|███████▍  | 222/300 [02:04<00:36,  2.16it/s]Progress: 77.00%
+---- avg training fps: 7.13

# Trainer step: 210, epoch: 15:  74%|███████▍  | 223/300 [02:04<00:35,  2.18it/s]
# Trainer step: 210, epoch: 15:  75%|███████▍  | 224/300 [02:04<00:34,  2.17it/s]
# Trainer step: 224, epoch: 16:  75%|███████▍  | 224/300 [02:05<00:34,  2.17it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 225/300 [02:05<00:33,  2.27it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 226/300 [02:05<00:33,  2.23it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 227/300 [02:06<00:32,  2.22it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 228/300 [02:06<00:32,  2.22it/s]Progress: 79.00%
+---- avg training fps: 7.17

# Trainer step: 224, epoch: 16:  76%|███████▋  | 229/300 [02:07<00:32,  2.21it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 230/300 [02:07<00:31,  2.20it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 231/300 [02:08<00:31,  2.19it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 232/300 [02:08<00:30,  2.19it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 233/300 [02:08<00:30,  2.20it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 234/300 [02:09<00:30,  2.20it/s]Progress: 81.00%
+---- avg training fps: 7.21

# Trainer step: 224, epoch: 16:  78%|███████▊  | 235/300 [02:09<00:29,  2.19it/s]
# Trainer step: 224, epoch: 16:  79%|███████▊  | 236/300 [02:10<00:29,  2.18it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 237/300 [02:10<00:28,  2.19it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 238/300 [02:11<00:28,  2.18it/s]
# Trainer step: 238, epoch: 17:  79%|███████▉  | 238/300 [02:11<00:28,  2.18it/s]
# Trainer step: 238, epoch: 17:  80%|███████▉  | 239/300 [02:11<00:26,  2.31it/s]
# Trainer step: 238, epoch: 17:  80%|████████  | 240/300 [02:12<00:26,  2.27it/s]Progress: 83.00%
+---- avg training fps: 7.24

# Trainer step: 238, epoch: 17:  80%|████████  | 241/300 [02:12<00:26,  2.24it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 242/300 [02:13<00:26,  2.22it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 243/300 [02:13<00:25,  2.22it/s]
# Trainer step: 238, epoch: 17:  81%|████████▏ | 244/300 [02:13<00:25,  2.21it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 245/300 [02:14<00:24,  2.20it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 246/300 [02:14<00:24,  2.19it/s]Progress: 85.00%
+---- avg training fps: 7.27

# Trainer step: 238, epoch: 17:  82%|████████▏ | 247/300 [02:15<00:24,  2.18it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 248/300 [02:15<00:23,  2.18it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 249/300 [02:16<00:23,  2.18it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 250/300 [02:16<00:22,  2.18it/s]
# Trainer step: 238, epoch: 17:  84%|████████▎ | 251/300 [02:17<00:22,  2.18it/s]
# Trainer step: 238, epoch: 17:  84%|████████▍ | 252/300 [02:20<01:08,  1.43s/it]Progress: 87.00%
+---- avg training fps: 7.14

# Trainer step: 252, epoch: 18:  84%|████████▍ | 252/300 [02:21<01:08,  1.43s/it]
# Trainer step: 252, epoch: 18:  84%|████████▍ | 253/300 [02:21<00:52,  1.12s/it]
# Trainer step: 252, epoch: 18:  85%|████████▍ | 254/300 [02:21<00:42,  1.08it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 255/300 [02:22<00:35,  1.27it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 256/300 [02:22<00:30,  1.46it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 257/300 [02:23<00:26,  1.61it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 258/300 [02:23<00:23,  1.75it/s]Progress: 89.00%
+---- avg training fps: 7.17

# Trainer step: 252, epoch: 18:  86%|████████▋ | 259/300 [02:24<00:22,  1.85it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 260/300 [02:24<00:20,  1.94it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 261/300 [02:24<00:19,  2.01it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 262/300 [02:25<00:18,  2.06it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 263/300 [02:25<00:17,  2.09it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 264/300 [02:26<00:17,  2.10it/s]Progress: 91.00%
+---- avg training fps: 7.19

# Trainer step: 252, epoch: 18:  88%|████████▊ | 265/300 [02:26<00:16,  2.12it/s]
# Trainer step: 252, epoch: 18:  89%|████████▊ | 266/300 [02:27<00:15,  2.14it/s]
# Trainer step: 266, epoch: 19:  89%|████████▊ | 266/300 [02:27<00:15,  2.14it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 267/300 [02:27<00:14,  2.24it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 268/300 [02:28<00:14,  2.22it/s]
# Trainer step: 266, epoch: 19:  90%|████████▉ | 269/300 [02:28<00:14,  2.21it/s]
# Trainer step: 266, epoch: 19:  90%|█████████ | 270/300 [02:29<00:13,  2.19it/s]Progress: 93.00%
+---- avg training fps: 7.23

# Trainer step: 266, epoch: 19:  90%|█████████ | 271/300 [02:29<00:13,  2.20it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 272/300 [02:29<00:12,  2.19it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 273/300 [02:30<00:12,  2.19it/s]
# Trainer step: 266, epoch: 19:  91%|█████████▏| 274/300 [02:30<00:11,  2.19it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 275/300 [02:31<00:11,  2.18it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 276/300 [02:31<00:11,  2.18it/s]Progress: 95.00%
+---- avg training fps: 7.25

# Trainer step: 266, epoch: 19:  92%|█████████▏| 277/300 [02:32<00:10,  2.18it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 278/300 [02:32<00:10,  2.18it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 279/300 [02:33<00:09,  2.18it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 280/300 [02:33<00:09,  2.18it/s]
# Trainer step: 280, epoch: 20:  93%|█████████▎| 280/300 [02:33<00:09,  2.18it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▎| 281/300 [02:33<00:08,  2.26it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▍| 282/300 [02:34<00:08,  2.24it/s]Progress: 97.00%
+---- avg training fps: 7.28

# Trainer step: 280, epoch: 20:  94%|█████████▍| 283/300 [02:34<00:07,  2.21it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▍| 284/300 [02:35<00:07,  2.20it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 285/300 [02:35<00:06,  2.20it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 286/300 [02:36<00:06,  2.19it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 287/300 [02:36<00:05,  2.18it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 288/300 [02:37<00:05,  2.17it/s]Progress: 99.00%
+---- avg training fps: 7.31

# Trainer step: 280, epoch: 20:  96%|█████████▋| 289/300 [02:37<00:05,  2.17it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 290/300 [02:38<00:04,  2.16it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 291/300 [02:38<00:04,  2.16it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 292/300 [02:39<00:03,  2.17it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 293/300 [02:39<00:03,  2.16it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 294/300 [02:40<00:02,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.33

# Trainer step: 294, epoch: 21:  98%|█████████▊| 294/300 [02:40<00:02,  2.16it/s]
# Trainer step: 294, epoch: 21:  98%|█████████▊| 295/300 [02:40<00:02,  2.25it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▊| 296/300 [02:40<00:01,  2.21it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 297/300 [02:41<00:01,  2.21it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 298/300 [02:41<00:00,  2.19it/s]
# Trainer step: 294, epoch: 21: 100%|█████████▉| 299/300 [02:42<00:00,  2.17it/s]
# Trainer step: 294, epoch: 21: 100%|██████████| 300/300 [02:42<00:00,  2.15it/s]Progress: 100.00%
+---- avg training fps: 7.35

# Trainer step: 294, epoch: 21: : 301it [02:43,  2.16it/s]                       Progress: 100.00%
Failed to plot token attention loss
+Reached max steps, stopping training!
+Saving checkpoint at step.. 301
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 294, epoch: 21: : 301it [04:03,  1.24it/s]
+------------------------------------------
+Training done :)
+2024-08-13 02:33:08.556638: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 02:33:08.649580: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 02:33:08.649608: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 02:33:08.668174: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 02:33:09.069232: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:33:09.069312: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:33:09.069321: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23101 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/good_styles/eden_crystals' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.75 freeze_unet_before_completion_f=0.0 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='eden_crystals' output_dir='lora_models/styles_final/eden_crystals_13_02-33-10-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723509190.8997006 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/eden_crystals_13_02-33-10-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.242
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants on a black background.
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants in the dark.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Futuristic natural elements blending with technology
+
+- In the style of TOK, a potted plant sits on a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of space filled with stars.
+- In the style of TOK, a flower with green leaves is on a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is on a black background.
+- In the style of TOK, a potted plant sits on a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of space filled with stars.
+- In the style of TOK, a flower with green leaves is on a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is in the dark.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Futuristic natural elements blending with technology
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 7619.43it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a potted plant s...
+1     in the style of <s0><s1><s2>, a futuristic cit...
+2     in the style of <s0><s1><s2>, a flower with gr...
+3     in the style of <s0><s1><s2>, a plant is growi...
+4     in the style of <s0><s1><s2>, a group of green...
+5     in the style of <s0><s1><s2>, a potted plant s...
+6     in the style of <s0><s1><s2>, a futuristic cit...
+7     in the style of <s0><s1><s2>, a flower with gr...
+8     in the style of <s0><s1><s2>, a plant is growi...
+9     in the style of <s0><s1><s2>, a group of green...
+10    in the style of <s0><s1><s2>, a potted plant s...
+11    in the style of <s0><s1><s2>, a futuristic cit...
+12    in the style of <s0><s1><s2>, a flower with gr...
+13    in the style of <s0><s1><s2>, a plant is growi...
+14    in the style of <s0><s1><s2>, a group of green...
+15    in the style of <s0><s1><s2>, a potted plant s...
+16    in the style of <s0><s1><s2>, a futuristic cit...
+17    in the style of <s0><s1><s2>, a flower with gr...
+18    in the style of <s0><s1><s2>, a plant is growi...
+19    in the style of <s0><s1><s2>, a group of green...
+20    in the style of <s0><s1><s2>, a potted plant s...
+21    in the style of <s0><s1><s2>, a futuristic cit...
+22    in the style of <s0><s1><s2>, a flower with gr...
+23    in the style of <s0><s1><s2>, a plant is growi...
+24    in the style of <s0><s1><s2>, a group of green...
+25    in the style of <s0><s1><s2>, a potted plant s...
+26    in the style of <s0><s1><s2>, a futuristic cit...
+27    in the style of <s0><s1><s2>, a flower with gr...
+28    in the style of <s0><s1><s2>, a plant is growi...
+29    in the style of <s0><s1><s2>, a group of green...
+30    in the style of <s0><s1><s2>, a potted plant s...
+31    in the style of <s0><s1><s2>, a futuristic cit...
+32    in the style of <s0><s1><s2>, a flower with gr...
+33    in the style of <s0><s1><s2>, a plant is growi...
+34    in the style of <s0><s1><s2>, a group of green...
+35    in the style of <s0><s1><s2>, a potted plant s...
+36    in the style of <s0><s1><s2>, a futuristic cit...
+37    in the style of <s0><s1><s2>, a flower with gr...
+38    in the style of <s0><s1><s2>, a plant is growi...
+39    in the style of <s0><s1><s2>, a group of green...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
  0%|          | 0/300 [00:00<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:02<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:02<14:41,  2.95s/it]
# Trainer step: 0, epoch: 0:   1%|          | 2/300 [00:06<16:37,  3.35s/it]
# Trainer step: 0, epoch: 0:   1%|          | 3/300 [00:07<10:00,  2.02s/it]
# Trainer step: 0, epoch: 0:   1%|▏         | 4/300 [00:07<06:54,  1.40s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:07<05:11,  1.06s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:08<04:09,  1.18it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.73

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:08<03:30,  1.39it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:09<03:04,  1.58it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 9/300 [00:09<02:47,  1.74it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 10/300 [00:10<02:35,  1.86it/s]
# Trainer step: 10, epoch: 1:   3%|▎         | 10/300 [00:10<02:35,  1.86it/s]
# Trainer step: 10, epoch: 1:   4%|▎         | 11/300 [00:10<02:27,  1.95it/s]
# Trainer step: 10, epoch: 1:   4%|▍         | 12/300 [00:11<02:23,  2.01it/s]Progress: 7.00%
+---- avg training fps: 4.16

# Trainer step: 10, epoch: 1:   4%|▍         | 13/300 [00:11<02:18,  2.06it/s]
# Trainer step: 10, epoch: 1:   5%|▍         | 14/300 [00:11<02:16,  2.10it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 15/300 [00:12<02:14,  2.12it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 16/300 [00:12<02:12,  2.14it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 17/300 [00:13<02:11,  2.16it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 18/300 [00:13<02:10,  2.17it/s]Progress: 9.00%
+---- avg training fps: 5.04

# Trainer step: 10, epoch: 1:   6%|▋         | 19/300 [00:14<02:09,  2.17it/s]
# Trainer step: 10, epoch: 1:   7%|▋         | 20/300 [00:14<02:08,  2.17it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 20/300 [00:15<02:08,  2.17it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 21/300 [00:15<02:07,  2.18it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 22/300 [00:15<02:07,  2.18it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 23/300 [00:16<02:06,  2.18it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 24/300 [00:16<02:06,  2.19it/s]Progress: 11.00%
+---- avg training fps: 5.64

# Trainer step: 20, epoch: 2:   8%|▊         | 25/300 [00:17<02:05,  2.19it/s]
# Trainer step: 20, epoch: 2:   9%|▊         | 26/300 [00:17<02:05,  2.19it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 27/300 [00:17<02:05,  2.18it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 28/300 [00:18<02:04,  2.18it/s]
# Trainer step: 20, epoch: 2:  10%|▉         | 29/300 [00:18<02:04,  2.18it/s]
# Trainer step: 20, epoch: 2:  10%|█         | 30/300 [00:19<02:04,  2.17it/s]Progress: 13.00%
+---- avg training fps: 6.07

# Trainer step: 30, epoch: 3:  10%|█         | 30/300 [00:19<02:04,  2.17it/s]
# Trainer step: 30, epoch: 3:  10%|█         | 31/300 [00:19<02:03,  2.17it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 32/300 [00:20<02:03,  2.17it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 33/300 [00:20<02:17,  1.94it/s]
# Trainer step: 30, epoch: 3:  11%|█▏        | 34/300 [00:21<02:12,  2.01it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 35/300 [00:21<02:08,  2.06it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 36/300 [00:22<02:05,  2.10it/s]Progress: 15.00%
+---- avg training fps: 6.34

# Trainer step: 30, epoch: 3:  12%|█▏        | 37/300 [00:22<02:03,  2.12it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 38/300 [00:23<02:01,  2.15it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 39/300 [00:23<02:00,  2.16it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 40/300 [00:24<01:59,  2.17it/s]
# Trainer step: 40, epoch: 4:  13%|█▎        | 40/300 [00:24<01:59,  2.17it/s]
# Trainer step: 40, epoch: 4:  14%|█▎        | 41/300 [00:24<01:58,  2.18it/s]
# Trainer step: 40, epoch: 4:  14%|█▍        | 42/300 [00:24<01:58,  2.18it/s]Progress: 17.00%
+---- avg training fps: 6.60

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:25<01:57,  2.19it/s]
# Trainer step: 40, epoch: 4:  15%|█▍        | 44/300 [00:25<01:57,  2.19it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 45/300 [00:26<01:56,  2.19it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 46/300 [00:26<01:56,  2.18it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 47/300 [00:27<01:55,  2.19it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:27<01:55,  2.18it/s]Progress: 19.00%
+---- avg training fps: 6.81

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:28<01:54,  2.18it/s]
# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:28<01:54,  2.18it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 50/300 [00:29<01:54,  2.18it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 51/300 [00:29<01:53,  2.19it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 52/300 [00:32<05:50,  1.41s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 53/300 [00:33<04:37,  1.12s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:33<03:47,  1.08it/s]Progress: 21.00%
+---- avg training fps: 6.33

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:34<03:12,  1.27it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:34<02:47,  1.46it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 57/300 [00:35<02:30,  1.62it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 58/300 [00:35<02:17,  1.76it/s]
# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:35<02:09,  1.87it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:36<02:03,  1.95it/s]Progress: 23.00%
+---- avg training fps: 6.51

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:36<02:03,  1.95it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:36<01:58,  2.01it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:37<01:54,  2.07it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:37<01:52,  2.10it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:38<02:04,  1.89it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:38<01:59,  1.97it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:39<01:55,  2.03it/s]Progress: 25.00%
+---- avg training fps: 6.64

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:39<01:51,  2.08it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:40<01:49,  2.11it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:40<01:48,  2.13it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:41<01:46,  2.15it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:41<01:46,  2.15it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:41<01:45,  2.17it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:42<01:44,  2.18it/s]Progress: 27.00%
+---- avg training fps: 6.77

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:42<01:44,  2.18it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:42<01:43,  2.18it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:43<01:42,  2.19it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:43<01:42,  2.18it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:44<01:42,  2.18it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:44<01:41,  2.18it/s]Progress: 29.00%
+---- avg training fps: 6.89

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:45<01:41,  2.18it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:45<01:40,  2.19it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:46<01:40,  2.19it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:46<01:40,  2.19it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:46<01:39,  2.18it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:47<01:39,  2.19it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:47<01:38,  2.20it/s]Progress: 31.00%
+---- avg training fps: 7.00

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:48<01:37,  2.20it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:48<01:37,  2.19it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:48<01:37,  2.19it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:49<01:36,  2.20it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:49<01:36,  2.20it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:50<01:35,  2.20it/s]Progress: 33.00%
+---- avg training fps: 7.10

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:50<01:35,  2.20it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:50<01:35,  2.20it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:51<01:34,  2.19it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:51<01:34,  2.20it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:52<01:33,  2.20it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:52<01:33,  2.19it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:53<01:33,  2.18it/s]Progress: 35.00%
+---- avg training fps: 7.18

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:53<01:32,  2.20it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:53<01:32,  2.19it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:54<01:32,  2.18it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:54<01:31,  2.18it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:55<01:31,  2.18it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:55<01:30,  2.19it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:55<01:30,  2.18it/s]Progress: 37.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.26

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:56<01:30,  2.19it/s]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [00:56<01:29,  2.18it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [00:57<01:29,  2.18it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [00:57<01:29,  2.18it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [00:58<01:28,  2.17it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [00:58<01:28,  2.17it/s]Progress: 39.00%
+---- avg training fps: 7.32

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [00:58<01:28,  2.17it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [00:59<01:27,  2.17it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [00:59<01:27,  2.17it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [00:59<01:27,  2.16it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:00<01:26,  2.16it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:00<01:26,  2.17it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:01<01:25,  2.17it/s]Progress: 41.00%
+---- avg training fps: 7.38

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:01<01:25,  2.17it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:02<01:24,  2.17it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:02<01:24,  2.17it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:03<01:24,  2.17it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:03<01:23,  2.17it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:04<01:23,  2.17it/s]Progress: 43.00%
+---- avg training fps: 7.44

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:04<01:23,  2.17it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:04<01:22,  2.17it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:04<01:21,  2.18it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:05<01:21,  2.18it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:05<01:20,  2.17it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:06<01:20,  2.17it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:06<01:19,  2.18it/s]Progress: 45.00%
+---- avg training fps: 7.49

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:07<01:19,  2.17it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:07<01:19,  2.17it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:08<01:19,  2.16it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:08<01:18,  2.16it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:09<01:18,  2.16it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:09<01:18,  2.17it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:09<01:17,  2.16it/s]Progress: 47.00%
+---- avg training fps: 7.54

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:10<01:17,  2.16it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:10<01:16,  2.17it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:10<01:16,  2.16it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:11<01:15,  2.16it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:11<01:15,  2.17it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:12<01:14,  2.17it/s]Progress: 49.00%
+---- avg training fps: 7.58

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:12<01:14,  2.16it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:13<01:14,  2.15it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:13<01:14,  2.15it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:13<01:13,  2.17it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:14<01:12,  2.17it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:14<01:12,  2.17it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:15<01:11,  2.17it/s]Progress: 51.00%
+---- avg training fps: 7.62

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:15<01:11,  2.18it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:16<01:10,  2.18it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:16<01:10,  2.17it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:16<01:10,  2.17it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:17<01:09,  2.17it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:17<01:09,  2.17it/s]Progress: 53.00%
+---- avg training fps: 7.66

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:18<01:09,  2.17it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:18<01:08,  2.16it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:22<03:41,  1.50s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:22<02:54,  1.19s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:23<02:21,  1.03it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:23<01:58,  1.23it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:24<01:41,  1.41it/s]Progress: 55.00%
+---- avg training fps: 7.38

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:24<01:30,  1.58it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:25<01:22,  1.73it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:25<01:16,  1.84it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:25<01:12,  1.93it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:26<01:12,  1.93it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:26<01:09,  1.99it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:26<01:07,  2.05it/s]Progress: 57.00%
+---- avg training fps: 7.42

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:27<01:05,  2.08it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:27<01:04,  2.10it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:28<01:03,  2.12it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:28<01:02,  2.15it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:29<01:01,  2.15it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:29<01:01,  2.15it/s]Progress: 59.00%
+---- avg training fps: 7.46

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:30<01:00,  2.15it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:30<01:00,  2.16it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:31<01:00,  2.16it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:31<00:59,  2.17it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:31<00:59,  2.16it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:31<00:58,  2.16it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:32<00:58,  2.17it/s]Progress: 61.00%
+---- avg training fps: 7.50

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:32<00:57,  2.16it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:33<00:57,  2.16it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:33<00:57,  2.16it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:34<00:56,  2.17it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:34<00:55,  2.16it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:35<00:55,  2.16it/s]Progress: 63.00%
+---- avg training fps: 7.53

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:35<00:55,  2.16it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:35<00:55,  2.16it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:36<00:54,  2.18it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:36<00:53,  2.17it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:37<00:53,  2.16it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:37<00:53,  2.15it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:37<00:52,  2.17it/s]Progress: 65.00%
+---- avg training fps: 7.56

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:38<00:52,  2.16it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:38<00:52,  2.15it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:39<00:51,  2.15it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:39<00:51,  2.15it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:40<00:51,  2.15it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:40<00:50,  2.15it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:40<00:50,  2.15it/s]Progress: 67.00%
+---- avg training fps: 7.59

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:41<00:49,  2.16it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:41<00:49,  2.16it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:42<00:48,  2.15it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:42<00:48,  2.15it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:43<00:47,  2.17it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:43<00:47,  2.15it/s]Progress: 69.00%
+---- avg training fps: 7.62

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:43<00:46,  2.15it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:44<00:46,  2.15it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:44<00:46,  2.15it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:44<00:45,  2.16it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:48<02:29,  1.52s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:49<01:56,  1.20s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:49<01:34,  1.02it/s]Progress: 71.00%
+---- avg training fps: 7.40

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:50<01:18,  1.22it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:50<01:07,  1.40it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:51<00:59,  1.57it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:51<00:53,  1.71it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:52<00:49,  1.83it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:52<00:46,  1.92it/s]Progress: 73.00%
+---- avg training fps: 7.43

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:53<00:46,  1.92it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:53<00:44,  1.99it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:53<00:42,  2.05it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:53<00:41,  2.08it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:54<00:40,  2.10it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [01:54<00:40,  2.12it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [01:55<00:39,  2.14it/s]Progress: 75.00%
+---- avg training fps: 7.46

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [01:55<00:38,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [01:56<00:38,  2.14it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [01:56<00:37,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [01:57<00:37,  2.15it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [01:57<00:37,  2.15it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [01:57<00:36,  2.15it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [01:58<00:36,  2.15it/s]Progress: 77.00%
+---- avg training fps: 7.49

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [01:58<00:35,  2.15it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [01:59<00:35,  2.16it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [01:59<00:34,  2.15it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [01:59<00:34,  2.15it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:00<00:33,  2.16it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:00<00:33,  2.16it/s]Progress: 79.00%
+---- avg training fps: 7.51

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:01<00:32,  2.16it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:01<00:32,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:02<00:32,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:02<00:31,  2.17it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:02<00:31,  2.16it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:03<00:31,  2.15it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:03<00:30,  2.15it/s]Progress: 81.00%
+---- avg training fps: 7.54

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:04<00:29,  2.17it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:04<00:29,  2.17it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:05<00:29,  2.16it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:05<00:28,  2.15it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:06<00:28,  2.16it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:06<00:27,  2.15it/s]Progress: 83.00%
+---- avg training fps: 7.56

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:06<00:27,  2.15it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:06<00:27,  2.16it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:07<00:26,  2.15it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:07<00:26,  2.16it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:08<00:26,  2.15it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:08<00:25,  2.15it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:09<00:24,  2.16it/s]Progress: 85.00%
+---- avg training fps: 7.59

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:09<00:24,  2.16it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:10<00:24,  2.16it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:10<00:23,  2.16it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:11<00:23,  2.17it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:11<00:23,  2.17it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:11<00:22,  2.17it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:12<00:22,  2.17it/s]Progress: 87.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.61

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:12<00:21,  2.16it/s]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:12<00:21,  2.17it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:13<00:20,  2.17it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:13<00:20,  2.16it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:14<00:19,  2.16it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:14<00:19,  2.17it/s]Progress: 89.00%
+---- avg training fps: 7.63

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:15<00:18,  2.17it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:15<00:18,  2.15it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:16<00:18,  2.15it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:16<00:18,  2.15it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:16<00:17,  2.17it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:17<00:17,  2.15it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:17<00:16,  2.15it/s]Progress: 91.00%
+---- avg training fps: 7.65

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:18<00:16,  2.14it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:18<00:15,  2.15it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:18<00:15,  2.15it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:19<00:14,  2.15it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:19<00:14,  2.16it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:20<00:13,  2.16it/s]Progress: 93.00%
+---- avg training fps: 7.67

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:20<00:13,  2.16it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:20<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:21<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:21<00:12,  2.17it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:22<00:12,  2.16it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:22<00:11,  2.16it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:23<00:11,  2.15it/s]Progress: 95.00%
+---- avg training fps: 7.69

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:23<00:10,  2.17it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:24<00:10,  2.16it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:24<00:09,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:25<00:09,  2.15it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:25<00:09,  2.15it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:25<00:08,  2.15it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:25<00:08,  2.15it/s]Progress: 97.00%
+---- avg training fps: 7.71

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:26<00:07,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:26<00:07,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:27<00:06,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:27<00:06,  2.14it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:28<00:06,  2.14it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:28<00:05,  2.14it/s]Progress: 99.00%
+---- avg training fps: 7.72

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:29<00:05,  2.13it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:29<00:04,  2.14it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:30<00:04,  2.14it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:30<00:04,  2.15it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:30<00:03,  2.15it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:31<00:03,  2.15it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:31<00:02,  2.14it/s]Progress: 100.00%
+---- avg training fps: 7.74

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:31<00:02,  2.16it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:32<00:01,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:32<00:01,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:33<00:01,  1.91it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:34<00:00,  2.00it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:34<00:00,  2.04it/s]Progress: 100.00%
+---- avg training fps: 7.74
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+
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+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [03:55<00:00,  1.27it/s]
+------------------------------------------
+Training done :)
+2024-08-13 02:37:35.900290: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 02:37:35.997660: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 02:37:35.997687: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 02:37:36.016597: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 02:37:36.415243: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:37:36.415323: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:37:36.415332: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23100 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.75 freeze_unet_before_completion_f=0.25 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='clipx.zip' output_dir='lora_models/styles_final/clipx.zip_13_02-37-38-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723509458.1703897 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Downloading training data...
+Downloading https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip to lora_models/styles_final/clipx.zip_13_02-37-38-style_512_florence_300/images_in/clipx.zip...
+Succesfully prepped 54 .jpg images in lora_models/styles_final/clipx.zip_13_02-37-38-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.314
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 27 images, 27 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 27 to 54)
+Generating 54 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal, whimsical, detailed, and quirky scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene is Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase describes the best and worst of times.
+- In the style of TOK, a blockchain is depicted using bees.
+- In the style of TOK, a gorgon made of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset shows Venus and Earth circling the sun.
+- In the style of TOK, there is an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is portrayed.
+- In the style of TOK, the phrase exemplifies the age of wisdom and the age of foolishness.
+- In the style of TOK, two people discuss quitting their boring corporation jobs together.
+- In the style of TOK, petroglyphs are presented.
+- In the style of TOK, a room and space exchange based on first and second-degree connections is depicted.
+- In the style of TOK, the fear known as trypophobia is illustrated.
+- In the style of TOK, there is a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing and singing beavers is highlighted.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with patterns akin to Hawaiian shirts.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a visual style like Tarsem Singh's.
+- In the style of TOK, a brigade of beavers is shown in a style similar to M. C. Escher's work.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is frozen solid and preserved, and rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting shows lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, and packing peanuts swims up through the sea to take a peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 1...
+----- GPT response: -----
+Style Description: Surrealistic, intricate, and fantastical scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is embodied in a visual representation.
+- In the style of TOK, a blockchain is composed of bees.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun is depicted.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is presented.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is embodied in a visual representation.
+- In the style of TOK, two individuals are depicted quitting their boring corporation job together.
+- In the style of TOK, petroglyphs are portrayed.
+- In the style of TOK, a room and space exchange is based on first and second-degree connections.
+- In the style of TOK, trypophobia is visually expressed.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of dancing and singing beavers is featured with a nod to Jim Henson's Muppets.
+- In the style of TOK, a beaver is shown chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a pattern like a Hawaiian shirt.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is designed in the style of M. C. Escher.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting showcases lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash is swimming up through the sea to take a peak above the water.
+- In the style of TOK, a mermaid made out of fishnets and other sea trash is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is embodied in a visual representation.
+- In the style of TOK, a blockchain is composed of bees.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun is depicted.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is presented.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is embodied in a visual representation.
+- In the style of TOK, two individuals are depicted quitting their boring corporation job together.
+- In the style of TOK, petroglyphs are portrayed.
+- In the style of TOK, a room and space exchange is based on first and second-degree connections.
+- In the style of TOK, trypophobia is visually expressed.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of dancing and singing beavers is featured with a nod to Jim Henson's Muppets.
+- In the style of TOK, a beaver is shown chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a pattern like a Hawaiian shirt.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is designed in the style of M. C. Escher.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting showcases lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash is swimming up through the sea to take a peak above the water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Using GPT concept name as CLIP-segmentation prompt: Surrealistic, intricate, and fantastical scenes
+Generating 54 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
54it [00:00, 14869.51it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 54 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a mermaid made o...
+1     in the style of <s0><s1><s2>, a throng of lill...
+2     in the style of <s0><s1><s2>, fort kochi in ke...
+3     in the style of <s0><s1><s2>, the phrase "they...
+4     in the style of <s0><s1><s2>, a blockchain is ...
+5     in the style of <s0><s1><s2>, a gorgon made ou...
+6     in the style of <s0><s1><s2>, a hillside at su...
+7     in the style of <s0><s1><s2>, an ancient templ...
+8     in the style of <s0><s1><s2>, cats are pooping...
+9     in the style of <s0><s1><s2>, a goblin goat is...
+10    in the style of <s0><s1><s2>, the phrase "it w...
+11    in the style of <s0><s1><s2>, two individuals ...
+12    in the style of <s0><s1><s2>, petroglyphs are ...
+13    in the style of <s0><s1><s2>, a room and space...
+14    in the style of <s0><s1><s2>, trypophobia is v...
+15    in the style of <s0><s1><s2>, a six-pack of ni...
+16    in the style of <s0><s1><s2>, a band of dancin...
+17    in the style of <s0><s1><s2>, a beaver is show...
+18    in the style of <s0><s1><s2>, a brigade of bea...
+19    in the style of <s0><s1><s2>, a brigade of bea...
+20    in the style of <s0><s1><s2>, a brigade of bea...
+21    in the style of <s0><s1><s2>, a castle is movi...
+22    in the style of <s0><s1><s2>, a chorus line of...
+23    in the style of <s0><s1><s2>, a dirty 1950s re...
+24    in the style of <s0><s1><s2>, a fashion show f...
+25    in the style of <s0><s1><s2>, a hyper-realisti...
+26    in the style of <s0><s1><s2>, a mermaid made o...
+27    in the style of <s0><s1><s2>, a mermaid made o...
+28    in the style of <s0><s1><s2>, a throng of lill...
+29    in the style of <s0><s1><s2>, fort kochi in ke...
+30    in the style of <s0><s1><s2>, the phrase "they...
+31    in the style of <s0><s1><s2>, a blockchain is ...
+32    in the style of <s0><s1><s2>, a gorgon made ou...
+33    in the style of <s0><s1><s2>, a hillside at su...
+34    in the style of <s0><s1><s2>, an ancient templ...
+35    in the style of <s0><s1><s2>, cats are pooping...
+36    in the style of <s0><s1><s2>, a goblin goat is...
+37    in the style of <s0><s1><s2>, the phrase "it w...
+38    in the style of <s0><s1><s2>, two individuals ...
+39    in the style of <s0><s1><s2>, petroglyphs are ...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 54
+--- Num batches each epoch = 14
+--- Num Epochs = 22
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:02<?, ?it/s]
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:10<04:54,  1.00s/it]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.26

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:10<04:00,  1.22it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:11<03:25,  1.42it/s]
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# Trainer step: 0, epoch: 0:   4%|▍         | 12/300 [00:12<02:27,  1.95it/s]Progress: 7.00%
+---- avg training fps: 3.59

# Trainer step: 0, epoch: 0:   4%|▍         | 13/300 [00:13<02:21,  2.02it/s]
# Trainer step: 0, epoch: 0:   5%|▍         | 14/300 [00:13<02:18,  2.07it/s]
# Trainer step: 14, epoch: 1:   5%|▍         | 14/300 [00:14<02:18,  2.07it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 15/300 [00:14<02:23,  1.98it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 16/300 [00:14<02:18,  2.05it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 17/300 [00:15<02:15,  2.10it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 18/300 [00:15<02:12,  2.13it/s]Progress: 9.00%
+---- avg training fps: 4.45

# Trainer step: 14, epoch: 1:   6%|▋         | 19/300 [00:16<02:10,  2.15it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 20/300 [00:16<02:09,  2.17it/s]
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# Trainer step: 14, epoch: 1:   7%|▋         | 22/300 [00:17<02:07,  2.18it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 23/300 [00:18<02:07,  2.17it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 24/300 [00:18<02:06,  2.18it/s]Progress: 11.00%
+---- avg training fps: 5.07

# Trainer step: 14, epoch: 1:   8%|▊         | 25/300 [00:18<02:06,  2.18it/s]
# Trainer step: 14, epoch: 1:   9%|▊         | 26/300 [00:19<02:06,  2.17it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 27/300 [00:19<02:05,  2.18it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 28/300 [00:20<02:05,  2.17it/s]
# Trainer step: 28, epoch: 2:   9%|▉         | 28/300 [00:20<02:05,  2.17it/s]
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# Trainer step: 28, epoch: 2:  10%|█         | 30/300 [00:21<02:00,  2.24it/s]Progress: 13.00%
+---- avg training fps: 5.55

# Trainer step: 28, epoch: 2:  10%|█         | 31/300 [00:21<02:01,  2.21it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 32/300 [00:22<02:02,  2.19it/s]
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# Trainer step: 28, epoch: 2:  11%|█▏        | 34/300 [00:23<02:01,  2.19it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 35/300 [00:23<02:01,  2.18it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 36/300 [00:23<02:01,  2.17it/s]Progress: 15.00%
+---- avg training fps: 5.90

# Trainer step: 28, epoch: 2:  12%|█▏        | 37/300 [00:24<02:01,  2.17it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 38/300 [00:24<02:01,  2.16it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 39/300 [00:25<02:00,  2.16it/s]
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# Trainer step: 28, epoch: 2:  14%|█▍        | 42/300 [00:26<01:58,  2.17it/s]Progress: 17.00%
+---- avg training fps: 6.20

# Trainer step: 42, epoch: 3:  14%|█▍        | 42/300 [00:27<01:58,  2.17it/s]
# Trainer step: 42, epoch: 3:  14%|█▍        | 43/300 [00:27<01:53,  2.27it/s]
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# Trainer step: 42, epoch: 3:  16%|█▌        | 47/300 [00:28<01:54,  2.20it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 48/300 [00:29<01:55,  2.18it/s]Progress: 19.00%
+---- avg training fps: 6.43

# Trainer step: 42, epoch: 3:  16%|█▋        | 49/300 [00:29<01:55,  2.18it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 50/300 [00:30<01:55,  2.17it/s]
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# Trainer step: 42, epoch: 3:  18%|█▊        | 54/300 [00:35<03:44,  1.10it/s]Progress: 21.00%
+---- avg training fps: 6.05

# Trainer step: 42, epoch: 3:  18%|█▊        | 55/300 [00:35<03:10,  1.29it/s]
# Trainer step: 42, epoch: 3:  19%|█▊        | 56/300 [00:36<02:46,  1.46it/s]
# Trainer step: 56, epoch: 4:  19%|█▊        | 56/300 [00:36<02:46,  1.46it/s]
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# Trainer step: 56, epoch: 4:  20%|█▉        | 59/300 [00:37<02:07,  1.89it/s]
# Trainer step: 56, epoch: 4:  20%|██        | 60/300 [00:37<02:01,  1.97it/s]Progress: 23.00%
+---- avg training fps: 6.24

# Trainer step: 56, epoch: 4:  20%|██        | 61/300 [00:38<01:58,  2.02it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 62/300 [00:38<01:55,  2.05it/s]
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# Trainer step: 56, epoch: 4:  21%|██▏       | 64/300 [00:40<02:11,  1.79it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 65/300 [00:40<02:04,  1.89it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 66/300 [00:41<01:58,  1.97it/s]Progress: 25.00%
+---- avg training fps: 6.36

# Trainer step: 56, epoch: 4:  22%|██▏       | 67/300 [00:41<01:55,  2.02it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 68/300 [00:41<01:52,  2.06it/s]
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# Trainer step: 56, epoch: 4:  23%|██▎       | 70/300 [00:42<01:48,  2.12it/s]
# Trainer step: 70, epoch: 5:  23%|██▎       | 70/300 [00:43<01:48,  2.12it/s]
# Trainer step: 70, epoch: 5:  24%|██▎       | 71/300 [00:43<01:42,  2.23it/s]
# Trainer step: 70, epoch: 5:  24%|██▍       | 72/300 [00:43<01:42,  2.22it/s]Progress: 27.00%
+---- avg training fps: 6.52

# Trainer step: 70, epoch: 5:  24%|██▍       | 73/300 [00:44<01:43,  2.19it/s]
# Trainer step: 70, epoch: 5:  25%|██▍       | 74/300 [00:44<01:43,  2.18it/s]
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# Trainer step: 70, epoch: 5:  26%|██▌       | 78/300 [00:46<01:42,  2.17it/s]Progress: 29.00%
+---- avg training fps: 6.65

# Trainer step: 70, epoch: 5:  26%|██▋       | 79/300 [00:46<01:41,  2.17it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 80/300 [00:47<01:41,  2.18it/s]
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# Trainer step: 70, epoch: 5:  28%|██▊       | 84/300 [00:49<01:39,  2.17it/s]Progress: 31.00%
+---- avg training fps: 6.77

# Trainer step: 84, epoch: 6:  28%|██▊       | 84/300 [00:49<01:39,  2.17it/s]
# Trainer step: 84, epoch: 6:  28%|██▊       | 85/300 [00:49<01:35,  2.26it/s]
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# Trainer step: 84, epoch: 6:  30%|███       | 90/300 [00:51<01:36,  2.17it/s]Progress: 33.00%
+---- avg training fps: 6.86

# Trainer step: 84, epoch: 6:  30%|███       | 91/300 [00:52<01:36,  2.16it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 92/300 [00:52<01:36,  2.16it/s]
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# Trainer step: 84, epoch: 6:  31%|███▏      | 94/300 [00:53<01:35,  2.15it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 95/300 [00:54<01:35,  2.15it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 96/300 [00:54<01:34,  2.16it/s]Progress: 35.00%
+---- avg training fps: 6.95

# Trainer step: 84, epoch: 6:  32%|███▏      | 97/300 [00:55<01:34,  2.16it/s]
# Trainer step: 84, epoch: 6:  33%|███▎      | 98/300 [00:55<01:33,  2.16it/s]
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# Trainer step: 98, epoch: 7:  34%|███▎      | 101/300 [00:57<01:30,  2.20it/s]
# Trainer step: 98, epoch: 7:  34%|███▍      | 102/300 [00:59<03:57,  1.20s/it]Progress: 37.00%
+---- avg training fps: 6.75

# Trainer step: 98, epoch: 7:  34%|███▍      | 103/300 [01:00<03:12,  1.02it/s]
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# Trainer step: 98, epoch: 7:  35%|███▌      | 105/300 [01:01<02:19,  1.40it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 106/300 [01:01<02:04,  1.56it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 107/300 [01:02<01:53,  1.70it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 108/300 [01:02<01:45,  1.82it/s]Progress: 39.00%
+---- avg training fps: 6.84

# Trainer step: 98, epoch: 7:  36%|███▋      | 109/300 [01:03<01:40,  1.90it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 110/300 [01:03<01:36,  1.97it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 111/300 [01:04<01:33,  2.02it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 112/300 [01:04<01:31,  2.06it/s]
# Trainer step: 112, epoch: 8:  37%|███▋      | 112/300 [01:04<01:31,  2.06it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 113/300 [01:04<01:25,  2.18it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 114/300 [01:05<01:25,  2.18it/s]Progress: 41.00%
+---- avg training fps: 6.92

# Trainer step: 112, epoch: 8:  38%|███▊      | 115/300 [01:05<01:25,  2.16it/s]
# Trainer step: 112, epoch: 8:  39%|███▊      | 116/300 [01:06<01:25,  2.15it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 117/300 [01:06<01:24,  2.16it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 118/300 [01:07<01:24,  2.16it/s]
# Trainer step: 112, epoch: 8:  40%|███▉      | 119/300 [01:07<01:33,  1.93it/s]
# Trainer step: 112, epoch: 8:  40%|████      | 120/300 [01:08<01:30,  1.99it/s]Progress: 43.00%
+---- avg training fps: 6.97

# Trainer step: 112, epoch: 8:  40%|████      | 121/300 [01:08<01:27,  2.05it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 122/300 [01:09<01:25,  2.08it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 123/300 [01:09<01:24,  2.10it/s]
# Trainer step: 112, epoch: 8:  41%|████▏     | 124/300 [01:10<01:23,  2.11it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 125/300 [01:10<01:22,  2.12it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 126/300 [01:11<01:21,  2.13it/s]Progress: 45.00%
+---- avg training fps: 7.04

# Trainer step: 126, epoch: 9:  42%|████▏     | 126/300 [01:11<01:21,  2.13it/s]
# Trainer step: 126, epoch: 9:  42%|████▏     | 127/300 [01:11<01:17,  2.24it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 128/300 [01:12<01:17,  2.21it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 129/300 [01:12<01:18,  2.19it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 130/300 [01:12<01:17,  2.19it/s]
# Trainer step: 126, epoch: 9:  44%|████▎     | 131/300 [01:13<01:17,  2.18it/s]
# Trainer step: 126, epoch: 9:  44%|████▍     | 132/300 [01:13<01:17,  2.18it/s]Progress: 47.00%
+---- avg training fps: 7.10

# Trainer step: 126, epoch: 9:  44%|████▍     | 133/300 [01:14<01:17,  2.16it/s]
# Trainer step: 126, epoch: 9:  45%|████▍     | 134/300 [01:14<01:16,  2.16it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 135/300 [01:15<01:16,  2.15it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 136/300 [01:15<01:16,  2.15it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 137/300 [01:16<01:15,  2.15it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 138/300 [01:16<01:15,  2.15it/s]Progress: 49.00%
+---- avg training fps: 7.15

# Trainer step: 126, epoch: 9:  46%|████▋     | 139/300 [01:17<01:14,  2.15it/s]
# Trainer step: 126, epoch: 9:  47%|████▋     | 140/300 [01:17<01:14,  2.15it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 140/300 [01:18<01:14,  2.15it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 141/300 [01:18<01:10,  2.25it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 142/300 [01:18<01:11,  2.21it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 143/300 [01:18<01:11,  2.19it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 144/300 [01:19<01:11,  2.17it/s]Progress: 51.00%
+---- avg training fps: 7.21

# Trainer step: 140, epoch: 10:  48%|████▊     | 145/300 [01:19<01:11,  2.16it/s]
# Trainer step: 140, epoch: 10:  49%|████▊     | 146/300 [01:20<01:11,  2.15it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 147/300 [01:20<01:11,  2.14it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 148/300 [01:21<01:11,  2.13it/s]
# Trainer step: 140, epoch: 10:  50%|████▉     | 149/300 [01:21<01:10,  2.15it/s]
# Trainer step: 140, epoch: 10:  50%|█████     | 150/300 [01:22<01:10,  2.14it/s]Progress: 53.00%
+---- avg training fps: 7.25

# Trainer step: 140, epoch: 10:  50%|█████     | 151/300 [01:22<01:09,  2.14it/s]
# Trainer step: 140, epoch: 10:  51%|█████     | 152/300 [01:27<04:30,  1.83s/it]
# Trainer step: 140, epoch: 10:  51%|█████     | 153/300 [01:28<03:28,  1.42s/it]
# Trainer step: 140, epoch: 10:  51%|█████▏    | 154/300 [01:28<02:45,  1.13s/it]
# Trainer step: 154, epoch: 11:  51%|█████▏    | 154/300 [01:29<02:45,  1.13s/it]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 155/300 [01:29<02:12,  1.10it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 156/300 [01:29<01:52,  1.29it/s]Progress: 55.00%
+---- avg training fps: 6.93

# Trainer step: 154, epoch: 11:  52%|█████▏    | 157/300 [01:29<01:37,  1.47it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 158/300 [01:30<01:27,  1.62it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 159/300 [01:30<01:20,  1.75it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 160/300 [01:31<01:15,  1.86it/s]
# Trainer step: 154, epoch: 11:  54%|█████▎    | 161/300 [01:31<01:11,  1.94it/s]
# Trainer step: 154, epoch: 11:  54%|█████▍    | 162/300 [01:32<01:09,  1.99it/s]Progress: 57.00%
+---- avg training fps: 6.99

# Trainer step: 154, epoch: 11:  54%|█████▍    | 163/300 [01:32<01:07,  2.04it/s]
# Trainer step: 154, epoch: 11:  55%|█████▍    | 164/300 [01:33<01:05,  2.06it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 165/300 [01:33<01:04,  2.09it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 166/300 [01:34<01:03,  2.10it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 167/300 [01:34<01:03,  2.11it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 168/300 [01:35<01:02,  2.13it/s]Progress: 59.00%
+---- avg training fps: 7.04

# Trainer step: 168, epoch: 12:  56%|█████▌    | 168/300 [01:35<01:02,  2.13it/s]
# Trainer step: 168, epoch: 12:  56%|█████▋    | 169/300 [01:35<00:58,  2.23it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 170/300 [01:35<00:58,  2.21it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 171/300 [01:36<00:59,  2.18it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 172/300 [01:36<00:59,  2.17it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 173/300 [01:37<00:58,  2.16it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 174/300 [01:37<00:58,  2.15it/s]Progress: 61.00%
+---- avg training fps: 7.08

# Trainer step: 168, epoch: 12:  58%|█████▊    | 175/300 [01:38<00:58,  2.15it/s]
# Trainer step: 168, epoch: 12:  59%|█████▊    | 176/300 [01:38<00:57,  2.14it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 177/300 [01:39<00:57,  2.14it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 178/300 [01:39<00:57,  2.14it/s]
# Trainer step: 168, epoch: 12:  60%|█████▉    | 179/300 [01:40<00:56,  2.14it/s]
# Trainer step: 168, epoch: 12:  60%|██████    | 180/300 [01:40<00:56,  2.14it/s]Progress: 63.00%
+---- avg training fps: 7.12

# Trainer step: 168, epoch: 12:  60%|██████    | 181/300 [01:41<00:55,  2.13it/s]
# Trainer step: 168, epoch: 12:  61%|██████    | 182/300 [01:41<00:55,  2.13it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 182/300 [01:41<00:55,  2.13it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 183/300 [01:41<00:52,  2.24it/s]
# Trainer step: 182, epoch: 13:  61%|██████▏   | 184/300 [01:42<00:52,  2.21it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 185/300 [01:42<00:52,  2.18it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 186/300 [01:43<00:52,  2.17it/s]Progress: 65.00%
+---- avg training fps: 7.16

# Trainer step: 182, epoch: 13:  62%|██████▏   | 187/300 [01:43<00:52,  2.15it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 188/300 [01:44<00:51,  2.16it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 189/300 [01:44<00:51,  2.15it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 190/300 [01:45<00:51,  2.15it/s]
# Trainer step: 182, epoch: 13:  64%|██████▎   | 191/300 [01:45<00:50,  2.14it/s]
# Trainer step: 182, epoch: 13:  64%|██████▍   | 192/300 [01:46<00:50,  2.14it/s]Progress: 67.00%
+---- avg training fps: 7.20

# Trainer step: 182, epoch: 13:  64%|██████▍   | 193/300 [01:46<00:49,  2.14it/s]
# Trainer step: 182, epoch: 13:  65%|██████▍   | 194/300 [01:47<00:49,  2.13it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 195/300 [01:47<00:48,  2.15it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 196/300 [01:48<00:48,  2.13it/s]
# Trainer step: 196, epoch: 14:  65%|██████▌   | 196/300 [01:48<00:48,  2.13it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 197/300 [01:48<00:46,  2.23it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 198/300 [01:48<00:46,  2.20it/s]Progress: 69.00%
+---- avg training fps: 7.24

# Trainer step: 196, epoch: 14:  66%|██████▋   | 199/300 [01:49<00:46,  2.18it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 200/300 [01:49<00:46,  2.17it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 201/300 [01:50<00:45,  2.15it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 202/300 [01:53<01:58,  1.21s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 203/300 [01:53<01:35,  1.01it/s]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 204/300 [01:54<01:19,  1.20it/s]Progress: 71.00%
+---- avg training fps: 7.10

# Trainer step: 196, epoch: 14:  68%|██████▊   | 205/300 [01:54<01:16,  1.24it/s]
# Trainer step: 196, epoch: 14:  69%|██████▊   | 206/300 [01:55<01:06,  1.42it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 207/300 [01:55<00:58,  1.58it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 208/300 [01:56<00:53,  1.72it/s]
# Trainer step: 196, epoch: 14:  70%|██████▉   | 209/300 [01:56<00:49,  1.83it/s]
# Trainer step: 196, epoch: 14:  70%|███████   | 210/300 [01:57<00:47,  1.91it/s]Progress: 73.00%
+---- avg training fps: 7.14

# Trainer step: 210, epoch: 15:  70%|███████   | 210/300 [01:57<00:47,  1.91it/s]
# Trainer step: 210, epoch: 15:  70%|███████   | 211/300 [01:57<00:43,  2.06it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 212/300 [01:58<00:42,  2.09it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 213/300 [01:58<00:41,  2.11it/s]
# Trainer step: 210, epoch: 15:  71%|███████▏  | 214/300 [01:59<00:40,  2.12it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 215/300 [01:59<00:39,  2.13it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 216/300 [02:00<00:39,  2.13it/s]Progress: 75.00%
+---- avg training fps: 7.17

# Trainer step: 210, epoch: 15:  72%|███████▏  | 217/300 [02:00<00:38,  2.14it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 218/300 [02:00<00:38,  2.13it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 219/300 [02:01<00:37,  2.14it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 220/300 [02:01<00:37,  2.14it/s]
# Trainer step: 210, epoch: 15:  74%|███████▎  | 221/300 [02:02<00:36,  2.15it/s]
# Trainer step: 210, epoch: 15:  74%|███████▍  | 222/300 [02:02<00:36,  2.14it/s]Progress: 77.00%
+---- avg training fps: 7.20

# Trainer step: 210, epoch: 15:  74%|███████▍  | 223/300 [02:03<00:35,  2.14it/s]
# Trainer step: 210, epoch: 15:  75%|███████▍  | 224/300 [02:03<00:35,  2.14it/s]
# Trainer step: 224, epoch: 16:  75%|███████▍  | 224/300 [02:04<00:35,  2.14it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 225/300 [02:04<00:33,  2.24it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 226/300 [02:04<00:33,  2.22it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 227/300 [02:05<00:33,  2.19it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 228/300 [02:05<00:33,  2.18it/s]Progress: 79.00%
+---- avg training fps: 7.24

# Trainer step: 224, epoch: 16:  76%|███████▋  | 229/300 [02:06<00:32,  2.18it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 230/300 [02:06<00:32,  2.18it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 231/300 [02:06<00:31,  2.17it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 232/300 [02:07<00:31,  2.16it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 233/300 [02:07<00:31,  2.16it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 234/300 [02:08<00:30,  2.15it/s]Progress: 81.00%
+---- avg training fps: 7.27

# Trainer step: 224, epoch: 16:  78%|███████▊  | 235/300 [02:08<00:30,  2.15it/s]
# Trainer step: 224, epoch: 16:  79%|███████▊  | 236/300 [02:09<00:29,  2.15it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 237/300 [02:09<00:29,  2.15it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 238/300 [02:10<00:28,  2.15it/s]
# Trainer step: 238, epoch: 17:  79%|███████▉  | 238/300 [02:10<00:28,  2.15it/s]
# Trainer step: 238, epoch: 17:  80%|███████▉  | 239/300 [02:10<00:27,  2.26it/s]
# Trainer step: 238, epoch: 17:  80%|████████  | 240/300 [02:11<00:26,  2.23it/s]Progress: 83.00%
+---- avg training fps: 7.30

# Trainer step: 238, epoch: 17:  80%|████████  | 241/300 [02:11<00:26,  2.20it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 242/300 [02:11<00:26,  2.19it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 243/300 [02:12<00:26,  2.17it/s]
# Trainer step: 238, epoch: 17:  81%|████████▏ | 244/300 [02:12<00:25,  2.17it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 245/300 [02:13<00:25,  2.17it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 246/300 [02:13<00:24,  2.17it/s]Progress: 85.00%
+---- avg training fps: 7.33

# Trainer step: 238, epoch: 17:  82%|████████▏ | 247/300 [02:14<00:24,  2.16it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 248/300 [02:14<00:24,  2.15it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 249/300 [02:15<00:23,  2.17it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 250/300 [02:15<00:23,  2.16it/s]
# Trainer step: 238, epoch: 17:  84%|████████▎ | 251/300 [02:16<00:22,  2.16it/s]
# Trainer step: 238, epoch: 17:  84%|████████▍ | 252/300 [02:19<01:07,  1.41s/it]Progress: 87.00%
+---- avg training fps: 7.19

# Trainer step: 252, epoch: 18:  84%|████████▍ | 252/300 [02:20<01:07,  1.41s/it]
# Trainer step: 252, epoch: 18:  84%|████████▍ | 253/300 [02:20<00:52,  1.11s/it]
# Trainer step: 252, epoch: 18:  85%|████████▍ | 254/300 [02:20<00:41,  1.10it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 255/300 [02:21<00:35,  1.29it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 256/300 [02:21<00:30,  1.46it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 257/300 [02:22<00:26,  1.62it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 258/300 [02:22<00:24,  1.75it/s]Progress: 89.00%
+---- avg training fps: 7.22

# Trainer step: 252, epoch: 18:  86%|████████▋ | 259/300 [02:22<00:22,  1.85it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 260/300 [02:23<00:20,  1.94it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 261/300 [02:23<00:19,  1.99it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 262/300 [02:24<00:18,  2.04it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 263/300 [02:24<00:17,  2.06it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 264/300 [02:25<00:17,  2.09it/s]Progress: 91.00%
+---- avg training fps: 7.24

# Trainer step: 252, epoch: 18:  88%|████████▊ | 265/300 [02:25<00:16,  2.10it/s]
# Trainer step: 252, epoch: 18:  89%|████████▊ | 266/300 [02:26<00:16,  2.12it/s]
# Trainer step: 266, epoch: 19:  89%|████████▊ | 266/300 [02:26<00:16,  2.12it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 267/300 [02:26<00:14,  2.22it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 268/300 [02:27<00:14,  2.20it/s]
# Trainer step: 266, epoch: 19:  90%|████████▉ | 269/300 [02:27<00:14,  2.19it/s]
# Trainer step: 266, epoch: 19:  90%|█████████ | 270/300 [02:28<00:13,  2.18it/s]Progress: 93.00%
+---- avg training fps: 7.27

# Trainer step: 266, epoch: 19:  90%|█████████ | 271/300 [02:28<00:13,  2.17it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 272/300 [02:28<00:12,  2.16it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 273/300 [02:29<00:12,  2.16it/s]
# Trainer step: 266, epoch: 19:  91%|█████████▏| 274/300 [02:29<00:12,  2.15it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 275/300 [02:30<00:11,  2.14it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 276/300 [02:30<00:11,  2.15it/s]Progress: 95.00%
+---- avg training fps: 7.30

# Trainer step: 266, epoch: 19:  92%|█████████▏| 277/300 [02:31<00:10,  2.15it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 278/300 [02:31<00:10,  2.14it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 279/300 [02:32<00:09,  2.14it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 280/300 [02:32<00:09,  2.15it/s]
# Trainer step: 280, epoch: 20:  93%|█████████▎| 280/300 [02:33<00:09,  2.15it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▎| 281/300 [02:33<00:08,  2.26it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▍| 282/300 [02:33<00:08,  2.22it/s]Progress: 97.00%
+---- avg training fps: 7.32

# Trainer step: 280, epoch: 20:  94%|█████████▍| 283/300 [02:34<00:07,  2.20it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▍| 284/300 [02:34<00:07,  2.18it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 285/300 [02:34<00:06,  2.17it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 286/300 [02:35<00:06,  2.16it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 287/300 [02:35<00:06,  2.15it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 288/300 [02:36<00:05,  2.16it/s]Progress: 99.00%
+---- avg training fps: 7.35

# Trainer step: 280, epoch: 20:  96%|█████████▋| 289/300 [02:36<00:05,  2.16it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 290/300 [02:37<00:04,  2.15it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 291/300 [02:37<00:04,  2.15it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 292/300 [02:38<00:03,  2.16it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 293/300 [02:38<00:03,  2.14it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 294/300 [02:39<00:02,  2.14it/s]Progress: 100.00%
+---- avg training fps: 7.37

# Trainer step: 294, epoch: 21:  98%|█████████▊| 294/300 [02:39<00:02,  2.14it/s]
# Trainer step: 294, epoch: 21:  98%|█████████▊| 295/300 [02:39<00:02,  2.24it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▊| 296/300 [02:40<00:01,  2.21it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 297/300 [02:40<00:01,  2.19it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 298/300 [02:40<00:00,  2.17it/s]
# Trainer step: 294, epoch: 21: 100%|█████████▉| 299/300 [02:41<00:00,  2.17it/s]
# Trainer step: 294, epoch: 21: 100%|██████████| 300/300 [02:41<00:00,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.39

# Trainer step: 294, epoch: 21: : 301it [02:42,  2.16it/s]                       Progress: 100.00%
Reached max steps, stopping training!
+Saving checkpoint at step.. 301
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 294, epoch: 21: : 301it [04:06,  1.22it/s]
+------------------------------------------
+Training done :)
+2024-08-13 02:42:30.257978: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 02:42:30.350279: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 02:42:30.350307: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 02:42:30.369065: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 02:42:30.771134: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:42:30.771189: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:42:30.771197: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23132 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/journey_small' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.75 freeze_unet_before_completion_f=0.25 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='journey_small' output_dir='lora_models/styles_final/journey_small_13_02-42-32-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723509752.5997176 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/journey_small_13_02-42-32-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 0.812
+New train_img_size: [448, 576]
+Validation_img_size was set to: [896, 1152]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red cloak standing in front of a building.
+- A person standing on top of a body of water.
+- A person walking in the desert with a camel in front of a mountain.
+- A man standing on top of a sandy beach next to the ocean.
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red dress is walking through an archway.
+- A person standing on top of a body of water.
+- A person walking in the desert with a dog on a leash.
+- A person walking on a beach next to a body of water.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal and dreamlike, bold compositions.
+
+- In the style of TOK: A sunset over a desert landscape with a mountain in the background.
+- In the style of TOK: A woman in a red cloak standing in front of a building.
+- In the style of TOK: A person standing on top of a body of water.
+- In the style of TOK: A person walking in the desert with a camel in front of a mountain.
+- In the style of TOK: A man standing on top of a sandy beach next to the ocean.
+- In the style of TOK: A sunset over a desert landscape with a mountain in the background.
+- In the style of TOK: A woman in a red dress walking through an archway.
+- In the style of TOK: A person standing on top of a body of water.
+- In the style of TOK: A person walking in the desert with a dog on a leash.
+- In the style of TOK: A person walking on a beach next to a body of water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Surreal and dreamlike, bold compositions.
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 10288.35it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>: a sunset over a ...
+1     in the style of <s0><s1><s2>: a woman in a red...
+2     in the style of <s0><s1><s2>: a person standin...
+3     in the style of <s0><s1><s2>: a person walking...
+4     in the style of <s0><s1><s2>: a man standing o...
+5     in the style of <s0><s1><s2>: a sunset over a ...
+6     in the style of <s0><s1><s2>: a woman in a red...
+7     in the style of <s0><s1><s2>: a person standin...
+8     in the style of <s0><s1><s2>: a person walking...
+9     in the style of <s0><s1><s2>: a person walking...
+10    in the style of <s0><s1><s2>: a sunset over a ...
+11    in the style of <s0><s1><s2>: a woman in a red...
+12    in the style of <s0><s1><s2>: a person standin...
+13    in the style of <s0><s1><s2>: a person walking...
+14    in the style of <s0><s1><s2>: a man standing o...
+15    in the style of <s0><s1><s2>: a sunset over a ...
+16    in the style of <s0><s1><s2>: a woman in a red...
+17    in the style of <s0><s1><s2>: a person standin...
+18    in the style of <s0><s1><s2>: a person walking...
+19    in the style of <s0><s1><s2>: a person walking...
+20    in the style of <s0><s1><s2>: a sunset over a ...
+21    in the style of <s0><s1><s2>: a woman in a red...
+22    in the style of <s0><s1><s2>: a person standin...
+23    in the style of <s0><s1><s2>: a person walking...
+24    in the style of <s0><s1><s2>: a man standing o...
+25    in the style of <s0><s1><s2>: a sunset over a ...
+26    in the style of <s0><s1><s2>: a woman in a red...
+27    in the style of <s0><s1><s2>: a person standin...
+28    in the style of <s0><s1><s2>: a person walking...
+29    in the style of <s0><s1><s2>: a person walking...
+30    in the style of <s0><s1><s2>: a sunset over a ...
+31    in the style of <s0><s1><s2>: a woman in a red...
+32    in the style of <s0><s1><s2>: a person standin...
+33    in the style of <s0><s1><s2>: a person walking...
+34    in the style of <s0><s1><s2>: a man standing o...
+35    in the style of <s0><s1><s2>: a sunset over a ...
+36    in the style of <s0><s1><s2>: a woman in a red...
+37    in the style of <s0><s1><s2>: a person standin...
+38    in the style of <s0><s1><s2>: a person walking...
+39    in the style of <s0><s1><s2>: a person walking...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:03<?, ?it/s]
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# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:09<05:55,  1.20s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:09<04:39,  1.05it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.40

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:10<03:51,  1.27it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:10<03:19,  1.46it/s]
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# Trainer step: 10, epoch: 1:   3%|▎         | 10/300 [00:11<02:44,  1.77it/s]
# Trainer step: 10, epoch: 1:   4%|▎         | 11/300 [00:11<02:34,  1.87it/s]
# Trainer step: 10, epoch: 1:   4%|▍         | 12/300 [00:12<02:27,  1.95it/s]Progress: 7.00%
+---- avg training fps: 3.70

# Trainer step: 10, epoch: 1:   4%|▍         | 13/300 [00:12<02:40,  1.79it/s]
# Trainer step: 10, epoch: 1:   5%|▍         | 14/300 [00:13<02:31,  1.89it/s]
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# Trainer step: 10, epoch: 1:   5%|▌         | 16/300 [00:14<02:19,  2.03it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 17/300 [00:14<02:17,  2.07it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 18/300 [00:15<02:14,  2.09it/s]Progress: 9.00%
+---- avg training fps: 4.58

# Trainer step: 10, epoch: 1:   6%|▋         | 19/300 [00:15<02:13,  2.11it/s]
# Trainer step: 10, epoch: 1:   7%|▋         | 20/300 [00:16<02:11,  2.12it/s]
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# Trainer step: 20, epoch: 2:   8%|▊         | 24/300 [00:18<02:07,  2.16it/s]Progress: 11.00%
+---- avg training fps: 5.19

# Trainer step: 20, epoch: 2:   8%|▊         | 25/300 [00:18<02:07,  2.16it/s]
# Trainer step: 20, epoch: 2:   9%|▊         | 26/300 [00:18<02:07,  2.15it/s]
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# Trainer step: 20, epoch: 2:  10%|█         | 30/300 [00:20<02:05,  2.16it/s]Progress: 13.00%
+---- avg training fps: 5.64

# Trainer step: 30, epoch: 3:  10%|█         | 30/300 [00:21<02:05,  2.16it/s]
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# Trainer step: 30, epoch: 3:  11%|█         | 32/300 [00:21<02:03,  2.16it/s]
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# Trainer step: 30, epoch: 3:  11%|█▏        | 34/300 [00:22<02:03,  2.15it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 35/300 [00:23<02:02,  2.16it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 36/300 [00:23<02:02,  2.15it/s]Progress: 15.00%
+---- avg training fps: 5.98

# Trainer step: 30, epoch: 3:  12%|█▏        | 37/300 [00:24<02:02,  2.14it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 38/300 [00:24<02:02,  2.15it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 39/300 [00:25<02:01,  2.15it/s]
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# Trainer step: 40, epoch: 4:  14%|█▍        | 42/300 [00:26<01:59,  2.16it/s]Progress: 17.00%
+---- avg training fps: 6.25

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:26<01:59,  2.16it/s]
# Trainer step: 40, epoch: 4:  15%|█▍        | 44/300 [00:27<01:58,  2.16it/s]
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# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:29<01:57,  2.15it/s]Progress: 19.00%
+---- avg training fps: 6.47

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:29<01:56,  2.15it/s]
# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:30<01:56,  2.14it/s]
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# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:35<04:08,  1.01s/it]Progress: 21.00%
+---- avg training fps: 5.97

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:36<03:27,  1.18it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:36<02:58,  1.36it/s]
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# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:38<02:14,  1.79it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:38<02:07,  1.89it/s]Progress: 23.00%
+---- avg training fps: 6.16

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:38<02:07,  1.89it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:38<02:02,  1.95it/s]
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# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:40<01:54,  2.07it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:40<01:52,  2.09it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:41<01:51,  2.10it/s]Progress: 25.00%
+---- avg training fps: 6.32

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:41<01:49,  2.12it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:42<01:49,  2.13it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:42<01:48,  2.13it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:43<01:47,  2.13it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:43<01:47,  2.13it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:43<01:47,  2.13it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:44<01:47,  2.13it/s]Progress: 27.00%
+---- avg training fps: 6.46

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:44<01:46,  2.13it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:45<01:45,  2.14it/s]
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# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:46<01:43,  2.14it/s]Progress: 29.00%
+---- avg training fps: 6.58

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:47<01:43,  2.14it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:47<01:43,  2.13it/s]
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# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:49<01:41,  2.14it/s]Progress: 31.00%
+---- avg training fps: 6.69

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:50<01:40,  2.14it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:50<01:40,  2.14it/s]
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# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:52<01:38,  2.14it/s]Progress: 33.00%
+---- avg training fps: 6.79

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:53<01:38,  2.14it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:53<01:37,  2.13it/s]
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# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:54<01:36,  2.13it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:54<01:36,  2.13it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:55<01:35,  2.13it/s]Progress: 35.00%
+---- avg training fps: 6.88

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:55<01:35,  2.14it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:56<01:35,  2.13it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:56<01:34,  2.13it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:57<01:34,  2.13it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:57<01:34,  2.13it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:57<01:33,  2.13it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [01:02<05:38,  1.71s/it]Progress: 37.00%
+---- avg training fps: 6.50

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [01:02<04:23,  1.34s/it]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [01:03<03:30,  1.07s/it]
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# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [01:04<02:28,  1.31it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:04<02:10,  1.48it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:05<01:57,  1.63it/s]Progress: 39.00%
+---- avg training fps: 6.59

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:05<01:48,  1.76it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:06<01:42,  1.86it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:06<01:42,  1.86it/s]
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# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:06<01:34,  2.00it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:07<01:32,  2.03it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:07<01:30,  2.06it/s]Progress: 41.00%
+---- avg training fps: 6.67

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:08<01:28,  2.08it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:08<01:27,  2.10it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:09<01:26,  2.11it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:09<01:26,  2.12it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:10<01:25,  2.12it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:10<01:24,  2.13it/s]Progress: 43.00%
+---- avg training fps: 6.74

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:11<01:24,  2.13it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:11<01:24,  2.13it/s]
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# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:12<01:22,  2.13it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:13<01:22,  2.12it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:13<01:21,  2.14it/s]Progress: 45.00%
+---- avg training fps: 6.81

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:13<01:21,  2.13it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:14<01:20,  2.13it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:14<01:20,  2.13it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:15<01:19,  2.13it/s]
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# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:15<01:19,  2.13it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:16<01:18,  2.13it/s]Progress: 47.00%
+---- avg training fps: 6.88

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:16<01:17,  2.14it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:17<01:17,  2.14it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:17<01:17,  2.13it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:18<01:17,  2.13it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:18<01:16,  2.13it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:19<01:16,  2.13it/s]Progress: 49.00%
+---- avg training fps: 6.93

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:19<01:15,  2.12it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:20<01:15,  2.12it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:20<01:15,  2.12it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:20<01:15,  2.11it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:21<01:14,  2.11it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:21<01:13,  2.13it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:21<01:13,  2.12it/s]Progress: 51.00%
+---- avg training fps: 6.99

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:22<01:13,  2.12it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:22<01:12,  2.13it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:23<01:11,  2.13it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:23<01:11,  2.13it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:24<01:11,  2.12it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:24<01:10,  2.12it/s]Progress: 53.00%
+---- avg training fps: 7.04

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:25<01:10,  2.12it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:25<01:10,  2.12it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:29<04:15,  1.73s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:30<03:18,  1.35s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:30<02:38,  1.08s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:31<02:10,  1.11it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:31<01:50,  1.30it/s]Progress: 55.00%
+---- avg training fps: 6.76

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:32<01:37,  1.47it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:32<01:27,  1.63it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:33<01:20,  1.75it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:33<01:15,  1.85it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:34<01:15,  1.85it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:34<01:12,  1.93it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:34<01:09,  1.99it/s]Progress: 57.00%
+---- avg training fps: 6.82

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:35<01:07,  2.03it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:35<01:06,  2.05it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:36<01:05,  2.07it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:36<01:04,  2.08it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:36<01:03,  2.09it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:37<01:02,  2.11it/s]Progress: 59.00%
+---- avg training fps: 6.86

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:37<01:01,  2.12it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:38<01:01,  2.12it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:38<01:01,  2.12it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:38<01:00,  2.12it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:39<01:00,  2.13it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:39<00:59,  2.13it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:40<00:59,  2.13it/s]Progress: 61.00%
+---- avg training fps: 6.91

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:40<00:58,  2.12it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:41<00:58,  2.12it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:41<00:57,  2.12it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:42<00:57,  2.12it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:42<00:56,  2.13it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:43<00:56,  2.13it/s]Progress: 63.00%
+---- avg training fps: 6.95

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:43<00:56,  2.13it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:43<00:55,  2.13it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:44<00:55,  2.12it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:44<00:55,  2.13it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:44<00:54,  2.12it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:45<00:54,  2.12it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:45<00:53,  2.14it/s]Progress: 65.00%
+---- avg training fps: 7.00

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:46<00:53,  2.13it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:46<00:52,  2.12it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:47<00:51,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:47<00:51,  2.13it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:48<00:51,  2.13it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:48<00:51,  2.13it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:48<00:50,  2.12it/s]Progress: 67.00%
+---- avg training fps: 7.03

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:49<00:50,  2.13it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:49<00:50,  2.12it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:50<00:49,  2.12it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:50<00:49,  2.11it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:51<00:48,  2.11it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:51<00:48,  2.11it/s]Progress: 69.00%
+---- avg training fps: 7.07

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:52<00:47,  2.13it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:52<00:47,  2.12it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:52<00:47,  2.12it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:52<00:46,  2.12it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:57<02:53,  1.77s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:58<02:13,  1.38s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:58<01:46,  1.11s/it]Progress: 71.00%
+---- avg training fps: 6.85

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:59<01:26,  1.09it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:59<01:13,  1.28it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [02:00<01:03,  1.45it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [02:00<00:57,  1.61it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [02:01<00:52,  1.74it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [02:01<00:48,  1.84it/s]Progress: 73.00%
+---- avg training fps: 6.89

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [02:02<00:48,  1.84it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [02:02<00:46,  1.91it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [02:02<00:44,  1.98it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [02:02<00:42,  2.02it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [02:03<00:41,  2.05it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [02:03<00:40,  2.08it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [02:04<00:40,  2.10it/s]Progress: 75.00%
+---- avg training fps: 6.92

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [02:04<00:39,  2.10it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [02:05<00:38,  2.11it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:05<00:38,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:06<00:37,  2.12it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:06<00:37,  2.12it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:06<00:37,  2.12it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:07<00:36,  2.12it/s]Progress: 77.00%
+---- avg training fps: 6.96

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:07<00:36,  2.12it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:08<00:35,  2.12it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:08<00:35,  2.12it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:09<00:34,  2.13it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:09<00:34,  2.13it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:09<00:33,  2.12it/s]Progress: 79.00%
+---- avg training fps: 6.99

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:10<00:33,  2.13it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:10<00:32,  2.13it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:11<00:32,  2.13it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:11<00:32,  2.13it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:11<00:32,  2.12it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:12<00:31,  2.13it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:12<00:30,  2.13it/s]Progress: 81.00%
+---- avg training fps: 7.02

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:13<00:30,  2.13it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:13<00:29,  2.14it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:14<00:29,  2.14it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:14<00:29,  2.13it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:15<00:28,  2.13it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:15<00:28,  2.13it/s]Progress: 83.00%
+---- avg training fps: 7.06

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:16<00:28,  2.13it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:16<00:27,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:16<00:27,  2.12it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:17<00:26,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:17<00:26,  2.13it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:17<00:25,  2.13it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:18<00:25,  2.13it/s]Progress: 85.00%
+---- avg training fps: 7.08

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:18<00:24,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:19<00:24,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:19<00:23,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:20<00:23,  2.14it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:20<00:23,  2.14it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:20<00:22,  2.13it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:25<01:23,  1.73s/it]Progress: 87.00%
+---- avg training fps: 6.91

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:25<01:03,  1.35s/it]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:26<00:49,  1.08s/it]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:26<00:40,  1.12it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:27<00:33,  1.30it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:27<00:29,  1.48it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:28<00:25,  1.63it/s]Progress: 89.00%
+---- avg training fps: 6.94

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:28<00:23,  1.76it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:29<00:21,  1.86it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:29<00:21,  1.86it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:29<00:20,  1.94it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:30<00:19,  1.99it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:30<00:18,  2.03it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:31<00:17,  2.06it/s]Progress: 91.00%
+---- avg training fps: 6.97

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:31<00:16,  2.08it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:31<00:16,  2.11it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:32<00:15,  2.12it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:32<00:15,  2.12it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:33<00:14,  2.12it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:33<00:14,  2.13it/s]Progress: 93.00%
+---- avg training fps: 7.00

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:34<00:14,  2.13it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:34<00:13,  2.13it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:34<00:13,  2.13it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:35<00:12,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:35<00:12,  2.14it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:36<00:11,  2.14it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:36<00:11,  2.13it/s]Progress: 95.00%
+---- avg training fps: 7.03

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:37<00:10,  2.14it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:37<00:10,  2.14it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:38<00:09,  2.13it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:38<00:09,  2.14it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:38<00:09,  2.14it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:38<00:08,  2.14it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:39<00:08,  2.13it/s]Progress: 97.00%
+---- avg training fps: 7.05

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:39<00:08,  2.12it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:40<00:07,  2.12it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:40<00:07,  2.12it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:41<00:06,  2.12it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:41<00:06,  2.14it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:42<00:05,  2.12it/s]Progress: 99.00%
+---- avg training fps: 7.08

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:42<00:05,  2.12it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:43<00:04,  2.12it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:43<00:04,  2.12it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:43<00:04,  2.13it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:44<00:03,  2.13it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:44<00:03,  2.13it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:45<00:02,  2.13it/s]Progress: 100.00%
+---- avg training fps: 7.10

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:45<00:02,  2.13it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:46<00:01,  2.08it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:46<00:01,  2.10it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:47<00:00,  2.10it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:47<00:00,  2.11it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:47<00:00,  2.12it/s]Progress: 100.00%
+---- avg training fps: 7.13
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [04:08<00:00,  1.21it/s]
+------------------------------------------
+Training done :)
+2024-08-13 02:47:06.925410: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 02:47:07.016927: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 02:47:07.016953: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 02:47:07.035677: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 02:47:07.434247: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:47:07.434328: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:47:07.434337: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23134 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/good_styles/eden_crystals' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=1.0 freeze_unet_before_completion_f=0.25 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='eden_crystals' output_dir='lora_models/styles_final/eden_crystals_13_02-47-09-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723510029.247109 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/eden_crystals_13_02-47-09-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.242
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants on a black background.
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants in the dark.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Futuristic natural elements blending with technology
+
+- In the style of TOK, a potted plant sits on a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves is on a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is on a black background.
+- In the style of TOK, a potted plant sits on a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves is on a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is in the dark.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Futuristic natural elements blending with technology
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 7293.81it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a potted plant s...
+1     in the style of <s0><s1><s2>, a futuristic cit...
+2     in the style of <s0><s1><s2>, a flower with gr...
+3     in the style of <s0><s1><s2>, a plant is growi...
+4     in the style of <s0><s1><s2>, a group of green...
+5     in the style of <s0><s1><s2>, a potted plant s...
+6     in the style of <s0><s1><s2>, a futuristic cit...
+7     in the style of <s0><s1><s2>, a flower with gr...
+8     in the style of <s0><s1><s2>, a plant is growi...
+9     in the style of <s0><s1><s2>, a group of green...
+10    in the style of <s0><s1><s2>, a potted plant s...
+11    in the style of <s0><s1><s2>, a futuristic cit...
+12    in the style of <s0><s1><s2>, a flower with gr...
+13    in the style of <s0><s1><s2>, a plant is growi...
+14    in the style of <s0><s1><s2>, a group of green...
+15    in the style of <s0><s1><s2>, a potted plant s...
+16    in the style of <s0><s1><s2>, a futuristic cit...
+17    in the style of <s0><s1><s2>, a flower with gr...
+18    in the style of <s0><s1><s2>, a plant is growi...
+19    in the style of <s0><s1><s2>, a group of green...
+20    in the style of <s0><s1><s2>, a potted plant s...
+21    in the style of <s0><s1><s2>, a futuristic cit...
+22    in the style of <s0><s1><s2>, a flower with gr...
+23    in the style of <s0><s1><s2>, a plant is growi...
+24    in the style of <s0><s1><s2>, a group of green...
+25    in the style of <s0><s1><s2>, a potted plant s...
+26    in the style of <s0><s1><s2>, a futuristic cit...
+27    in the style of <s0><s1><s2>, a flower with gr...
+28    in the style of <s0><s1><s2>, a plant is growi...
+29    in the style of <s0><s1><s2>, a group of green...
+30    in the style of <s0><s1><s2>, a potted plant s...
+31    in the style of <s0><s1><s2>, a futuristic cit...
+32    in the style of <s0><s1><s2>, a flower with gr...
+33    in the style of <s0><s1><s2>, a plant is growi...
+34    in the style of <s0><s1><s2>, a group of green...
+35    in the style of <s0><s1><s2>, a potted plant s...
+36    in the style of <s0><s1><s2>, a futuristic cit...
+37    in the style of <s0><s1><s2>, a flower with gr...
+38    in the style of <s0><s1><s2>, a plant is growi...
+39    in the style of <s0><s1><s2>, a group of green...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
  0%|          | 0/300 [00:00<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:02<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:02<14:39,  2.94s/it]
# Trainer step: 0, epoch: 0:   1%|          | 2/300 [00:06<16:39,  3.35s/it]
# Trainer step: 0, epoch: 0:   1%|          | 3/300 [00:07<10:02,  2.03s/it]
# Trainer step: 0, epoch: 0:   1%|▏         | 4/300 [00:07<06:55,  1.40s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:07<05:12,  1.06s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:08<04:11,  1.17it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.72

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:08<03:32,  1.38it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:09<03:06,  1.57it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 9/300 [00:09<02:48,  1.72it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 10/300 [00:10<02:37,  1.84it/s]
# Trainer step: 10, epoch: 1:   3%|▎         | 10/300 [00:10<02:37,  1.84it/s]
# Trainer step: 10, epoch: 1:   4%|▎         | 11/300 [00:10<02:28,  1.94it/s]
# Trainer step: 10, epoch: 1:   4%|▍         | 12/300 [00:11<02:23,  2.01it/s]Progress: 7.00%
+---- avg training fps: 4.15

# Trainer step: 10, epoch: 1:   4%|▍         | 13/300 [00:11<02:19,  2.06it/s]
# Trainer step: 10, epoch: 1:   5%|▍         | 14/300 [00:12<02:17,  2.08it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 15/300 [00:12<02:14,  2.12it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 16/300 [00:12<02:12,  2.14it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 17/300 [00:13<02:11,  2.15it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 18/300 [00:13<02:10,  2.16it/s]Progress: 9.00%
+---- avg training fps: 5.03

# Trainer step: 10, epoch: 1:   6%|▋         | 19/300 [00:14<02:10,  2.16it/s]
# Trainer step: 10, epoch: 1:   7%|▋         | 20/300 [00:14<02:08,  2.17it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 20/300 [00:15<02:08,  2.17it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 21/300 [00:15<02:08,  2.17it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 22/300 [00:15<02:07,  2.17it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 23/300 [00:16<02:07,  2.18it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 24/300 [00:16<02:06,  2.19it/s]Progress: 11.00%
+---- avg training fps: 5.62

# Trainer step: 20, epoch: 2:   8%|▊         | 25/300 [00:17<02:06,  2.18it/s]
# Trainer step: 20, epoch: 2:   9%|▊         | 26/300 [00:17<02:05,  2.18it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 27/300 [00:17<02:05,  2.18it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 28/300 [00:18<02:04,  2.19it/s]
# Trainer step: 20, epoch: 2:  10%|▉         | 29/300 [00:18<02:04,  2.18it/s]
# Trainer step: 20, epoch: 2:  10%|█         | 30/300 [00:19<02:04,  2.17it/s]Progress: 13.00%
+---- avg training fps: 6.05

# Trainer step: 30, epoch: 3:  10%|█         | 30/300 [00:19<02:04,  2.17it/s]
# Trainer step: 30, epoch: 3:  10%|█         | 31/300 [00:19<02:03,  2.17it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 32/300 [00:20<02:02,  2.18it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 33/300 [00:20<02:17,  1.94it/s]
# Trainer step: 30, epoch: 3:  11%|█▏        | 34/300 [00:21<02:12,  2.01it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 35/300 [00:21<02:08,  2.06it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 36/300 [00:22<02:06,  2.09it/s]Progress: 15.00%
+---- avg training fps: 6.33

# Trainer step: 30, epoch: 3:  12%|█▏        | 37/300 [00:22<02:04,  2.12it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 38/300 [00:23<02:02,  2.14it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 39/300 [00:23<02:01,  2.15it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 40/300 [00:24<02:00,  2.16it/s]
# Trainer step: 40, epoch: 4:  13%|█▎        | 40/300 [00:24<02:00,  2.16it/s]
# Trainer step: 40, epoch: 4:  14%|█▎        | 41/300 [00:24<01:59,  2.17it/s]
# Trainer step: 40, epoch: 4:  14%|█▍        | 42/300 [00:25<01:58,  2.17it/s]Progress: 17.00%
+---- avg training fps: 6.58

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:25<01:58,  2.17it/s]
# Trainer step: 40, epoch: 4:  15%|█▍        | 44/300 [00:25<01:57,  2.17it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 45/300 [00:26<01:57,  2.17it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 46/300 [00:26<01:57,  2.17it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 47/300 [00:27<01:56,  2.17it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:27<01:56,  2.17it/s]Progress: 19.00%
+---- avg training fps: 6.79

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:28<01:55,  2.18it/s]
# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:28<01:54,  2.18it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 50/300 [00:29<01:54,  2.18it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 51/300 [00:29<01:54,  2.18it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 52/300 [00:32<05:46,  1.40s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 53/300 [00:33<04:35,  1.12s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:33<03:45,  1.09it/s]Progress: 21.00%
+---- avg training fps: 6.32

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:34<03:11,  1.28it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:34<02:46,  1.46it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 57/300 [00:35<02:30,  1.62it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 58/300 [00:35<02:18,  1.75it/s]
# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:36<02:09,  1.86it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:36<02:03,  1.94it/s]Progress: 23.00%
+---- avg training fps: 6.50

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:36<02:03,  1.94it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:36<01:59,  2.01it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:37<01:55,  2.05it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:37<01:53,  2.09it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:38<01:51,  2.12it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:38<01:50,  2.13it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:39<02:02,  1.91it/s]Progress: 25.00%
+---- avg training fps: 6.62

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:39<01:57,  1.98it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:40<01:54,  2.03it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:40<01:51,  2.07it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:41<01:49,  2.10it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:41<01:49,  2.10it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:41<01:47,  2.12it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:42<01:46,  2.14it/s]Progress: 27.00%
+---- avg training fps: 6.76

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:42<01:45,  2.16it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:43<01:44,  2.16it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:43<01:43,  2.17it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:44<01:43,  2.17it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:44<01:42,  2.18it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:44<01:41,  2.18it/s]Progress: 29.00%
+---- avg training fps: 6.88

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:45<01:41,  2.18it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:45<01:40,  2.18it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:46<01:40,  2.18it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:46<01:40,  2.18it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:46<01:40,  2.18it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:47<01:39,  2.18it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:47<01:39,  2.18it/s]Progress: 31.00%
+---- avg training fps: 6.98

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:48<01:38,  2.18it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:48<01:38,  2.18it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:49<01:37,  2.18it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:49<01:37,  2.18it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:49<01:36,  2.18it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:50<01:36,  2.18it/s]Progress: 33.00%
+---- avg training fps: 7.07

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:50<01:36,  2.18it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:50<01:36,  2.17it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:51<01:35,  2.17it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:51<01:35,  2.17it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:52<01:34,  2.18it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:52<01:34,  2.17it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:53<01:34,  2.17it/s]Progress: 35.00%
+---- avg training fps: 7.16

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:53<01:33,  2.17it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:54<01:33,  2.17it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:54<01:32,  2.17it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:55<01:32,  2.17it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:55<01:32,  2.17it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:55<01:31,  2.17it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:58<04:18,  1.31s/it]Progress: 37.00%
+---- avg training fps: 6.89

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:59<03:27,  1.05s/it]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [00:59<02:51,  1.14it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [01:00<02:26,  1.33it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [01:00<02:08,  1.51it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:01<02:07,  1.51it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:01<01:55,  1.67it/s]Progress: 39.00%
+---- avg training fps: 6.95

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:02<01:46,  1.79it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:02<01:40,  1.89it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:03<01:40,  1.89it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:03<01:35,  1.98it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:03<01:32,  2.03it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:04<01:30,  2.07it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:04<01:28,  2.10it/s]Progress: 41.00%
+---- avg training fps: 7.02

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:04<01:27,  2.13it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:05<01:26,  2.14it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:05<01:25,  2.15it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:06<01:24,  2.16it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:06<01:23,  2.17it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:07<01:22,  2.18it/s]Progress: 43.00%
+---- avg training fps: 7.09

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:07<01:22,  2.18it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:07<01:22,  2.18it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:08<01:21,  2.17it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:08<01:21,  2.17it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:09<01:21,  2.17it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:09<01:20,  2.17it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:10<01:20,  2.17it/s]Progress: 45.00%
+---- avg training fps: 7.15

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:10<01:19,  2.18it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:10<01:19,  2.18it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:11<01:18,  2.17it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:11<01:18,  2.17it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:12<01:18,  2.17it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:12<01:17,  2.17it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:12<01:17,  2.17it/s]Progress: 47.00%
+---- avg training fps: 7.21

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:13<01:17,  2.17it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:13<01:16,  2.17it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:14<01:16,  2.17it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:14<01:15,  2.17it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:15<01:15,  2.17it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:15<01:14,  2.17it/s]Progress: 49.00%
+---- avg training fps: 7.26

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:15<01:13,  2.18it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:16<01:13,  2.17it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:16<01:13,  2.17it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:16<01:13,  2.17it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:17<01:12,  2.17it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:17<01:11,  2.18it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:18<01:11,  2.18it/s]Progress: 51.00%
+---- avg training fps: 7.31

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:18<01:11,  2.17it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:19<01:11,  2.17it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:19<01:10,  2.17it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:20<01:10,  2.16it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:20<01:09,  2.16it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:21<01:09,  2.16it/s]Progress: 53.00%
+---- avg training fps: 7.36

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:21<01:09,  2.16it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:21<01:08,  2.17it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:25<03:51,  1.56s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:26<03:01,  1.23s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:26<02:26,  1.00s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:27<02:01,  1.19it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:27<01:44,  1.38it/s]Progress: 55.00%
+---- avg training fps: 7.09

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:27<01:32,  1.55it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:28<01:23,  1.69it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:28<01:17,  1.81it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:29<01:13,  1.90it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:29<01:13,  1.90it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:29<01:10,  1.97it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:30<01:08,  2.01it/s]Progress: 57.00%
+---- avg training fps: 7.14

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:30<01:06,  2.06it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:31<01:05,  2.09it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:31<01:03,  2.11it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:32<01:03,  2.12it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:32<01:01,  2.15it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:33<01:01,  2.15it/s]Progress: 59.00%
+---- avg training fps: 7.19

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:33<01:01,  2.15it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:34<01:00,  2.14it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:34<01:00,  2.14it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:34<01:00,  2.14it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:34<00:59,  2.14it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:35<00:59,  2.15it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:35<00:58,  2.15it/s]Progress: 61.00%
+---- avg training fps: 7.23

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:36<00:58,  2.15it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:36<00:57,  2.15it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:37<00:57,  2.15it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:37<00:56,  2.16it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:38<00:56,  2.16it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:38<00:55,  2.15it/s]Progress: 63.00%
+---- avg training fps: 7.26

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:39<00:55,  2.15it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:39<00:55,  2.15it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:39<00:54,  2.15it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:40<00:54,  2.15it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:40<00:54,  2.15it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:40<00:53,  2.15it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:41<00:53,  2.15it/s]Progress: 65.00%
+---- avg training fps: 7.30

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:41<00:52,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:42<00:52,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:42<00:51,  2.15it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:43<00:51,  2.15it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:43<00:51,  2.15it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:43<00:50,  2.15it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:44<00:50,  2.14it/s]Progress: 67.00%
+---- avg training fps: 7.34

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:44<00:49,  2.15it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:45<00:49,  2.15it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:45<00:49,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:46<00:48,  2.15it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:46<00:47,  2.15it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:47<00:47,  2.15it/s]Progress: 69.00%
+---- avg training fps: 7.37

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:47<00:47,  2.14it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:47<00:46,  2.16it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:48<00:46,  2.16it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:48<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:52<02:29,  1.53s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:52<01:57,  1.21s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:53<01:34,  1.02it/s]Progress: 71.00%
+---- avg training fps: 7.17

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:53<01:18,  1.21it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:54<01:07,  1.40it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:54<00:59,  1.56it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:55<00:54,  1.70it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:55<00:50,  1.81it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:56<00:47,  1.91it/s]Progress: 73.00%
+---- avg training fps: 7.20

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:56<00:47,  1.91it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:56<00:45,  1.98it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:57<00:43,  2.03it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:57<00:42,  2.07it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:57<00:40,  2.10it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [01:58<00:40,  2.12it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [01:58<00:39,  2.13it/s]Progress: 75.00%
+---- avg training fps: 7.24

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [01:59<00:38,  2.14it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [01:59<00:38,  2.15it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:00<00:37,  2.16it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:00<00:37,  2.15it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:01<00:37,  2.15it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:01<00:36,  2.15it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:01<00:36,  2.15it/s]Progress: 77.00%
+---- avg training fps: 7.27

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:02<00:35,  2.15it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:02<00:35,  2.14it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:03<00:34,  2.15it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:03<00:34,  2.15it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:04<00:34,  2.15it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:04<00:33,  2.14it/s]Progress: 79.00%
+---- avg training fps: 7.30

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:04<00:32,  2.16it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:05<00:32,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:05<00:32,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:05<00:32,  2.15it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:06<00:31,  2.15it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:06<00:31,  2.16it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:07<00:30,  2.16it/s]Progress: 81.00%
+---- avg training fps: 7.33

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:07<00:30,  2.15it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:08<00:29,  2.15it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:08<00:29,  2.14it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:09<00:29,  2.13it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:09<00:28,  2.13it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:10<00:28,  2.14it/s]Progress: 83.00%
+---- avg training fps: 7.35

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:10<00:28,  2.14it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:10<00:27,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:11<00:27,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:11<00:26,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:11<00:26,  2.13it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:12<00:25,  2.13it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:12<00:25,  2.13it/s]Progress: 85.00%
+---- avg training fps: 7.38

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:13<00:24,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:13<00:24,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:14<00:23,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:14<00:23,  2.13it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:15<00:23,  2.13it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:15<00:22,  2.13it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:19<01:17,  1.61s/it]Progress: 87.00%
+---- avg training fps: 7.20

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:19<00:59,  1.26s/it]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:20<00:47,  1.03s/it]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:20<00:38,  1.16it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:21<00:32,  1.35it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:21<00:28,  1.52it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:22<00:25,  1.67it/s]Progress: 89.00%
+---- avg training fps: 7.23

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:22<00:22,  1.79it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:23<00:21,  1.85it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:23<00:21,  1.85it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:23<00:20,  1.93it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:24<00:19,  1.99it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:24<00:18,  2.03it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:25<00:17,  2.06it/s]Progress: 91.00%
+---- avg training fps: 7.25

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:25<00:16,  2.08it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:26<00:16,  2.10it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:26<00:15,  2.11it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:27<00:15,  2.12it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:27<00:14,  2.13it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:27<00:14,  2.13it/s]Progress: 93.00%
+---- avg training fps: 7.28

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:28<00:14,  2.13it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:28<00:13,  2.13it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:28<00:13,  2.12it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:29<00:12,  2.12it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:29<00:12,  2.12it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:30<00:11,  2.13it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:30<00:11,  2.14it/s]Progress: 95.00%
+---- avg training fps: 7.30

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:31<00:10,  2.14it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:31<00:10,  2.13it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:32<00:09,  2.13it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:32<00:09,  2.13it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:33<00:09,  2.13it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:33<00:08,  2.13it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:33<00:08,  2.13it/s]Progress: 97.00%
+---- avg training fps: 7.32

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:34<00:07,  2.14it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:34<00:07,  2.14it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:34<00:07,  2.13it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:35<00:06,  2.13it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:35<00:06,  2.13it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:36<00:05,  2.13it/s]Progress: 99.00%
+---- avg training fps: 7.34

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:36<00:05,  2.12it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:37<00:04,  2.14it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:37<00:04,  2.14it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:37<00:04,  2.13it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:38<00:03,  2.13it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:38<00:03,  2.14it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:39<00:02,  2.13it/s]Progress: 100.00%
+---- avg training fps: 7.37

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:39<00:02,  2.13it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:40<00:01,  2.13it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:40<00:01,  2.12it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:41<00:00,  2.12it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:41<00:00,  2.12it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:42<00:00,  2.13it/s]Progress: 100.00%
+---- avg training fps: 7.38
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [04:02<00:00,  1.24it/s]
+------------------------------------------
+Training done :)
+2024-08-13 02:51:41.127441: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 02:51:41.218470: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 02:51:41.218498: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 02:51:41.237292: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 02:51:41.637444: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:51:41.637529: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:51:41.637538: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23134 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.75 freeze_unet_before_completion_f=0.5 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='clipx.zip' output_dir='lora_models/styles_final/clipx.zip_13_02-51-43-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723510303.4602222 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Downloading training data...
+Downloading https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip to lora_models/styles_final/clipx.zip_13_02-51-43-style_512_florence_300/images_in/clipx.zip...
+Succesfully prepped 54 .jpg images in lora_models/styles_final/clipx.zip_13_02-51-43-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.314
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 27 images, 27 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 27 to 54)
+Generating 54 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal, whimsical, detailed, and quirky scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene is Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase describes the best and worst of times.
+- In the style of TOK, bees are forming a blockchain.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset shows Venus and Earth circling the sun.
+- In the style of TOK, there is an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is depicted.
+- In the style of TOK, the phrase exemplifies the age of wisdom and the age of foolishness.
+- In the style of TOK, two people discuss quitting their boring corporation jobs together.
+- In the style of TOK, petroglyphs are presented.
+- In the style of TOK, a room and space exchange based on first and second-degree connections is shown.
+- In the style of TOK, the fear known as trypophobia is depicted.
+- In the style of TOK, there is a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers is highlighted.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with patterns akin to Hawaiian shirts.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a sense of visual storytelling similar to Tarsem Singh.
+- In the style of TOK, a brigade of beavers is illustrated in an M. C. Escher-inspired design.
+- In the style of TOK, a castle moves along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dances in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models and egrets seated in the audience.
+- In the style of TOK, there is a hyper-realistic painting of lemon-lime architecture.
+--------------------------
+GPT-4 returned the wrong number of prompts 26 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 1...
+----- GPT response: -----
+Style Description: Surrealistic, intricate, and fantastical scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is embodied in a visual representation.
+- In the style of TOK, a blockchain is composed of bees.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun is depicted.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is presented.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is embodied in a visual representation.
+- In the style of TOK, two individuals decide to quit their boring corporation job together.
+- In the style of TOK, petroglyphs are depicted.
+- In the style of TOK, a room and space exchange is based on first and second-degree connections.
+- In the style of TOK, trypophobia is visually represented.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of dancing and singing beavers is featured with a nod to Jim Henson's Muppets.
+- In the style of TOK, a beaver is shown chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a pattern like a Hawaiian shirt.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is depicted, reminiscent of M. C. Escher's style.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models and egrets as the audience.
+- In the style of TOK, a hyper-realistic painting showcases lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash is swimming up through the sea to take a peak above the water.
+- In the style of TOK, a mermaid made out of fishnets and other sea trash is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is embodied in a visual representation.
+- In the style of TOK, a blockchain is composed of bees.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun is depicted.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is presented.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is embodied in a visual representation.
+- In the style of TOK, two individuals decide to quit their boring corporation job together.
+- In the style of TOK, petroglyphs are depicted.
+- In the style of TOK, a room and space exchange is based on first and second-degree connections.
+- In the style of TOK, trypophobia is visually represented.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of dancing and singing beavers is featured with a nod to Jim Henson's Muppets.
+- In the style of TOK, a beaver is shown chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a pattern like a Hawaiian shirt.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is depicted, reminiscent of M. C. Escher's style.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models and egrets as the audience.
+- In the style of TOK, a hyper-realistic painting showcases lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash is swimming up through the sea to take a peak above the water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Using GPT concept name as CLIP-segmentation prompt: Surrealistic, intricate, and fantastical scenes
+Generating 54 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
54it [00:00, 15137.84it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 54 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a mermaid made o...
+1     in the style of <s0><s1><s2>, a throng of lill...
+2     in the style of <s0><s1><s2>, fort kochi in ke...
+3     in the style of <s0><s1><s2>, the phrase "they...
+4     in the style of <s0><s1><s2>, a blockchain is ...
+5     in the style of <s0><s1><s2>, a gorgon made ou...
+6     in the style of <s0><s1><s2>, a hillside at su...
+7     in the style of <s0><s1><s2>, an ancient templ...
+8     in the style of <s0><s1><s2>, cats are pooping...
+9     in the style of <s0><s1><s2>, a goblin goat is...
+10    in the style of <s0><s1><s2>, the phrase "it w...
+11    in the style of <s0><s1><s2>, two individuals ...
+12    in the style of <s0><s1><s2>, petroglyphs are ...
+13    in the style of <s0><s1><s2>, a room and space...
+14    in the style of <s0><s1><s2>, trypophobia is v...
+15    in the style of <s0><s1><s2>, a six-pack of ni...
+16    in the style of <s0><s1><s2>, a band of dancin...
+17    in the style of <s0><s1><s2>, a beaver is show...
+18    in the style of <s0><s1><s2>, a brigade of bea...
+19    in the style of <s0><s1><s2>, a brigade of bea...
+20    in the style of <s0><s1><s2>, a brigade of bea...
+21    in the style of <s0><s1><s2>, a castle is movi...
+22    in the style of <s0><s1><s2>, a chorus line of...
+23    in the style of <s0><s1><s2>, a dirty 1950s re...
+24    in the style of <s0><s1><s2>, a fashion show f...
+25    in the style of <s0><s1><s2>, a hyper-realisti...
+26    in the style of <s0><s1><s2>, a mermaid made o...
+27    in the style of <s0><s1><s2>, a mermaid made o...
+28    in the style of <s0><s1><s2>, a throng of lill...
+29    in the style of <s0><s1><s2>, fort kochi in ke...
+30    in the style of <s0><s1><s2>, the phrase "they...
+31    in the style of <s0><s1><s2>, a blockchain is ...
+32    in the style of <s0><s1><s2>, a gorgon made ou...
+33    in the style of <s0><s1><s2>, a hillside at su...
+34    in the style of <s0><s1><s2>, an ancient templ...
+35    in the style of <s0><s1><s2>, cats are pooping...
+36    in the style of <s0><s1><s2>, a goblin goat is...
+37    in the style of <s0><s1><s2>, the phrase "it w...
+38    in the style of <s0><s1><s2>, two individuals ...
+39    in the style of <s0><s1><s2>, petroglyphs are ...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 54
+--- Num batches each epoch = 14
+--- Num Epochs = 22
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:02<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:02<14:34,  2.92s/it]
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# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:09<05:59,  1.22s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:09<04:40,  1.05it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.40

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:10<03:51,  1.27it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:10<03:18,  1.47it/s]
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# Trainer step: 0, epoch: 0:   4%|▍         | 12/300 [00:12<02:24,  1.99it/s]Progress: 7.00%
+---- avg training fps: 3.78

# Trainer step: 0, epoch: 0:   4%|▍         | 13/300 [00:12<02:19,  2.05it/s]
# Trainer step: 0, epoch: 0:   5%|▍         | 14/300 [00:13<02:15,  2.10it/s]
# Trainer step: 14, epoch: 1:   5%|▍         | 14/300 [00:13<02:15,  2.10it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 15/300 [00:13<02:22,  2.00it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 16/300 [00:14<02:17,  2.07it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 17/300 [00:14<02:14,  2.11it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 18/300 [00:15<02:11,  2.14it/s]Progress: 9.00%
+---- avg training fps: 4.64

# Trainer step: 14, epoch: 1:   6%|▋         | 19/300 [00:15<02:10,  2.16it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 20/300 [00:15<02:08,  2.18it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 21/300 [00:16<02:07,  2.19it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 22/300 [00:16<02:06,  2.19it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 23/300 [00:17<02:06,  2.20it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 24/300 [00:17<02:05,  2.20it/s]Progress: 11.00%
+---- avg training fps: 5.27

# Trainer step: 14, epoch: 1:   8%|▊         | 25/300 [00:18<02:04,  2.21it/s]
# Trainer step: 14, epoch: 1:   9%|▊         | 26/300 [00:18<02:03,  2.21it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 27/300 [00:19<02:03,  2.22it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 28/300 [00:19<02:02,  2.22it/s]
# Trainer step: 28, epoch: 2:   9%|▉         | 28/300 [00:19<02:02,  2.22it/s]
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# Trainer step: 28, epoch: 2:  10%|█         | 30/300 [00:20<01:58,  2.27it/s]Progress: 13.00%
+---- avg training fps: 5.75

# Trainer step: 28, epoch: 2:  10%|█         | 31/300 [00:20<01:59,  2.25it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 32/300 [00:21<01:59,  2.23it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 33/300 [00:21<02:00,  2.22it/s]
# Trainer step: 28, epoch: 2:  11%|█▏        | 34/300 [00:22<02:00,  2.21it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 35/300 [00:22<02:00,  2.20it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 36/300 [00:23<01:59,  2.21it/s]Progress: 15.00%
+---- avg training fps: 6.10

# Trainer step: 28, epoch: 2:  12%|█▏        | 37/300 [00:23<01:59,  2.21it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 38/300 [00:24<01:59,  2.20it/s]
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# Trainer step: 28, epoch: 2:  13%|█▎        | 40/300 [00:24<01:59,  2.18it/s]
# Trainer step: 28, epoch: 2:  14%|█▎        | 41/300 [00:25<01:59,  2.17it/s]
# Trainer step: 28, epoch: 2:  14%|█▍        | 42/300 [00:25<01:58,  2.17it/s]Progress: 17.00%
+---- avg training fps: 6.39

# Trainer step: 42, epoch: 3:  14%|█▍        | 42/300 [00:26<01:58,  2.17it/s]
# Trainer step: 42, epoch: 3:  14%|█▍        | 43/300 [00:26<01:54,  2.25it/s]
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# Trainer step: 42, epoch: 3:  15%|█▌        | 45/300 [00:27<01:55,  2.20it/s]
# Trainer step: 42, epoch: 3:  15%|█▌        | 46/300 [00:27<01:56,  2.19it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 47/300 [00:28<01:56,  2.18it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 48/300 [00:28<01:56,  2.17it/s]Progress: 19.00%
+---- avg training fps: 6.60

# Trainer step: 42, epoch: 3:  16%|█▋        | 49/300 [00:29<01:56,  2.16it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 50/300 [00:29<01:56,  2.15it/s]
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# Trainer step: 42, epoch: 3:  17%|█▋        | 52/300 [00:33<05:59,  1.45s/it]
# Trainer step: 42, epoch: 3:  18%|█▊        | 53/300 [00:34<04:44,  1.15s/it]
# Trainer step: 42, epoch: 3:  18%|█▊        | 54/300 [00:34<03:52,  1.06it/s]Progress: 21.00%
+---- avg training fps: 6.14

# Trainer step: 42, epoch: 3:  18%|█▊        | 55/300 [00:35<03:16,  1.25it/s]
# Trainer step: 42, epoch: 3:  19%|█▊        | 56/300 [00:35<02:51,  1.43it/s]
# Trainer step: 56, epoch: 4:  19%|█▊        | 56/300 [00:36<02:51,  1.43it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 57/300 [00:36<02:28,  1.63it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 58/300 [00:36<02:17,  1.76it/s]
# Trainer step: 56, epoch: 4:  20%|█▉        | 59/300 [00:36<02:09,  1.86it/s]
# Trainer step: 56, epoch: 4:  20%|██        | 60/300 [00:37<02:04,  1.93it/s]Progress: 23.00%
+---- avg training fps: 6.33

# Trainer step: 56, epoch: 4:  20%|██        | 61/300 [00:37<01:59,  2.00it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 62/300 [00:38<01:57,  2.03it/s]
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# Trainer step: 56, epoch: 4:  21%|██▏       | 64/300 [00:39<01:53,  2.08it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 65/300 [00:39<01:52,  2.09it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 66/300 [00:40<01:51,  2.10it/s]Progress: 25.00%
+---- avg training fps: 6.48

# Trainer step: 56, epoch: 4:  22%|██▏       | 67/300 [00:40<01:50,  2.10it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 68/300 [00:41<01:50,  2.11it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 69/300 [00:41<01:49,  2.12it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 70/300 [00:42<01:48,  2.12it/s]
# Trainer step: 70, epoch: 5:  23%|██▎       | 70/300 [00:42<01:48,  2.12it/s]
# Trainer step: 70, epoch: 5:  24%|██▎       | 71/300 [00:42<01:43,  2.21it/s]
# Trainer step: 70, epoch: 5:  24%|██▍       | 72/300 [00:43<01:43,  2.20it/s]Progress: 27.00%
+---- avg training fps: 6.62

# Trainer step: 70, epoch: 5:  24%|██▍       | 73/300 [00:43<01:44,  2.17it/s]
# Trainer step: 70, epoch: 5:  25%|██▍       | 74/300 [00:43<01:44,  2.16it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 75/300 [00:44<01:44,  2.15it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 76/300 [00:44<01:44,  2.14it/s]
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# Trainer step: 70, epoch: 5:  26%|██▌       | 78/300 [00:45<01:44,  2.13it/s]Progress: 29.00%
+---- avg training fps: 6.74

# Trainer step: 70, epoch: 5:  26%|██▋       | 79/300 [00:46<01:43,  2.14it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 80/300 [00:46<01:43,  2.13it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 81/300 [00:47<01:42,  2.13it/s]
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# Trainer step: 70, epoch: 5:  28%|██▊       | 83/300 [00:48<01:42,  2.12it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 84/300 [00:48<01:41,  2.12it/s]Progress: 31.00%
+---- avg training fps: 6.85

# Trainer step: 84, epoch: 6:  28%|██▊       | 84/300 [00:49<01:41,  2.12it/s]
# Trainer step: 84, epoch: 6:  28%|██▊       | 85/300 [00:49<01:37,  2.21it/s]
# Trainer step: 84, epoch: 6:  29%|██▊       | 86/300 [00:49<01:37,  2.19it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 87/300 [00:50<01:38,  2.17it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 88/300 [00:50<01:38,  2.15it/s]
# Trainer step: 84, epoch: 6:  30%|██▉       | 89/300 [00:50<01:38,  2.14it/s]
# Trainer step: 84, epoch: 6:  30%|███       | 90/300 [00:51<01:38,  2.14it/s]Progress: 33.00%
+---- avg training fps: 6.94

# Trainer step: 84, epoch: 6:  30%|███       | 91/300 [00:51<01:38,  2.13it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 92/300 [00:52<01:37,  2.13it/s]
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# Trainer step: 84, epoch: 6:  31%|███▏      | 94/300 [00:53<01:37,  2.12it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 95/300 [00:53<01:36,  2.12it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 96/300 [00:54<01:35,  2.13it/s]Progress: 35.00%
+---- avg training fps: 7.02

# Trainer step: 84, epoch: 6:  32%|███▏      | 97/300 [00:54<01:35,  2.13it/s]
# Trainer step: 84, epoch: 6:  33%|███▎      | 98/300 [00:55<01:35,  2.12it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 98/300 [00:55<01:35,  2.12it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 99/300 [00:55<01:30,  2.21it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 100/300 [00:56<01:31,  2.19it/s]
# Trainer step: 98, epoch: 7:  34%|███▎      | 101/300 [00:56<01:31,  2.17it/s]
# Trainer step: 98, epoch: 7:  34%|███▍      | 102/300 [00:59<03:58,  1.20s/it]Progress: 37.00%
+---- avg training fps: 6.80

# Trainer step: 98, epoch: 7:  34%|███▍      | 103/300 [00:59<03:13,  1.02it/s]
# Trainer step: 98, epoch: 7:  35%|███▍      | 104/300 [01:00<02:42,  1.21it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 105/300 [01:00<02:21,  1.38it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 106/300 [01:01<02:05,  1.54it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 107/300 [01:01<01:55,  1.67it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 108/300 [01:02<01:47,  1.79it/s]Progress: 39.00%
+---- avg training fps: 6.88

# Trainer step: 98, epoch: 7:  36%|███▋      | 109/300 [01:02<01:41,  1.87it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 110/300 [01:03<01:37,  1.94it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 111/300 [01:03<01:34,  1.99it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 112/300 [01:04<01:32,  2.03it/s]
# Trainer step: 112, epoch: 8:  37%|███▋      | 112/300 [01:04<01:32,  2.03it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 113/300 [01:04<01:27,  2.13it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 114/300 [01:05<01:27,  2.13it/s]Progress: 41.00%
+---- avg training fps: 6.95

# Trainer step: 112, epoch: 8:  38%|███▊      | 115/300 [01:05<01:26,  2.13it/s]
# Trainer step: 112, epoch: 8:  39%|███▊      | 116/300 [01:06<01:26,  2.13it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 117/300 [01:06<01:26,  2.12it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 118/300 [01:07<01:39,  1.83it/s]
# Trainer step: 112, epoch: 8:  40%|███▉      | 119/300 [01:07<01:34,  1.91it/s]
# Trainer step: 112, epoch: 8:  40%|████      | 120/300 [01:08<01:31,  1.97it/s]Progress: 43.00%
+---- avg training fps: 6.99

# Trainer step: 112, epoch: 8:  40%|████      | 121/300 [01:08<01:28,  2.02it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 122/300 [01:09<01:26,  2.05it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 123/300 [01:09<01:25,  2.07it/s]
# Trainer step: 112, epoch: 8:  41%|████▏     | 124/300 [01:10<01:24,  2.09it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 125/300 [01:10<01:23,  2.10it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 126/300 [01:10<01:22,  2.11it/s]Progress: 45.00%
+---- avg training fps: 7.06

# Trainer step: 126, epoch: 9:  42%|████▏     | 126/300 [01:11<01:22,  2.11it/s]
# Trainer step: 126, epoch: 9:  42%|████▏     | 127/300 [01:11<01:18,  2.21it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 128/300 [01:11<01:18,  2.18it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 129/300 [01:12<01:18,  2.17it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 130/300 [01:12<01:18,  2.16it/s]
# Trainer step: 126, epoch: 9:  44%|████▎     | 131/300 [01:13<01:18,  2.15it/s]
# Trainer step: 126, epoch: 9:  44%|████▍     | 132/300 [01:13<01:18,  2.15it/s]Progress: 47.00%
+---- avg training fps: 7.12

# Trainer step: 126, epoch: 9:  44%|████▍     | 133/300 [01:14<01:17,  2.14it/s]
# Trainer step: 126, epoch: 9:  45%|████▍     | 134/300 [01:14<01:17,  2.14it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 135/300 [01:15<01:17,  2.14it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 136/300 [01:15<01:16,  2.14it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 137/300 [01:16<01:16,  2.13it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 138/300 [01:16<01:15,  2.13it/s]Progress: 49.00%
+---- avg training fps: 7.17

# Trainer step: 126, epoch: 9:  46%|████▋     | 139/300 [01:17<01:15,  2.14it/s]
# Trainer step: 126, epoch: 9:  47%|████▋     | 140/300 [01:17<01:14,  2.14it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 140/300 [01:17<01:14,  2.14it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 141/300 [01:17<01:11,  2.23it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 142/300 [01:18<01:11,  2.20it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 143/300 [01:18<01:12,  2.17it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 144/300 [01:19<01:12,  2.16it/s]Progress: 51.00%
+---- avg training fps: 7.22

# Trainer step: 140, epoch: 10:  48%|████▊     | 145/300 [01:19<01:12,  2.15it/s]
# Trainer step: 140, epoch: 10:  49%|████▊     | 146/300 [01:20<01:11,  2.14it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 147/300 [01:20<01:11,  2.15it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 148/300 [01:21<01:10,  2.15it/s]
# Trainer step: 140, epoch: 10:  50%|████▉     | 149/300 [01:21<01:10,  2.14it/s]
# Trainer step: 140, epoch: 10:  50%|█████     | 150/300 [01:22<01:10,  2.13it/s]Progress: 53.00%
+---- avg training fps: 7.27

# Trainer step: 140, epoch: 10:  50%|█████     | 151/300 [01:22<01:09,  2.13it/s]
# Trainer step: 140, epoch: 10:  51%|█████     | 152/300 [01:27<04:18,  1.75s/it]
# Trainer step: 140, epoch: 10:  51%|█████     | 153/300 [01:27<03:20,  1.36s/it]
# Trainer step: 140, epoch: 10:  51%|█████▏    | 154/300 [01:28<02:39,  1.09s/it]
# Trainer step: 154, epoch: 11:  51%|█████▏    | 154/300 [01:28<02:39,  1.09s/it]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 155/300 [01:28<02:08,  1.13it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 156/300 [01:29<01:49,  1.31it/s]Progress: 55.00%
+---- avg training fps: 6.97

# Trainer step: 154, epoch: 11:  52%|█████▏    | 157/300 [01:29<01:36,  1.48it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 158/300 [01:30<01:27,  1.63it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 159/300 [01:30<01:20,  1.75it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 160/300 [01:31<01:15,  1.84it/s]
# Trainer step: 154, epoch: 11:  54%|█████▎    | 161/300 [01:31<01:12,  1.92it/s]
# Trainer step: 154, epoch: 11:  54%|█████▍    | 162/300 [01:31<01:09,  1.97it/s]Progress: 57.00%
+---- avg training fps: 7.01

# Trainer step: 154, epoch: 11:  54%|█████▍    | 163/300 [01:32<01:08,  2.01it/s]
# Trainer step: 154, epoch: 11:  55%|█████▍    | 164/300 [01:32<01:06,  2.05it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 165/300 [01:33<01:05,  2.07it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 166/300 [01:33<01:04,  2.08it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 167/300 [01:34<01:03,  2.09it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 168/300 [01:34<01:02,  2.10it/s]Progress: 59.00%
+---- avg training fps: 7.06

# Trainer step: 168, epoch: 12:  56%|█████▌    | 168/300 [01:35<01:02,  2.10it/s]
# Trainer step: 168, epoch: 12:  56%|█████▋    | 169/300 [01:35<00:59,  2.19it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 170/300 [01:35<00:59,  2.18it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 171/300 [01:36<00:59,  2.16it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 172/300 [01:36<00:59,  2.15it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 173/300 [01:37<00:59,  2.14it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 174/300 [01:37<00:59,  2.13it/s]Progress: 61.00%
+---- avg training fps: 7.10

# Trainer step: 168, epoch: 12:  58%|█████▊    | 175/300 [01:38<00:58,  2.13it/s]
# Trainer step: 168, epoch: 12:  59%|█████▊    | 176/300 [01:38<00:58,  2.12it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 177/300 [01:38<00:57,  2.13it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 178/300 [01:39<00:57,  2.12it/s]
# Trainer step: 168, epoch: 12:  60%|█████▉    | 179/300 [01:39<00:57,  2.12it/s]
# Trainer step: 168, epoch: 12:  60%|██████    | 180/300 [01:40<00:56,  2.13it/s]Progress: 63.00%
+---- avg training fps: 7.14

# Trainer step: 168, epoch: 12:  60%|██████    | 181/300 [01:40<00:56,  2.11it/s]
# Trainer step: 168, epoch: 12:  61%|██████    | 182/300 [01:41<00:55,  2.11it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 182/300 [01:41<00:55,  2.11it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 183/300 [01:41<00:53,  2.20it/s]
# Trainer step: 182, epoch: 13:  61%|██████▏   | 184/300 [01:42<00:53,  2.18it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 185/300 [01:42<00:53,  2.16it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 186/300 [01:43<00:53,  2.15it/s]Progress: 65.00%
+---- avg training fps: 7.18

# Trainer step: 182, epoch: 13:  62%|██████▏   | 187/300 [01:43<00:52,  2.14it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 188/300 [01:44<00:52,  2.13it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 189/300 [01:44<00:52,  2.13it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 190/300 [01:45<00:51,  2.12it/s]
# Trainer step: 182, epoch: 13:  64%|██████▎   | 191/300 [01:45<00:51,  2.12it/s]
# Trainer step: 182, epoch: 13:  64%|██████▍   | 192/300 [01:45<00:50,  2.12it/s]Progress: 67.00%
+---- avg training fps: 7.21

# Trainer step: 182, epoch: 13:  64%|██████▍   | 193/300 [01:46<00:50,  2.11it/s]
# Trainer step: 182, epoch: 13:  65%|██████▍   | 194/300 [01:46<00:49,  2.12it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 195/300 [01:47<00:49,  2.11it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 196/300 [01:47<00:49,  2.10it/s]
# Trainer step: 196, epoch: 14:  65%|██████▌   | 196/300 [01:48<00:49,  2.10it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 197/300 [01:48<00:46,  2.20it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 198/300 [01:48<00:46,  2.19it/s]Progress: 69.00%
+---- avg training fps: 7.25

# Trainer step: 196, epoch: 14:  66%|██████▋   | 199/300 [01:49<00:46,  2.17it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 200/300 [01:49<00:46,  2.15it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 201/300 [01:50<00:46,  2.14it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 202/300 [01:53<01:59,  1.21s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 203/300 [01:53<01:44,  1.08s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 204/300 [01:54<01:25,  1.12it/s]Progress: 71.00%
+---- avg training fps: 7.11

# Trainer step: 196, epoch: 14:  68%|██████▊   | 205/300 [01:54<01:12,  1.30it/s]
# Trainer step: 196, epoch: 14:  69%|██████▊   | 206/300 [01:55<01:03,  1.48it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 207/300 [01:55<00:57,  1.62it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 208/300 [01:56<00:52,  1.75it/s]
# Trainer step: 196, epoch: 14:  70%|██████▉   | 209/300 [01:56<00:49,  1.85it/s]
# Trainer step: 196, epoch: 14:  70%|███████   | 210/300 [01:57<00:46,  1.93it/s]Progress: 73.00%
+---- avg training fps: 7.14

# Trainer step: 210, epoch: 15:  70%|███████   | 210/300 [01:57<00:46,  1.93it/s]
# Trainer step: 210, epoch: 15:  70%|███████   | 211/300 [01:57<00:43,  2.07it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 212/300 [01:58<00:42,  2.09it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 213/300 [01:58<00:41,  2.10it/s]
# Trainer step: 210, epoch: 15:  71%|███████▏  | 214/300 [01:58<00:40,  2.11it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 215/300 [01:59<00:40,  2.11it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 216/300 [01:59<00:39,  2.11it/s]Progress: 75.00%
+---- avg training fps: 7.18

# Trainer step: 210, epoch: 15:  72%|███████▏  | 217/300 [02:00<00:39,  2.11it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 218/300 [02:00<00:38,  2.12it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 219/300 [02:01<00:38,  2.12it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 220/300 [02:01<00:37,  2.12it/s]
# Trainer step: 210, epoch: 15:  74%|███████▎  | 221/300 [02:02<00:37,  2.13it/s]
# Trainer step: 210, epoch: 15:  74%|███████▍  | 222/300 [02:02<00:36,  2.13it/s]Progress: 77.00%
+---- avg training fps: 7.21

# Trainer step: 210, epoch: 15:  74%|███████▍  | 223/300 [02:03<00:36,  2.13it/s]
# Trainer step: 210, epoch: 15:  75%|███████▍  | 224/300 [02:03<00:35,  2.13it/s]
# Trainer step: 224, epoch: 16:  75%|███████▍  | 224/300 [02:04<00:35,  2.13it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 225/300 [02:04<00:33,  2.23it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 226/300 [02:04<00:33,  2.19it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 227/300 [02:05<00:33,  2.17it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 228/300 [02:05<00:33,  2.15it/s]Progress: 79.00%
+---- avg training fps: 7.24

# Trainer step: 224, epoch: 16:  76%|███████▋  | 229/300 [02:05<00:32,  2.15it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 230/300 [02:06<00:32,  2.14it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 231/300 [02:06<00:32,  2.13it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 232/300 [02:07<00:31,  2.13it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 233/300 [02:07<00:31,  2.12it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 234/300 [02:08<00:31,  2.12it/s]Progress: 81.00%
+---- avg training fps: 7.27

# Trainer step: 224, epoch: 16:  78%|███████▊  | 235/300 [02:08<00:30,  2.12it/s]
# Trainer step: 224, epoch: 16:  79%|███████▊  | 236/300 [02:09<00:30,  2.13it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 237/300 [02:09<00:29,  2.13it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 238/300 [02:10<00:29,  2.13it/s]
# Trainer step: 238, epoch: 17:  79%|███████▉  | 238/300 [02:10<00:29,  2.13it/s]
# Trainer step: 238, epoch: 17:  80%|███████▉  | 239/300 [02:10<00:27,  2.23it/s]
# Trainer step: 238, epoch: 17:  80%|████████  | 240/300 [02:11<00:27,  2.21it/s]Progress: 83.00%
+---- avg training fps: 7.30

# Trainer step: 238, epoch: 17:  80%|████████  | 241/300 [02:11<00:26,  2.19it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 242/300 [02:12<00:26,  2.17it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 243/300 [02:12<00:26,  2.16it/s]
# Trainer step: 238, epoch: 17:  81%|████████▏ | 244/300 [02:12<00:25,  2.16it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 245/300 [02:13<00:25,  2.15it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 246/300 [02:13<00:25,  2.15it/s]Progress: 85.00%
+---- avg training fps: 7.32

# Trainer step: 238, epoch: 17:  82%|████████▏ | 247/300 [02:14<00:24,  2.14it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 248/300 [02:14<00:24,  2.13it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 249/300 [02:15<00:24,  2.12it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 250/300 [02:15<00:23,  2.12it/s]
# Trainer step: 238, epoch: 17:  84%|████████▎ | 251/300 [02:16<00:23,  2.13it/s]
# Trainer step: 238, epoch: 17:  84%|████████▍ | 252/300 [02:20<01:10,  1.46s/it]Progress: 87.00%
+---- avg training fps: 7.18

# Trainer step: 252, epoch: 18:  84%|████████▍ | 252/300 [02:20<01:10,  1.46s/it]
# Trainer step: 252, epoch: 18:  84%|████████▍ | 253/300 [02:20<00:53,  1.14s/it]
# Trainer step: 252, epoch: 18:  85%|████████▍ | 254/300 [02:20<00:43,  1.06it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 255/300 [02:21<00:36,  1.25it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 256/300 [02:21<00:30,  1.43it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 257/300 [02:22<00:27,  1.59it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 258/300 [02:22<00:24,  1.72it/s]Progress: 89.00%
+---- avg training fps: 7.21

# Trainer step: 252, epoch: 18:  86%|████████▋ | 259/300 [02:23<00:22,  1.83it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 260/300 [02:23<00:20,  1.91it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 261/300 [02:24<00:19,  1.97it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 262/300 [02:24<00:18,  2.02it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 263/300 [02:25<00:18,  2.03it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 264/300 [02:25<00:17,  2.05it/s]Progress: 91.00%
+---- avg training fps: 7.23

# Trainer step: 252, epoch: 18:  88%|████████▊ | 265/300 [02:26<00:16,  2.08it/s]
# Trainer step: 252, epoch: 18:  89%|████████▊ | 266/300 [02:26<00:16,  2.09it/s]
# Trainer step: 266, epoch: 19:  89%|████████▊ | 266/300 [02:26<00:16,  2.09it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 267/300 [02:26<00:15,  2.20it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 268/300 [02:27<00:14,  2.17it/s]
# Trainer step: 266, epoch: 19:  90%|████████▉ | 269/300 [02:27<00:14,  2.16it/s]
# Trainer step: 266, epoch: 19:  90%|█████████ | 270/300 [02:28<00:13,  2.15it/s]Progress: 93.00%
+---- avg training fps: 7.26

# Trainer step: 266, epoch: 19:  90%|█████████ | 271/300 [02:28<00:13,  2.14it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 272/300 [02:29<00:13,  2.14it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 273/300 [02:29<00:12,  2.13it/s]
# Trainer step: 266, epoch: 19:  91%|█████████▏| 274/300 [02:30<00:12,  2.12it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 275/300 [02:30<00:11,  2.11it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 276/300 [02:31<00:11,  2.12it/s]Progress: 95.00%
+---- avg training fps: 7.28

# Trainer step: 266, epoch: 19:  92%|█████████▏| 277/300 [02:31<00:10,  2.11it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 278/300 [02:32<00:10,  2.12it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 279/300 [02:32<00:09,  2.12it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 280/300 [02:33<00:09,  2.12it/s]
# Trainer step: 280, epoch: 20:  93%|█████████▎| 280/300 [02:33<00:09,  2.12it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▎| 281/300 [02:33<00:08,  2.22it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▍| 282/300 [02:33<00:08,  2.19it/s]Progress: 97.00%
+---- avg training fps: 7.30

# Trainer step: 280, epoch: 20:  94%|█████████▍| 283/300 [02:34<00:07,  2.16it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▍| 284/300 [02:34<00:07,  2.15it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 285/300 [02:35<00:07,  2.14it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 286/300 [02:35<00:06,  2.13it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 287/300 [02:36<00:06,  2.14it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 288/300 [02:36<00:05,  2.13it/s]Progress: 99.00%
+---- avg training fps: 7.33

# Trainer step: 280, epoch: 20:  96%|█████████▋| 289/300 [02:37<00:05,  2.13it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 290/300 [02:37<00:04,  2.13it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 291/300 [02:38<00:04,  2.13it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 292/300 [02:38<00:03,  2.13it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 293/300 [02:39<00:03,  2.12it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 294/300 [02:39<00:02,  2.13it/s]Progress: 100.00%
+---- avg training fps: 7.35

# Trainer step: 294, epoch: 21:  98%|█████████▊| 294/300 [02:40<00:02,  2.13it/s]
# Trainer step: 294, epoch: 21:  98%|█████████▊| 295/300 [02:40<00:02,  2.22it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▊| 296/300 [02:40<00:01,  2.19it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 297/300 [02:40<00:01,  2.16it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 298/300 [02:41<00:00,  2.16it/s]
# Trainer step: 294, epoch: 21: 100%|█████████▉| 299/300 [02:41<00:00,  2.14it/s]
# Trainer step: 294, epoch: 21: 100%|██████████| 300/300 [02:42<00:00,  2.13it/s]Progress: 100.00%
+---- avg training fps: 7.37

# Trainer step: 294, epoch: 21: : 301it [02:42,  2.13it/s]                       Progress: 100.00%
Reached max steps, stopping training!
+Saving checkpoint at step.. 301
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 294, epoch: 21: : 301it [04:06,  1.22it/s]
+------------------------------------------
+Training done :)
+2024-08-13 02:56:39.413052: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 02:56:39.504259: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 02:56:39.504287: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 02:56:39.523641: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 02:56:39.926560: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:56:39.926642: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 02:56:39.926651: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23135 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/journey_small' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.75 freeze_unet_before_completion_f=0.5 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='journey_small' output_dir='lora_models/styles_final/journey_small_13_02-56-41-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723510601.7071629 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/journey_small_13_02-56-41-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 0.812
+New train_img_size: [448, 576]
+Validation_img_size was set to: [896, 1152]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red cloak standing in front of a building.
+- A person standing on top of a body of water.
+- A person walking in the desert with a camel in front of a mountain.
+- A man standing on top of a sandy beach next to the ocean.
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red dress is walking through an archway.
+- A person standing on top of a body of water.
+- A person walking in the desert with a dog on a leash.
+- A person walking on a beach next to a body of water.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal and dreamlike, focus on vastness.
+
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red cloak stands in front of a building.
+- In the style of TOK, a person stands on top of a body of water.
+- In the style of TOK, a person walks in the desert with a camel in front of a mountain.
+- In the style of TOK, a man stands on top of a sandy beach next to the ocean.
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red dress walks through an archway.
+- In the style of TOK, a person stands on top of a body of water.
+- In the style of TOK, a person walks in the desert with a dog on a leash.
+- In the style of TOK, a person walks on a beach next to a body of water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Surreal and dreamlike, focus on vastness.
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 10286.46it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a sunset over a ...
+1     in the style of <s0><s1><s2>, a woman in a red...
+2     in the style of <s0><s1><s2>, a person stands ...
+3     in the style of <s0><s1><s2>, a person walks i...
+4     in the style of <s0><s1><s2>, a man stands on ...
+5     in the style of <s0><s1><s2>, a sunset over a ...
+6     in the style of <s0><s1><s2>, a woman in a red...
+7     in the style of <s0><s1><s2>, a person stands ...
+8     in the style of <s0><s1><s2>, a person walks i...
+9     in the style of <s0><s1><s2>, a person walks o...
+10    in the style of <s0><s1><s2>, a sunset over a ...
+11    in the style of <s0><s1><s2>, a woman in a red...
+12    in the style of <s0><s1><s2>, a person stands ...
+13    in the style of <s0><s1><s2>, a person walks i...
+14    in the style of <s0><s1><s2>, a man stands on ...
+15    in the style of <s0><s1><s2>, a sunset over a ...
+16    in the style of <s0><s1><s2>, a woman in a red...
+17    in the style of <s0><s1><s2>, a person stands ...
+18    in the style of <s0><s1><s2>, a person walks i...
+19    in the style of <s0><s1><s2>, a person walks o...
+20    in the style of <s0><s1><s2>, a sunset over a ...
+21    in the style of <s0><s1><s2>, a woman in a red...
+22    in the style of <s0><s1><s2>, a person stands ...
+23    in the style of <s0><s1><s2>, a person walks i...
+24    in the style of <s0><s1><s2>, a man stands on ...
+25    in the style of <s0><s1><s2>, a sunset over a ...
+26    in the style of <s0><s1><s2>, a woman in a red...
+27    in the style of <s0><s1><s2>, a person stands ...
+28    in the style of <s0><s1><s2>, a person walks i...
+29    in the style of <s0><s1><s2>, a person walks o...
+30    in the style of <s0><s1><s2>, a sunset over a ...
+31    in the style of <s0><s1><s2>, a woman in a red...
+32    in the style of <s0><s1><s2>, a person stands ...
+33    in the style of <s0><s1><s2>, a person walks i...
+34    in the style of <s0><s1><s2>, a man stands on ...
+35    in the style of <s0><s1><s2>, a sunset over a ...
+36    in the style of <s0><s1><s2>, a woman in a red...
+37    in the style of <s0><s1><s2>, a person stands ...
+38    in the style of <s0><s1><s2>, a person walks i...
+39    in the style of <s0><s1><s2>, a person walks o...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
  0%|          | 0/300 [00:00<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:02<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:02<14:36,  2.93s/it]
# Trainer step: 0, epoch: 0:   1%|          | 2/300 [00:07<19:21,  3.90s/it]
# Trainer step: 0, epoch: 0:   1%|          | 3/300 [00:07<11:30,  2.32s/it]
# Trainer step: 0, epoch: 0:   1%|▏         | 4/300 [00:08<07:48,  1.58s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:08<05:46,  1.17s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:09<04:32,  1.08it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.46

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:09<03:45,  1.30it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:10<03:15,  1.50it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 9/300 [00:10<02:54,  1.67it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 10/300 [00:11<02:40,  1.81it/s]
# Trainer step: 10, epoch: 1:   3%|▎         | 10/300 [00:11<02:40,  1.81it/s]
# Trainer step: 10, epoch: 1:   4%|▎         | 11/300 [00:11<02:31,  1.91it/s]
# Trainer step: 10, epoch: 1:   4%|▍         | 12/300 [00:12<02:24,  1.99it/s]Progress: 7.00%
+---- avg training fps: 3.80

# Trainer step: 10, epoch: 1:   4%|▍         | 13/300 [00:12<02:36,  1.83it/s]
# Trainer step: 10, epoch: 1:   5%|▍         | 14/300 [00:13<02:27,  1.94it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 15/300 [00:13<02:21,  2.01it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 16/300 [00:14<02:17,  2.06it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 17/300 [00:14<02:14,  2.10it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 18/300 [00:14<02:12,  2.14it/s]Progress: 9.00%
+---- avg training fps: 4.69

# Trainer step: 10, epoch: 1:   6%|▋         | 19/300 [00:15<02:09,  2.16it/s]
# Trainer step: 10, epoch: 1:   7%|▋         | 20/300 [00:15<02:09,  2.17it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 20/300 [00:16<02:09,  2.17it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 21/300 [00:16<02:08,  2.18it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 22/300 [00:16<02:07,  2.18it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 23/300 [00:17<02:06,  2.19it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 24/300 [00:17<02:05,  2.20it/s]Progress: 11.00%
+---- avg training fps: 5.31

# Trainer step: 20, epoch: 2:   8%|▊         | 25/300 [00:18<02:04,  2.20it/s]
# Trainer step: 20, epoch: 2:   9%|▊         | 26/300 [00:18<02:04,  2.20it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 27/300 [00:19<02:04,  2.20it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 28/300 [00:19<02:04,  2.19it/s]
# Trainer step: 20, epoch: 2:  10%|▉         | 29/300 [00:19<02:03,  2.19it/s]
# Trainer step: 20, epoch: 2:  10%|█         | 30/300 [00:20<02:03,  2.19it/s]Progress: 13.00%
+---- avg training fps: 5.76

# Trainer step: 30, epoch: 3:  10%|█         | 30/300 [00:20<02:03,  2.19it/s]
# Trainer step: 30, epoch: 3:  10%|█         | 31/300 [00:20<02:02,  2.19it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 32/300 [00:21<02:01,  2.20it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 33/300 [00:21<02:01,  2.19it/s]
# Trainer step: 30, epoch: 3:  11%|█▏        | 34/300 [00:22<02:01,  2.19it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 35/300 [00:22<02:00,  2.19it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 36/300 [00:23<01:59,  2.20it/s]Progress: 15.00%
+---- avg training fps: 6.11

# Trainer step: 30, epoch: 3:  12%|█▏        | 37/300 [00:23<01:59,  2.20it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 38/300 [00:24<01:59,  2.20it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 39/300 [00:24<01:59,  2.19it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 40/300 [00:24<01:58,  2.19it/s]
# Trainer step: 40, epoch: 4:  13%|█▎        | 40/300 [00:25<01:58,  2.19it/s]
# Trainer step: 40, epoch: 4:  14%|█▎        | 41/300 [00:25<01:58,  2.19it/s]
# Trainer step: 40, epoch: 4:  14%|█▍        | 42/300 [00:25<01:57,  2.19it/s]Progress: 17.00%
+---- avg training fps: 6.39

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:26<01:57,  2.19it/s]
# Trainer step: 40, epoch: 4:  15%|█▍        | 44/300 [00:26<01:56,  2.19it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 45/300 [00:27<01:55,  2.20it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 46/300 [00:27<01:55,  2.20it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 47/300 [00:28<01:55,  2.20it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:28<01:54,  2.19it/s]Progress: 19.00%
+---- avg training fps: 6.61

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:29<01:54,  2.19it/s]
# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:29<01:53,  2.20it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 50/300 [00:29<01:53,  2.20it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 51/300 [00:29<01:53,  2.19it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 52/300 [00:34<06:22,  1.54s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 53/300 [00:34<05:00,  1.22s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:34<04:02,  1.01it/s]Progress: 21.00%
+---- avg training fps: 6.10

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:35<03:22,  1.21it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:35<02:54,  1.40it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 57/300 [00:36<02:35,  1.56it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 58/300 [00:36<02:21,  1.71it/s]
# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:37<02:11,  1.83it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:37<02:04,  1.93it/s]Progress: 23.00%
+---- avg training fps: 6.29

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:38<02:04,  1.93it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:38<02:00,  1.99it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:38<01:56,  2.04it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:39<01:53,  2.08it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:39<01:51,  2.12it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:39<01:50,  2.13it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:40<01:49,  2.15it/s]Progress: 25.00%
+---- avg training fps: 6.46

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:40<01:48,  2.16it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:41<01:46,  2.17it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:41<01:46,  2.17it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:42<01:45,  2.17it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:42<01:45,  2.17it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:42<01:45,  2.18it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:43<01:44,  2.18it/s]Progress: 27.00%
+---- avg training fps: 6.60

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:43<01:44,  2.18it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:44<01:43,  2.18it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:44<01:43,  2.18it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:45<01:42,  2.18it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:45<01:42,  2.17it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:45<01:42,  2.17it/s]Progress: 29.00%
+---- avg training fps: 6.73

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:46<01:41,  2.18it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:46<01:41,  2.18it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:47<01:41,  2.18it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:47<01:40,  2.18it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:47<01:40,  2.17it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:48<01:39,  2.17it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:48<01:39,  2.17it/s]Progress: 31.00%
+---- avg training fps: 6.84

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:49<01:38,  2.17it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:49<01:38,  2.17it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:50<01:37,  2.17it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:50<01:37,  2.17it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:50<01:36,  2.18it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:51<01:36,  2.18it/s]Progress: 33.00%
+---- avg training fps: 6.94

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:51<01:36,  2.18it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:51<01:36,  2.18it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:52<01:35,  2.18it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:52<01:34,  2.18it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:53<01:34,  2.18it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:53<01:34,  2.17it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:54<01:33,  2.17it/s]Progress: 35.00%
+---- avg training fps: 7.03

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:54<01:33,  2.18it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:55<01:32,  2.18it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:55<01:32,  2.18it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:56<01:31,  2.17it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:56<01:31,  2.17it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:56<01:31,  2.18it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [01:00<05:10,  1.57s/it]Progress: 37.00%
+---- avg training fps: 6.65

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [01:01<04:18,  1.31s/it]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [01:01<03:27,  1.06s/it]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [01:02<02:51,  1.14it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [01:02<02:25,  1.33it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:03<02:07,  1.51it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:03<01:55,  1.67it/s]Progress: 39.00%
+---- avg training fps: 6.74

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:04<01:46,  1.80it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:04<01:39,  1.90it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:05<01:39,  1.90it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:05<01:35,  1.98it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:05<01:32,  2.04it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:05<01:29,  2.09it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:06<01:27,  2.12it/s]Progress: 41.00%
+---- avg training fps: 6.82

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:06<01:26,  2.14it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:07<01:25,  2.16it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:07<01:24,  2.17it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:08<01:23,  2.18it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:08<01:23,  2.18it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:09<01:22,  2.18it/s]Progress: 43.00%
+---- avg training fps: 6.90

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:09<01:22,  2.18it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:09<01:21,  2.19it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:10<01:21,  2.19it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:10<01:20,  2.19it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:10<01:20,  2.19it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:11<01:19,  2.19it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:11<01:19,  2.20it/s]Progress: 45.00%
+---- avg training fps: 6.97

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:12<01:18,  2.20it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:12<01:18,  2.19it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:13<01:17,  2.19it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:13<01:17,  2.19it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:14<01:17,  2.19it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:14<01:17,  2.19it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:14<01:16,  2.19it/s]Progress: 47.00%
+---- avg training fps: 7.03

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:15<01:16,  2.19it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:15<01:16,  2.18it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:15<01:15,  2.19it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:16<01:15,  2.18it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:16<01:14,  2.18it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:17<01:14,  2.18it/s]Progress: 49.00%
+---- avg training fps: 7.10

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:17<01:13,  2.19it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:18<01:13,  2.18it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:18<01:13,  2.18it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:18<01:12,  2.18it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:19<01:12,  2.18it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:19<01:11,  2.19it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:20<01:11,  2.19it/s]Progress: 51.00%
+---- avg training fps: 7.15

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:20<01:10,  2.18it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:21<01:10,  2.18it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:21<01:10,  2.18it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:21<01:09,  2.18it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:22<01:09,  2.18it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:22<01:08,  2.18it/s]Progress: 53.00%
+---- avg training fps: 7.20

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:23<01:08,  2.18it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:23<01:08,  2.18it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:27<04:00,  1.62s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:28<03:07,  1.27s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:28<02:30,  1.03s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:29<02:04,  1.17it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:29<01:45,  1.36it/s]Progress: 55.00%
+---- avg training fps: 6.94

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:29<01:33,  1.53it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:30<01:24,  1.68it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:30<01:18,  1.80it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:31<01:13,  1.90it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:31<01:13,  1.90it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:31<01:10,  1.97it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:32<01:07,  2.03it/s]Progress: 57.00%
+---- avg training fps: 6.99

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:32<01:06,  2.07it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:33<01:04,  2.10it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:33<01:03,  2.12it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:34<01:02,  2.14it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:34<01:01,  2.15it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:34<01:01,  2.16it/s]Progress: 59.00%
+---- avg training fps: 7.04

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:35<01:00,  2.16it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:35<01:00,  2.16it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:36<01:00,  2.16it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:36<00:59,  2.16it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:36<00:58,  2.17it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:37<00:58,  2.17it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:37<00:58,  2.17it/s]Progress: 61.00%
+---- avg training fps: 7.09

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:38<00:57,  2.17it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:38<00:57,  2.17it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:39<00:56,  2.17it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:39<00:56,  2.17it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:40<00:55,  2.17it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:40<00:55,  2.17it/s]Progress: 63.00%
+---- avg training fps: 7.13

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:40<00:55,  2.17it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:40<00:54,  2.17it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:41<00:54,  2.17it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:41<00:54,  2.16it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:42<00:53,  2.17it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:42<00:53,  2.16it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:43<00:52,  2.17it/s]Progress: 65.00%
+---- avg training fps: 7.17

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:43<00:52,  2.16it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:44<00:51,  2.17it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:44<00:51,  2.16it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:45<00:50,  2.16it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:45<00:50,  2.16it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:45<00:50,  2.16it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:46<00:49,  2.17it/s]Progress: 67.00%
+---- avg training fps: 7.21

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:46<00:49,  2.16it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:46<00:49,  2.16it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:47<00:48,  2.16it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:47<00:48,  2.16it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:48<00:47,  2.15it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:48<00:47,  2.14it/s]Progress: 69.00%
+---- avg training fps: 7.24

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:49<00:47,  2.13it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:49<00:46,  2.13it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:50<00:46,  2.13it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:50<00:46,  2.12it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:54<02:49,  1.73s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:55<02:11,  1.35s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:55<01:44,  1.09s/it]Progress: 71.00%
+---- avg training fps: 7.01

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:56<01:25,  1.11it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:56<01:12,  1.29it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:57<01:03,  1.47it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:57<00:56,  1.62it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:58<00:52,  1.75it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:58<00:48,  1.85it/s]Progress: 73.00%
+---- avg training fps: 7.05

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:59<00:48,  1.85it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:59<00:46,  1.92it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:59<00:44,  1.98it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [02:00<00:43,  2.02it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [02:00<00:41,  2.05it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [02:01<00:40,  2.07it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [02:01<00:40,  2.09it/s]Progress: 75.00%
+---- avg training fps: 7.08

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [02:01<00:39,  2.10it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [02:02<00:38,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:02<00:38,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:03<00:37,  2.12it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:03<00:37,  2.12it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:03<00:37,  2.12it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:04<00:36,  2.12it/s]Progress: 77.00%
+---- avg training fps: 7.11

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:04<00:36,  2.12it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:05<00:35,  2.12it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:05<00:35,  2.12it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:06<00:34,  2.12it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:06<00:34,  2.11it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:07<00:33,  2.13it/s]Progress: 79.00%
+---- avg training fps: 7.15

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:07<00:33,  2.12it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:08<00:32,  2.12it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:08<00:32,  2.12it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:08<00:32,  2.12it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:09<00:31,  2.13it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:09<00:31,  2.12it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:09<00:31,  2.13it/s]Progress: 81.00%
+---- avg training fps: 7.17

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:10<00:30,  2.13it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:10<00:30,  2.13it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:11<00:29,  2.13it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:11<00:29,  2.14it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:12<00:28,  2.13it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:12<00:28,  2.13it/s]Progress: 83.00%
+---- avg training fps: 7.20

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:13<00:28,  2.13it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:13<00:27,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:13<00:27,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:14<00:26,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:14<00:26,  2.12it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:15<00:25,  2.13it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:15<00:25,  2.12it/s]Progress: 85.00%
+---- avg training fps: 7.23

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:16<00:24,  2.12it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:16<00:24,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:17<00:24,  2.12it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:17<00:23,  2.09it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:18<00:23,  2.09it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:18<00:23,  2.10it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:22<01:19,  1.66s/it]Progress: 87.00%
+---- avg training fps: 7.05

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:22<01:01,  1.30s/it]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:23<00:48,  1.05s/it]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:23<00:39,  1.14it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:24<00:33,  1.33it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:24<00:28,  1.50it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:25<00:25,  1.65it/s]Progress: 89.00%
+---- avg training fps: 7.08

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:25<00:23,  1.78it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:26<00:21,  1.88it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:26<00:21,  1.88it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:26<00:19,  1.96it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:27<00:18,  2.01it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:27<00:18,  2.05it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:28<00:17,  2.08it/s]Progress: 91.00%
+---- avg training fps: 7.11

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:28<00:16,  2.10it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:28<00:16,  2.12it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:29<00:15,  2.13it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:29<00:14,  2.14it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:30<00:14,  2.14it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:30<00:13,  2.15it/s]Progress: 93.00%
+---- avg training fps: 7.14

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:31<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:31<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:31<00:12,  2.16it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:32<00:12,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:32<00:12,  2.15it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:33<00:11,  2.16it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:33<00:11,  2.16it/s]Progress: 95.00%
+---- avg training fps: 7.17

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:34<00:10,  2.16it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:34<00:10,  2.16it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:34<00:09,  2.16it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:35<00:09,  2.16it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:35<00:09,  2.16it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:35<00:08,  2.16it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:36<00:08,  2.16it/s]Progress: 97.00%
+---- avg training fps: 7.19

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:36<00:07,  2.16it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:37<00:07,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:37<00:06,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:38<00:06,  2.15it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:38<00:06,  2.15it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:39<00:05,  2.15it/s]Progress: 99.00%
+---- avg training fps: 7.22

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:39<00:05,  2.15it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:40<00:04,  2.15it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:40<00:04,  2.15it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:40<00:04,  2.15it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:40<00:03,  2.16it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:41<00:03,  2.16it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:41<00:02,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.24

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:42<00:02,  2.16it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:42<00:01,  2.16it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:43<00:01,  2.16it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:43<00:00,  2.16it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:44<00:00,  2.16it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:44<00:00,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.27
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [04:04<00:00,  1.23it/s]
+------------------------------------------
+Training done :)
+2024-08-13 03:01:13.466052: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 03:01:13.557897: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 03:01:13.557922: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 03:01:13.576468: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 03:01:13.979733: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:01:13.979813: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:01:13.979822: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23126 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.5 freeze_unet_before_completion_f=0.5 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='clipx.zip' output_dir='lora_models/styles_final/clipx.zip_13_03-01-15-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723510875.7981896 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Downloading training data...
+Downloading https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip to lora_models/styles_final/clipx.zip_13_03-01-15-style_512_florence_300/images_in/clipx.zip...
+Succesfully prepped 54 .jpg images in lora_models/styles_final/clipx.zip_13_03-01-15-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.314
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 27 images, 27 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 27 to 54)
+Generating 54 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal, whimsical, detailed, and quirky scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene is Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase describes the best and worst of times.
+- In the style of TOK, a blockchain is depicted using bees.
+- In the style of TOK, a gorgon made of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset shows Venus and Earth circling the sun.
+- In the style of TOK, there is an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is portrayed.
+- In the style of TOK, the phrase exemplifies the age of wisdom and the age of foolishness.
+- In the style of TOK, two people discuss quitting their boring corporation jobs together.
+- In the style of TOK, petroglyphs are presented.
+- In the style of TOK, a room and space exchange based on first and second-degree connections is depicted.
+- In the style of TOK, the fear known as trypophobia is illustrated.
+- In the style of TOK, there is a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing and singing beavers is highlighted.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with patterns akin to Hawaiian shirts.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a visual style like Tarsem Singh's.
+- In the style of TOK, a brigade of beavers is shown in a style similar to M. C. Escher's work.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is frozen solid and preserved, and rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting shows lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash is swimming up through the sea to peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 1...
+----- GPT response: -----
+Style Description: Surrealistic, intricate, and fantastical scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is embodied in a visual representation.
+- In the style of TOK, a blockchain is composed of bees.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun is depicted.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is presented.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is embodied in a visual representation.
+- In the style of TOK, two individuals are depicted quitting their boring corporation job together.
+- In the style of TOK, petroglyphs are portrayed.
+- In the style of TOK, a room and space exchange is based on first and second-degree connections.
+- In the style of TOK, trypophobia is visually expressed.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of dancing and singing beavers is featured with a nod to Jim Henson's Muppets.
+- In the style of TOK, a beaver is shown chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a pattern like a Hawaiian shirt.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is designed in the style of M. C. Escher.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting showcases lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash is swimming up through the sea to take a peak above the water.
+- In the style of TOK, a mermaid made out of fishnets and other sea trash is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is embodied in a visual representation.
+- In the style of TOK, a blockchain is composed of bees.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun is depicted.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is presented.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is embodied in a visual representation.
+- In the style of TOK, two individuals are depicted quitting their boring corporation job together.
+- In the style of TOK, petroglyphs are portrayed.
+- In the style of TOK, a room and space exchange is based on first and second-degree connections.
+- In the style of TOK, trypophobia is visually expressed.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of dancing and singing beavers is featured with a nod to Jim Henson's Muppets.
+- In the style of TOK, a beaver is shown chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a pattern like a Hawaiian shirt.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is designed in the style of M. C. Escher.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting showcases lemon-lime architecture.
+--------------------------
+GPT-4 returned the wrong number of prompts 53 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 2...
+----- GPT response: -----
+Style Description: Surreal, eclectic, and whimsical themes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi, Kerala, India.
+- In the style of TOK, "They were the best of times, they were the worst of times."
+- In the style of TOK, a blockchain of bees.
+- In the style of TOK, a gorgon made out of sharks eating each other swims underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats pooping in zero-gravity.
+- In the style of TOK, a goblin goat.
+- In the style of TOK, "It was the age of wisdom, it was the age of foolishness."
+- In the style of TOK, “Let's quit our boring corporation job together.”
+- In the style of TOK, petroglyphs.
+- In the style of TOK, a room and space exchange based on first and second-degree connections.
+- In the style of TOK, trypophobia.
+- In the style of TOK, a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers.
+- In the style of TOK, a beaver chewing a rifle in half.
+- In the style of TOK, a brigade of beavers quickly building a dam.
+- In the style of TOK, a castle moving along the grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show with elephant seals as runway models and egrets as the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 25 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 3...
+----- GPT response: -----
+Style Description: Surreal and intricate marine or forest scenes
+
+- In the style of TOK, a mermaid crafted from waste materials swims underwater in the ocean.
+- In the style of TOK, a throng of tiny Lilliputians are packed inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene is set in Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase "best of times, worst of times" represents contrasting periods.
+- In the style of TOK, bees are organized in a blockchain formation.
+- In the style of TOK, a gorgon composed of sharks cannibalizes each other while swimming underwater.
+- In the style of TOK, Venus and Earth are depicted circling the sun against a sunset hillside.
+- In the style of TOK, an ancient temple is found in the depths of the Amazon jungle.
+- In the style of TOK, cats are depicted pooping in zero-gravity.
+- In the style of TOK, a goblin-goat hybrid is featured.
+- In the style of TOK, the phrase "age of wisdom, age of foolishness" denotes contrasting periods.
+- In the style of TOK, two people discuss quitting their monotonous corporate jobs together.
+- In the style of TOK, petroglyphs are presented.
+- In the style of TOK, room and space exchange occurs based on first and second-degree connections.
+- In the style of TOK, images invoke a sense of trypophobia.
+- In the style of TOK, a 6-pack is metaphorically described as nightmare fuel.
+- In the style of TOK, a band of beavers are dancing and singing, reminiscent of Jim Henson's Muppets.
+- In the style of TOK, a beaver is seen gnawing a rifle in half.
+- In the style of TOK, a brigade of beavers rapidly constructs a dam as if on a Hawaiian-shirt pattern.
+- In the style of TOK, a brigade of beavers rapidly constructs a dam in a manner reminiscent of Tarsem Singh.
+- In the style of TOK, a brigade of beavers creates structures in an M. C. Escher-like fashion.
+- In the style of TOK, a castle moves along grassy hills.
+- In the style of TOK, a chorus line of beavers dance adorably in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is frozen solid and preserved, discovered in the space age.
+- In the style of TOK, elephant seals model on a runway while egrets sit in the audience at a fashion show.
+- In the style of TOK, a hyper-realistic painting features lemon-lime architecture.
+- In the style of TOK, a mermaid made from debris and trash rises through the water's surface.
+- In the style of TOK, a mermaid made from fishnets and sea trash swims underwater in the ocean.
+- In the style of TOK, a mass of Lilliputians fill the pores of a giant sea-sponge.
+- In the style of TOK, the scene depicts Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase "best of times, worst of times" symbolizes contrasting periods.
+- In the style of TOK, bees are arranged in a blockchain.
+- In the style of TOK, a shark-based gorgon engages in cannibalistic behavior underwater.
+- In the style of TOK, Venus and Earth are shown circling the sun over a sunset hillside.
+- In the style of TOK, an ancient temple is set deep within the Amazon jungle.
+- In the style of TOK, cats defy gravity to poop in zero-gravity.
+- In the style of TOK, a goblin-goat hybrid creature is present.
+- In the style of TOK, the phrase "age of wisdom, age of foolishness" suggests contrasting periods.
+- In the style of TOK, individuals discuss quitting their dull corporate jobs.
+- In the style of TOK, petroglyphs are depicted.
+- In the style of TOK, room and space exchange is portrayed based on connections.
+- In the style of TOK, expressions evoke trypophobia.
+- In the style of TOK, a 6-pack evokes a nightmare-like quality.
+- In the style of TOK, a group of beavers engages in singing and dancing similar to Jim Henson's Muppets.
+- In the style of TOK, a beaver gnaws a rifle in half.
+- In the style of TOK, beavers rapidly construct a dam influenced by Hawaiian-shirt patterns.
+- In the style of TOK, beavers build a dam reminiscent of Tarsem Singh’s work.
+- In the style of TOK, beavers craft structures in an Escher-like manner.
+- In the style of TOK, a moving castle traverses grassy hills.
+- In the style of TOK, a chorus line of beavers dance in syncronization.
+- In the style of TOK, a 1950s restaurant kitchen is found frozen and preserved in the space age.
+- In the style of TOK, elephant seals model while egrets watch at a fashion show.
+- In the style of TOK, lemon-lime architecture is depicted in hyper-realistic painting.
+--------------------------
+GPT-4 returned the wrong number of prompts 53 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 4...
+----- GPT response: -----
+Style Description: Surreal and fantastical imagery
+
+- In the style of TOK, a mermaid constructed from fishnets and sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, a scenic view of Fort Kochi, Kerala, India.
+- In the style of TOK, a depiction of the phrase "the best of times, the worst of times."
+- In the style of TOK, a blockchain made up of bees.
+- In the style of TOK, a gorgon composed of sharks eating each other, swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin is depicted as a goat.
+- In the style of TOK, the phrase "it was the age of wisdom, it was the age of foolishness."
+- In the style of TOK, two characters decide to quit their boring corporate job together.
+- In the style of TOK, petroglyphs are etched into stone.
+- In the style of TOK, a room and space exchange based on first and second-degree connections.
+- In the style of TOK, an image invoking trypophobia.
+- In the style of TOK, a six-pack of nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers quickly builds a dam.
+- In the style of TOK, a brigade of beavers builds a dam as seen in a Hawaiian-shirt pattern.
+- In the style of TOK, a brigade of beavers constructs a dam in a Tarsem Singh-inspired scene.
+- In the style of TOK, a brigade of beavers is depicted in an M. C. Escher style.
+- In the style of TOK, a castle moves along the ground on grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dances in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting depicts lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and sea trash swims up through the sea to peek above the water.
+
+--------------------------
+GPT-4 returned the wrong number of prompts 28 instead of 54, retrying...
+WARNING: no captioning text was given and we're not doing chatgpt cleanup...
+Concept mode:  style
+Using GPT concept name as CLIP-segmentation prompt: Surreal and fantastical imagery
+Generating 54 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
54it [00:00, 15030.35it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 54 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a mermaid made o...
+1     in the style of <s0><s1><s2>, a throng of lill...
+2     in the style of <s0><s1><s2>, fort kochi, kera...
+3     in the style of <s0><s1><s2>, they were the be...
+4     in the style of <s0><s1><s2>, a blockchain of ...
+5     in the style of <s0><s1><s2>, a gorgon made ou...
+6     in the style of <s0><s1><s2>, a hillside at su...
+7     in the style of <s0><s1><s2>, an ancient templ...
+8     in the style of <s0><s1><s2>, cats pooping in ...
+9             in the style of <s0><s1><s2>, goblin goat
+10    in the style of <s0><s1><s2>, it was the age o...
+11    in the style of <s0><s1><s2>, let's quit our b...
+12            in the style of <s0><s1><s2>, petroglyphs
+13    in the style of <s0><s1><s2>, room and space e...
+14            in the style of <s0><s1><s2>, trypophobia
+15    in the style of <s0><s1><s2>, a 6-pack of nigh...
+16    in the style of <s0><s1><s2>, a band of dancin...
+17    in the style of <s0><s1><s2>, a beaver chewing...
+18    in the style of <s0><s1><s2>, a brigade of bea...
+19    in the style of <s0><s1><s2>, a brigade of bea...
+20    in the style of <s0><s1><s2>, a brigade of bea...
+21    in the style of <s0><s1><s2>, a castle that is...
+22    in the style of <s0><s1><s2>, a chorus line of...
+23    in the style of <s0><s1><s2>, a dirty 1950s re...
+24    in the style of <s0><s1><s2>, a fashion show w...
+25    in the style of <s0><s1><s2>, a hyper-realisti...
+26    in the style of <s0><s1><s2>, a mermaid made o...
+27    in the style of <s0><s1><s2>, a mermaid made o...
+28    in the style of <s0><s1><s2>, a throng of lill...
+29    in the style of <s0><s1><s2>, fort kochi, kera...
+30    in the style of <s0><s1><s2>, they were the be...
+31    in the style of <s0><s1><s2>, a blockchain of ...
+32    in the style of <s0><s1><s2>, a gorgon made ou...
+33    in the style of <s0><s1><s2>, a hillside at su...
+34    in the style of <s0><s1><s2>, an ancient templ...
+35    in the style of <s0><s1><s2>, cats pooping in ...
+36            in the style of <s0><s1><s2>, goblin goat
+37    in the style of <s0><s1><s2>, it was the age o...
+38    in the style of <s0><s1><s2>, let's quit our b...
+39            in the style of <s0><s1><s2>, petroglyphs
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 54
+--- Num batches each epoch = 14
+--- Num Epochs = 22
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
  0%|          | 0/300 [00:00<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:03<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:03<15:05,  3.03s/it]
# Trainer step: 0, epoch: 0:   1%|          | 2/300 [00:08<22:16,  4.49s/it]
# Trainer step: 0, epoch: 0:   1%|          | 3/300 [00:08<13:07,  2.65s/it]
# Trainer step: 0, epoch: 0:   1%|▏         | 4/300 [00:09<08:49,  1.79s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:09<06:27,  1.31s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:10<05:01,  1.03s/it]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.21

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:10<04:07,  1.19it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:11<03:31,  1.38it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 9/300 [00:11<03:07,  1.55it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 10/300 [00:12<02:51,  1.69it/s]
# Trainer step: 0, epoch: 0:   4%|▎         | 11/300 [00:12<02:39,  1.81it/s]
# Trainer step: 0, epoch: 0:   4%|▍         | 12/300 [00:13<02:31,  1.89it/s]Progress: 7.00%
+---- avg training fps: 3.51

# Trainer step: 0, epoch: 0:   4%|▍         | 13/300 [00:13<02:26,  1.96it/s]
# Trainer step: 0, epoch: 0:   5%|▍         | 14/300 [00:14<02:22,  2.01it/s]
# Trainer step: 14, epoch: 1:   5%|▍         | 14/300 [00:14<02:22,  2.01it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 15/300 [00:14<02:27,  1.93it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 16/300 [00:15<02:23,  1.98it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 17/300 [00:15<02:19,  2.02it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 18/300 [00:16<02:17,  2.05it/s]Progress: 9.00%
+---- avg training fps: 4.34

# Trainer step: 14, epoch: 1:   6%|▋         | 19/300 [00:16<02:15,  2.08it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 20/300 [00:17<02:13,  2.09it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 21/300 [00:17<02:12,  2.10it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 22/300 [00:18<02:11,  2.11it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 23/300 [00:18<02:10,  2.12it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 24/300 [00:18<02:10,  2.12it/s]Progress: 11.00%
+---- avg training fps: 4.94

# Trainer step: 14, epoch: 1:   8%|▊         | 25/300 [00:19<02:09,  2.13it/s]
# Trainer step: 14, epoch: 1:   9%|▊         | 26/300 [00:19<02:09,  2.12it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 27/300 [00:20<02:08,  2.13it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 28/300 [00:20<02:08,  2.12it/s]
# Trainer step: 28, epoch: 2:   9%|▉         | 28/300 [00:21<02:08,  2.12it/s]
# Trainer step: 28, epoch: 2:  10%|▉         | 29/300 [00:21<02:01,  2.22it/s]
# Trainer step: 28, epoch: 2:  10%|█         | 30/300 [00:21<02:03,  2.19it/s]Progress: 13.00%
+---- avg training fps: 5.41

# Trainer step: 28, epoch: 2:  10%|█         | 31/300 [00:22<02:03,  2.18it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 32/300 [00:22<02:04,  2.16it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 33/300 [00:23<02:04,  2.15it/s]
# Trainer step: 28, epoch: 2:  11%|█▏        | 34/300 [00:23<02:04,  2.14it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 35/300 [00:24<02:03,  2.14it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 36/300 [00:24<02:03,  2.14it/s]Progress: 15.00%
+---- avg training fps: 5.76

# Trainer step: 28, epoch: 2:  12%|█▏        | 37/300 [00:24<02:03,  2.13it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 38/300 [00:25<02:02,  2.13it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 39/300 [00:25<02:02,  2.13it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 40/300 [00:26<02:02,  2.13it/s]
# Trainer step: 28, epoch: 2:  14%|█▎        | 41/300 [00:26<02:01,  2.13it/s]
# Trainer step: 28, epoch: 2:  14%|█▍        | 42/300 [00:27<02:00,  2.14it/s]Progress: 17.00%
+---- avg training fps: 6.06

# Trainer step: 42, epoch: 3:  14%|█▍        | 42/300 [00:27<02:00,  2.14it/s]
# Trainer step: 42, epoch: 3:  14%|█▍        | 43/300 [00:27<01:55,  2.23it/s]
# Trainer step: 42, epoch: 3:  15%|█▍        | 44/300 [00:28<01:56,  2.20it/s]
# Trainer step: 42, epoch: 3:  15%|█▌        | 45/300 [00:28<01:57,  2.18it/s]
# Trainer step: 42, epoch: 3:  15%|█▌        | 46/300 [00:29<01:57,  2.16it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 47/300 [00:29<01:57,  2.15it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 48/300 [00:30<01:57,  2.14it/s]Progress: 19.00%
+---- avg training fps: 6.28

# Trainer step: 42, epoch: 3:  16%|█▋        | 49/300 [00:30<01:57,  2.13it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 50/300 [00:31<01:58,  2.12it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 51/300 [00:31<01:57,  2.12it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 52/300 [00:34<05:20,  1.29s/it]
# Trainer step: 42, epoch: 3:  18%|█▊        | 53/300 [00:35<04:18,  1.05s/it]
# Trainer step: 42, epoch: 3:  18%|█▊        | 54/300 [00:35<03:35,  1.14it/s]Progress: 21.00%
+---- avg training fps: 5.98

# Trainer step: 42, epoch: 3:  18%|█▊        | 55/300 [00:36<03:04,  1.33it/s]
# Trainer step: 42, epoch: 3:  19%|█▊        | 56/300 [00:36<02:43,  1.49it/s]
# Trainer step: 56, epoch: 4:  19%|█▊        | 56/300 [00:37<02:43,  1.49it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 57/300 [00:37<02:23,  1.70it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 58/300 [00:37<02:14,  1.80it/s]
# Trainer step: 56, epoch: 4:  20%|█▉        | 59/300 [00:37<02:07,  1.89it/s]
# Trainer step: 56, epoch: 4:  20%|██        | 60/300 [00:38<02:03,  1.95it/s]Progress: 23.00%
+---- avg training fps: 6.17

# Trainer step: 56, epoch: 4:  20%|██        | 61/300 [00:38<01:59,  2.00it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 62/300 [00:39<01:56,  2.04it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 63/300 [00:39<01:55,  2.06it/s]
# Trainer step: 56, epoch: 4:  21%|██▏       | 64/300 [00:40<01:53,  2.08it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 65/300 [00:40<01:52,  2.09it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 66/300 [00:41<01:51,  2.10it/s]Progress: 25.00%
+---- avg training fps: 6.33

# Trainer step: 56, epoch: 4:  22%|██▏       | 67/300 [00:41<01:50,  2.11it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 68/300 [00:42<01:49,  2.11it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 69/300 [00:42<01:49,  2.12it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 70/300 [00:43<01:48,  2.12it/s]
# Trainer step: 70, epoch: 5:  23%|██▎       | 70/300 [00:43<01:48,  2.12it/s]
# Trainer step: 70, epoch: 5:  24%|██▎       | 71/300 [00:43<01:43,  2.21it/s]
# Trainer step: 70, epoch: 5:  24%|██▍       | 72/300 [00:44<01:44,  2.19it/s]Progress: 27.00%
+---- avg training fps: 6.48

# Trainer step: 70, epoch: 5:  24%|██▍       | 73/300 [00:44<01:44,  2.17it/s]
# Trainer step: 70, epoch: 5:  25%|██▍       | 74/300 [00:44<01:45,  2.15it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 75/300 [00:45<01:45,  2.14it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 76/300 [00:45<01:44,  2.14it/s]
# Trainer step: 70, epoch: 5:  26%|██▌       | 77/300 [00:46<02:03,  1.81it/s]
# Trainer step: 70, epoch: 5:  26%|██▌       | 78/300 [00:47<01:57,  1.89it/s]Progress: 29.00%
+---- avg training fps: 6.56

# Trainer step: 70, epoch: 5:  26%|██▋       | 79/300 [00:47<01:53,  1.95it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 80/300 [00:48<01:49,  2.01it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 81/300 [00:48<01:47,  2.04it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 82/300 [00:48<01:45,  2.07it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 83/300 [00:49<01:43,  2.09it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 84/300 [00:49<01:43,  2.10it/s]Progress: 31.00%
+---- avg training fps: 6.68

# Trainer step: 84, epoch: 6:  28%|██▊       | 84/300 [00:50<01:43,  2.10it/s]
# Trainer step: 84, epoch: 6:  28%|██▊       | 85/300 [00:50<01:37,  2.20it/s]
# Trainer step: 84, epoch: 6:  29%|██▊       | 86/300 [00:50<01:38,  2.18it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 87/300 [00:51<01:38,  2.17it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 88/300 [00:51<01:38,  2.16it/s]
# Trainer step: 84, epoch: 6:  30%|██▉       | 89/300 [00:52<01:38,  2.15it/s]
# Trainer step: 84, epoch: 6:  30%|███       | 90/300 [00:52<01:37,  2.15it/s]Progress: 33.00%
+---- avg training fps: 6.77

# Trainer step: 84, epoch: 6:  30%|███       | 91/300 [00:53<01:37,  2.15it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 92/300 [00:53<01:37,  2.14it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 93/300 [00:54<01:36,  2.14it/s]
# Trainer step: 84, epoch: 6:  31%|███▏      | 94/300 [00:54<01:36,  2.14it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 95/300 [00:55<01:35,  2.14it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 96/300 [00:55<01:35,  2.14it/s]Progress: 35.00%
+---- avg training fps: 6.86

# Trainer step: 84, epoch: 6:  32%|███▏      | 97/300 [00:55<01:35,  2.12it/s]
# Trainer step: 84, epoch: 6:  33%|███▎      | 98/300 [00:56<01:34,  2.13it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 98/300 [00:56<01:34,  2.13it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 99/300 [00:56<01:30,  2.22it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 100/300 [00:57<01:30,  2.20it/s]
# Trainer step: 98, epoch: 7:  34%|███▎      | 101/300 [00:57<01:31,  2.18it/s]
# Trainer step: 98, epoch: 7:  34%|███▍      | 102/300 [01:00<03:31,  1.07s/it]Progress: 37.00%
+---- avg training fps: 6.72

# Trainer step: 98, epoch: 7:  34%|███▍      | 103/300 [01:00<02:54,  1.13it/s]
# Trainer step: 98, epoch: 7:  35%|███▍      | 104/300 [01:01<02:29,  1.31it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 105/300 [01:01<02:11,  1.49it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 106/300 [01:02<01:58,  1.63it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 107/300 [01:02<01:49,  1.76it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 108/300 [01:03<01:43,  1.85it/s]Progress: 39.00%
+---- avg training fps: 6.80

# Trainer step: 98, epoch: 7:  36%|███▋      | 109/300 [01:03<01:39,  1.93it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 110/300 [01:04<01:36,  1.98it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 111/300 [01:04<01:33,  2.02it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 112/300 [01:04<01:31,  2.05it/s]
# Trainer step: 112, epoch: 8:  37%|███▋      | 112/300 [01:05<01:31,  2.05it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 113/300 [01:05<01:26,  2.16it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 114/300 [01:05<01:26,  2.15it/s]Progress: 41.00%
+---- avg training fps: 6.88

# Trainer step: 112, epoch: 8:  38%|███▊      | 115/300 [01:06<01:26,  2.14it/s]
# Trainer step: 112, epoch: 8:  39%|███▊      | 116/300 [01:06<01:26,  2.13it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 117/300 [01:07<01:26,  2.13it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 118/300 [01:07<01:25,  2.13it/s]
# Trainer step: 112, epoch: 8:  40%|███▉      | 119/300 [01:08<01:24,  2.13it/s]
# Trainer step: 112, epoch: 8:  40%|████      | 120/300 [01:08<01:24,  2.13it/s]Progress: 43.00%
+---- avg training fps: 6.94

# Trainer step: 112, epoch: 8:  40%|████      | 121/300 [01:09<01:24,  2.13it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 122/300 [01:09<01:23,  2.12it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 123/300 [01:10<01:23,  2.13it/s]
# Trainer step: 112, epoch: 8:  41%|████▏     | 124/300 [01:10<01:23,  2.12it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 125/300 [01:11<01:22,  2.12it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 126/300 [01:11<01:21,  2.12it/s]Progress: 45.00%
+---- avg training fps: 7.01

# Trainer step: 126, epoch: 9:  42%|████▏     | 126/300 [01:11<01:21,  2.12it/s]
# Trainer step: 126, epoch: 9:  42%|████▏     | 127/300 [01:11<01:17,  2.22it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 128/300 [01:12<01:18,  2.19it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 129/300 [01:12<01:18,  2.17it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 130/300 [01:13<01:18,  2.16it/s]
# Trainer step: 126, epoch: 9:  44%|████▎     | 131/300 [01:13<01:18,  2.15it/s]
# Trainer step: 126, epoch: 9:  44%|████▍     | 132/300 [01:14<01:18,  2.14it/s]Progress: 47.00%
+---- avg training fps: 7.07

# Trainer step: 126, epoch: 9:  44%|████▍     | 133/300 [01:14<01:18,  2.13it/s]
# Trainer step: 126, epoch: 9:  45%|████▍     | 134/300 [01:15<01:18,  2.13it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 135/300 [01:15<01:17,  2.12it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 136/300 [01:16<01:17,  2.13it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 137/300 [01:16<01:16,  2.13it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 138/300 [01:17<01:16,  2.13it/s]Progress: 49.00%
+---- avg training fps: 7.12

# Trainer step: 126, epoch: 9:  46%|████▋     | 139/300 [01:17<01:15,  2.13it/s]
# Trainer step: 126, epoch: 9:  47%|████▋     | 140/300 [01:18<01:15,  2.12it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 140/300 [01:18<01:15,  2.12it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 141/300 [01:18<01:11,  2.22it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 142/300 [01:18<01:12,  2.19it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 143/300 [01:19<01:12,  2.17it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 144/300 [01:19<01:12,  2.15it/s]Progress: 51.00%
+---- avg training fps: 7.17

# Trainer step: 140, epoch: 10:  48%|████▊     | 145/300 [01:20<01:12,  2.14it/s]
# Trainer step: 140, epoch: 10:  49%|████▊     | 146/300 [01:20<01:12,  2.13it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 147/300 [01:21<01:11,  2.13it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 148/300 [01:21<01:19,  1.92it/s]
# Trainer step: 140, epoch: 10:  50%|████▉     | 149/300 [01:22<01:16,  1.98it/s]
# Trainer step: 140, epoch: 10:  50%|█████     | 150/300 [01:22<01:14,  2.02it/s]Progress: 53.00%
+---- avg training fps: 7.20

# Trainer step: 140, epoch: 10:  50%|█████     | 151/300 [01:23<01:12,  2.05it/s]
# Trainer step: 140, epoch: 10:  51%|█████     | 152/300 [01:26<03:30,  1.42s/it]
# Trainer step: 140, epoch: 10:  51%|█████     | 153/300 [01:27<02:46,  1.14s/it]
# Trainer step: 140, epoch: 10:  51%|█████▏    | 154/300 [01:27<02:16,  1.07it/s]
# Trainer step: 154, epoch: 11:  51%|█████▏    | 154/300 [01:28<02:16,  1.07it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 155/300 [01:28<01:52,  1.29it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 156/300 [01:28<01:38,  1.46it/s]Progress: 55.00%
+---- avg training fps: 7.00

# Trainer step: 154, epoch: 11:  52%|█████▏    | 157/300 [01:29<01:28,  1.61it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 158/300 [01:29<01:21,  1.74it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 159/300 [01:30<01:16,  1.84it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 160/300 [01:30<01:13,  1.92it/s]
# Trainer step: 154, epoch: 11:  54%|█████▎    | 161/300 [01:31<01:10,  1.97it/s]
# Trainer step: 154, epoch: 11:  54%|█████▍    | 162/300 [01:31<01:08,  2.01it/s]Progress: 57.00%
+---- avg training fps: 7.04

# Trainer step: 154, epoch: 11:  54%|█████▍    | 163/300 [01:32<01:07,  2.04it/s]
# Trainer step: 154, epoch: 11:  55%|█████▍    | 164/300 [01:32<01:05,  2.07it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 165/300 [01:32<01:04,  2.08it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 166/300 [01:33<01:03,  2.10it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 167/300 [01:33<01:03,  2.10it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 168/300 [01:34<01:02,  2.11it/s]Progress: 59.00%
+---- avg training fps: 7.09

# Trainer step: 168, epoch: 12:  56%|█████▌    | 168/300 [01:34<01:02,  2.11it/s]
# Trainer step: 168, epoch: 12:  56%|█████▋    | 169/300 [01:34<00:59,  2.20it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 170/300 [01:35<00:59,  2.18it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 171/300 [01:35<00:59,  2.16it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 172/300 [01:36<00:59,  2.15it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 173/300 [01:36<00:59,  2.14it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 174/300 [01:37<00:59,  2.13it/s]Progress: 61.00%
+---- avg training fps: 7.13

# Trainer step: 168, epoch: 12:  58%|█████▊    | 175/300 [01:37<00:58,  2.12it/s]
# Trainer step: 168, epoch: 12:  59%|█████▊    | 176/300 [01:38<00:58,  2.12it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 177/300 [01:38<00:58,  2.12it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 178/300 [01:39<00:57,  2.12it/s]
# Trainer step: 168, epoch: 12:  60%|█████▉    | 179/300 [01:39<00:57,  2.12it/s]
# Trainer step: 168, epoch: 12:  60%|██████    | 180/300 [01:39<00:56,  2.12it/s]Progress: 63.00%
+---- avg training fps: 7.17

# Trainer step: 168, epoch: 12:  60%|██████    | 181/300 [01:40<00:56,  2.12it/s]
# Trainer step: 168, epoch: 12:  61%|██████    | 182/300 [01:40<00:55,  2.12it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 182/300 [01:41<00:55,  2.12it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 183/300 [01:41<00:52,  2.22it/s]
# Trainer step: 182, epoch: 13:  61%|██████▏   | 184/300 [01:41<01:00,  1.93it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 185/300 [01:42<00:57,  1.98it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 186/300 [01:42<00:56,  2.03it/s]Progress: 65.00%
+---- avg training fps: 7.20

# Trainer step: 182, epoch: 13:  62%|██████▏   | 187/300 [01:43<00:54,  2.06it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 188/300 [01:43<00:53,  2.08it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 189/300 [01:44<00:53,  2.09it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 190/300 [01:44<00:52,  2.10it/s]
# Trainer step: 182, epoch: 13:  64%|██████▎   | 191/300 [01:45<00:51,  2.11it/s]
# Trainer step: 182, epoch: 13:  64%|██████▍   | 192/300 [01:45<00:51,  2.12it/s]Progress: 67.00%
+---- avg training fps: 7.23

# Trainer step: 182, epoch: 13:  64%|██████▍   | 193/300 [01:46<00:50,  2.12it/s]
# Trainer step: 182, epoch: 13:  65%|██████▍   | 194/300 [01:46<00:49,  2.12it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 195/300 [01:47<00:49,  2.12it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 196/300 [01:47<00:48,  2.13it/s]
# Trainer step: 196, epoch: 14:  65%|██████▌   | 196/300 [01:48<00:48,  2.13it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 197/300 [01:48<00:46,  2.22it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 198/300 [01:48<00:46,  2.19it/s]Progress: 69.00%
+---- avg training fps: 7.27

# Trainer step: 196, epoch: 14:  66%|██████▋   | 199/300 [01:48<00:46,  2.17it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 200/300 [01:49<00:46,  2.16it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 201/300 [01:49<00:46,  2.15it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 202/300 [01:52<01:41,  1.04s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 203/300 [01:52<01:23,  1.16it/s]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 204/300 [01:53<01:11,  1.34it/s]Progress: 71.00%
+---- avg training fps: 7.18

# Trainer step: 196, epoch: 14:  68%|██████▊   | 205/300 [01:53<01:03,  1.51it/s]
# Trainer step: 196, epoch: 14:  69%|██████▊   | 206/300 [01:54<00:56,  1.65it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 207/300 [01:54<00:52,  1.77it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 208/300 [01:55<00:49,  1.87it/s]
# Trainer step: 196, epoch: 14:  70%|██████▉   | 209/300 [01:55<00:47,  1.93it/s]
# Trainer step: 196, epoch: 14:  70%|███████   | 210/300 [01:56<00:45,  1.98it/s]Progress: 73.00%
+---- avg training fps: 7.21

# Trainer step: 210, epoch: 15:  70%|███████   | 210/300 [01:56<00:45,  1.98it/s]
# Trainer step: 210, epoch: 15:  70%|███████   | 211/300 [01:56<00:42,  2.11it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 212/300 [01:56<00:41,  2.12it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 213/300 [01:57<00:40,  2.12it/s]
# Trainer step: 210, epoch: 15:  71%|███████▏  | 214/300 [01:57<00:40,  2.13it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 215/300 [01:58<00:40,  2.12it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 216/300 [01:58<00:39,  2.13it/s]Progress: 75.00%
+---- avg training fps: 7.24

# Trainer step: 210, epoch: 15:  72%|███████▏  | 217/300 [01:59<00:38,  2.13it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 218/300 [01:59<00:38,  2.13it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 219/300 [02:00<00:38,  2.12it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 220/300 [02:00<00:37,  2.12it/s]
# Trainer step: 210, epoch: 15:  74%|███████▎  | 221/300 [02:01<00:37,  2.12it/s]
# Trainer step: 210, epoch: 15:  74%|███████▍  | 222/300 [02:01<00:36,  2.12it/s]Progress: 77.00%
+---- avg training fps: 7.27

# Trainer step: 210, epoch: 15:  74%|███████▍  | 223/300 [02:02<00:36,  2.12it/s]
# Trainer step: 210, epoch: 15:  75%|███████▍  | 224/300 [02:02<00:37,  2.05it/s]
# Trainer step: 224, epoch: 16:  75%|███████▍  | 224/300 [02:03<00:37,  2.05it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 225/300 [02:03<00:34,  2.16it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 226/300 [02:03<00:34,  2.16it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 227/300 [02:03<00:33,  2.16it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 228/300 [02:04<00:33,  2.16it/s]Progress: 79.00%
+---- avg training fps: 7.30

# Trainer step: 224, epoch: 16:  76%|███████▋  | 229/300 [02:04<00:33,  2.15it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 230/300 [02:05<00:32,  2.16it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 231/300 [02:05<00:32,  2.15it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 232/300 [02:06<00:31,  2.16it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 233/300 [02:06<00:31,  2.14it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 234/300 [02:07<00:30,  2.14it/s]Progress: 81.00%
+---- avg training fps: 7.33

# Trainer step: 224, epoch: 16:  78%|███████▊  | 235/300 [02:07<00:30,  2.14it/s]
# Trainer step: 224, epoch: 16:  79%|███████▊  | 236/300 [02:08<00:29,  2.15it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 237/300 [02:08<00:29,  2.14it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 238/300 [02:09<00:28,  2.14it/s]
# Trainer step: 238, epoch: 17:  79%|███████▉  | 238/300 [02:09<00:28,  2.14it/s]
# Trainer step: 238, epoch: 17:  80%|███████▉  | 239/300 [02:09<00:26,  2.26it/s]
# Trainer step: 238, epoch: 17:  80%|████████  | 240/300 [02:09<00:26,  2.23it/s]Progress: 83.00%
+---- avg training fps: 7.36

# Trainer step: 238, epoch: 17:  80%|████████  | 241/300 [02:10<00:26,  2.20it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 242/300 [02:10<00:26,  2.19it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 243/300 [02:11<00:26,  2.18it/s]
# Trainer step: 238, epoch: 17:  81%|████████▏ | 244/300 [02:11<00:25,  2.17it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 245/300 [02:12<00:25,  2.16it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 246/300 [02:12<00:24,  2.16it/s]Progress: 85.00%
+---- avg training fps: 7.39

# Trainer step: 238, epoch: 17:  82%|████████▏ | 247/300 [02:13<00:24,  2.15it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 248/300 [02:13<00:24,  2.15it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 249/300 [02:14<00:23,  2.15it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 250/300 [02:14<00:23,  2.17it/s]
# Trainer step: 238, epoch: 17:  84%|████████▎ | 251/300 [02:15<00:22,  2.15it/s]
# Trainer step: 238, epoch: 17:  84%|████████▍ | 252/300 [02:18<01:05,  1.37s/it]Progress: 87.00%
+---- avg training fps: 7.26

# Trainer step: 252, epoch: 18:  84%|████████▍ | 252/300 [02:18<01:05,  1.37s/it]
# Trainer step: 252, epoch: 18:  84%|████████▍ | 253/300 [02:18<00:50,  1.08s/it]
# Trainer step: 252, epoch: 18:  85%|████████▍ | 254/300 [02:19<00:41,  1.12it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 255/300 [02:19<00:34,  1.31it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 256/300 [02:20<00:29,  1.48it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 257/300 [02:20<00:26,  1.64it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 258/300 [02:21<00:23,  1.77it/s]Progress: 89.00%
+---- avg training fps: 7.28

# Trainer step: 252, epoch: 18:  86%|████████▋ | 259/300 [02:21<00:21,  1.86it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 260/300 [02:22<00:20,  1.94it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 261/300 [02:22<00:19,  2.00it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 262/300 [02:23<00:18,  2.05it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 263/300 [02:23<00:17,  2.08it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 264/300 [02:24<00:17,  2.09it/s]Progress: 91.00%
+---- avg training fps: 7.31

# Trainer step: 252, epoch: 18:  88%|████████▊ | 265/300 [02:24<00:16,  2.11it/s]
# Trainer step: 252, epoch: 18:  89%|████████▊ | 266/300 [02:24<00:15,  2.13it/s]
# Trainer step: 266, epoch: 19:  89%|████████▊ | 266/300 [02:25<00:15,  2.13it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 267/300 [02:25<00:14,  2.23it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 268/300 [02:25<00:14,  2.22it/s]
# Trainer step: 266, epoch: 19:  90%|████████▉ | 269/300 [02:26<00:14,  2.19it/s]
# Trainer step: 266, epoch: 19:  90%|█████████ | 270/300 [02:26<00:13,  2.18it/s]Progress: 93.00%
+---- avg training fps: 7.34

# Trainer step: 266, epoch: 19:  90%|█████████ | 271/300 [02:27<00:13,  2.17it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 272/300 [02:27<00:12,  2.18it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 273/300 [02:28<00:12,  2.16it/s]
# Trainer step: 266, epoch: 19:  91%|█████████▏| 274/300 [02:28<00:12,  2.16it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 275/300 [02:29<00:11,  2.15it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 276/300 [02:29<00:11,  2.16it/s]Progress: 95.00%
+---- avg training fps: 7.36

# Trainer step: 266, epoch: 19:  92%|█████████▏| 277/300 [02:30<00:10,  2.16it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 278/300 [02:30<00:10,  2.16it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 279/300 [02:30<00:09,  2.15it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 280/300 [02:31<00:09,  2.15it/s]
# Trainer step: 280, epoch: 20:  93%|█████████▎| 280/300 [02:31<00:09,  2.15it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▎| 281/300 [02:31<00:08,  2.24it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▍| 282/300 [02:32<00:08,  2.21it/s]Progress: 97.00%
+---- avg training fps: 7.39

# Trainer step: 280, epoch: 20:  94%|█████████▍| 283/300 [02:32<00:07,  2.19it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▍| 284/300 [02:33<00:07,  2.19it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 285/300 [02:33<00:06,  2.18it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 286/300 [02:34<00:06,  2.17it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 287/300 [02:34<00:06,  2.15it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 288/300 [02:35<00:05,  2.15it/s]Progress: 99.00%
+---- avg training fps: 7.41

# Trainer step: 280, epoch: 20:  96%|█████████▋| 289/300 [02:35<00:05,  2.15it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 290/300 [02:35<00:04,  2.15it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 291/300 [02:36<00:04,  2.15it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 292/300 [02:36<00:03,  2.15it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 293/300 [02:37<00:03,  2.16it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 294/300 [02:37<00:02,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.43

# Trainer step: 294, epoch: 21:  98%|█████████▊| 294/300 [02:38<00:02,  2.16it/s]
# Trainer step: 294, epoch: 21:  98%|█████████▊| 295/300 [02:38<00:02,  2.26it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▊| 296/300 [02:38<00:01,  2.23it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 297/300 [02:39<00:01,  2.21it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 298/300 [02:39<00:00,  2.19it/s]
# Trainer step: 294, epoch: 21: 100%|█████████▉| 299/300 [02:40<00:00,  2.18it/s]
# Trainer step: 294, epoch: 21: 100%|██████████| 300/300 [02:40<00:00,  2.18it/s]Progress: 100.00%
+---- avg training fps: 7.45

# Trainer step: 294, epoch: 21: : 301it [02:41,  2.17it/s]                       Progress: 100.00%
Failed to plot token attention loss
+Reached max steps, stopping training!
+Saving checkpoint at step.. 301
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 294, epoch: 21: : 301it [04:00,  1.25it/s]
+------------------------------------------
+Training done :)
+2024-08-13 03:06:27.930809: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 03:06:28.022152: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 03:06:28.022179: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 03:06:28.040942: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 03:06:28.440973: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:06:28.441054: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:06:28.441063: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23096 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=1.0 freeze_unet_before_completion_f=0.25 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='clipx.zip' output_dir='lora_models/styles_final/clipx.zip_13_03-06-30-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723511190.2575362 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Downloading training data...
+Downloading https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip to lora_models/styles_final/clipx.zip_13_03-06-30-style_512_florence_300/images_in/clipx.zip...
+Succesfully prepped 54 .jpg images in lora_models/styles_final/clipx.zip_13_03-06-30-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.314
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 27 images, 27 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 27 to 54)
+Generating 54 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal, whimsical, detailed, and quirky scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene is Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase describes the best and worst of times.
+- In the style of TOK, a blockchain is depicted using bees.
+- In the style of TOK, a gorgon made of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset shows Venus and Earth circling the sun.
+- In the style of TOK, there is an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is portrayed.
+- In the style of TOK, the phrase exemplifies the age of wisdom and foolishness.
+- In the style of TOK, the message is to quit a boring corporation job together.
+- In the style of TOK, petroglyphs are depicted.
+- In the style of TOK, room and space exchange are based on first and second-degree connections.
+- In the style of TOK, the concept of trypophobia is represented.
+- In the style of TOK, six-pack containers burst with nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers performs.
+- In the style of TOK, a beaver chews a rifle in half.
+- In the style of TOK, a brigade of beavers quickly builds a dam.
+- In the style of TOK, a castle moves along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dances in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets as the audience.
+- In the style of TOK, there is a hyper-realistic painting of lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, and other sea trash swims up through the sea to peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 25 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 1...
+----- GPT response: -----
+Style Description: Surrealistic, intricate, and fantastical scenes
+
+- In the style of TOK, a mermaid made out of fishnets and sea trash, is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is embodied in a visual representation.
+- In the style of TOK, a blockchain is composed of bees.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun is depicted.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is presented.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is embodied in a visual representation.
+- In the style of TOK, two individuals are depicted quitting their boring corporation job together.
+- In the style of TOK, petroglyphs are portrayed.
+- In the style of TOK, a room and space exchange is based on first and second-degree connections.
+- In the style of TOK, trypophobia is visually expressed.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of dancing and singing beavers is created with a nod to Jim Henson's Muppets.
+- In the style of TOK, a beaver is shown chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a pattern like a Hawaiian shirt.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is designed in the style of M. C. Escher.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models and egrets as the audience.
+- In the style of TOK, a hyper-realistic painting depicts lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and sea trash, is swimming up through the sea to take a peak above water.
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 2...
+----- GPT response: -----
+Style Description: Surreal, eclectic, and whimsical themes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi, Kerala, India.
+- In the style of TOK, "They were the best of times, they were the worst of times."
+- In the style of TOK, a blockchain of bees.
+- In the style of TOK, a gorgon made out of sharks eating each other swims underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats pooping in zero-gravity.
+- In the style of TOK, a goblin goat.
+- In the style of TOK, "It was the age of wisdom, it was the age of foolishness."
+- In the style of TOK, “Let's quit our boring corporation job together.”
+- In the style of TOK, petroglyphs.
+- In the style of TOK, a room and space exchange based on first and second-degree connections.
+- In the style of TOK, trypophobia.
+- In the style of TOK, a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers.
+- In the style of TOK, a beaver chewing a rifle in half.
+- In the style of TOK, a brigade of beavers quickly building a dam.
+- In the style of TOK, a castle moving along the grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show with elephant seals as runway models and egrets as the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 25 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 3...
+----- GPT response: -----
+Style Description: Surreal and intricate marine or forest scenes
+
+- In the style of TOK, a mermaid crafted from waste materials swims underwater.
+- In the style of TOK, a group of tiny Lilliputians is inside a giant sea-sponge.
+- In the style of TOK, the scene depicts Fort Kochi, Kerala, India.
+- In the style of TOK, the statement illustrates the contrast of best and worst times.
+- In the style of TOK, a chain of bees is linked together.
+- In the style of TOK, a gorgon composed of intertwining sharks swims underwater.
+- In the style of TOK, Venus and Earth circle the sun on a hillside at sunset.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats defecate in zero-gravity.
+- In the style of TOK, a goblin that resembles a goat.
+- In the style of TOK, the phrase contrasts wisdom and foolishness.
+- In the style of TOK, two people discuss quitting their corporation jobs together.
+- In the style of TOK, ancient petroglyphs are depicted.
+- In the style of TOK, room and space exchange is based on connections.
+- In the style of TOK, the imagery evokes trypophobia.
+- In the style of TOK, a 6-pack contains nightmarish substances.
+- In the style of TOK, a band of beavers sings and dances, similar to Jim Henson's Muppets.
+- In the style of TOK, a beaver chews a rifle in half.
+- In the style of TOK, a brigade of beavers rapidly constructs a dam, patterned like Hawaiian shirts.
+- In the style of TOK, a brigade of beavers builds a dam quickly, similar to a Tarsem Singh film.
+- In the style of TOK, a brigade of beavers is presented as if in an M. C. Escher artwork.
+- In the style of TOK, a castle moves across grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dances in unison.
+- In the style of TOK, a 1950s restaurant kitchen, frozen and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as models and egrets in the audience.
+- In the style of TOK, a hyper-realistic painting showcases lemon-lime architecture.
+- In the style of TOK, a mermaid composed of waste materials swims to peek above the sea.
+- In the style of TOK, a mermaid fashioned from waste swims underwater.
+- In the style of TOK, many tiny Lilliputians inhabit a giant sea-sponge.
+- In the style of TOK, the setting is Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase contrasts the best and worst of times.
+- In the style of TOK, a blockchain composed of bees.
+- In the style of TOK, a gorgon made of sharks is underwater.
+- In the style of TOK, at sunset, Venus and Earth circle the sun on a hillside.
+- In the style of TOK, deep in the Amazon jungle, an ancient temple stands.
+- In the style of TOK, cats are defecating in zero-gravity.
+- In the style of TOK, a fantastical goblin goat is depicted.
+- In the style of TOK, the phrase contrasts wisdom and foolishness.
+- In the style of TOK, individuals decide to quit their boring corporate jobs.
+- In the style of TOK, ancient petroglyphs are displayed.
+- In the style of TOK, room and space exchange is based on connections.
+- In the style of TOK, the visual triggers trypophobia.
+- In the style of TOK, a 6-pack contains nightmare-inducing imagery.
+- In the style of TOK, singing and dancing beavers perform like Jim Henson's Muppets.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers rapidly constructs a dam with Hawaiian-themed patterns.
+- In the style of TOK, a brigade of beavers builds a dam swiftly, resembling a Tarsem Singh scene.
+- In the style of TOK, a brigade of beavers is illustrated in an M. C. Escher style.
+- In the style of TOK, a mobile castle traverses grassy hills.
+- In the style of TOK, dancing beavers perform in unison.
+- In the style of TOK, a preserved 1950s restaurant kitchen is rediscovered in the space age.
+- In the style of TOK, elephant seals model in a fashion show with egrets as spectators.
+- In the style of TOK, a painting of lemon-lime architecture appears hyper-realistic.
+- In the style of TOK, a mermaid constructed from sea trash swims up to peek above the water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Using GPT concept name as CLIP-segmentation prompt: Surreal and intricate marine or forest scenes
+Generating 54 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
54it [00:00, 15582.55it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 54 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a mermaid crafte...
+1     in the style of <s0><s1><s2>, a group of tiny ...
+2     in the style of <s0><s1><s2>, the scene depict...
+3     in the style of <s0><s1><s2>, the statement il...
+4     in the style of <s0><s1><s2>, a chain of bees ...
+5     in the style of <s0><s1><s2>, a gorgon compose...
+6     in the style of <s0><s1><s2>, venus and earth ...
+7     in the style of <s0><s1><s2>, an ancient templ...
+8     in the style of <s0><s1><s2>, cats defecate in...
+9     in the style of <s0><s1><s2>, a goblin that re...
+10    in the style of <s0><s1><s2>, the phrase contr...
+11    in the style of <s0><s1><s2>, two people discu...
+12    in the style of <s0><s1><s2>, ancient petrogly...
+13    in the style of <s0><s1><s2>, room and space e...
+14    in the style of <s0><s1><s2>, the imagery evok...
+15    in the style of <s0><s1><s2>, a 6-pack contain...
+16    in the style of <s0><s1><s2>, a band of beaver...
+17    in the style of <s0><s1><s2>, a beaver chews a...
+18    in the style of <s0><s1><s2>, a brigade of bea...
+19    in the style of <s0><s1><s2>, a brigade of bea...
+20    in the style of <s0><s1><s2>, a brigade of bea...
+21    in the style of <s0><s1><s2>, a castle moves a...
+22    in the style of <s0><s1><s2>, a chorus line of...
+23    in the style of <s0><s1><s2>, a 1950s restaura...
+24    in the style of <s0><s1><s2>, a fashion show f...
+25    in the style of <s0><s1><s2>, a hyper-realisti...
+26    in the style of <s0><s1><s2>, a mermaid compos...
+27    in the style of <s0><s1><s2>, a mermaid fashio...
+28    in the style of <s0><s1><s2>, many tiny lillip...
+29    in the style of <s0><s1><s2>, the setting is f...
+30    in the style of <s0><s1><s2>, the phrase contr...
+31    in the style of <s0><s1><s2>, a blockchain com...
+32    in the style of <s0><s1><s2>, a gorgon made of...
+33    in the style of <s0><s1><s2>, at sunset, venus...
+34    in the style of <s0><s1><s2>, deep in the amaz...
+35    in the style of <s0><s1><s2>, cats are defecat...
+36    in the style of <s0><s1><s2>, a fantastical go...
+37    in the style of <s0><s1><s2>, the phrase contr...
+38    in the style of <s0><s1><s2>, individuals deci...
+39    in the style of <s0><s1><s2>, ancient petrogly...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 54
+--- Num batches each epoch = 14
+--- Num Epochs = 22
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
  0%|          | 0/300 [00:00<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:02<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:02<14:29,  2.91s/it]
# Trainer step: 0, epoch: 0:   1%|          | 2/300 [00:07<18:56,  3.81s/it]
# Trainer step: 0, epoch: 0:   1%|          | 3/300 [00:07<11:16,  2.28s/it]
# Trainer step: 0, epoch: 0:   1%|▏         | 4/300 [00:08<08:04,  1.64s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:08<05:55,  1.21s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:09<04:38,  1.06it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.45

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:09<03:49,  1.28it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:10<03:17,  1.48it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 9/300 [00:10<02:55,  1.66it/s]
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# Trainer step: 0, epoch: 0:   4%|▎         | 11/300 [00:11<02:31,  1.91it/s]
# Trainer step: 0, epoch: 0:   4%|▍         | 12/300 [00:12<02:23,  2.00it/s]Progress: 7.00%
+---- avg training fps: 3.85

# Trainer step: 0, epoch: 0:   4%|▍         | 13/300 [00:12<02:18,  2.07it/s]
# Trainer step: 0, epoch: 0:   5%|▍         | 14/300 [00:12<02:15,  2.12it/s]
# Trainer step: 14, epoch: 1:   5%|▍         | 14/300 [00:13<02:15,  2.12it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 15/300 [00:13<02:21,  2.02it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 16/300 [00:13<02:16,  2.08it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 17/300 [00:14<02:13,  2.13it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 18/300 [00:14<02:10,  2.16it/s]Progress: 9.00%
+---- avg training fps: 4.72

# Trainer step: 14, epoch: 1:   6%|▋         | 19/300 [00:15<02:08,  2.18it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 20/300 [00:15<02:07,  2.20it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 21/300 [00:16<02:05,  2.22it/s]
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# Trainer step: 14, epoch: 1:   8%|▊         | 23/300 [00:17<02:04,  2.22it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 24/300 [00:17<02:03,  2.23it/s]Progress: 11.00%
+---- avg training fps: 5.35

# Trainer step: 14, epoch: 1:   8%|▊         | 25/300 [00:17<02:03,  2.23it/s]
# Trainer step: 14, epoch: 1:   9%|▊         | 26/300 [00:18<02:02,  2.23it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 27/300 [00:18<02:02,  2.23it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 28/300 [00:19<02:02,  2.23it/s]
# Trainer step: 28, epoch: 2:   9%|▉         | 28/300 [00:19<02:02,  2.23it/s]
# Trainer step: 28, epoch: 2:  10%|▉         | 29/300 [00:19<01:57,  2.31it/s]
# Trainer step: 28, epoch: 2:  10%|█         | 30/300 [00:20<01:57,  2.29it/s]Progress: 13.00%
+---- avg training fps: 5.84

# Trainer step: 28, epoch: 2:  10%|█         | 31/300 [00:20<01:58,  2.27it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 32/300 [00:21<01:58,  2.26it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 33/300 [00:21<01:58,  2.25it/s]
# Trainer step: 28, epoch: 2:  11%|█▏        | 34/300 [00:21<01:58,  2.24it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 35/300 [00:22<01:58,  2.24it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 36/300 [00:22<01:58,  2.24it/s]Progress: 15.00%
+---- avg training fps: 6.19

# Trainer step: 28, epoch: 2:  12%|█▏        | 37/300 [00:23<01:57,  2.23it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 38/300 [00:23<01:57,  2.23it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 39/300 [00:24<01:57,  2.23it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 40/300 [00:24<01:56,  2.23it/s]
# Trainer step: 28, epoch: 2:  14%|█▎        | 41/300 [00:25<01:56,  2.23it/s]
# Trainer step: 28, epoch: 2:  14%|█▍        | 42/300 [00:25<01:56,  2.22it/s]Progress: 17.00%
+---- avg training fps: 6.49

# Trainer step: 42, epoch: 3:  14%|█▍        | 42/300 [00:25<01:56,  2.22it/s]
# Trainer step: 42, epoch: 3:  14%|█▍        | 43/300 [00:25<01:51,  2.31it/s]
# Trainer step: 42, epoch: 3:  15%|█▍        | 44/300 [00:26<01:52,  2.28it/s]
# Trainer step: 42, epoch: 3:  15%|█▌        | 45/300 [00:26<01:52,  2.26it/s]
# Trainer step: 42, epoch: 3:  15%|█▌        | 46/300 [00:27<01:52,  2.25it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 47/300 [00:27<01:52,  2.24it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 48/300 [00:28<01:52,  2.23it/s]Progress: 19.00%
+---- avg training fps: 6.71

# Trainer step: 42, epoch: 3:  16%|█▋        | 49/300 [00:28<01:53,  2.22it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 50/300 [00:29<01:53,  2.20it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 51/300 [00:29<01:52,  2.21it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 52/300 [00:32<05:11,  1.26s/it]
# Trainer step: 42, epoch: 3:  18%|█▊        | 53/300 [00:33<04:10,  1.02s/it]
# Trainer step: 42, epoch: 3:  18%|█▊        | 54/300 [00:33<03:41,  1.11it/s]Progress: 21.00%
+---- avg training fps: 6.32

# Trainer step: 42, epoch: 3:  18%|█▊        | 55/300 [00:34<03:07,  1.31it/s]
# Trainer step: 42, epoch: 3:  19%|█▊        | 56/300 [00:34<02:43,  1.49it/s]
# Trainer step: 56, epoch: 4:  19%|█▊        | 56/300 [00:35<02:43,  1.49it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 57/300 [00:35<02:22,  1.71it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 58/300 [00:35<02:12,  1.83it/s]
# Trainer step: 56, epoch: 4:  20%|█▉        | 59/300 [00:35<02:05,  1.92it/s]
# Trainer step: 56, epoch: 4:  20%|██        | 60/300 [00:36<01:59,  2.00it/s]Progress: 23.00%
+---- avg training fps: 6.51

# Trainer step: 56, epoch: 4:  20%|██        | 61/300 [00:36<01:55,  2.06it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 62/300 [00:37<01:52,  2.11it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 63/300 [00:37<01:50,  2.14it/s]
# Trainer step: 56, epoch: 4:  21%|██▏       | 64/300 [00:38<01:49,  2.16it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 65/300 [00:38<01:48,  2.16it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 66/300 [00:39<01:47,  2.18it/s]Progress: 25.00%
+---- avg training fps: 6.67

# Trainer step: 56, epoch: 4:  22%|██▏       | 67/300 [00:39<01:46,  2.19it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 68/300 [00:40<01:45,  2.19it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 69/300 [00:40<01:45,  2.19it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 70/300 [00:40<01:44,  2.19it/s]
# Trainer step: 70, epoch: 5:  23%|██▎       | 70/300 [00:41<01:44,  2.19it/s]
# Trainer step: 70, epoch: 5:  24%|██▎       | 71/300 [00:41<01:39,  2.29it/s]
# Trainer step: 70, epoch: 5:  24%|██▍       | 72/300 [00:41<01:40,  2.26it/s]Progress: 27.00%
+---- avg training fps: 6.82

# Trainer step: 70, epoch: 5:  24%|██▍       | 73/300 [00:42<01:41,  2.24it/s]
# Trainer step: 70, epoch: 5:  25%|██▍       | 74/300 [00:42<01:41,  2.22it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 75/300 [00:43<01:41,  2.21it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 76/300 [00:43<01:41,  2.20it/s]
# Trainer step: 70, epoch: 5:  26%|██▌       | 77/300 [00:44<01:40,  2.21it/s]
# Trainer step: 70, epoch: 5:  26%|██▌       | 78/300 [00:44<01:40,  2.20it/s]Progress: 29.00%
+---- avg training fps: 6.94

# Trainer step: 70, epoch: 5:  26%|██▋       | 79/300 [00:44<01:40,  2.20it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 80/300 [00:45<01:40,  2.19it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 81/300 [00:45<01:39,  2.19it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 82/300 [00:46<01:39,  2.20it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 83/300 [00:46<01:39,  2.19it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 84/300 [00:47<01:38,  2.19it/s]Progress: 31.00%
+---- avg training fps: 7.05

# Trainer step: 84, epoch: 6:  28%|██▊       | 84/300 [00:47<01:38,  2.19it/s]
# Trainer step: 84, epoch: 6:  28%|██▊       | 85/300 [00:47<01:34,  2.28it/s]
# Trainer step: 84, epoch: 6:  29%|██▊       | 86/300 [00:48<01:35,  2.25it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 87/300 [00:48<01:35,  2.24it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 88/300 [00:49<01:35,  2.22it/s]
# Trainer step: 84, epoch: 6:  30%|██▉       | 89/300 [00:49<01:35,  2.21it/s]
# Trainer step: 84, epoch: 6:  30%|███       | 90/300 [00:49<01:35,  2.21it/s]Progress: 33.00%
+---- avg training fps: 7.15

# Trainer step: 84, epoch: 6:  30%|███       | 91/300 [00:50<01:34,  2.20it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 92/300 [00:50<01:35,  2.18it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 93/300 [00:51<01:34,  2.19it/s]
# Trainer step: 84, epoch: 6:  31%|███▏      | 94/300 [00:51<01:34,  2.19it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 95/300 [00:52<01:33,  2.19it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 96/300 [00:52<01:32,  2.19it/s]Progress: 35.00%
+---- avg training fps: 7.23

# Trainer step: 84, epoch: 6:  32%|███▏      | 97/300 [00:53<01:32,  2.19it/s]
# Trainer step: 84, epoch: 6:  33%|███▎      | 98/300 [00:53<01:32,  2.19it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 98/300 [00:53<01:32,  2.19it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 99/300 [00:53<01:28,  2.28it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 100/300 [00:54<01:28,  2.26it/s]
# Trainer step: 98, epoch: 7:  34%|███▎      | 101/300 [00:54<01:29,  2.23it/s]
# Trainer step: 98, epoch: 7:  34%|███▍      | 102/300 [00:57<03:58,  1.21s/it]Progress: 37.00%
+---- avg training fps: 7.00

# Trainer step: 98, epoch: 7:  34%|███▍      | 103/300 [00:58<03:13,  1.02it/s]
# Trainer step: 98, epoch: 7:  35%|███▍      | 104/300 [00:58<02:52,  1.14it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 105/300 [00:59<02:26,  1.33it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 106/300 [00:59<02:08,  1.51it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 107/300 [01:00<01:55,  1.67it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 108/300 [01:00<01:47,  1.79it/s]Progress: 39.00%
+---- avg training fps: 7.05

# Trainer step: 98, epoch: 7:  36%|███▋      | 109/300 [01:01<01:40,  1.90it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 110/300 [01:01<01:36,  1.98it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 111/300 [01:02<01:33,  2.03it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 112/300 [01:02<01:30,  2.08it/s]
# Trainer step: 112, epoch: 8:  37%|███▋      | 112/300 [01:03<01:30,  2.08it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 113/300 [01:03<01:25,  2.19it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 114/300 [01:03<01:24,  2.19it/s]Progress: 41.00%
+---- avg training fps: 7.13

# Trainer step: 112, epoch: 8:  38%|███▊      | 115/300 [01:03<01:24,  2.20it/s]
# Trainer step: 112, epoch: 8:  39%|███▊      | 116/300 [01:04<01:23,  2.20it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 117/300 [01:04<01:23,  2.19it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 118/300 [01:05<01:23,  2.19it/s]
# Trainer step: 112, epoch: 8:  40%|███▉      | 119/300 [01:05<01:22,  2.19it/s]
# Trainer step: 112, epoch: 8:  40%|████      | 120/300 [01:06<01:22,  2.19it/s]Progress: 43.00%
+---- avg training fps: 7.20

# Trainer step: 112, epoch: 8:  40%|████      | 121/300 [01:06<01:21,  2.19it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 122/300 [01:07<01:21,  2.19it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 123/300 [01:07<01:20,  2.19it/s]
# Trainer step: 112, epoch: 8:  41%|████▏     | 124/300 [01:08<01:19,  2.20it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 125/300 [01:08<01:19,  2.19it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 126/300 [01:08<01:19,  2.19it/s]Progress: 45.00%
+---- avg training fps: 7.27

# Trainer step: 126, epoch: 9:  42%|████▏     | 126/300 [01:09<01:19,  2.19it/s]
# Trainer step: 126, epoch: 9:  42%|████▏     | 127/300 [01:09<01:15,  2.29it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 128/300 [01:09<01:16,  2.25it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 129/300 [01:10<01:16,  2.24it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 130/300 [01:10<01:16,  2.23it/s]
# Trainer step: 126, epoch: 9:  44%|████▎     | 131/300 [01:11<01:16,  2.22it/s]
# Trainer step: 126, epoch: 9:  44%|████▍     | 132/300 [01:11<01:16,  2.21it/s]Progress: 47.00%
+---- avg training fps: 7.33

# Trainer step: 126, epoch: 9:  44%|████▍     | 133/300 [01:12<01:15,  2.20it/s]
# Trainer step: 126, epoch: 9:  45%|████▍     | 134/300 [01:12<01:15,  2.20it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 135/300 [01:12<01:15,  2.20it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 136/300 [01:13<01:14,  2.19it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 137/300 [01:13<01:14,  2.19it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 138/300 [01:14<01:13,  2.20it/s]Progress: 49.00%
+---- avg training fps: 7.38

# Trainer step: 126, epoch: 9:  46%|████▋     | 139/300 [01:14<01:13,  2.19it/s]
# Trainer step: 126, epoch: 9:  47%|████▋     | 140/300 [01:15<01:13,  2.19it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 140/300 [01:15<01:13,  2.19it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 141/300 [01:15<01:09,  2.28it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 142/300 [01:16<01:10,  2.25it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 143/300 [01:16<01:10,  2.23it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 144/300 [01:17<01:10,  2.23it/s]Progress: 51.00%
+---- avg training fps: 7.43

# Trainer step: 140, epoch: 10:  48%|████▊     | 145/300 [01:17<01:10,  2.21it/s]
# Trainer step: 140, epoch: 10:  49%|████▊     | 146/300 [01:17<01:09,  2.20it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 147/300 [01:18<01:09,  2.20it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 148/300 [01:18<01:09,  2.20it/s]
# Trainer step: 140, epoch: 10:  50%|████▉     | 149/300 [01:19<01:08,  2.20it/s]
# Trainer step: 140, epoch: 10:  50%|█████     | 150/300 [01:19<01:08,  2.19it/s]Progress: 53.00%
+---- avg training fps: 7.48

# Trainer step: 140, epoch: 10:  50%|█████     | 151/300 [01:20<01:08,  2.19it/s]
# Trainer step: 140, epoch: 10:  51%|█████     | 152/300 [01:24<04:17,  1.74s/it]
# Trainer step: 140, epoch: 10:  51%|█████     | 153/300 [01:25<03:18,  1.35s/it]
# Trainer step: 140, epoch: 10:  51%|█████▏    | 154/300 [01:25<02:38,  1.08s/it]
# Trainer step: 154, epoch: 11:  51%|█████▏    | 154/300 [01:26<02:38,  1.08s/it]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 155/300 [01:26<02:07,  1.14it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 156/300 [01:26<01:48,  1.33it/s]Progress: 55.00%
+---- avg training fps: 7.16

# Trainer step: 154, epoch: 11:  52%|█████▏    | 157/300 [01:27<01:34,  1.51it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 158/300 [01:27<01:25,  1.66it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 159/300 [01:28<01:18,  1.80it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 160/300 [01:28<01:13,  1.90it/s]
# Trainer step: 154, epoch: 11:  54%|█████▎    | 161/300 [01:29<01:10,  1.97it/s]
# Trainer step: 154, epoch: 11:  54%|█████▍    | 162/300 [01:29<01:07,  2.03it/s]Progress: 57.00%
+---- avg training fps: 7.21

# Trainer step: 154, epoch: 11:  54%|█████▍    | 163/300 [01:29<01:06,  2.07it/s]
# Trainer step: 154, epoch: 11:  55%|█████▍    | 164/300 [01:30<01:04,  2.09it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 165/300 [01:30<01:03,  2.12it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 166/300 [01:31<01:02,  2.13it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 167/300 [01:31<01:01,  2.15it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 168/300 [01:32<01:01,  2.16it/s]Progress: 59.00%
+---- avg training fps: 7.26

# Trainer step: 168, epoch: 12:  56%|█████▌    | 168/300 [01:32<01:01,  2.16it/s]
# Trainer step: 168, epoch: 12:  56%|█████▋    | 169/300 [01:32<00:58,  2.25it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 170/300 [01:33<00:58,  2.23it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 171/300 [01:33<00:58,  2.21it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 172/300 [01:34<00:58,  2.20it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 173/300 [01:34<00:57,  2.19it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 174/300 [01:34<00:57,  2.19it/s]Progress: 61.00%
+---- avg training fps: 7.30

# Trainer step: 168, epoch: 12:  58%|█████▊    | 175/300 [01:35<00:57,  2.18it/s]
# Trainer step: 168, epoch: 12:  59%|█████▊    | 176/300 [01:35<00:56,  2.18it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 177/300 [01:36<00:56,  2.18it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 178/300 [01:36<00:55,  2.18it/s]
# Trainer step: 168, epoch: 12:  60%|█████▉    | 179/300 [01:37<00:55,  2.18it/s]
# Trainer step: 168, epoch: 12:  60%|██████    | 180/300 [01:37<00:55,  2.18it/s]Progress: 63.00%
+---- avg training fps: 7.34

# Trainer step: 168, epoch: 12:  60%|██████    | 181/300 [01:38<00:54,  2.19it/s]
# Trainer step: 168, epoch: 12:  61%|██████    | 182/300 [01:38<00:54,  2.18it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 182/300 [01:38<00:54,  2.18it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 183/300 [01:38<00:51,  2.27it/s]
# Trainer step: 182, epoch: 13:  61%|██████▏   | 184/300 [01:39<00:51,  2.24it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 185/300 [01:39<00:51,  2.22it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 186/300 [01:40<00:51,  2.22it/s]Progress: 65.00%
+---- avg training fps: 7.38

# Trainer step: 182, epoch: 13:  62%|██████▏   | 187/300 [01:40<00:51,  2.20it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 188/300 [01:41<00:51,  2.19it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 189/300 [01:41<00:50,  2.19it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 190/300 [01:42<00:50,  2.18it/s]
# Trainer step: 182, epoch: 13:  64%|██████▎   | 191/300 [01:42<00:50,  2.18it/s]
# Trainer step: 182, epoch: 13:  64%|██████▍   | 192/300 [01:43<00:49,  2.18it/s]Progress: 67.00%
+---- avg training fps: 7.41

# Trainer step: 182, epoch: 13:  64%|██████▍   | 193/300 [01:43<00:49,  2.17it/s]
# Trainer step: 182, epoch: 13:  65%|██████▍   | 194/300 [01:44<00:48,  2.18it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 195/300 [01:44<00:48,  2.18it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 196/300 [01:44<00:47,  2.17it/s]
# Trainer step: 196, epoch: 14:  65%|██████▌   | 196/300 [01:45<00:47,  2.17it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 197/300 [01:45<00:45,  2.27it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 198/300 [01:45<00:45,  2.24it/s]Progress: 69.00%
+---- avg training fps: 7.45

# Trainer step: 196, epoch: 14:  66%|██████▋   | 199/300 [01:46<00:45,  2.22it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 200/300 [01:46<00:45,  2.21it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 201/300 [01:47<00:45,  2.20it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 202/300 [01:50<02:03,  1.26s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 203/300 [01:50<01:39,  1.02s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 204/300 [01:51<01:22,  1.17it/s]Progress: 71.00%
+---- avg training fps: 7.30

# Trainer step: 196, epoch: 14:  68%|██████▊   | 205/300 [01:51<01:09,  1.36it/s]
# Trainer step: 196, epoch: 14:  69%|██████▊   | 206/300 [01:52<01:01,  1.53it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 207/300 [01:52<00:55,  1.68it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 208/300 [01:53<00:50,  1.81it/s]
# Trainer step: 196, epoch: 14:  70%|██████▉   | 209/300 [01:53<00:47,  1.90it/s]
# Trainer step: 196, epoch: 14:  70%|███████   | 210/300 [01:54<00:45,  1.98it/s]Progress: 73.00%
+---- avg training fps: 7.34

# Trainer step: 210, epoch: 15:  70%|███████   | 210/300 [01:54<00:45,  1.98it/s]
# Trainer step: 210, epoch: 15:  70%|███████   | 211/300 [01:54<00:42,  2.11it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 212/300 [01:54<00:41,  2.13it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 213/300 [01:55<00:40,  2.15it/s]
# Trainer step: 210, epoch: 15:  71%|███████▏  | 214/300 [01:55<00:39,  2.15it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 215/300 [01:56<00:39,  2.16it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 216/300 [01:56<00:38,  2.16it/s]Progress: 75.00%
+---- avg training fps: 7.37

# Trainer step: 210, epoch: 15:  72%|███████▏  | 217/300 [01:57<00:38,  2.17it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 218/300 [01:57<00:38,  2.13it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 219/300 [01:58<00:37,  2.14it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 220/300 [01:58<00:37,  2.15it/s]
# Trainer step: 210, epoch: 15:  74%|███████▎  | 221/300 [01:59<00:36,  2.15it/s]
# Trainer step: 210, epoch: 15:  74%|███████▍  | 222/300 [01:59<00:36,  2.16it/s]Progress: 77.00%
+---- avg training fps: 7.40

# Trainer step: 210, epoch: 15:  74%|███████▍  | 223/300 [01:59<00:35,  2.16it/s]
# Trainer step: 210, epoch: 15:  75%|███████▍  | 224/300 [02:00<00:35,  2.17it/s]
# Trainer step: 224, epoch: 16:  75%|███████▍  | 224/300 [02:00<00:35,  2.17it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 225/300 [02:00<00:33,  2.26it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 226/300 [02:01<00:33,  2.24it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 227/300 [02:01<00:33,  2.21it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 228/300 [02:02<00:32,  2.20it/s]Progress: 79.00%
+---- avg training fps: 7.43

# Trainer step: 224, epoch: 16:  76%|███████▋  | 229/300 [02:02<00:32,  2.19it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 230/300 [02:03<00:32,  2.18it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 231/300 [02:03<00:31,  2.17it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 232/300 [02:04<00:31,  2.17it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 233/300 [02:04<00:30,  2.17it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 234/300 [02:04<00:30,  2.17it/s]Progress: 81.00%
+---- avg training fps: 7.46

# Trainer step: 224, epoch: 16:  78%|███████▊  | 235/300 [02:05<00:29,  2.17it/s]
# Trainer step: 224, epoch: 16:  79%|███████▊  | 236/300 [02:05<00:29,  2.16it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 237/300 [02:06<00:29,  2.17it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 238/300 [02:06<00:28,  2.17it/s]
# Trainer step: 238, epoch: 17:  79%|███████▉  | 238/300 [02:07<00:28,  2.17it/s]
# Trainer step: 238, epoch: 17:  80%|███████▉  | 239/300 [02:07<00:26,  2.26it/s]
# Trainer step: 238, epoch: 17:  80%|████████  | 240/300 [02:07<00:26,  2.23it/s]Progress: 83.00%
+---- avg training fps: 7.49

# Trainer step: 238, epoch: 17:  80%|████████  | 241/300 [02:08<00:26,  2.21it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 242/300 [02:08<00:26,  2.19it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 243/300 [02:09<00:26,  2.18it/s]
# Trainer step: 238, epoch: 17:  81%|████████▏ | 244/300 [02:09<00:25,  2.18it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 245/300 [02:10<00:25,  2.17it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 246/300 [02:10<00:24,  2.17it/s]Progress: 85.00%
+---- avg training fps: 7.52

# Trainer step: 238, epoch: 17:  82%|████████▏ | 247/300 [02:10<00:24,  2.17it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 248/300 [02:11<00:23,  2.17it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 249/300 [02:11<00:23,  2.16it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 250/300 [02:12<00:22,  2.18it/s]
# Trainer step: 238, epoch: 17:  84%|████████▎ | 251/300 [02:12<00:22,  2.17it/s]
# Trainer step: 238, epoch: 17:  84%|████████▍ | 252/300 [02:16<01:03,  1.33s/it]Progress: 87.00%
+---- avg training fps: 7.38

# Trainer step: 252, epoch: 18:  84%|████████▍ | 252/300 [02:16<01:03,  1.33s/it]
# Trainer step: 252, epoch: 18:  84%|████████▍ | 253/300 [02:16<00:49,  1.05s/it]
# Trainer step: 252, epoch: 18:  85%|████████▍ | 254/300 [02:17<00:40,  1.14it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 255/300 [02:17<00:33,  1.33it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 256/300 [02:17<00:29,  1.51it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 257/300 [02:18<00:25,  1.66it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 258/300 [02:18<00:23,  1.78it/s]Progress: 89.00%
+---- avg training fps: 7.41

# Trainer step: 252, epoch: 18:  86%|████████▋ | 259/300 [02:19<00:21,  1.89it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 260/300 [02:19<00:20,  1.96it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 261/300 [02:20<00:19,  2.02it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 262/300 [02:20<00:18,  2.06it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 263/300 [02:21<00:17,  2.10it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 264/300 [02:21<00:17,  2.12it/s]Progress: 91.00%
+---- avg training fps: 7.43

# Trainer step: 252, epoch: 18:  88%|████████▊ | 265/300 [02:22<00:16,  2.13it/s]
# Trainer step: 252, epoch: 18:  89%|████████▊ | 266/300 [02:22<00:15,  2.14it/s]
# Trainer step: 266, epoch: 19:  89%|████████▊ | 266/300 [02:22<00:15,  2.14it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 267/300 [02:22<00:14,  2.23it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 268/300 [02:23<00:14,  2.22it/s]
# Trainer step: 266, epoch: 19:  90%|████████▉ | 269/300 [02:23<00:14,  2.20it/s]
# Trainer step: 266, epoch: 19:  90%|█████████ | 270/300 [02:24<00:13,  2.19it/s]Progress: 93.00%
+---- avg training fps: 7.46

# Trainer step: 266, epoch: 19:  90%|█████████ | 271/300 [02:24<00:13,  2.18it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 272/300 [02:25<00:12,  2.18it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 273/300 [02:25<00:12,  2.18it/s]
# Trainer step: 266, epoch: 19:  91%|█████████▏| 274/300 [02:26<00:11,  2.18it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 275/300 [02:26<00:11,  2.17it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 276/300 [02:27<00:11,  2.17it/s]Progress: 95.00%
+---- avg training fps: 7.48

# Trainer step: 266, epoch: 19:  92%|█████████▏| 277/300 [02:27<00:10,  2.16it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 278/300 [02:28<00:10,  2.16it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 279/300 [02:28<00:09,  2.16it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 280/300 [02:28<00:09,  2.17it/s]
# Trainer step: 280, epoch: 20:  93%|█████████▎| 280/300 [02:29<00:09,  2.17it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▎| 281/300 [02:29<00:08,  2.27it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▍| 282/300 [02:29<00:08,  2.23it/s]Progress: 97.00%
+---- avg training fps: 7.51

# Trainer step: 280, epoch: 20:  94%|█████████▍| 283/300 [02:30<00:07,  2.21it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▍| 284/300 [02:30<00:07,  2.19it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 285/300 [02:31<00:06,  2.19it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 286/300 [02:31<00:06,  2.18it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 287/300 [02:32<00:05,  2.17it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 288/300 [02:32<00:05,  2.17it/s]Progress: 99.00%
+---- avg training fps: 7.53

# Trainer step: 280, epoch: 20:  96%|█████████▋| 289/300 [02:33<00:05,  2.17it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 290/300 [02:33<00:04,  2.17it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 291/300 [02:33<00:04,  2.16it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 292/300 [02:34<00:03,  2.16it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 293/300 [02:34<00:03,  2.16it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 294/300 [02:35<00:02,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.55

# Trainer step: 294, epoch: 21:  98%|█████████▊| 294/300 [02:35<00:02,  2.16it/s]
# Trainer step: 294, epoch: 21:  98%|█████████▊| 295/300 [02:35<00:02,  2.26it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▊| 296/300 [02:36<00:01,  2.23it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 297/300 [02:36<00:01,  2.21it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 298/300 [02:37<00:00,  2.19it/s]
# Trainer step: 294, epoch: 21: 100%|█████████▉| 299/300 [02:37<00:00,  2.18it/s]
# Trainer step: 294, epoch: 21: 100%|██████████| 300/300 [02:38<00:00,  2.17it/s]Progress: 100.00%
+---- avg training fps: 7.57

# Trainer step: 294, epoch: 21: : 301it [02:38,  2.17it/s]                       Progress: 100.00%
Reached max steps, stopping training!
+Saving checkpoint at step.. 301
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 294, epoch: 21: : 301it [04:01,  1.25it/s]
+------------------------------------------
+Training done :)
+2024-08-13 03:11:26.322417: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 03:11:26.414215: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 03:11:26.414243: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 03:11:26.432774: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 03:11:26.833095: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:11:26.833178: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:11:26.833187: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23112 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/good_styles/eden_crystals' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=1.0 freeze_unet_before_completion_f=0.0 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='eden_crystals' output_dir='lora_models/styles_final/eden_crystals_13_03-11-28-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723511488.6430585 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/eden_crystals_13_03-11-28-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.242
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants on a black background.
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants in the dark.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal blending of nature and technology
+
+- In the style of TOK, a potted plant sitting on top of a table next to a lamp.
+- In the style of TOK, a futuristic city in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves on a dark blue background.
+- In the style of TOK, a plant growing out of a circuit board.
+- In the style of TOK, a group of green and red plants on a black background.
+- In the style of TOK, a potted plant sitting on top of a table next to a lamp.
+- In the style of TOK, a futuristic city in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves on a dark blue background.
+- In the style of TOK, a plant growing out of a circuit board.
+- In the style of TOK, a group of green and red plants in the dark.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Surreal blending of nature and technology
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 7292.86it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a potted plant s...
+1     in the style of <s0><s1><s2>, a futuristic cit...
+2     in the style of <s0><s1><s2>, a flower with gr...
+3     in the style of <s0><s1><s2>, a plant growing ...
+4     in the style of <s0><s1><s2>, a group of green...
+5     in the style of <s0><s1><s2>, a potted plant s...
+6     in the style of <s0><s1><s2>, a futuristic cit...
+7     in the style of <s0><s1><s2>, a flower with gr...
+8     in the style of <s0><s1><s2>, a plant growing ...
+9     in the style of <s0><s1><s2>, a group of green...
+10    in the style of <s0><s1><s2>, a potted plant s...
+11    in the style of <s0><s1><s2>, a futuristic cit...
+12    in the style of <s0><s1><s2>, a flower with gr...
+13    in the style of <s0><s1><s2>, a plant growing ...
+14    in the style of <s0><s1><s2>, a group of green...
+15    in the style of <s0><s1><s2>, a potted plant s...
+16    in the style of <s0><s1><s2>, a futuristic cit...
+17    in the style of <s0><s1><s2>, a flower with gr...
+18    in the style of <s0><s1><s2>, a plant growing ...
+19    in the style of <s0><s1><s2>, a group of green...
+20    in the style of <s0><s1><s2>, a potted plant s...
+21    in the style of <s0><s1><s2>, a futuristic cit...
+22    in the style of <s0><s1><s2>, a flower with gr...
+23    in the style of <s0><s1><s2>, a plant growing ...
+24    in the style of <s0><s1><s2>, a group of green...
+25    in the style of <s0><s1><s2>, a potted plant s...
+26    in the style of <s0><s1><s2>, a futuristic cit...
+27    in the style of <s0><s1><s2>, a flower with gr...
+28    in the style of <s0><s1><s2>, a plant growing ...
+29    in the style of <s0><s1><s2>, a group of green...
+30    in the style of <s0><s1><s2>, a potted plant s...
+31    in the style of <s0><s1><s2>, a futuristic cit...
+32    in the style of <s0><s1><s2>, a flower with gr...
+33    in the style of <s0><s1><s2>, a plant growing ...
+34    in the style of <s0><s1><s2>, a group of green...
+35    in the style of <s0><s1><s2>, a potted plant s...
+36    in the style of <s0><s1><s2>, a futuristic cit...
+37    in the style of <s0><s1><s2>, a flower with gr...
+38    in the style of <s0><s1><s2>, a plant growing ...
+39    in the style of <s0><s1><s2>, a group of green...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:08<04:06,  1.19it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.77

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+---- avg training fps: 4.20

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+---- avg training fps: 5.07

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+---- avg training fps: 5.65

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+---- avg training fps: 6.08

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+---- avg training fps: 6.35

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+---- avg training fps: 6.61

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+---- avg training fps: 6.82

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+---- avg training fps: 6.34

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# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:35<02:09,  1.86it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:36<02:03,  1.95it/s]Progress: 23.00%
+---- avg training fps: 6.52

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:36<02:03,  1.95it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:36<01:58,  2.02it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:37<01:55,  2.06it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:37<01:53,  2.09it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:38<01:51,  2.12it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:38<01:49,  2.14it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:39<01:48,  2.15it/s]Progress: 25.00%
+---- avg training fps: 6.67

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:39<01:48,  2.15it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:40<01:47,  2.16it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:40<01:46,  2.17it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:40<01:45,  2.18it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:41<01:45,  2.18it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:41<01:45,  2.18it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:41<01:44,  2.18it/s]Progress: 27.00%
+---- avg training fps: 6.78

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:42<01:57,  1.93it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:42<01:52,  2.01it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:43<01:49,  2.06it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:43<01:47,  2.08it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:44<01:45,  2.11it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:44<01:43,  2.14it/s]Progress: 29.00%
+---- avg training fps: 6.90

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:45<01:42,  2.15it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:45<01:41,  2.16it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:46<01:41,  2.16it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:46<01:41,  2.17it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:46<01:40,  2.17it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:47<01:39,  2.18it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:47<01:39,  2.18it/s]Progress: 31.00%
+---- avg training fps: 7.00

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:48<01:38,  2.18it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:48<01:38,  2.18it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:48<01:37,  2.19it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:49<01:36,  2.19it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:49<01:36,  2.18it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:50<01:36,  2.18it/s]Progress: 33.00%
+---- avg training fps: 7.10

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:50<01:36,  2.18it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:50<01:35,  2.19it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:51<01:34,  2.19it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:51<01:34,  2.19it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:52<01:34,  2.19it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:52<01:33,  2.19it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:53<01:33,  2.19it/s]Progress: 35.00%
+---- avg training fps: 7.18

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:53<01:32,  2.19it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:53<01:32,  2.18it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:54<01:32,  2.18it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:54<01:31,  2.18it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:55<01:31,  2.18it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:55<01:31,  2.18it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:58<04:14,  1.28s/it]Progress: 37.00%
+---- avg training fps: 6.92

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:58<03:24,  1.04s/it]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [00:59<02:49,  1.16it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [00:59<02:24,  1.35it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [01:00<02:07,  1.52it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:00<01:55,  1.67it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:01<01:46,  1.80it/s]Progress: 39.00%
+---- avg training fps: 7.00

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:01<01:40,  1.90it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:02<01:36,  1.98it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:02<01:36,  1.98it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:02<01:32,  2.03it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:03<01:30,  2.08it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:03<01:28,  2.10it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:04<01:27,  2.13it/s]Progress: 41.00%
+---- avg training fps: 7.07

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:04<01:26,  2.15it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:04<01:25,  2.16it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:05<01:24,  2.16it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:06<01:34,  1.93it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:06<01:30,  2.01it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:06<01:27,  2.05it/s]Progress: 43.00%
+---- avg training fps: 7.12

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:07<01:27,  2.05it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:07<01:25,  2.09it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:07<01:24,  2.12it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:08<01:22,  2.15it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:08<01:21,  2.16it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:09<01:20,  2.16it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:09<01:20,  2.17it/s]Progress: 45.00%
+---- avg training fps: 7.18

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:10<01:19,  2.17it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:10<01:19,  2.17it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:11<01:18,  2.17it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:11<01:18,  2.17it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:12<01:18,  2.17it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:12<01:17,  2.18it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:12<01:17,  2.18it/s]Progress: 47.00%
+---- avg training fps: 7.24

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:12<01:16,  2.18it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:13<01:16,  2.18it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:13<01:15,  2.18it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:14<01:14,  2.19it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:14<01:14,  2.19it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:15<01:14,  2.18it/s]Progress: 49.00%
+---- avg training fps: 7.29

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:15<01:13,  2.18it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:16<01:13,  2.19it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:16<01:13,  2.19it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:16<01:12,  2.19it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:17<01:12,  2.18it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:17<01:12,  2.18it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:17<01:11,  2.18it/s]Progress: 51.00%
+---- avg training fps: 7.35

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:18<01:11,  2.18it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:18<01:10,  2.18it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:19<01:10,  2.18it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:19<01:09,  2.18it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:20<01:10,  2.15it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:20<01:09,  2.15it/s]Progress: 53.00%
+---- avg training fps: 7.39

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:21<01:09,  2.15it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:21<01:09,  2.16it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:25<03:42,  1.50s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:25<02:54,  1.19s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:26<02:21,  1.03it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:26<01:58,  1.22it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:26<01:42,  1.41it/s]Progress: 55.00%
+---- avg training fps: 7.14

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:27<01:30,  1.58it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:27<01:22,  1.72it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:28<01:16,  1.84it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:28<01:12,  1.93it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:29<01:12,  1.93it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:29<01:09,  2.00it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:29<01:07,  2.04it/s]Progress: 57.00%
+---- avg training fps: 7.19

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:30<01:06,  2.07it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:30<01:04,  2.10it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:31<01:03,  2.12it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:31<01:02,  2.13it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:32<01:02,  2.13it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:32<01:01,  2.14it/s]Progress: 59.00%
+---- avg training fps: 7.23

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:32<01:00,  2.16it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:33<01:00,  2.16it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:33<01:00,  2.16it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:33<00:59,  2.16it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:34<00:59,  2.17it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:34<00:58,  2.17it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:35<00:58,  2.17it/s]Progress: 61.00%
+---- avg training fps: 7.27

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:35<00:57,  2.16it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:36<00:57,  2.17it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:36<00:56,  2.17it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:37<00:56,  2.17it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:37<00:55,  2.17it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:38<00:55,  2.17it/s]Progress: 63.00%
+---- avg training fps: 7.31

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:38<00:55,  2.17it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:38<00:54,  2.17it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:38<00:54,  2.17it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:39<00:54,  2.17it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:39<00:53,  2.17it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:40<00:53,  2.16it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:40<00:52,  2.16it/s]Progress: 65.00%
+---- avg training fps: 7.35

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:41<00:52,  2.16it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:41<00:51,  2.17it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:42<00:51,  2.16it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:42<00:50,  2.16it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:43<00:50,  2.16it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:43<00:50,  2.16it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:43<00:49,  2.17it/s]Progress: 67.00%
+---- avg training fps: 7.38

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:44<00:49,  2.17it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:44<00:49,  2.16it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:44<00:48,  2.16it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:45<00:47,  2.17it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:45<00:47,  2.17it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:46<00:47,  2.17it/s]Progress: 69.00%
+---- avg training fps: 7.42

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:46<00:46,  2.16it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:47<00:46,  2.17it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:47<00:46,  2.17it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:47<00:45,  2.17it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:51<02:19,  1.43s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:51<01:50,  1.14s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:52<01:29,  1.07it/s]Progress: 71.00%
+---- avg training fps: 7.24

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:52<01:15,  1.27it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:53<01:04,  1.45it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:53<00:57,  1.61it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:54<01:00,  1.52it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:54<00:54,  1.67it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:55<00:50,  1.80it/s]Progress: 73.00%
+---- avg training fps: 7.25

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:55<00:50,  1.80it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:55<00:46,  1.90it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:56<00:44,  1.98it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:56<00:42,  2.04it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:57<00:41,  2.08it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [01:57<00:40,  2.11it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [01:58<00:39,  2.14it/s]Progress: 75.00%
+---- avg training fps: 7.29

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [01:58<00:38,  2.15it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [01:59<00:37,  2.16it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [01:59<00:37,  2.17it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [01:59<00:36,  2.17it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:00<00:36,  2.17it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:00<00:36,  2.17it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:00<00:35,  2.17it/s]Progress: 77.00%
+---- avg training fps: 7.32

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:01<00:35,  2.18it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:01<00:35,  2.16it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:02<00:34,  2.17it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:02<00:34,  2.17it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:03<00:33,  2.16it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:03<00:33,  2.16it/s]Progress: 79.00%
+---- avg training fps: 7.35

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:04<00:32,  2.16it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:04<00:32,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:05<00:32,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:05<00:31,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:05<00:31,  2.16it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:05<00:31,  2.16it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:06<00:30,  2.16it/s]Progress: 81.00%
+---- avg training fps: 7.38

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:06<00:30,  2.16it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:07<00:29,  2.16it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:07<00:29,  2.16it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:08<00:28,  2.16it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:08<00:28,  2.16it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:09<00:27,  2.16it/s]Progress: 83.00%
+---- avg training fps: 7.40

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:09<00:27,  2.16it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:09<00:27,  2.16it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:10<00:26,  2.16it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:10<00:26,  2.16it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:11<00:25,  2.16it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:11<00:25,  2.16it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:11<00:25,  2.16it/s]Progress: 85.00%
+---- avg training fps: 7.43

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:12<00:24,  2.16it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:12<00:24,  2.16it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:13<00:23,  2.16it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:13<00:23,  2.16it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:14<00:23,  2.16it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:14<00:22,  2.16it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:18<01:12,  1.51s/it]Progress: 87.00%
+---- avg training fps: 7.27

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:18<00:56,  1.19s/it]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:19<00:44,  1.02it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:19<00:37,  1.22it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:20<00:31,  1.40it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:20<00:27,  1.56it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:21<00:24,  1.70it/s]Progress: 89.00%
+---- avg training fps: 7.29

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:21<00:22,  1.81it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:21<00:21,  1.90it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:22<00:21,  1.90it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:22<00:19,  1.97it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:22<00:18,  2.02it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:23<00:17,  2.06it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:23<00:17,  2.08it/s]Progress: 91.00%
+---- avg training fps: 7.32

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:24<00:16,  2.11it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:24<00:16,  2.08it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:25<00:15,  2.10it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:25<00:15,  2.11it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:26<00:14,  2.12it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:26<00:14,  2.13it/s]Progress: 93.00%
+---- avg training fps: 7.34

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:27<00:14,  2.13it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:27<00:13,  2.13it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:27<00:13,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:28<00:12,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:28<00:12,  2.14it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:28<00:11,  2.15it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:29<00:11,  2.15it/s]Progress: 95.00%
+---- avg training fps: 7.37

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:29<00:10,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:30<00:10,  2.14it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:30<00:09,  2.13it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:31<00:09,  2.14it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:31<00:09,  2.14it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:31<00:08,  2.14it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:32<00:08,  2.14it/s]Progress: 97.00%
+---- avg training fps: 7.39

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:32<00:07,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:33<00:07,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:33<00:06,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:34<00:06,  2.14it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:34<00:06,  2.15it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:35<00:05,  2.15it/s]Progress: 99.00%
+---- avg training fps: 7.41

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:35<00:05,  2.15it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:35<00:04,  2.15it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:36<00:04,  2.15it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:36<00:04,  2.15it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:36<00:03,  2.15it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:37<00:03,  2.15it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:37<00:02,  2.14it/s]Progress: 100.00%
+---- avg training fps: 7.43

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:38<00:02,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:38<00:01,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:39<00:01,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:39<00:00,  2.15it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:40<00:00,  2.14it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:40<00:00,  2.15it/s]Progress: 100.00%
+---- avg training fps: 7.45
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [04:00<00:00,  1.25it/s]
+------------------------------------------
+Training done :)
+2024-08-13 03:15:58.420114: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 03:15:58.510816: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 03:15:58.510845: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 03:15:58.529756: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 03:15:58.931335: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:15:58.931417: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:15:58.931426: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23104 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=1.0 freeze_unet_before_completion_f=0.0 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='clipx.zip' output_dir='lora_models/styles_final/clipx.zip_13_03-16-00-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723511760.7760966 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Downloading training data...
+Downloading https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip to lora_models/styles_final/clipx.zip_13_03-16-00-style_512_florence_300/images_in/clipx.zip...
+Succesfully prepped 54 .jpg images in lora_models/styles_final/clipx.zip_13_03-16-00-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.314
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 27 images, 27 of which have captions.
+upscaling imgs..
+
  0%|          | 0/27 [00:00<?, ?it/s]
100%|██████████| 27/27 [00:00<00:00, 760041.66it/s]
+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 27 to 54)
+Generating 54 captions using mode: style...
+
  0%|          | 0/54 [00:00<?, ?it/s]
100%|██████████| 54/54 [00:00<00:00, 1562016.66it/s]
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal, whimsical, detailed, and quirky scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene is Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase describes the best and worst of times.
+- In the style of TOK, a blockchain is depicted using bees.
+- In the style of TOK, a gorgon made of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset shows Venus and Earth circling the sun.
+- In the style of TOK, there is an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is portrayed.
+- In the style of TOK, the phrase exemplifies the age of wisdom and the age of foolishness.
+- In the style of TOK, two people discuss quitting their boring corporation jobs together.
+- In the style of TOK, petroglyphs are presented.
+- In the style of TOK, a room and space exchange based on first and second-degree connections is depicted.
+- In the style of TOK, the fear known as trypophobia is illustrated.
+- In the style of TOK, there is a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing and singing beavers is highlighted.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with patterns akin to Hawaiian shirts.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a visual style like Tarsem Singh's.
+- In the style of TOK, a brigade of beavers is shown in a style similar to M. C. Escher's works.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is frozen solid and preserved, and rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting shows lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, and packing peanuts swims up through the sea to take a peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 1...
+----- GPT response: -----
+Style Description: Surrealistic, intricate, and fantastical scenes
+
+- In the style of TOK, a mermaid swims underwater in the ocean, crafted from fishnets and sea trash.
+- In the style of TOK, a throng of Lilliputians are cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi, Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is illustrated.
+- In the style of TOK, a blockchain of bees is depicted.
+- In the style of TOK, a gorgon made out of sharks is depicted swimming underwater, the sharks eating each other.
+- In the style of TOK, a hillside at sunset is shown with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple deep in the Amazon jungle is illustrated.
+- In the style of TOK, cats are shown pooping in zero-gravity.
+- In the style of TOK, a goblin goat is depicted.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is illustrated.
+- In the style of TOK, the phrase "Let's quit our boring corporation job together" is illustrated.
+- In the style of TOK, petroglyphs are depicted.
+- In the style of TOK, room and space exchange based on first and second-degree connection is depicted.
+- In the style of TOK, trypophobia is illustrated.
+- In the style of TOK, a six-pack of nightmare fuel is depicted.
+- In the style of TOK, a band of dancing, singing beavers is shown.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam in a Hawaiian-shirt pattern.
+- In the style of TOK, a brigade of beavers is quickly building a dam as depicted by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is depicted in the manner of M. C. Escher.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is shown, frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture is depicted.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, and packing peanuts is swimming up through the sea to peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 2...
+----- GPT response: -----
+Style Description: Surreal, eclectic, and whimsical themes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi, Kerala, India.
+- In the style of TOK, "They were the best of times, they were the worst of times."
+- In the style of TOK, a blockchain of bees.
+- In the style of TOK, a gorgon made out of sharks eating each other swims underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats pooping in zero-gravity.
+- In the style of TOK, a goblin goat.
+- In the style of TOK, "It was the age of wisdom, it was the age of foolishness."
+- In the style of TOK, “Let's quit our boring corporation job together.”
+- In the style of TOK, petroglyphs.
+- In the style of TOK, a room and space exchange based on first and second-degree connections.
+- In the style of TOK, trypophobia.
+- In the style of TOK, a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers.
+- In the style of TOK, a beaver chewing a rifle in half.
+- In the style of TOK, a brigade of beavers quickly building a dam.
+- In the style of TOK, a castle moving along the grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show with elephant seals as runway models and egrets as the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 25 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 3...
+----- GPT response: -----
+Style Description: Surreal and intricate marine or forest scenes
+
+- In the style of TOK, a mermaid crafted from waste materials swims underwater in the ocean.
+- In the style of TOK, a throng of tiny Lilliputians are packed inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene is set in Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase "best of times, worst of times" is visually depicted.
+- In the style of TOK, bees are organized in a blockchain formation.
+- In the style of TOK, a gorgon composed of sharks cannibalizes each other while swimming underwater.
+- In the style of TOK, Venus and Earth are seen circling the sun against a sunset hillside.
+- In the style of TOK, an ancient temple is found in the depths of the Amazon jungle.
+- In the style of TOK, cats are depicted pooping in zero-gravity.
+- In the style of TOK, a goblin-goat hybrid is featured.
+- In the style of TOK, the concept "age of wisdom, age of foolishness" comes to life.
+- In the style of TOK, two people decide to quit their mundane corporate jobs together.
+- In the style of TOK, petroglyphs are presented.
+- In the style of TOK, room and space exchange occurs based on first and second-degree connections.
+- In the style of TOK, images invoke a sense of trypophobia.
+- In the style of TOK, a 6-pack container brings forth nightmare-inducing contents.
+- In the style of TOK, a band of beavers are dancing and singing, reminiscent of Jim Henson's Muppets.
+- In the style of TOK, a beaver is seen gnawing a rifle in half.
+- In the style of TOK, a brigade of beavers rapidly constructs a dam as if on a Hawaiian-shirt pattern.
+- In the style of TOK, a brigade of beavers rapidly constructs a dam in a manner reminiscent of Tarsem Singh's visuals.
+- In the style of TOK, a brigade of beavers is depicted with an M. C. Escher inspiration.
+- In the style of TOK, a castle advances across grassy hills.
+- In the style of TOK, a chorus line of beavers dances in synchrony.
+- In the style of TOK, a 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, elephant seals strut in a fashion show while egrets watch from the audience.
+- In the style of TOK, architectural elements are rendered in hyper-realistic lemon and lime hues.
+- In the style of TOK, a mermaid made from debris and dead fish emerges from the sea to glimpse above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 4...
+----- GPT response: -----
+Style Description: Surreal and whimsical elements.
+
+- In the style of TOK, a mermaid swimming underwater made out of fishnets and sea trash.
+- In the style of TOK, Lilliputians cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene is set in Fort Kochi, Kerala, India.
+- In the style of TOK, depicting the saying "They were the best of times, they were the worst of times."
+- In the style of TOK, a blockchain visualized as a series of bees.
+- In the style of TOK, a gorgon made of sharks eating each other swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple found deep in the Amazon jungle.
+- In the style of TOK, cats are experienced to be pooping in zero-gravity conditions.
+- In the style of TOK, a depiction of a goblin goat.
+- In the style of TOK, illustrating "it was the age of wisdom, it was the age of foolishness."
+- In the style of TOK, two characters deciding to quit their boring corporate job together.
+- In the style of TOK, depicting ancient petroglyphs.
+- In the style of TOK, a scene showing room and space exchange based on first and second-degree connections.
+- In the style of TOK, an illustration evoking trypophobia.
+- In the style of TOK, a 6-pack containing nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers.
+- In the style of TOK, a beaver chewing a rifle in half.
+- In the style of TOK, a brigade of beavers quickly building a dam.
+- In the style of TOK, a brigade of beavers quickly building a dam.
+- In the style of TOK, a brigade of beavers building a dam.
+- In the style of TOK, a moving castle traveling through grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and rediscovered in the space age.
+- In the style of TOK, a fashion show featuring elephant seals as runway models and egrets in the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture.
+- In the style of TOK, a mermaid swimming up made out of fishnets, dead fish, packing peanuts, and sea trash.
+
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+WARNING: no captioning text was given and we're not doing chatgpt cleanup...
+Concept mode:  style
+Using GPT concept name as CLIP-segmentation prompt: Surreal and whimsical elements.
+Generating 54 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
54it [00:00, 15261.26it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 54 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a mermaid made o...
+1     in the style of <s0><s1><s2>, a throng of lill...
+2     in the style of <s0><s1><s2>, fort kochi, kera...
+3     in the style of <s0><s1><s2>, they were the be...
+4     in the style of <s0><s1><s2>, a blockchain of ...
+5     in the style of <s0><s1><s2>, a gorgon made ou...
+6     in the style of <s0><s1><s2>, a hillside at su...
+7     in the style of <s0><s1><s2>, an ancient templ...
+8     in the style of <s0><s1><s2>, cats pooping in ...
+9             in the style of <s0><s1><s2>, goblin goat
+10    in the style of <s0><s1><s2>, it was the age o...
+11    in the style of <s0><s1><s2>, let's quit our b...
+12            in the style of <s0><s1><s2>, petroglyphs
+13    in the style of <s0><s1><s2>, room and space e...
+14            in the style of <s0><s1><s2>, trypophobia
+15    in the style of <s0><s1><s2>, a 6-pack of nigh...
+16    in the style of <s0><s1><s2>, a band of dancin...
+17    in the style of <s0><s1><s2>, a beaver chewing...
+18    in the style of <s0><s1><s2>, a brigade of bea...
+19    in the style of <s0><s1><s2>, a brigade of bea...
+20    in the style of <s0><s1><s2>, a brigade of bea...
+21    in the style of <s0><s1><s2>, a castle that is...
+22    in the style of <s0><s1><s2>, a chorus line of...
+23    in the style of <s0><s1><s2>, a dirty 1950s re...
+24    in the style of <s0><s1><s2>, a fashion show w...
+25    in the style of <s0><s1><s2>, a hyper-realisti...
+26    in the style of <s0><s1><s2>, a mermaid made o...
+27    in the style of <s0><s1><s2>, a mermaid made o...
+28    in the style of <s0><s1><s2>, a throng of lill...
+29    in the style of <s0><s1><s2>, fort kochi, kera...
+30    in the style of <s0><s1><s2>, they were the be...
+31    in the style of <s0><s1><s2>, a blockchain of ...
+32    in the style of <s0><s1><s2>, a gorgon made ou...
+33    in the style of <s0><s1><s2>, a hillside at su...
+34    in the style of <s0><s1><s2>, an ancient templ...
+35    in the style of <s0><s1><s2>, cats pooping in ...
+36            in the style of <s0><s1><s2>, goblin goat
+37    in the style of <s0><s1><s2>, it was the age o...
+38    in the style of <s0><s1><s2>, let's quit our b...
+39            in the style of <s0><s1><s2>, petroglyphs
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 54
+--- Num batches each epoch = 14
+--- Num Epochs = 22
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:02<?, ?it/s]
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:10<04:54,  1.00s/it]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.27

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:10<04:01,  1.21it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:11<03:26,  1.41it/s]
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# Trainer step: 0, epoch: 0:   4%|▍         | 12/300 [00:12<02:29,  1.93it/s]Progress: 7.00%
+---- avg training fps: 3.60

# Trainer step: 0, epoch: 0:   4%|▍         | 13/300 [00:13<02:24,  1.99it/s]
# Trainer step: 0, epoch: 0:   5%|▍         | 14/300 [00:13<02:20,  2.04it/s]
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# Trainer step: 14, epoch: 1:   6%|▌         | 18/300 [00:15<02:15,  2.09it/s]Progress: 9.00%
+---- avg training fps: 4.44

# Trainer step: 14, epoch: 1:   6%|▋         | 19/300 [00:16<02:13,  2.11it/s]
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# Trainer step: 14, epoch: 1:   8%|▊         | 24/300 [00:18<02:09,  2.14it/s]Progress: 11.00%
+---- avg training fps: 5.05

# Trainer step: 14, epoch: 1:   8%|▊         | 25/300 [00:19<02:08,  2.15it/s]
# Trainer step: 14, epoch: 1:   9%|▊         | 26/300 [00:19<02:07,  2.14it/s]
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# Trainer step: 28, epoch: 2:  10%|█         | 30/300 [00:21<02:02,  2.21it/s]Progress: 13.00%
+---- avg training fps: 5.52

# Trainer step: 28, epoch: 2:  10%|█         | 31/300 [00:21<02:02,  2.19it/s]
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# Trainer step: 28, epoch: 2:  12%|█▏        | 36/300 [00:24<02:02,  2.16it/s]Progress: 15.00%
+---- avg training fps: 5.87

# Trainer step: 28, epoch: 2:  12%|█▏        | 37/300 [00:24<02:02,  2.15it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 38/300 [00:25<02:01,  2.15it/s]
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# Trainer step: 28, epoch: 2:  14%|█▍        | 42/300 [00:26<01:59,  2.15it/s]Progress: 17.00%
+---- avg training fps: 6.17

# Trainer step: 42, epoch: 3:  14%|█▍        | 42/300 [00:27<01:59,  2.15it/s]
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# Trainer step: 42, epoch: 3:  16%|█▌        | 48/300 [00:29<01:56,  2.16it/s]Progress: 19.00%
+---- avg training fps: 6.39

# Trainer step: 42, epoch: 3:  16%|█▋        | 49/300 [00:30<01:56,  2.16it/s]
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# Trainer step: 42, epoch: 3:  18%|█▊        | 54/300 [00:35<03:31,  1.16it/s]Progress: 21.00%
+---- avg training fps: 6.08

# Trainer step: 42, epoch: 3:  18%|█▊        | 55/300 [00:35<03:01,  1.35it/s]
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# Trainer step: 56, epoch: 4:  20%|██        | 60/300 [00:37<02:01,  1.98it/s]Progress: 23.00%
+---- avg training fps: 6.28

# Trainer step: 56, epoch: 4:  20%|██        | 61/300 [00:38<01:57,  2.03it/s]
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# Trainer step: 56, epoch: 4:  22%|██▏       | 66/300 [00:40<01:49,  2.13it/s]Progress: 25.00%
+---- avg training fps: 6.43

# Trainer step: 56, epoch: 4:  22%|██▏       | 67/300 [00:41<01:48,  2.14it/s]
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# Trainer step: 70, epoch: 5:  24%|██▍       | 72/300 [00:43<01:42,  2.22it/s]Progress: 27.00%
+---- avg training fps: 6.58

# Trainer step: 70, epoch: 5:  24%|██▍       | 73/300 [00:43<01:43,  2.20it/s]
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# Trainer step: 70, epoch: 5:  26%|██▌       | 78/300 [00:46<01:48,  2.04it/s]Progress: 29.00%
+---- avg training fps: 6.67

# Trainer step: 70, epoch: 5:  26%|██▋       | 79/300 [00:46<01:46,  2.07it/s]
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# Trainer step: 70, epoch: 5:  28%|██▊       | 84/300 [00:49<01:40,  2.14it/s]Progress: 31.00%
+---- avg training fps: 6.79

# Trainer step: 84, epoch: 6:  28%|██▊       | 84/300 [00:49<01:40,  2.14it/s]
# Trainer step: 84, epoch: 6:  28%|██▊       | 85/300 [00:49<01:35,  2.25it/s]
# Trainer step: 84, epoch: 6:  29%|██▊       | 86/300 [00:49<01:36,  2.22it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 87/300 [00:50<01:36,  2.21it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 88/300 [00:50<01:36,  2.20it/s]
# Trainer step: 84, epoch: 6:  30%|██▉       | 89/300 [00:51<01:36,  2.18it/s]
# Trainer step: 84, epoch: 6:  30%|███       | 90/300 [00:51<01:36,  2.18it/s]Progress: 33.00%
+---- avg training fps: 6.89

# Trainer step: 84, epoch: 6:  30%|███       | 91/300 [00:52<01:36,  2.17it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 92/300 [00:52<01:35,  2.17it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 93/300 [00:53<01:35,  2.17it/s]
# Trainer step: 84, epoch: 6:  31%|███▏      | 94/300 [00:53<01:34,  2.17it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 95/300 [00:54<01:34,  2.17it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 96/300 [00:54<01:34,  2.16it/s]Progress: 35.00%
+---- avg training fps: 6.97

# Trainer step: 84, epoch: 6:  32%|███▏      | 97/300 [00:55<01:33,  2.16it/s]
# Trainer step: 84, epoch: 6:  33%|███▎      | 98/300 [00:55<01:33,  2.16it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 98/300 [00:55<01:33,  2.16it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 99/300 [00:55<01:28,  2.27it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 100/300 [00:56<01:29,  2.24it/s]
# Trainer step: 98, epoch: 7:  34%|███▎      | 101/300 [00:56<01:29,  2.21it/s]
# Trainer step: 98, epoch: 7:  34%|███▍      | 102/300 [00:59<03:25,  1.04s/it]Progress: 37.00%
+---- avg training fps: 6.83

# Trainer step: 98, epoch: 7:  34%|███▍      | 103/300 [00:59<02:50,  1.15it/s]
# Trainer step: 98, epoch: 7:  35%|███▍      | 104/300 [01:00<02:26,  1.34it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 105/300 [01:00<02:08,  1.51it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 106/300 [01:01<01:56,  1.66it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 107/300 [01:01<01:48,  1.78it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 108/300 [01:02<01:42,  1.88it/s]Progress: 39.00%
+---- avg training fps: 6.91

# Trainer step: 98, epoch: 7:  36%|███▋      | 109/300 [01:02<01:37,  1.96it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 110/300 [01:02<01:34,  2.01it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 111/300 [01:03<01:32,  2.05it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 112/300 [01:03<01:30,  2.08it/s]
# Trainer step: 112, epoch: 8:  37%|███▋      | 112/300 [01:04<01:30,  2.08it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 113/300 [01:04<01:24,  2.20it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 114/300 [01:04<01:25,  2.18it/s]Progress: 41.00%
+---- avg training fps: 6.99

# Trainer step: 112, epoch: 8:  38%|███▊      | 115/300 [01:05<01:25,  2.17it/s]
# Trainer step: 112, epoch: 8:  39%|███▊      | 116/300 [01:05<01:25,  2.16it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 117/300 [01:06<01:24,  2.16it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 118/300 [01:06<01:24,  2.16it/s]
# Trainer step: 112, epoch: 8:  40%|███▉      | 119/300 [01:07<01:23,  2.16it/s]
# Trainer step: 112, epoch: 8:  40%|████      | 120/300 [01:07<01:23,  2.16it/s]Progress: 43.00%
+---- avg training fps: 7.06

# Trainer step: 112, epoch: 8:  40%|████      | 121/300 [01:08<01:23,  2.15it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 122/300 [01:08<01:23,  2.14it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 123/300 [01:08<01:22,  2.15it/s]
# Trainer step: 112, epoch: 8:  41%|████▏     | 124/300 [01:09<01:21,  2.15it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 125/300 [01:09<01:21,  2.15it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 126/300 [01:10<01:20,  2.15it/s]Progress: 45.00%
+---- avg training fps: 7.13

# Trainer step: 126, epoch: 9:  42%|████▏     | 126/300 [01:10<01:20,  2.15it/s]
# Trainer step: 126, epoch: 9:  42%|████▏     | 127/300 [01:10<01:16,  2.26it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 128/300 [01:11<01:17,  2.22it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 129/300 [01:11<01:17,  2.20it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 130/300 [01:12<01:17,  2.19it/s]
# Trainer step: 126, epoch: 9:  44%|████▎     | 131/300 [01:12<01:17,  2.18it/s]
# Trainer step: 126, epoch: 9:  44%|████▍     | 132/300 [01:13<01:17,  2.17it/s]Progress: 47.00%
+---- avg training fps: 7.18

# Trainer step: 126, epoch: 9:  44%|████▍     | 133/300 [01:13<01:17,  2.16it/s]
# Trainer step: 126, epoch: 9:  45%|████▍     | 134/300 [01:13<01:16,  2.16it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 135/300 [01:14<01:16,  2.15it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 136/300 [01:14<01:16,  2.16it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 137/300 [01:15<01:15,  2.16it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 138/300 [01:15<01:15,  2.15it/s]Progress: 49.00%
+---- avg training fps: 7.23

# Trainer step: 126, epoch: 9:  46%|████▋     | 139/300 [01:16<01:14,  2.15it/s]
# Trainer step: 126, epoch: 9:  47%|████▋     | 140/300 [01:16<01:14,  2.15it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 140/300 [01:17<01:14,  2.15it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 141/300 [01:17<01:10,  2.26it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 142/300 [01:17<01:10,  2.23it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 143/300 [01:18<01:11,  2.20it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 144/300 [01:18<01:11,  2.18it/s]Progress: 51.00%
+---- avg training fps: 7.29

# Trainer step: 140, epoch: 10:  48%|████▊     | 145/300 [01:19<01:11,  2.17it/s]
# Trainer step: 140, epoch: 10:  49%|████▊     | 146/300 [01:19<01:11,  2.17it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 147/300 [01:20<01:18,  1.95it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 148/300 [01:20<01:15,  2.01it/s]
# Trainer step: 140, epoch: 10:  50%|████▉     | 149/300 [01:21<01:13,  2.05it/s]
# Trainer step: 140, epoch: 10:  50%|█████     | 150/300 [01:21<01:11,  2.09it/s]Progress: 53.00%
+---- avg training fps: 7.32

# Trainer step: 140, epoch: 10:  50%|█████     | 151/300 [01:21<01:10,  2.11it/s]
# Trainer step: 140, epoch: 10:  51%|█████     | 152/300 [01:25<03:24,  1.38s/it]
# Trainer step: 140, epoch: 10:  51%|█████     | 153/300 [01:25<02:42,  1.11s/it]
# Trainer step: 140, epoch: 10:  51%|█████▏    | 154/300 [01:26<02:13,  1.09it/s]
# Trainer step: 154, epoch: 11:  51%|█████▏    | 154/300 [01:26<02:13,  1.09it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 155/300 [01:26<01:49,  1.32it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 156/300 [01:27<01:36,  1.49it/s]Progress: 55.00%
+---- avg training fps: 7.11

# Trainer step: 154, epoch: 11:  52%|█████▏    | 157/300 [01:27<01:27,  1.64it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 158/300 [01:28<01:20,  1.77it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 159/300 [01:28<01:15,  1.86it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 160/300 [01:29<01:12,  1.94it/s]
# Trainer step: 154, epoch: 11:  54%|█████▎    | 161/300 [01:29<01:09,  2.00it/s]
# Trainer step: 154, epoch: 11:  54%|█████▍    | 162/300 [01:30<01:07,  2.04it/s]Progress: 57.00%
+---- avg training fps: 7.16

# Trainer step: 154, epoch: 11:  54%|█████▍    | 163/300 [01:30<01:05,  2.08it/s]
# Trainer step: 154, epoch: 11:  55%|█████▍    | 164/300 [01:30<01:04,  2.10it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 165/300 [01:31<01:03,  2.12it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 166/300 [01:31<01:03,  2.13it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 167/300 [01:32<01:02,  2.14it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 168/300 [01:32<01:01,  2.14it/s]Progress: 59.00%
+---- avg training fps: 7.21

# Trainer step: 168, epoch: 12:  56%|█████▌    | 168/300 [01:33<01:01,  2.14it/s]
# Trainer step: 168, epoch: 12:  56%|█████▋    | 169/300 [01:33<00:58,  2.25it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 170/300 [01:33<00:58,  2.22it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 171/300 [01:34<00:58,  2.21it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 172/300 [01:34<00:58,  2.19it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 173/300 [01:35<00:58,  2.17it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 174/300 [01:35<00:58,  2.17it/s]Progress: 61.00%
+---- avg training fps: 7.25

# Trainer step: 168, epoch: 12:  58%|█████▊    | 175/300 [01:36<00:57,  2.16it/s]
# Trainer step: 168, epoch: 12:  59%|█████▊    | 176/300 [01:36<00:57,  2.15it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 177/300 [01:36<00:57,  2.15it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 178/300 [01:37<00:56,  2.15it/s]
# Trainer step: 168, epoch: 12:  60%|█████▉    | 179/300 [01:37<00:56,  2.15it/s]
# Trainer step: 168, epoch: 12:  60%|██████    | 180/300 [01:38<00:55,  2.15it/s]Progress: 63.00%
+---- avg training fps: 7.29

# Trainer step: 168, epoch: 12:  60%|██████    | 181/300 [01:38<00:55,  2.15it/s]
# Trainer step: 168, epoch: 12:  61%|██████    | 182/300 [01:39<00:54,  2.15it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 182/300 [01:39<00:54,  2.15it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 183/300 [01:39<00:52,  2.24it/s]
# Trainer step: 182, epoch: 13:  61%|██████▏   | 184/300 [01:40<00:59,  1.96it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 185/300 [01:40<00:57,  2.01it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 186/300 [01:41<00:55,  2.05it/s]Progress: 65.00%
+---- avg training fps: 7.31

# Trainer step: 182, epoch: 13:  62%|██████▏   | 187/300 [01:41<00:54,  2.08it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 188/300 [01:42<00:53,  2.11it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 189/300 [01:42<00:52,  2.12it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 190/300 [01:43<00:51,  2.14it/s]
# Trainer step: 182, epoch: 13:  64%|██████▎   | 191/300 [01:43<00:50,  2.14it/s]
# Trainer step: 182, epoch: 13:  64%|██████▍   | 192/300 [01:44<00:50,  2.14it/s]Progress: 67.00%
+---- avg training fps: 7.35

# Trainer step: 182, epoch: 13:  64%|██████▍   | 193/300 [01:44<00:49,  2.15it/s]
# Trainer step: 182, epoch: 13:  65%|██████▍   | 194/300 [01:44<00:49,  2.15it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 195/300 [01:45<00:48,  2.16it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 196/300 [01:45<00:48,  2.16it/s]
# Trainer step: 196, epoch: 14:  65%|██████▌   | 196/300 [01:46<00:48,  2.16it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 197/300 [01:46<00:45,  2.25it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 198/300 [01:46<00:46,  2.21it/s]Progress: 69.00%
+---- avg training fps: 7.39

# Trainer step: 196, epoch: 14:  66%|██████▋   | 199/300 [01:47<00:46,  2.20it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 200/300 [01:47<00:45,  2.18it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 201/300 [01:48<00:45,  2.18it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 202/300 [01:50<01:38,  1.00s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 203/300 [01:50<01:21,  1.19it/s]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 204/300 [01:51<01:09,  1.37it/s]Progress: 71.00%
+---- avg training fps: 7.30

# Trainer step: 196, epoch: 14:  68%|██████▊   | 205/300 [01:51<01:01,  1.54it/s]
# Trainer step: 196, epoch: 14:  69%|██████▊   | 206/300 [01:52<00:55,  1.68it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 207/300 [01:52<00:51,  1.81it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 208/300 [01:53<00:48,  1.90it/s]
# Trainer step: 196, epoch: 14:  70%|██████▉   | 209/300 [01:53<00:46,  1.97it/s]
# Trainer step: 196, epoch: 14:  70%|███████   | 210/300 [01:54<00:44,  2.02it/s]Progress: 73.00%
+---- avg training fps: 7.33

# Trainer step: 210, epoch: 15:  70%|███████   | 210/300 [01:54<00:44,  2.02it/s]
# Trainer step: 210, epoch: 15:  70%|███████   | 211/300 [01:54<00:41,  2.14it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 212/300 [01:55<00:41,  2.14it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 213/300 [01:55<00:40,  2.15it/s]
# Trainer step: 210, epoch: 15:  71%|███████▏  | 214/300 [01:55<00:40,  2.15it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 215/300 [01:56<00:39,  2.15it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 216/300 [01:56<00:39,  2.15it/s]Progress: 75.00%
+---- avg training fps: 7.36

# Trainer step: 210, epoch: 15:  72%|███████▏  | 217/300 [01:57<00:38,  2.16it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 218/300 [01:57<00:38,  2.15it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 219/300 [01:58<00:37,  2.15it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 220/300 [01:58<00:37,  2.15it/s]
# Trainer step: 210, epoch: 15:  74%|███████▎  | 221/300 [01:59<00:36,  2.15it/s]
# Trainer step: 210, epoch: 15:  74%|███████▍  | 222/300 [01:59<00:36,  2.15it/s]Progress: 77.00%
+---- avg training fps: 7.39

# Trainer step: 210, epoch: 15:  74%|███████▍  | 223/300 [02:00<00:35,  2.15it/s]
# Trainer step: 210, epoch: 15:  75%|███████▍  | 224/300 [02:00<00:35,  2.15it/s]
# Trainer step: 224, epoch: 16:  75%|███████▍  | 224/300 [02:00<00:35,  2.15it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 225/300 [02:00<00:33,  2.25it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 226/300 [02:01<00:33,  2.22it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 227/300 [02:01<00:33,  2.19it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 228/300 [02:02<00:32,  2.18it/s]Progress: 79.00%
+---- avg training fps: 7.42

# Trainer step: 224, epoch: 16:  76%|███████▋  | 229/300 [02:02<00:32,  2.17it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 230/300 [02:03<00:32,  2.16it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 231/300 [02:03<00:31,  2.16it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 232/300 [02:04<00:31,  2.16it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 233/300 [02:04<00:31,  2.15it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 234/300 [02:05<00:30,  2.15it/s]Progress: 81.00%
+---- avg training fps: 7.45

# Trainer step: 224, epoch: 16:  78%|███████▊  | 235/300 [02:05<00:30,  2.15it/s]
# Trainer step: 224, epoch: 16:  79%|███████▊  | 236/300 [02:06<00:29,  2.15it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 237/300 [02:06<00:29,  2.15it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 238/300 [02:07<00:28,  2.15it/s]
# Trainer step: 238, epoch: 17:  79%|███████▉  | 238/300 [02:07<00:28,  2.15it/s]
# Trainer step: 238, epoch: 17:  80%|███████▉  | 239/300 [02:07<00:26,  2.28it/s]
# Trainer step: 238, epoch: 17:  80%|████████  | 240/300 [02:07<00:26,  2.24it/s]Progress: 83.00%
+---- avg training fps: 7.48

# Trainer step: 238, epoch: 17:  80%|████████  | 241/300 [02:08<00:26,  2.21it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 242/300 [02:08<00:26,  2.19it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 243/300 [02:09<00:26,  2.18it/s]
# Trainer step: 238, epoch: 17:  81%|████████▏ | 244/300 [02:09<00:25,  2.17it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 245/300 [02:10<00:25,  2.16it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 246/300 [02:10<00:25,  2.16it/s]Progress: 85.00%
+---- avg training fps: 7.50

# Trainer step: 238, epoch: 17:  82%|████████▏ | 247/300 [02:11<00:24,  2.16it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 248/300 [02:11<00:24,  2.15it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 249/300 [02:12<00:23,  2.15it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 250/300 [02:12<00:23,  2.16it/s]
# Trainer step: 238, epoch: 17:  84%|████████▎ | 251/300 [02:12<00:22,  2.15it/s]
# Trainer step: 238, epoch: 17:  84%|████████▍ | 252/300 [02:16<01:04,  1.35s/it]Progress: 87.00%
+---- avg training fps: 7.37

# Trainer step: 252, epoch: 18:  84%|████████▍ | 252/300 [02:16<01:04,  1.35s/it]
# Trainer step: 252, epoch: 18:  84%|████████▍ | 253/300 [02:16<00:50,  1.07s/it]
# Trainer step: 252, epoch: 18:  85%|████████▍ | 254/300 [02:17<00:40,  1.13it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 255/300 [02:17<00:34,  1.32it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 256/300 [02:18<00:29,  1.49it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 257/300 [02:18<00:26,  1.64it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 258/300 [02:19<00:23,  1.77it/s]Progress: 89.00%
+---- avg training fps: 7.39

# Trainer step: 252, epoch: 18:  86%|████████▋ | 259/300 [02:19<00:21,  1.87it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 260/300 [02:20<00:20,  1.94it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 261/300 [02:20<00:19,  2.01it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 262/300 [02:20<00:18,  2.05it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 263/300 [02:21<00:17,  2.08it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 264/300 [02:21<00:17,  2.10it/s]Progress: 91.00%
+---- avg training fps: 7.42

# Trainer step: 252, epoch: 18:  88%|████████▊ | 265/300 [02:22<00:16,  2.11it/s]
# Trainer step: 252, epoch: 18:  89%|████████▊ | 266/300 [02:22<00:16,  2.12it/s]
# Trainer step: 266, epoch: 19:  89%|████████▊ | 266/300 [02:23<00:16,  2.12it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 267/300 [02:23<00:14,  2.23it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 268/300 [02:23<00:14,  2.19it/s]
# Trainer step: 266, epoch: 19:  90%|████████▉ | 269/300 [02:24<00:14,  2.18it/s]
# Trainer step: 266, epoch: 19:  90%|█████████ | 270/300 [02:24<00:13,  2.17it/s]Progress: 93.00%
+---- avg training fps: 7.44

# Trainer step: 266, epoch: 19:  90%|█████████ | 271/300 [02:25<00:13,  2.16it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 272/300 [02:25<00:12,  2.15it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 273/300 [02:26<00:12,  2.15it/s]
# Trainer step: 266, epoch: 19:  91%|█████████▏| 274/300 [02:26<00:12,  2.15it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 275/300 [02:26<00:11,  2.15it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 276/300 [02:27<00:11,  2.15it/s]Progress: 95.00%
+---- avg training fps: 7.46

# Trainer step: 266, epoch: 19:  92%|█████████▏| 277/300 [02:27<00:10,  2.15it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 278/300 [02:28<00:10,  2.16it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 279/300 [02:28<00:09,  2.16it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 280/300 [02:29<00:09,  2.15it/s]
# Trainer step: 280, epoch: 20:  93%|█████████▎| 280/300 [02:29<00:09,  2.15it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▎| 281/300 [02:29<00:08,  2.25it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▍| 282/300 [02:30<00:08,  2.22it/s]Progress: 97.00%
+---- avg training fps: 7.49

# Trainer step: 280, epoch: 20:  94%|█████████▍| 283/300 [02:30<00:07,  2.20it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▍| 284/300 [02:31<00:07,  2.19it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 285/300 [02:31<00:06,  2.18it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 286/300 [02:32<00:06,  2.17it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 287/300 [02:32<00:06,  2.17it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 288/300 [02:32<00:05,  2.16it/s]Progress: 99.00%
+---- avg training fps: 7.51

# Trainer step: 280, epoch: 20:  96%|█████████▋| 289/300 [02:33<00:05,  2.15it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 290/300 [02:33<00:04,  2.15it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 291/300 [02:34<00:04,  2.15it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 292/300 [02:34<00:03,  2.15it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 293/300 [02:35<00:03,  2.15it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 294/300 [02:35<00:02,  2.14it/s]Progress: 100.00%
+---- avg training fps: 7.53

# Trainer step: 294, epoch: 21:  98%|█████████▊| 294/300 [02:36<00:02,  2.14it/s]
# Trainer step: 294, epoch: 21:  98%|█████████▊| 295/300 [02:36<00:02,  2.24it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▊| 296/300 [02:36<00:01,  2.21it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 297/300 [02:37<00:01,  2.20it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 298/300 [02:37<00:00,  2.19it/s]
# Trainer step: 294, epoch: 21: 100%|█████████▉| 299/300 [02:38<00:00,  2.17it/s]
# Trainer step: 294, epoch: 21: 100%|██████████| 300/300 [02:38<00:00,  2.17it/s]Progress: 100.00%
+---- avg training fps: 7.55

# Trainer step: 294, epoch: 21: : 301it [02:38,  2.16it/s]                       Progress: 100.00%
Failed to plot token attention loss
+Reached max steps, stopping training!
+Saving checkpoint at step.. 301
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 294, epoch: 21: : 301it [03:57,  1.27it/s]
+------------------------------------------
+Training done :)
+2024-08-13 03:21:01.703569: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 03:21:01.794616: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 03:21:01.794644: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 03:21:01.813268: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 03:21:02.212838: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:21:02.212919: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:21:02.212928: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23128 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/good_styles/eden_crystals' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.5 freeze_unet_before_completion_f=0.25 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='eden_crystals' output_dir='lora_models/styles_final/eden_crystals_13_03-21-03-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723512064.038661 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/eden_crystals_13_03-21-03-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.242
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants on a black background.
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants in the dark.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Futuristic natural elements blending with technology.
+
+- In the style of TOK, a potted plant sits on a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of space filled with stars.
+- In the style of TOK, a flower with green leaves is on a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is on a black background.
+- In the style of TOK, a potted plant sits on a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of space filled with stars.
+- In the style of TOK, a flower with green leaves is on a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is in the dark.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Futuristic natural elements blending with technology.
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 7732.15it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a potted plant s...
+1     in the style of <s0><s1><s2>, a futuristic cit...
+2     in the style of <s0><s1><s2>, a flower with gr...
+3     in the style of <s0><s1><s2>, a plant is growi...
+4     in the style of <s0><s1><s2>, a group of green...
+5     in the style of <s0><s1><s2>, a potted plant s...
+6     in the style of <s0><s1><s2>, a futuristic cit...
+7     in the style of <s0><s1><s2>, a flower with gr...
+8     in the style of <s0><s1><s2>, a plant is growi...
+9     in the style of <s0><s1><s2>, a group of green...
+10    in the style of <s0><s1><s2>, a potted plant s...
+11    in the style of <s0><s1><s2>, a futuristic cit...
+12    in the style of <s0><s1><s2>, a flower with gr...
+13    in the style of <s0><s1><s2>, a plant is growi...
+14    in the style of <s0><s1><s2>, a group of green...
+15    in the style of <s0><s1><s2>, a potted plant s...
+16    in the style of <s0><s1><s2>, a futuristic cit...
+17    in the style of <s0><s1><s2>, a flower with gr...
+18    in the style of <s0><s1><s2>, a plant is growi...
+19    in the style of <s0><s1><s2>, a group of green...
+20    in the style of <s0><s1><s2>, a potted plant s...
+21    in the style of <s0><s1><s2>, a futuristic cit...
+22    in the style of <s0><s1><s2>, a flower with gr...
+23    in the style of <s0><s1><s2>, a plant is growi...
+24    in the style of <s0><s1><s2>, a group of green...
+25    in the style of <s0><s1><s2>, a potted plant s...
+26    in the style of <s0><s1><s2>, a futuristic cit...
+27    in the style of <s0><s1><s2>, a flower with gr...
+28    in the style of <s0><s1><s2>, a plant is growi...
+29    in the style of <s0><s1><s2>, a group of green...
+30    in the style of <s0><s1><s2>, a potted plant s...
+31    in the style of <s0><s1><s2>, a futuristic cit...
+32    in the style of <s0><s1><s2>, a flower with gr...
+33    in the style of <s0><s1><s2>, a plant is growi...
+34    in the style of <s0><s1><s2>, a group of green...
+35    in the style of <s0><s1><s2>, a potted plant s...
+36    in the style of <s0><s1><s2>, a futuristic cit...
+37    in the style of <s0><s1><s2>, a flower with gr...
+38    in the style of <s0><s1><s2>, a plant is growi...
+39    in the style of <s0><s1><s2>, a group of green...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:08<04:18,  1.14it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.64

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+---- avg training fps: 4.03

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+---- avg training fps: 4.89

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+---- avg training fps: 5.47

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+---- avg training fps: 5.88

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+---- avg training fps: 6.15

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+---- avg training fps: 6.41

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+---- avg training fps: 6.61

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+---- avg training fps: 6.15

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+---- avg training fps: 6.33

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+---- avg training fps: 6.44

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+---- avg training fps: 6.58

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+---- avg training fps: 6.69

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+---- avg training fps: 6.80

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+---- avg training fps: 6.89

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# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:54<01:35,  2.14it/s]Progress: 35.00%
+---- avg training fps: 6.98

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:55<01:34,  2.14it/s]
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Failed to plot token attention loss
+
+---- avg training fps: 7.06

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:57<01:31,  2.14it/s]
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# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:00<01:29,  2.14it/s]Progress: 39.00%
+---- avg training fps: 7.13

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:00<01:29,  2.13it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:01<01:28,  2.13it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:01<01:28,  2.13it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:01<01:28,  2.14it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:02<01:28,  2.14it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:02<01:27,  2.14it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:02<01:26,  2.14it/s]Progress: 41.00%
+---- avg training fps: 7.19

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:03<01:26,  2.14it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:03<01:25,  2.14it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:04<01:25,  2.14it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:04<01:25,  2.14it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:05<01:24,  2.14it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:05<01:24,  2.14it/s]Progress: 43.00%
+---- avg training fps: 7.25

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:06<01:24,  2.14it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:06<01:23,  2.14it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:06<01:23,  2.14it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:07<01:22,  2.14it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:07<01:22,  2.14it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:08<01:21,  2.14it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:08<01:21,  2.14it/s]Progress: 45.00%
+---- avg training fps: 7.30

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:09<01:20,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:09<01:20,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:09<01:20,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:10<01:19,  2.14it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:10<01:19,  2.14it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:10<01:19,  2.14it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:11<01:18,  2.14it/s]Progress: 47.00%
+---- avg training fps: 7.35

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:11<01:18,  2.14it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:12<01:17,  2.14it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:12<01:17,  2.14it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:13<01:16,  2.14it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:13<01:16,  2.14it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:14<01:15,  2.14it/s]Progress: 49.00%
+---- avg training fps: 7.39

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:14<01:15,  2.14it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:15<01:14,  2.14it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:15<01:14,  2.14it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:15<01:14,  2.13it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:16<01:13,  2.14it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:16<01:13,  2.14it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:17<01:12,  2.14it/s]Progress: 51.00%
+---- avg training fps: 7.44

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:17<01:12,  2.14it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:17<01:12,  2.14it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:18<01:11,  2.14it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:18<01:11,  2.14it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:19<01:10,  2.14it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:19<01:10,  2.14it/s]Progress: 53.00%
+---- avg training fps: 7.47

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:20<01:10,  2.14it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:20<01:09,  2.13it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:24<03:44,  1.51s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:24<02:56,  1.20s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:25<02:23,  1.02it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:25<01:59,  1.21it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:26<01:43,  1.39it/s]Progress: 55.00%
+---- avg training fps: 7.21

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:26<01:31,  1.56it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:27<01:23,  1.70it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:27<01:17,  1.81it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:27<01:13,  1.90it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:28<01:13,  1.90it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:28<01:10,  1.96it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:28<01:08,  2.02it/s]Progress: 57.00%
+---- avg training fps: 7.25

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:29<01:07,  2.04it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:29<01:05,  2.06it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:30<01:04,  2.09it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:30<01:03,  2.10it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:31<01:03,  2.11it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:31<01:02,  2.12it/s]Progress: 59.00%
+---- avg training fps: 7.29

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:32<01:01,  2.13it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:32<01:01,  2.12it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:33<01:01,  2.12it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:33<01:00,  2.13it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:33<00:59,  2.14it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:34<00:59,  2.14it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:34<00:58,  2.14it/s]Progress: 61.00%
+---- avg training fps: 7.33

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:34<00:58,  2.14it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:35<00:57,  2.14it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:35<00:57,  2.14it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:36<00:57,  2.14it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:36<00:56,  2.14it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:37<00:56,  2.14it/s]Progress: 63.00%
+---- avg training fps: 7.36

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:37<00:56,  2.14it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:37<00:55,  2.14it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:38<00:55,  2.14it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:38<00:54,  2.14it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:39<00:54,  2.14it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:39<00:53,  2.14it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:40<00:53,  2.14it/s]Progress: 65.00%
+---- avg training fps: 7.40

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:40<00:52,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:41<00:52,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:41<00:51,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:41<00:51,  2.14it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:42<00:51,  2.14it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:42<00:50,  2.14it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:42<00:50,  2.14it/s]Progress: 67.00%
+---- avg training fps: 7.43

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:43<00:50,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:43<00:49,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:44<00:49,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:44<00:48,  2.14it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:45<00:48,  2.14it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:45<00:47,  2.14it/s]Progress: 69.00%
+---- avg training fps: 7.46

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:46<00:47,  2.14it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:46<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:47<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:47<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:51<02:32,  1.56s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:51<01:59,  1.23s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:52<01:35,  1.00it/s]Progress: 71.00%
+---- avg training fps: 7.25

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:52<01:19,  1.19it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:53<01:08,  1.38it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:53<01:00,  1.54it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:54<00:54,  1.68it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:54<00:50,  1.80it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:54<00:47,  1.89it/s]Progress: 73.00%
+---- avg training fps: 7.28

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:55<00:47,  1.89it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:55<00:45,  1.95it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:55<00:43,  2.00it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:56<00:42,  2.04it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:56<00:41,  2.07it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [01:57<00:40,  2.09it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [01:57<00:39,  2.11it/s]Progress: 75.00%
+---- avg training fps: 7.31

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [01:58<00:39,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [01:58<00:38,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [01:59<00:38,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [01:59<00:37,  2.13it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:00<00:37,  2.13it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:00<00:37,  2.13it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:00<00:36,  2.13it/s]Progress: 77.00%
+---- avg training fps: 7.34

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:01<00:36,  2.14it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:01<00:35,  2.14it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:01<00:35,  2.13it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:02<00:34,  2.14it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:02<00:34,  2.14it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:03<00:33,  2.14it/s]Progress: 79.00%
+---- avg training fps: 7.36

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:03<00:33,  2.14it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:04<00:32,  2.13it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:04<00:32,  2.13it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:04<00:32,  2.14it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:05<00:31,  2.13it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:05<00:31,  2.13it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:06<00:30,  2.13it/s]Progress: 81.00%
+---- avg training fps: 7.39

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:06<00:30,  2.13it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:07<00:29,  2.13it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:07<00:29,  2.13it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:08<00:29,  2.14it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:08<00:28,  2.13it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:09<00:28,  2.14it/s]Progress: 83.00%
+---- avg training fps: 7.41

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:09<00:28,  2.14it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:09<00:27,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:09<00:27,  2.14it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:10<00:26,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:10<00:26,  2.13it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:11<00:25,  2.14it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:11<00:25,  2.14it/s]Progress: 85.00%
+---- avg training fps: 7.44

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:12<00:24,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:12<00:24,  2.14it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:13<00:23,  2.14it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:13<00:23,  2.13it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:14<00:23,  2.13it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:14<00:22,  2.14it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:14<00:22,  2.14it/s]Progress: 87.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.46

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:15<00:21,  2.14it/s]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:15<00:21,  2.14it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:16<00:21,  2.14it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:16<00:20,  2.14it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:16<00:20,  2.14it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:17<00:19,  2.14it/s]Progress: 89.00%
+---- avg training fps: 7.48

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:17<00:19,  2.14it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:18<00:18,  2.14it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:18<00:18,  2.14it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:18<00:18,  2.14it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:19<00:17,  2.14it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:19<00:17,  2.14it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:20<00:16,  2.14it/s]Progress: 91.00%
+---- avg training fps: 7.51

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:20<00:16,  2.14it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:21<00:15,  2.14it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:21<00:15,  2.14it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:22<00:14,  2.14it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:22<00:14,  2.14it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:23<00:14,  2.14it/s]Progress: 93.00%
+---- avg training fps: 7.53

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:23<00:14,  2.14it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:23<00:13,  2.11it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:23<00:13,  2.12it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:24<00:14,  1.88it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:25<00:13,  1.95it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:25<00:12,  2.01it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:26<00:11,  2.05it/s]Progress: 95.00%
+---- avg training fps: 7.54

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:26<00:11,  2.08it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:26<00:10,  2.10it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:27<00:09,  2.11it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:27<00:09,  2.12it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:28<00:09,  2.12it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:28<00:08,  2.13it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:28<00:08,  2.14it/s]Progress: 97.00%
+---- avg training fps: 7.55

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:29<00:07,  2.14it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:29<00:07,  2.14it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:30<00:07,  2.14it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:30<00:06,  2.15it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:31<00:06,  2.15it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:31<00:05,  2.15it/s]Progress: 99.00%
+---- avg training fps: 7.57

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:32<00:05,  2.15it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:32<00:04,  2.15it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:33<00:04,  2.15it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:33<00:04,  2.16it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:33<00:03,  2.15it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:33<00:03,  2.15it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:34<00:02,  2.15it/s]Progress: 100.00%
+---- avg training fps: 7.59

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:34<00:02,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:35<00:01,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:35<00:01,  2.14it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:36<00:00,  2.15it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:36<00:00,  2.15it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:37<00:00,  2.15it/s]Progress: 100.00%
+---- avg training fps: 7.61
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [03:56<00:00,  1.27it/s]
+------------------------------------------
+Training done :)
+2024-08-13 03:25:30.352316: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 03:25:30.443540: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 03:25:30.443566: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 03:25:30.462405: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 03:25:30.861651: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:25:30.861732: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:25:30.861741: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23132 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/journey_small' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.75 freeze_unet_before_completion_f=0.5 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='journey_small' output_dir='lora_models/styles_final/journey_small_13_03-25-32-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723512332.6346176 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/journey_small_13_03-25-32-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 0.812
+New train_img_size: [448, 576]
+Validation_img_size was set to: [896, 1152]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red cloak standing in front of a building.
+- A person standing on top of a body of water.
+- A person walking in the desert with a camel in front of a mountain.
+- A man standing on top of a sandy beach next to the ocean.
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red dress is walking through an archway.
+- A person standing on top of a body of water.
+- A person walking in the desert with a dog on a leash.
+- A person walking on a beach next to a body of water.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal and dreamlike scenes.
+
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red cloak standing in front of a building.
+- In the style of TOK, a person standing on top of a body of water.
+- In the style of TOK, a person walking in the desert with a camel in front of a mountain.
+- In the style of TOK, a man standing on top of a sandy beach next to the ocean.
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red dress is walking through an archway.
+- In the style of TOK, a person standing on top of a body of water.
+- In the style of TOK, a person walking in the desert with a dog on a leash.
+- In the style of TOK, a person walking on a beach next to a body of water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Surreal and dreamlike scenes.
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 11399.89it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a sunset over a ...
+1     in the style of <s0><s1><s2>, a woman in a red...
+2     in the style of <s0><s1><s2>, a person standin...
+3     in the style of <s0><s1><s2>, a person walking...
+4     in the style of <s0><s1><s2>, a man standing o...
+5     in the style of <s0><s1><s2>, a sunset over a ...
+6     in the style of <s0><s1><s2>, a woman in a red...
+7     in the style of <s0><s1><s2>, a person standin...
+8     in the style of <s0><s1><s2>, a person walking...
+9     in the style of <s0><s1><s2>, a person walking...
+10    in the style of <s0><s1><s2>, a sunset over a ...
+11    in the style of <s0><s1><s2>, a woman in a red...
+12    in the style of <s0><s1><s2>, a person standin...
+13    in the style of <s0><s1><s2>, a person walking...
+14    in the style of <s0><s1><s2>, a man standing o...
+15    in the style of <s0><s1><s2>, a sunset over a ...
+16    in the style of <s0><s1><s2>, a woman in a red...
+17    in the style of <s0><s1><s2>, a person standin...
+18    in the style of <s0><s1><s2>, a person walking...
+19    in the style of <s0><s1><s2>, a person walking...
+20    in the style of <s0><s1><s2>, a sunset over a ...
+21    in the style of <s0><s1><s2>, a woman in a red...
+22    in the style of <s0><s1><s2>, a person standin...
+23    in the style of <s0><s1><s2>, a person walking...
+24    in the style of <s0><s1><s2>, a man standing o...
+25    in the style of <s0><s1><s2>, a sunset over a ...
+26    in the style of <s0><s1><s2>, a woman in a red...
+27    in the style of <s0><s1><s2>, a person standin...
+28    in the style of <s0><s1><s2>, a person walking...
+29    in the style of <s0><s1><s2>, a person walking...
+30    in the style of <s0><s1><s2>, a sunset over a ...
+31    in the style of <s0><s1><s2>, a woman in a red...
+32    in the style of <s0><s1><s2>, a person standin...
+33    in the style of <s0><s1><s2>, a person walking...
+34    in the style of <s0><s1><s2>, a man standing o...
+35    in the style of <s0><s1><s2>, a sunset over a ...
+36    in the style of <s0><s1><s2>, a woman in a red...
+37    in the style of <s0><s1><s2>, a person standin...
+38    in the style of <s0><s1><s2>, a person walking...
+39    in the style of <s0><s1><s2>, a person walking...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:02<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:02<14:56,  3.00s/it]
# Trainer step: 0, epoch: 0:   1%|          | 2/300 [00:07<19:49,  3.99s/it]
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# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:09<05:56,  1.21s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:09<04:41,  1.04it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.39

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:10<03:54,  1.25it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:10<03:22,  1.44it/s]
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# Trainer step: 10, epoch: 1:   3%|▎         | 10/300 [00:11<02:47,  1.73it/s]
# Trainer step: 10, epoch: 1:   4%|▎         | 11/300 [00:11<02:36,  1.84it/s]
# Trainer step: 10, epoch: 1:   4%|▍         | 12/300 [00:12<02:30,  1.92it/s]Progress: 7.00%
+---- avg training fps: 3.68

# Trainer step: 10, epoch: 1:   4%|▍         | 13/300 [00:13<02:42,  1.76it/s]
# Trainer step: 10, epoch: 1:   5%|▍         | 14/300 [00:13<02:33,  1.86it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 15/300 [00:13<02:27,  1.93it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 16/300 [00:14<02:23,  1.98it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 17/300 [00:14<02:19,  2.02it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 18/300 [00:15<02:17,  2.05it/s]Progress: 9.00%
+---- avg training fps: 4.54

# Trainer step: 10, epoch: 1:   6%|▋         | 19/300 [00:15<02:15,  2.08it/s]
# Trainer step: 10, epoch: 1:   7%|▋         | 20/300 [00:16<02:14,  2.09it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 20/300 [00:16<02:14,  2.09it/s]
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# Trainer step: 20, epoch: 2:   8%|▊         | 23/300 [00:17<02:11,  2.11it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 24/300 [00:18<02:10,  2.12it/s]Progress: 11.00%
+---- avg training fps: 5.13

# Trainer step: 20, epoch: 2:   8%|▊         | 25/300 [00:18<02:09,  2.12it/s]
# Trainer step: 20, epoch: 2:   9%|▊         | 26/300 [00:19<02:09,  2.12it/s]
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# Trainer step: 20, epoch: 2:  10%|█         | 30/300 [00:21<02:07,  2.11it/s]Progress: 13.00%
+---- avg training fps: 5.57

# Trainer step: 30, epoch: 3:  10%|█         | 30/300 [00:21<02:07,  2.11it/s]
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# Trainer step: 30, epoch: 3:  11%|█         | 32/300 [00:22<02:06,  2.11it/s]
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# Trainer step: 30, epoch: 3:  11%|█▏        | 34/300 [00:22<02:05,  2.12it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 35/300 [00:23<02:04,  2.13it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 36/300 [00:23<02:04,  2.13it/s]Progress: 15.00%
+---- avg training fps: 5.91

# Trainer step: 30, epoch: 3:  12%|█▏        | 37/300 [00:24<02:03,  2.13it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 38/300 [00:24<02:02,  2.13it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 39/300 [00:25<02:02,  2.13it/s]
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# Trainer step: 40, epoch: 4:  13%|█▎        | 40/300 [00:26<02:02,  2.13it/s]
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# Trainer step: 40, epoch: 4:  14%|█▍        | 42/300 [00:26<02:01,  2.12it/s]Progress: 17.00%
+---- avg training fps: 6.18

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:27<02:00,  2.13it/s]
# Trainer step: 40, epoch: 4:  15%|█▍        | 44/300 [00:27<02:00,  2.12it/s]
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# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:29<01:58,  2.12it/s]Progress: 19.00%
+---- avg training fps: 6.40

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:30<01:58,  2.13it/s]
# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:30<01:57,  2.13it/s]
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# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:36<04:09,  1.01s/it]Progress: 21.00%
+---- avg training fps: 5.92

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:36<03:28,  1.17it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:36<02:59,  1.36it/s]
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# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:38<02:15,  1.77it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:38<02:08,  1.87it/s]Progress: 23.00%
+---- avg training fps: 6.10

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:39<02:08,  1.87it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:39<02:03,  1.93it/s]
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# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:41<01:53,  2.07it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:41<01:52,  2.08it/s]Progress: 25.00%
+---- avg training fps: 6.26

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:42<01:51,  2.10it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:42<01:50,  2.10it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:43<01:49,  2.10it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:43<01:49,  2.11it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:44<01:49,  2.11it/s]
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# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:44<01:48,  2.11it/s]Progress: 27.00%
+---- avg training fps: 6.40

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:45<01:47,  2.11it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:45<01:46,  2.11it/s]
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# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:47<01:45,  2.11it/s]Progress: 29.00%
+---- avg training fps: 6.52

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:47<01:44,  2.11it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:48<01:44,  2.11it/s]
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# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:50<01:42,  2.11it/s]Progress: 31.00%
+---- avg training fps: 6.63

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:50<01:41,  2.12it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:51<01:41,  2.11it/s]
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# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:53<01:38,  2.13it/s]Progress: 33.00%
+---- avg training fps: 6.73

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:53<01:38,  2.13it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:53<01:37,  2.13it/s]
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# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:54<01:36,  2.13it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:55<01:36,  2.13it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:55<01:35,  2.13it/s]Progress: 35.00%
+---- avg training fps: 6.82

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:56<01:35,  2.13it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:56<01:34,  2.13it/s]
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# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:57<01:34,  2.12it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:58<01:34,  2.12it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:58<01:33,  2.13it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:58<01:32,  2.13it/s]Progress: 37.00%
Failed to plot token attention loss
+
+---- avg training fps: 6.90

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:59<01:32,  2.13it/s]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [00:59<01:32,  2.13it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [01:00<01:31,  2.12it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [01:00<01:31,  2.12it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:01<01:31,  2.12it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:01<01:30,  2.12it/s]Progress: 39.00%
+---- avg training fps: 6.97

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:02<01:30,  2.12it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:02<01:29,  2.12it/s]
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# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:02<01:29,  2.12it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:03<01:28,  2.12it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:03<01:28,  2.12it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:04<01:27,  2.12it/s]Progress: 41.00%
+---- avg training fps: 7.03

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:04<01:26,  2.13it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:05<01:26,  2.12it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:05<01:26,  2.11it/s]
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# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:06<01:25,  2.12it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:07<01:24,  2.12it/s]Progress: 43.00%
+---- avg training fps: 7.09

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:07<01:24,  2.12it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:07<01:24,  2.13it/s]
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# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:08<01:22,  2.13it/s]
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# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:09<01:22,  2.12it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:10<01:22,  2.11it/s]Progress: 45.00%
+---- avg training fps: 7.15

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:10<01:22,  2.11it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:10<01:21,  2.11it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:11<01:21,  2.11it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:11<01:20,  2.11it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:12<01:20,  2.11it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:12<01:20,  2.10it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:12<01:19,  2.10it/s]Progress: 47.00%
+---- avg training fps: 7.20

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:13<01:19,  2.10it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:13<01:18,  2.11it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:14<01:18,  2.10it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:14<01:18,  2.10it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:15<01:17,  2.09it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:15<01:17,  2.09it/s]Progress: 49.00%
+---- avg training fps: 7.24

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:16<01:17,  2.09it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:16<01:16,  2.09it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:17<01:16,  2.09it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:17<01:15,  2.10it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:17<01:15,  2.10it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:18<01:14,  2.11it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:18<01:14,  2.11it/s]Progress: 51.00%
+---- avg training fps: 7.29

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:19<01:13,  2.10it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:19<01:13,  2.10it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:20<01:12,  2.10it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:20<01:12,  2.10it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:20<01:11,  2.10it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:21<01:11,  2.10it/s]Progress: 53.00%
+---- avg training fps: 7.32

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:21<01:11,  2.10it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:21<01:11,  2.10it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:26<04:13,  1.71s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:27<03:17,  1.34s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:27<02:37,  1.08s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:27<02:10,  1.11it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:28<01:51,  1.29it/s]Progress: 55.00%
+---- avg training fps: 7.02

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:28<01:37,  1.46it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:29<01:27,  1.62it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:29<01:21,  1.74it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:30<01:16,  1.83it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:30<01:16,  1.83it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:30<01:12,  1.91it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:31<01:10,  1.96it/s]Progress: 57.00%
+---- avg training fps: 7.06

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:31<01:08,  2.00it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:32<01:06,  2.03it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:32<01:05,  2.06it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:33<01:04,  2.07it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:33<01:03,  2.09it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:34<01:03,  2.09it/s]Progress: 59.00%
+---- avg training fps: 7.11

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:34<01:02,  2.10it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:35<01:02,  2.09it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:35<01:02,  2.09it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:35<01:01,  2.10it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:36<01:01,  2.10it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:36<01:00,  2.10it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:36<00:59,  2.11it/s]Progress: 61.00%
+---- avg training fps: 7.14

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:37<00:59,  2.10it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:37<00:58,  2.11it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:38<00:58,  2.11it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:38<00:57,  2.11it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:39<00:57,  2.11it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:39<00:56,  2.11it/s]Progress: 63.00%
+---- avg training fps: 7.18

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:40<00:56,  2.11it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:40<00:56,  2.11it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:40<00:55,  2.11it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:41<00:55,  2.11it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:41<00:55,  2.11it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:42<00:54,  2.11it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:42<00:54,  2.10it/s]Progress: 65.00%
+---- avg training fps: 7.21

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:43<00:53,  2.11it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:43<00:53,  2.11it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:44<00:52,  2.11it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:44<00:52,  2.11it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:45<00:52,  2.11it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:45<00:51,  2.11it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:45<00:51,  2.10it/s]Progress: 67.00%
+---- avg training fps: 7.25

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:45<00:50,  2.10it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:46<00:50,  2.10it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:46<00:49,  2.11it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:47<00:49,  2.11it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:47<00:48,  2.11it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:48<00:48,  2.11it/s]Progress: 69.00%
+---- avg training fps: 7.28

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:48<00:47,  2.11it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:49<00:47,  2.11it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:49<00:47,  2.11it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:49<00:46,  2.11it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:54<02:51,  1.75s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:54<02:12,  1.37s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:55<01:45,  1.10s/it]Progress: 71.00%
+---- avg training fps: 7.04

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:55<01:26,  1.10it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:56<01:13,  1.28it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:56<01:03,  1.46it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:57<00:57,  1.60it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:57<00:52,  1.73it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:58<00:49,  1.83it/s]Progress: 73.00%
+---- avg training fps: 7.07

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:58<00:49,  1.83it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:58<00:46,  1.90it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:59<00:45,  1.95it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:59<00:43,  2.00it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [02:00<00:42,  2.03it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [02:00<00:41,  2.05it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [02:01<00:40,  2.06it/s]Progress: 75.00%
+---- avg training fps: 7.11

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [02:01<00:39,  2.08it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [02:02<00:39,  2.09it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:02<00:38,  2.09it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:03<00:38,  2.10it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:03<00:38,  2.10it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:03<00:37,  2.10it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:03<00:37,  2.10it/s]Progress: 77.00%
+---- avg training fps: 7.14

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:04<00:36,  2.11it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:04<00:36,  2.11it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:05<00:35,  2.10it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:05<00:35,  2.10it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:06<00:34,  2.10it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:06<00:34,  2.11it/s]Progress: 79.00%
+---- avg training fps: 7.16

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:07<00:33,  2.11it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:07<00:33,  2.11it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:08<00:33,  2.11it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:08<00:32,  2.11it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:08<00:32,  2.12it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:09<00:31,  2.11it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:09<00:31,  2.11it/s]Progress: 81.00%
+---- avg training fps: 7.19

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:10<00:30,  2.11it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:10<00:30,  2.11it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:11<00:29,  2.10it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:11<00:29,  2.11it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:12<00:28,  2.11it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:12<00:28,  2.11it/s]Progress: 83.00%
+---- avg training fps: 7.22

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:12<00:28,  2.11it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:12<00:27,  2.11it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:13<00:27,  2.12it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:13<00:26,  2.12it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:14<00:26,  2.11it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:14<00:25,  2.12it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:15<00:25,  2.12it/s]Progress: 85.00%
+---- avg training fps: 7.24

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:15<00:25,  2.12it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:16<00:24,  2.12it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:16<00:24,  2.12it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:17<00:23,  2.12it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:17<00:23,  2.12it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:17<00:23,  2.12it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:18<00:22,  2.12it/s]Progress: 87.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.27

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:18<00:22,  2.12it/s]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:19<00:21,  2.13it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:19<00:21,  2.12it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:20<00:20,  2.12it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:20<00:20,  2.12it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:21<00:19,  2.12it/s]Progress: 89.00%
+---- avg training fps: 7.29

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:21<00:19,  2.12it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:21<00:18,  2.12it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:22<00:18,  2.12it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:22<00:18,  2.12it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:22<00:17,  2.12it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:23<00:17,  2.12it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:23<00:16,  2.12it/s]Progress: 91.00%
+---- avg training fps: 7.32

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:24<00:16,  2.12it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:24<00:16,  2.12it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:25<00:15,  2.11it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:25<00:15,  2.12it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:26<00:14,  2.12it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:26<00:14,  2.11it/s]Progress: 93.00%
+---- avg training fps: 7.34

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:27<00:14,  2.11it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:27<00:13,  2.11it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:27<00:13,  2.12it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:28<00:12,  2.12it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:28<00:12,  2.12it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:29<00:11,  2.12it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:29<00:11,  2.11it/s]Progress: 95.00%
+---- avg training fps: 7.36

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:29<00:10,  2.11it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:30<00:10,  2.11it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:30<00:10,  2.09it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:31<00:09,  2.05it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:31<00:09,  2.05it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:31<00:09,  2.07it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:32<00:08,  2.07it/s]Progress: 97.00%
+---- avg training fps: 7.38

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:32<00:08,  2.08it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:33<00:07,  2.09it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:33<00:07,  2.10it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:34<00:06,  2.11it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:34<00:06,  2.11it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:35<00:05,  2.11it/s]Progress: 99.00%
+---- avg training fps: 7.40

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:35<00:05,  2.11it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:36<00:05,  1.87it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:36<00:05,  1.87it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:36<00:04,  1.95it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:37<00:03,  2.00it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:37<00:03,  2.04it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:38<00:02,  2.05it/s]Progress: 100.00%
+---- avg training fps: 7.41

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:38<00:02,  2.08it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:39<00:01,  2.10it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:39<00:01,  2.10it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:40<00:00,  2.11it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:40<00:00,  2.12it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:41<00:00,  2.13it/s]Progress: 100.00%
+---- avg training fps: 7.43
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [04:00<00:00,  1.25it/s]
+------------------------------------------
+Training done :)
+2024-08-13 03:29:57.560146: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 03:29:57.650042: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 03:29:57.650066: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 03:29:57.668821: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 03:29:58.058335: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:29:58.058413: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:29:58.058423: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23135 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/journey_small' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.5 freeze_unet_before_completion_f=0.25 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='journey_small' output_dir='lora_models/styles_final/journey_small_13_03-29-59-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723512599.8059993 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/journey_small_13_03-29-59-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 0.812
+New train_img_size: [448, 576]
+Validation_img_size was set to: [896, 1152]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red cloak standing in front of a building.
+- A person standing on top of a body of water.
+- A person walking in the desert with a camel in front of a mountain.
+- A man standing on top of a sandy beach next to the ocean.
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red dress is walking through an archway.
+- A person standing on top of a body of water.
+- A person walking in the desert with a dog on a leash.
+- A person walking on a beach next to a body of water.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal and dreamlike scenes.
+
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red cloak standing in front of a building.
+- In the style of TOK, a person standing on top of a body of water.
+- In the style of TOK, a person walking in the desert with a camel in front of a mountain.
+- In the style of TOK, a man standing on top of a sandy beach next to the ocean.
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red dress is walking through an archway.
+- In the style of TOK, a person standing on top of a body of water.
+- In the style of TOK, a person walking in the desert with a dog on a leash.
+- In the style of TOK, a person walking on a beach next to a body of water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Surreal and dreamlike scenes.
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 9005.97it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a sunset over a ...
+1     in the style of <s0><s1><s2>, a woman in a red...
+2     in the style of <s0><s1><s2>, a person standin...
+3     in the style of <s0><s1><s2>, a person walking...
+4     in the style of <s0><s1><s2>, a man standing o...
+5     in the style of <s0><s1><s2>, a sunset over a ...
+6     in the style of <s0><s1><s2>, a woman in a red...
+7     in the style of <s0><s1><s2>, a person standin...
+8     in the style of <s0><s1><s2>, a person walking...
+9     in the style of <s0><s1><s2>, a person walking...
+10    in the style of <s0><s1><s2>, a sunset over a ...
+11    in the style of <s0><s1><s2>, a woman in a red...
+12    in the style of <s0><s1><s2>, a person standin...
+13    in the style of <s0><s1><s2>, a person walking...
+14    in the style of <s0><s1><s2>, a man standing o...
+15    in the style of <s0><s1><s2>, a sunset over a ...
+16    in the style of <s0><s1><s2>, a woman in a red...
+17    in the style of <s0><s1><s2>, a person standin...
+18    in the style of <s0><s1><s2>, a person walking...
+19    in the style of <s0><s1><s2>, a person walking...
+20    in the style of <s0><s1><s2>, a sunset over a ...
+21    in the style of <s0><s1><s2>, a woman in a red...
+22    in the style of <s0><s1><s2>, a person standin...
+23    in the style of <s0><s1><s2>, a person walking...
+24    in the style of <s0><s1><s2>, a man standing o...
+25    in the style of <s0><s1><s2>, a sunset over a ...
+26    in the style of <s0><s1><s2>, a woman in a red...
+27    in the style of <s0><s1><s2>, a person standin...
+28    in the style of <s0><s1><s2>, a person walking...
+29    in the style of <s0><s1><s2>, a person walking...
+30    in the style of <s0><s1><s2>, a sunset over a ...
+31    in the style of <s0><s1><s2>, a woman in a red...
+32    in the style of <s0><s1><s2>, a person standin...
+33    in the style of <s0><s1><s2>, a person walking...
+34    in the style of <s0><s1><s2>, a man standing o...
+35    in the style of <s0><s1><s2>, a sunset over a ...
+36    in the style of <s0><s1><s2>, a woman in a red...
+37    in the style of <s0><s1><s2>, a person standin...
+38    in the style of <s0><s1><s2>, a person walking...
+39    in the style of <s0><s1><s2>, a person walking...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
  0%|          | 0/300 [00:00<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:03<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:03<15:09,  3.04s/it]
# Trainer step: 0, epoch: 0:   1%|          | 2/300 [00:07<19:57,  4.02s/it]
# Trainer step: 0, epoch: 0:   1%|          | 3/300 [00:08<11:52,  2.40s/it]
# Trainer step: 0, epoch: 0:   1%|▏         | 4/300 [00:08<08:04,  1.64s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:09<05:58,  1.22s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:09<04:43,  1.04it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.38

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:10<03:55,  1.25it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:10<03:23,  1.43it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 9/300 [00:11<03:02,  1.59it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 10/300 [00:11<02:47,  1.73it/s]
# Trainer step: 10, epoch: 1:   3%|▎         | 10/300 [00:11<02:47,  1.73it/s]
# Trainer step: 10, epoch: 1:   4%|▎         | 11/300 [00:11<02:38,  1.82it/s]
# Trainer step: 10, epoch: 1:   4%|▍         | 12/300 [00:12<02:31,  1.90it/s]Progress: 7.00%
+---- avg training fps: 3.65

# Trainer step: 10, epoch: 1:   4%|▍         | 13/300 [00:13<02:43,  1.75it/s]
# Trainer step: 10, epoch: 1:   5%|▍         | 14/300 [00:13<02:34,  1.85it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 15/300 [00:14<02:28,  1.92it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 16/300 [00:14<02:23,  1.98it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 17/300 [00:15<02:20,  2.02it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 18/300 [00:15<02:17,  2.05it/s]Progress: 9.00%
+---- avg training fps: 4.51

# Trainer step: 10, epoch: 1:   6%|▋         | 19/300 [00:15<02:15,  2.07it/s]
# Trainer step: 10, epoch: 1:   7%|▋         | 20/300 [00:16<02:14,  2.09it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 20/300 [00:16<02:14,  2.09it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 21/300 [00:16<02:12,  2.10it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 22/300 [00:17<02:11,  2.11it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 23/300 [00:17<02:11,  2.11it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 24/300 [00:18<02:10,  2.11it/s]Progress: 11.00%
+---- avg training fps: 5.11

# Trainer step: 20, epoch: 2:   8%|▊         | 25/300 [00:18<02:10,  2.12it/s]
# Trainer step: 20, epoch: 2:   9%|▊         | 26/300 [00:19<02:09,  2.12it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 27/300 [00:19<02:08,  2.12it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 28/300 [00:20<02:08,  2.12it/s]
# Trainer step: 20, epoch: 2:  10%|▉         | 29/300 [00:20<02:08,  2.12it/s]
# Trainer step: 20, epoch: 2:  10%|█         | 30/300 [00:21<02:07,  2.12it/s]Progress: 13.00%
+---- avg training fps: 5.55

# Trainer step: 30, epoch: 3:  10%|█         | 30/300 [00:21<02:07,  2.12it/s]
# Trainer step: 30, epoch: 3:  10%|█         | 31/300 [00:21<02:07,  2.12it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 32/300 [00:22<02:06,  2.12it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 33/300 [00:22<02:06,  2.12it/s]
# Trainer step: 30, epoch: 3:  11%|█▏        | 34/300 [00:23<02:05,  2.12it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 35/300 [00:23<02:05,  2.12it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 36/300 [00:23<02:04,  2.12it/s]Progress: 15.00%
+---- avg training fps: 5.89

# Trainer step: 30, epoch: 3:  12%|█▏        | 37/300 [00:24<02:04,  2.12it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 38/300 [00:24<02:03,  2.12it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 39/300 [00:25<02:02,  2.12it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 40/300 [00:25<02:02,  2.12it/s]
# Trainer step: 40, epoch: 4:  13%|█▎        | 40/300 [00:26<02:02,  2.12it/s]
# Trainer step: 40, epoch: 4:  14%|█▎        | 41/300 [00:26<02:01,  2.13it/s]
# Trainer step: 40, epoch: 4:  14%|█▍        | 42/300 [00:26<02:01,  2.13it/s]Progress: 17.00%
+---- avg training fps: 6.16

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:27<02:00,  2.13it/s]
# Trainer step: 40, epoch: 4:  15%|█▍        | 44/300 [00:27<02:00,  2.13it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 45/300 [00:28<01:59,  2.13it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 46/300 [00:28<01:59,  2.12it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 47/300 [00:29<01:59,  2.12it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:29<01:58,  2.12it/s]Progress: 19.00%
+---- avg training fps: 6.38

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:30<01:57,  2.13it/s]
# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:30<01:57,  2.13it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 50/300 [00:31<01:57,  2.13it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 51/300 [00:31<01:56,  2.13it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 52/300 [00:35<06:32,  1.58s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 53/300 [00:35<05:08,  1.25s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:36<04:09,  1.01s/it]Progress: 21.00%
+---- avg training fps: 5.90

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:36<03:28,  1.17it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:37<03:00,  1.35it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 57/300 [00:37<02:39,  1.52it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 58/300 [00:38<02:25,  1.66it/s]
# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:38<02:15,  1.77it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:38<02:08,  1.87it/s]Progress: 23.00%
+---- avg training fps: 6.08

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:39<02:08,  1.87it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:39<02:03,  1.93it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:39<01:59,  1.98it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:40<01:57,  2.02it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:40<01:55,  2.05it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:41<01:53,  2.07it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:41<01:52,  2.08it/s]Progress: 25.00%
+---- avg training fps: 6.24

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:42<01:51,  2.10it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:42<01:50,  2.10it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:43<01:49,  2.11it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:43<01:48,  2.11it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:44<01:48,  2.11it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:44<01:48,  2.11it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:44<01:47,  2.11it/s]Progress: 27.00%
+---- avg training fps: 6.38

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:45<01:47,  2.11it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:45<01:46,  2.12it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:46<01:46,  2.12it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:46<01:45,  2.12it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:47<01:45,  2.12it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:47<01:44,  2.12it/s]Progress: 29.00%
+---- avg training fps: 6.51

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:47<01:44,  2.12it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:48<01:44,  2.11it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:48<01:44,  2.11it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:48<01:43,  2.12it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:49<01:42,  2.12it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:49<01:42,  2.12it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:50<01:42,  2.12it/s]Progress: 31.00%
+---- avg training fps: 6.61

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:50<01:41,  2.12it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:51<01:41,  2.12it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:51<01:40,  2.12it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:52<01:40,  2.12it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:52<01:39,  2.11it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:53<01:39,  2.11it/s]Progress: 33.00%
+---- avg training fps: 6.71

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:53<01:39,  2.11it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:53<01:39,  2.11it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:54<01:38,  2.11it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:54<01:37,  2.12it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:55<01:37,  2.12it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:55<01:36,  2.12it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:55<01:36,  2.12it/s]Progress: 35.00%
+---- avg training fps: 6.80

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:56<01:35,  2.12it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:56<01:35,  2.12it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:57<01:34,  2.12it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:57<01:34,  2.12it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:58<01:34,  2.12it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:58<01:33,  2.12it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [01:02<05:37,  1.70s/it]Progress: 37.00%
+---- avg training fps: 6.44

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [01:03<04:22,  1.33s/it]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [01:03<03:30,  1.07s/it]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [01:04<02:54,  1.12it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [01:04<02:28,  1.30it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:05<02:10,  1.47it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:05<01:58,  1.62it/s]Progress: 39.00%
+---- avg training fps: 6.52

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:06<01:49,  1.75it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:06<01:43,  1.84it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:07<01:43,  1.84it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:07<01:38,  1.92it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:07<01:35,  1.97it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:08<01:32,  2.01it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:08<01:31,  2.04it/s]Progress: 41.00%
+---- avg training fps: 6.60

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:09<01:29,  2.06it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:09<01:28,  2.08it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:10<01:27,  2.09it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:10<01:26,  2.10it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:10<01:26,  2.10it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:11<01:25,  2.10it/s]Progress: 43.00%
+---- avg training fps: 6.68

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:11<01:25,  2.10it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:11<01:24,  2.11it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:12<01:24,  2.11it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:12<01:23,  2.11it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:13<01:23,  2.11it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:13<01:22,  2.11it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:14<01:22,  2.12it/s]Progress: 45.00%
+---- avg training fps: 6.74

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:14<01:21,  2.12it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:15<01:21,  2.12it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:15<01:20,  2.11it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:16<01:20,  2.12it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:16<01:20,  2.12it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:16<01:19,  2.12it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:17<01:19,  2.12it/s]Progress: 47.00%
+---- avg training fps: 6.81

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:17<01:18,  2.11it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:18<01:18,  2.11it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:18<01:18,  2.11it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:18<01:17,  2.11it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:19<01:17,  2.11it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:19<01:16,  2.11it/s]Progress: 49.00%
+---- avg training fps: 6.86

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:20<01:16,  2.11it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:20<01:15,  2.11it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:21<01:15,  2.11it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:21<01:15,  2.11it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:21<01:14,  2.11it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:22<01:14,  2.12it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:22<01:13,  2.12it/s]Progress: 51.00%
+---- avg training fps: 6.92

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:23<01:13,  2.12it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:23<01:13,  2.11it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:24<01:12,  2.10it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:24<01:12,  2.11it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:25<01:11,  2.11it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:25<01:11,  2.11it/s]Progress: 53.00%
+---- avg training fps: 6.97

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:26<01:11,  2.11it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:26<01:10,  2.11it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:30<04:13,  1.71s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:31<03:16,  1.34s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:31<02:37,  1.08s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:32<02:09,  1.12it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:32<01:50,  1.30it/s]Progress: 55.00%
+---- avg training fps: 6.71

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:33<01:37,  1.47it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:33<01:27,  1.62it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:33<01:20,  1.75it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:34<01:15,  1.85it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:34<01:15,  1.85it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:34<01:11,  1.93it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:35<01:09,  1.99it/s]Progress: 57.00%
+---- avg training fps: 6.76

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:35<01:07,  2.04it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:36<01:05,  2.07it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:36<01:04,  2.10it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:37<01:03,  2.12it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:37<01:02,  2.13it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:38<01:01,  2.13it/s]Progress: 59.00%
+---- avg training fps: 6.81

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:38<01:01,  2.14it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:39<01:00,  2.14it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:39<01:00,  2.14it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:39<01:00,  2.14it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:40<00:59,  2.15it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:40<00:59,  2.15it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:40<00:58,  2.15it/s]Progress: 61.00%
+---- avg training fps: 6.86

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:41<00:58,  2.15it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:41<00:57,  2.16it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:42<00:57,  2.15it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:42<00:56,  2.15it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:43<00:56,  2.16it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:43<00:55,  2.16it/s]Progress: 63.00%
+---- avg training fps: 6.91

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:44<00:55,  2.16it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:44<00:55,  2.16it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:44<00:54,  2.16it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:45<00:54,  2.16it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:45<00:53,  2.16it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:46<00:53,  2.16it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:46<00:52,  2.16it/s]Progress: 65.00%
+---- avg training fps: 6.95

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:46<00:52,  2.16it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:47<00:51,  2.16it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:47<00:51,  2.16it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:48<00:50,  2.16it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:48<00:50,  2.16it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:48<00:50,  2.15it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:49<00:50,  2.16it/s]Progress: 67.00%
+---- avg training fps: 7.00

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:49<00:49,  2.16it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:50<00:49,  2.16it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:50<00:48,  2.16it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:51<00:48,  2.16it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:51<00:47,  2.16it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:52<00:47,  2.16it/s]Progress: 69.00%
+---- avg training fps: 7.04

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:52<00:46,  2.16it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:53<00:46,  2.16it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:53<00:46,  2.16it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:53<00:45,  2.16it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:57<02:39,  1.63s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:58<02:04,  1.28s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:58<01:39,  1.03s/it]Progress: 71.00%
+---- avg training fps: 6.83

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:59<01:30,  1.05it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:59<01:15,  1.25it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [02:00<01:05,  1.43it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [02:00<00:57,  1.60it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [02:01<00:52,  1.73it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [02:01<00:48,  1.85it/s]Progress: 73.00%
+---- avg training fps: 6.87

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [02:02<00:48,  1.85it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [02:02<00:45,  1.94it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [02:02<00:43,  2.00it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [02:03<00:42,  2.05it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [02:03<00:41,  2.08it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [02:04<00:40,  2.11it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [02:04<00:39,  2.13it/s]Progress: 75.00%
+---- avg training fps: 6.91

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [02:05<00:38,  2.15it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [02:05<00:38,  2.15it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:05<00:37,  2.16it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:06<00:36,  2.17it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:06<00:36,  2.17it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:06<00:36,  2.17it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:07<00:35,  2.17it/s]Progress: 77.00%
+---- avg training fps: 6.95

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:07<00:35,  2.17it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:08<00:34,  2.18it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:08<00:34,  2.18it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:09<00:34,  2.18it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:09<00:33,  2.18it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:10<00:33,  2.18it/s]Progress: 79.00%
+---- avg training fps: 6.99

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:10<00:32,  2.18it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:10<00:32,  2.18it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:11<00:32,  2.18it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:11<00:31,  2.18it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:11<00:31,  2.18it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:12<00:30,  2.18it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:12<00:30,  2.18it/s]Progress: 81.00%
+---- avg training fps: 7.02

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:13<00:29,  2.18it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:13<00:29,  2.18it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:14<00:28,  2.18it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:14<00:28,  2.18it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:15<00:28,  2.17it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:15<00:27,  2.17it/s]Progress: 83.00%
+---- avg training fps: 7.06

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:16<00:27,  2.17it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:16<00:27,  2.17it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:16<00:26,  2.17it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:16<00:26,  2.18it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:17<00:25,  2.18it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:17<00:25,  2.18it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:18<00:24,  2.18it/s]Progress: 85.00%
+---- avg training fps: 7.09

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:18<00:24,  2.18it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:19<00:23,  2.18it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:19<00:23,  2.18it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:20<00:22,  2.18it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:20<00:22,  2.18it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:20<00:22,  2.17it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:24<01:18,  1.63s/it]Progress: 87.00%
+---- avg training fps: 6.93

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:25<01:00,  1.28s/it]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:25<00:47,  1.03s/it]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:26<00:38,  1.16it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:26<00:32,  1.35it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:27<00:28,  1.52it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:27<00:25,  1.67it/s]Progress: 89.00%
+---- avg training fps: 6.96

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:28<00:22,  1.79it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:28<00:21,  1.89it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:29<00:21,  1.89it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:29<00:19,  1.96it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:29<00:18,  2.01it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:30<00:18,  2.05it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:30<00:17,  2.08it/s]Progress: 91.00%
+---- avg training fps: 6.99

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:30<00:16,  2.11it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:31<00:16,  2.12it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:31<00:15,  2.13it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:32<00:14,  2.14it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:32<00:14,  2.15it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:33<00:13,  2.15it/s]Progress: 93.00%
+---- avg training fps: 7.02

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:33<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:33<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:34<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:34<00:12,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:35<00:12,  2.16it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:35<00:11,  2.16it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:36<00:11,  2.16it/s]Progress: 95.00%
+---- avg training fps: 7.05

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:36<00:10,  2.16it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:37<00:10,  2.16it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:37<00:09,  2.16it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:37<00:09,  2.16it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:38<00:09,  2.16it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:38<00:08,  2.16it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:38<00:08,  2.16it/s]Progress: 97.00%
+---- avg training fps: 7.08

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:39<00:07,  2.16it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:39<00:07,  2.16it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:40<00:06,  2.16it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:40<00:06,  2.16it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:41<00:06,  2.16it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:41<00:05,  2.16it/s]Progress: 99.00%
+---- avg training fps: 7.11

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:42<00:05,  2.16it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:42<00:04,  2.16it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:43<00:04,  2.16it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:43<00:04,  2.16it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:43<00:03,  2.16it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:43<00:03,  2.16it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:44<00:02,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.13

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:44<00:02,  2.16it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:45<00:01,  2.16it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:45<00:01,  2.16it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:46<00:00,  2.16it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:46<00:00,  2.16it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:47<00:00,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.16
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [04:06<00:00,  1.22it/s]
+------------------------------------------
+Training done :)
+2024-08-13 03:34:31.227219: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 03:34:31.319832: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 03:34:31.319856: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 03:34:31.338315: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 03:34:31.737792: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:34:31.737870: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:34:31.737879: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23134 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/good_styles/eden_crystals' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=1.0 freeze_unet_before_completion_f=0.5 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='eden_crystals' output_dir='lora_models/styles_final/eden_crystals_13_03-34-33-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723512873.5619853 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/eden_crystals_13_03-34-33-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.242
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants on a black background.
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants in the dark.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal blending of nature and technology
+
+- In the style of TOK, a potted plant sits on top of a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves is placed on a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is set against a black background.
+- In the style of TOK, a potted plant sits on top of a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves is placed on a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is in the dark.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Surreal blending of nature and technology
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 6753.57it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a potted plant s...
+1     in the style of <s0><s1><s2>, a futuristic cit...
+2     in the style of <s0><s1><s2>, a flower with gr...
+3     in the style of <s0><s1><s2>, a plant is growi...
+4     in the style of <s0><s1><s2>, a group of green...
+5     in the style of <s0><s1><s2>, a potted plant s...
+6     in the style of <s0><s1><s2>, a futuristic cit...
+7     in the style of <s0><s1><s2>, a flower with gr...
+8     in the style of <s0><s1><s2>, a plant is growi...
+9     in the style of <s0><s1><s2>, a group of green...
+10    in the style of <s0><s1><s2>, a potted plant s...
+11    in the style of <s0><s1><s2>, a futuristic cit...
+12    in the style of <s0><s1><s2>, a flower with gr...
+13    in the style of <s0><s1><s2>, a plant is growi...
+14    in the style of <s0><s1><s2>, a group of green...
+15    in the style of <s0><s1><s2>, a potted plant s...
+16    in the style of <s0><s1><s2>, a futuristic cit...
+17    in the style of <s0><s1><s2>, a flower with gr...
+18    in the style of <s0><s1><s2>, a plant is growi...
+19    in the style of <s0><s1><s2>, a group of green...
+20    in the style of <s0><s1><s2>, a potted plant s...
+21    in the style of <s0><s1><s2>, a futuristic cit...
+22    in the style of <s0><s1><s2>, a flower with gr...
+23    in the style of <s0><s1><s2>, a plant is growi...
+24    in the style of <s0><s1><s2>, a group of green...
+25    in the style of <s0><s1><s2>, a potted plant s...
+26    in the style of <s0><s1><s2>, a futuristic cit...
+27    in the style of <s0><s1><s2>, a flower with gr...
+28    in the style of <s0><s1><s2>, a plant is growi...
+29    in the style of <s0><s1><s2>, a group of green...
+30    in the style of <s0><s1><s2>, a potted plant s...
+31    in the style of <s0><s1><s2>, a futuristic cit...
+32    in the style of <s0><s1><s2>, a flower with gr...
+33    in the style of <s0><s1><s2>, a plant is growi...
+34    in the style of <s0><s1><s2>, a group of green...
+35    in the style of <s0><s1><s2>, a potted plant s...
+36    in the style of <s0><s1><s2>, a futuristic cit...
+37    in the style of <s0><s1><s2>, a flower with gr...
+38    in the style of <s0><s1><s2>, a plant is growi...
+39    in the style of <s0><s1><s2>, a group of green...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:08<04:20,  1.13it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.63

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# Trainer step: 0, epoch: 0:   3%|▎         | 10/300 [00:10<02:42,  1.78it/s]
# Trainer step: 10, epoch: 1:   3%|▎         | 10/300 [00:11<02:42,  1.78it/s]
# Trainer step: 10, epoch: 1:   4%|▎         | 11/300 [00:11<02:34,  1.88it/s]
# Trainer step: 10, epoch: 1:   4%|▍         | 12/300 [00:11<02:27,  1.95it/s]Progress: 7.00%
+---- avg training fps: 4.01

# Trainer step: 10, epoch: 1:   4%|▍         | 13/300 [00:11<02:23,  2.00it/s]
# Trainer step: 10, epoch: 1:   5%|▍         | 14/300 [00:12<02:20,  2.03it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 15/300 [00:12<02:18,  2.05it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 16/300 [00:13<02:16,  2.07it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 17/300 [00:13<02:15,  2.09it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 18/300 [00:14<02:14,  2.10it/s]Progress: 9.00%
+---- avg training fps: 4.87

# Trainer step: 10, epoch: 1:   6%|▋         | 19/300 [00:14<02:13,  2.11it/s]
# Trainer step: 10, epoch: 1:   7%|▋         | 20/300 [00:15<02:12,  2.11it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 20/300 [00:15<02:12,  2.11it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 21/300 [00:15<02:12,  2.11it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 22/300 [00:16<02:11,  2.12it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 23/300 [00:16<02:10,  2.12it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 24/300 [00:17<02:09,  2.12it/s]Progress: 11.00%
+---- avg training fps: 5.45

# Trainer step: 20, epoch: 2:   8%|▊         | 25/300 [00:17<02:09,  2.12it/s]
# Trainer step: 20, epoch: 2:   9%|▊         | 26/300 [00:18<02:08,  2.12it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 27/300 [00:18<02:08,  2.12it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 28/300 [00:19<02:08,  2.12it/s]
# Trainer step: 20, epoch: 2:  10%|▉         | 29/300 [00:19<02:08,  2.12it/s]
# Trainer step: 20, epoch: 2:  10%|█         | 30/300 [00:19<02:07,  2.11it/s]Progress: 13.00%
+---- avg training fps: 5.87

# Trainer step: 30, epoch: 3:  10%|█         | 30/300 [00:20<02:07,  2.11it/s]
# Trainer step: 30, epoch: 3:  10%|█         | 31/300 [00:20<02:07,  2.12it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 32/300 [00:20<02:06,  2.11it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 33/300 [00:21<02:20,  1.89it/s]
# Trainer step: 30, epoch: 3:  11%|█▏        | 34/300 [00:22<02:15,  1.96it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 35/300 [00:22<02:11,  2.01it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 36/300 [00:22<02:09,  2.04it/s]Progress: 15.00%
+---- avg training fps: 6.14

# Trainer step: 30, epoch: 3:  12%|█▏        | 37/300 [00:23<02:06,  2.07it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 38/300 [00:23<02:05,  2.09it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 39/300 [00:24<02:04,  2.10it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 40/300 [00:24<02:02,  2.11it/s]
# Trainer step: 40, epoch: 4:  13%|█▎        | 40/300 [00:25<02:02,  2.11it/s]
# Trainer step: 40, epoch: 4:  14%|█▎        | 41/300 [00:25<02:02,  2.12it/s]
# Trainer step: 40, epoch: 4:  14%|█▍        | 42/300 [00:25<02:01,  2.12it/s]Progress: 17.00%
+---- avg training fps: 6.40

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:26<02:00,  2.13it/s]
# Trainer step: 40, epoch: 4:  15%|█▍        | 44/300 [00:26<02:00,  2.12it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 45/300 [00:27<01:59,  2.13it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 46/300 [00:27<01:59,  2.13it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 47/300 [00:28<01:59,  2.12it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:28<01:58,  2.12it/s]Progress: 19.00%
+---- avg training fps: 6.60

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:29<01:58,  2.12it/s]
# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:29<01:57,  2.13it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 50/300 [00:30<01:57,  2.13it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 51/300 [00:30<01:57,  2.12it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 52/300 [00:33<06:11,  1.50s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 53/300 [00:34<04:54,  1.19s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:34<03:59,  1.03it/s]Progress: 21.00%
+---- avg training fps: 6.11

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:35<03:21,  1.21it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:35<02:55,  1.39it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 57/300 [00:36<02:36,  1.55it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 58/300 [00:36<02:23,  1.69it/s]
# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:37<02:13,  1.80it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:37<02:07,  1.89it/s]Progress: 23.00%
+---- avg training fps: 6.29

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:38<02:07,  1.89it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:38<02:02,  1.95it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:38<02:12,  1.79it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:39<02:05,  1.89it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:39<02:00,  1.95it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:40<01:57,  2.00it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:40<01:54,  2.04it/s]Progress: 25.00%
+---- avg training fps: 6.41

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:41<01:52,  2.06it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:41<01:51,  2.08it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:42<01:50,  2.09it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:42<01:49,  2.10it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:43<01:49,  2.10it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:43<01:48,  2.10it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:43<01:47,  2.11it/s]Progress: 27.00%
+---- avg training fps: 6.54

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:44<01:47,  2.11it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:44<01:46,  2.12it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:44<01:46,  2.12it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:45<01:45,  2.12it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:45<01:45,  2.12it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:46<01:44,  2.12it/s]Progress: 29.00%
+---- avg training fps: 6.66

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:46<01:44,  2.12it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:47<01:43,  2.12it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:47<01:43,  2.12it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:47<01:43,  2.12it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:48<01:43,  2.11it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:48<01:42,  2.12it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:49<01:41,  2.13it/s]Progress: 31.00%
+---- avg training fps: 6.77

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:49<01:41,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:50<01:40,  2.12it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:50<01:40,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:51<01:39,  2.13it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:51<01:39,  2.13it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:52<01:38,  2.13it/s]Progress: 33.00%
+---- avg training fps: 6.86

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:52<01:38,  2.13it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:52<01:37,  2.14it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:52<01:37,  2.14it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:53<01:36,  2.14it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:53<01:36,  2.14it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:54<01:35,  2.14it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:54<01:35,  2.14it/s]Progress: 35.00%
+---- avg training fps: 6.95

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:55<01:35,  2.13it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:55<01:34,  2.13it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:56<01:34,  2.13it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:56<01:34,  2.12it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:57<01:34,  2.12it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:57<01:33,  2.13it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:57<01:33,  2.13it/s]Progress: 37.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.02

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:58<01:32,  2.12it/s]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [00:58<01:32,  2.12it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [00:59<01:32,  2.12it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [00:59<01:31,  2.12it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:00<01:31,  2.11it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:00<01:30,  2.11it/s]Progress: 39.00%
+---- avg training fps: 7.09

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:00<01:30,  2.11it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:01<01:29,  2.11it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:01<01:29,  2.11it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:01<01:29,  2.11it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:02<01:29,  2.10it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:02<01:28,  2.11it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:03<01:28,  2.11it/s]Progress: 41.00%
+---- avg training fps: 7.15

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:03<01:27,  2.12it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:04<01:26,  2.12it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:04<01:26,  2.12it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:05<01:25,  2.12it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:05<01:25,  2.13it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:06<01:24,  2.12it/s]Progress: 43.00%
+---- avg training fps: 7.20

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:06<01:24,  2.12it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:06<01:24,  2.12it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:07<01:23,  2.13it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:07<01:23,  2.13it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:08<01:23,  2.12it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:08<01:22,  2.12it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:08<01:22,  2.12it/s]Progress: 45.00%
+---- avg training fps: 7.26

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:09<01:21,  2.12it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:09<01:21,  2.12it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:10<01:21,  2.11it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:10<01:20,  2.11it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:11<01:20,  2.11it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:11<01:20,  2.11it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:11<01:20,  2.10it/s]Progress: 47.00%
+---- avg training fps: 7.30

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:12<01:19,  2.10it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:12<01:18,  2.11it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:13<01:18,  2.11it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:13<01:17,  2.11it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:14<01:17,  2.11it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:14<01:16,  2.11it/s]Progress: 49.00%
+---- avg training fps: 7.35

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:15<01:16,  2.11it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:15<01:15,  2.12it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:16<01:15,  2.12it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:16<01:15,  2.12it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:16<01:14,  2.12it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:17<01:13,  2.12it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:17<01:13,  2.12it/s]Progress: 51.00%
+---- avg training fps: 7.39

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:17<01:13,  2.12it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:18<01:12,  2.12it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:18<01:12,  2.11it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:19<01:11,  2.11it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:19<01:11,  2.12it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:20<01:10,  2.12it/s]Progress: 53.00%
+---- avg training fps: 7.42

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:20<01:10,  2.12it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:20<01:10,  2.11it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:25<03:56,  1.60s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:25<03:05,  1.26s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:26<02:30,  1.03s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:26<02:04,  1.16it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:26<01:47,  1.34it/s]Progress: 55.00%
+---- avg training fps: 7.14

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:27<01:34,  1.51it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:27<01:26,  1.65it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:28<01:19,  1.77it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:28<01:15,  1.86it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:29<01:15,  1.86it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:29<01:12,  1.93it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:29<01:09,  1.98it/s]Progress: 57.00%
+---- avg training fps: 7.18

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:30<01:07,  2.02it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:30<01:06,  2.05it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:31<01:05,  2.07it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:31<01:04,  2.08it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:32<01:03,  2.09it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:32<01:02,  2.10it/s]Progress: 59.00%
+---- avg training fps: 7.22

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:33<01:02,  2.09it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:33<01:01,  2.10it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:34<01:01,  2.10it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:34<01:01,  2.10it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:34<01:00,  2.11it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:34<01:00,  2.10it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:35<00:59,  2.10it/s]Progress: 61.00%
+---- avg training fps: 7.25

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:35<00:59,  2.10it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:36<00:58,  2.11it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:36<00:58,  2.11it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:37<00:57,  2.11it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:37<00:57,  2.11it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:38<00:56,  2.11it/s]Progress: 63.00%
+---- avg training fps: 7.29

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:38<00:56,  2.11it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:38<00:56,  2.12it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:39<00:55,  2.12it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:39<00:55,  2.11it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:40<00:54,  2.11it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:40<00:54,  2.11it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:41<00:54,  2.10it/s]Progress: 65.00%
+---- avg training fps: 7.32

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:41<00:53,  2.10it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:42<00:53,  2.11it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:42<00:52,  2.11it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:43<00:52,  2.11it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:43<00:52,  2.11it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:43<00:51,  2.10it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:44<00:51,  2.10it/s]Progress: 67.00%
+---- avg training fps: 7.35

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:44<00:50,  2.10it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:44<00:50,  2.11it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:45<00:49,  2.10it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:45<00:49,  2.11it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:46<00:48,  2.11it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:46<00:48,  2.11it/s]Progress: 69.00%
+---- avg training fps: 7.38

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:47<00:47,  2.11it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:47<00:47,  2.12it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:48<00:47,  2.12it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:48<00:46,  2.12it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:52<02:38,  1.62s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:53<02:04,  1.28s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:53<01:39,  1.04s/it]Progress: 71.00%
+---- avg training fps: 7.16

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:53<01:22,  1.15it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:54<01:10,  1.34it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:54<01:02,  1.50it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:55<00:56,  1.64it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:55<00:51,  1.76it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:56<00:48,  1.85it/s]Progress: 73.00%
+---- avg training fps: 7.19

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:56<00:48,  1.85it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:56<00:46,  1.91it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:57<00:44,  1.96it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:57<00:43,  2.00it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:58<00:42,  2.03it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [01:58<00:41,  2.06it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [01:59<00:40,  2.07it/s]Progress: 75.00%
+---- avg training fps: 7.22

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [01:59<00:39,  2.08it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [02:00<00:39,  2.09it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:00<00:38,  2.09it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:01<00:38,  2.10it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:01<00:38,  2.10it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:01<00:37,  2.09it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:02<00:37,  2.09it/s]Progress: 77.00%
+---- avg training fps: 7.25

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:02<00:36,  2.10it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:03<00:36,  2.10it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:03<00:35,  2.10it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:03<00:35,  2.11it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:04<00:34,  2.11it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:04<00:34,  2.11it/s]Progress: 79.00%
+---- avg training fps: 7.27

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:05<00:33,  2.11it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:05<00:33,  2.11it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:06<00:33,  2.11it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:06<00:32,  2.11it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:06<00:32,  2.11it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:07<00:31,  2.10it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:07<00:31,  2.11it/s]Progress: 81.00%
+---- avg training fps: 7.30

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:08<00:30,  2.11it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:08<00:30,  2.11it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:09<00:29,  2.10it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:09<00:29,  2.10it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:10<00:29,  2.10it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:10<00:28,  2.10it/s]Progress: 83.00%
+---- avg training fps: 7.32

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:11<00:28,  2.10it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:11<00:28,  2.09it/s]
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# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:12<00:26,  2.11it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:13<00:26,  2.11it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:13<00:25,  2.11it/s]Progress: 85.00%
+---- avg training fps: 7.35

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:13<00:25,  2.11it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:14<00:24,  2.12it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:14<00:24,  2.12it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:15<00:23,  2.11it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:15<00:23,  2.11it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:15<00:23,  2.11it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:16<00:22,  2.12it/s]Progress: 87.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.37

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:16<00:22,  2.11it/s]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:17<00:21,  2.12it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:17<00:21,  2.11it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:18<00:20,  2.11it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:18<00:20,  2.11it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:19<00:19,  2.11it/s]Progress: 89.00%
+---- avg training fps: 7.39

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:19<00:19,  2.11it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:20<00:18,  2.11it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:20<00:18,  2.11it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:20<00:18,  2.10it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:21<00:18,  2.11it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:21<00:17,  2.11it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:21<00:17,  2.11it/s]Progress: 91.00%
+---- avg training fps: 7.41

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:22<00:16,  2.12it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:22<00:16,  2.12it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:23<00:15,  2.12it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:23<00:15,  2.12it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:24<00:16,  1.87it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:25<00:15,  1.94it/s]Progress: 93.00%
+---- avg training fps: 7.42

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:25<00:15,  1.94it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:25<00:14,  1.98it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:25<00:13,  2.02it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:26<00:13,  2.05it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:26<00:12,  2.07it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:27<00:11,  2.09it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:27<00:11,  2.10it/s]Progress: 95.00%
+---- avg training fps: 7.44

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:28<00:10,  2.10it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:28<00:10,  2.10it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:29<00:09,  2.11it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:29<00:09,  2.11it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:30<00:09,  2.11it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:30<00:08,  2.11it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:30<00:08,  2.12it/s]Progress: 97.00%
+---- avg training fps: 7.46

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:31<00:08,  2.12it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:31<00:07,  2.11it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:32<00:07,  2.12it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:32<00:06,  2.12it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:33<00:06,  2.11it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:33<00:05,  2.11it/s]Progress: 99.00%
+---- avg training fps: 7.48

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:34<00:05,  2.11it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:34<00:04,  2.12it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:34<00:04,  2.12it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:34<00:04,  2.12it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:35<00:03,  2.12it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:35<00:03,  2.12it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:36<00:02,  2.12it/s]Progress: 100.00%
+---- avg training fps: 7.50

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:36<00:02,  2.12it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:37<00:01,  2.12it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:37<00:01,  2.12it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:38<00:00,  2.12it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:38<00:00,  2.12it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:39<00:00,  2.12it/s]Progress: 100.00%
+---- avg training fps: 7.52
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [03:58<00:00,  1.26it/s]
+------------------------------------------
+Training done :)
+2024-08-13 03:39:02.238881: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 03:39:02.329762: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 03:39:02.329790: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 03:39:02.349380: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 03:39:02.751738: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:39:02.751793: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:39:02.751802: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23117 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/good_styles/eden_crystals' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.75 freeze_unet_before_completion_f=0.5 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='eden_crystals' output_dir='lora_models/styles_final/eden_crystals_13_03-39-04-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723513144.5762262 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/eden_crystals_13_03-39-04-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.242
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants on a black background.
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants in the dark.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Futuristic natural elements blending with technology
+
+- In the style of TOK, a potted plant sitting on top of a table next to a lamp.
+- In the style of TOK, a futuristic city in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves on a dark blue background.
+- In the style of TOK, a plant growing out of a circuit board.
+- In the style of TOK, a group of green and red plants on a black background.
+- In the style of TOK, a potted plant sitting on top of a table next to a lamp.
+- In the style of TOK, a futuristic city in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves on a dark blue background.
+- In the style of TOK, a plant growing out of a circuit board.
+- In the style of TOK, a group of green and red plants in the dark.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Futuristic natural elements blending with technology
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 7594.25it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a potted plant s...
+1     in the style of <s0><s1><s2>, a futuristic cit...
+2     in the style of <s0><s1><s2>, a flower with gr...
+3     in the style of <s0><s1><s2>, a plant growing ...
+4     in the style of <s0><s1><s2>, a group of green...
+5     in the style of <s0><s1><s2>, a potted plant s...
+6     in the style of <s0><s1><s2>, a futuristic cit...
+7     in the style of <s0><s1><s2>, a flower with gr...
+8     in the style of <s0><s1><s2>, a plant growing ...
+9     in the style of <s0><s1><s2>, a group of green...
+10    in the style of <s0><s1><s2>, a potted plant s...
+11    in the style of <s0><s1><s2>, a futuristic cit...
+12    in the style of <s0><s1><s2>, a flower with gr...
+13    in the style of <s0><s1><s2>, a plant growing ...
+14    in the style of <s0><s1><s2>, a group of green...
+15    in the style of <s0><s1><s2>, a potted plant s...
+16    in the style of <s0><s1><s2>, a futuristic cit...
+17    in the style of <s0><s1><s2>, a flower with gr...
+18    in the style of <s0><s1><s2>, a plant growing ...
+19    in the style of <s0><s1><s2>, a group of green...
+20    in the style of <s0><s1><s2>, a potted plant s...
+21    in the style of <s0><s1><s2>, a futuristic cit...
+22    in the style of <s0><s1><s2>, a flower with gr...
+23    in the style of <s0><s1><s2>, a plant growing ...
+24    in the style of <s0><s1><s2>, a group of green...
+25    in the style of <s0><s1><s2>, a potted plant s...
+26    in the style of <s0><s1><s2>, a futuristic cit...
+27    in the style of <s0><s1><s2>, a flower with gr...
+28    in the style of <s0><s1><s2>, a plant growing ...
+29    in the style of <s0><s1><s2>, a group of green...
+30    in the style of <s0><s1><s2>, a potted plant s...
+31    in the style of <s0><s1><s2>, a futuristic cit...
+32    in the style of <s0><s1><s2>, a flower with gr...
+33    in the style of <s0><s1><s2>, a plant growing ...
+34    in the style of <s0><s1><s2>, a group of green...
+35    in the style of <s0><s1><s2>, a potted plant s...
+36    in the style of <s0><s1><s2>, a futuristic cit...
+37    in the style of <s0><s1><s2>, a flower with gr...
+38    in the style of <s0><s1><s2>, a plant growing ...
+39    in the style of <s0><s1><s2>, a group of green...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:08<04:09,  1.18it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.73

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+---- avg training fps: 4.17

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+---- avg training fps: 5.04

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+---- avg training fps: 5.63

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# Trainer step: 20, epoch: 2:  10%|█         | 30/300 [00:19<02:04,  2.17it/s]Progress: 13.00%
+---- avg training fps: 6.06

# Trainer step: 30, epoch: 3:  10%|█         | 30/300 [00:19<02:04,  2.17it/s]
# Trainer step: 30, epoch: 3:  10%|█         | 31/300 [00:19<02:03,  2.17it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 32/300 [00:20<02:03,  2.17it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 33/300 [00:20<02:02,  2.17it/s]
# Trainer step: 30, epoch: 3:  11%|█▏        | 34/300 [00:21<02:02,  2.17it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 35/300 [00:21<02:02,  2.17it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 36/300 [00:22<02:01,  2.17it/s]Progress: 15.00%
+---- avg training fps: 6.33

# Trainer step: 30, epoch: 3:  12%|█▏        | 37/300 [00:22<02:15,  1.94it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 38/300 [00:23<02:10,  2.01it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 39/300 [00:23<02:07,  2.05it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 40/300 [00:24<02:04,  2.09it/s]
# Trainer step: 40, epoch: 4:  13%|█▎        | 40/300 [00:24<02:04,  2.09it/s]
# Trainer step: 40, epoch: 4:  14%|█▎        | 41/300 [00:24<02:01,  2.12it/s]
# Trainer step: 40, epoch: 4:  14%|█▍        | 42/300 [00:25<02:00,  2.14it/s]Progress: 17.00%
+---- avg training fps: 6.59

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:25<01:59,  2.15it/s]
# Trainer step: 40, epoch: 4:  15%|█▍        | 44/300 [00:25<01:58,  2.16it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 45/300 [00:26<01:57,  2.16it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 46/300 [00:26<01:57,  2.16it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 47/300 [00:27<01:57,  2.16it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:27<01:56,  2.16it/s]Progress: 19.00%
+---- avg training fps: 6.79

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:28<01:55,  2.16it/s]
# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:28<01:55,  2.16it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 50/300 [00:29<01:55,  2.16it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 51/300 [00:29<01:54,  2.17it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 52/300 [00:32<05:47,  1.40s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 53/300 [00:33<04:36,  1.12s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:33<03:46,  1.09it/s]Progress: 21.00%
+---- avg training fps: 6.32

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:34<03:11,  1.28it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:34<02:47,  1.46it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 57/300 [00:35<02:30,  1.62it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 58/300 [00:35<02:18,  1.75it/s]
# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:36<02:09,  1.86it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:36<02:03,  1.95it/s]Progress: 23.00%
+---- avg training fps: 6.50

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:36<02:03,  1.95it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:36<01:59,  2.00it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:37<01:55,  2.06it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:37<01:53,  2.09it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:38<01:51,  2.12it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:38<01:50,  2.13it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:39<01:49,  2.14it/s]Progress: 25.00%
+---- avg training fps: 6.65

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:39<01:48,  2.15it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:40<01:47,  2.16it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:40<01:46,  2.16it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:41<01:46,  2.16it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:41<01:46,  2.16it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:41<01:45,  2.16it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:42<01:44,  2.17it/s]Progress: 27.00%
+---- avg training fps: 6.78

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:42<01:44,  2.16it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:43<01:57,  1.92it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:43<01:52,  1.99it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:44<01:49,  2.05it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:44<01:46,  2.09it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:44<01:45,  2.11it/s]Progress: 29.00%
+---- avg training fps: 6.87

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:45<01:43,  2.13it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:45<01:42,  2.14it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:46<01:42,  2.14it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:46<01:41,  2.15it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:46<01:41,  2.15it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:47<01:40,  2.16it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:47<01:39,  2.17it/s]Progress: 31.00%
+---- avg training fps: 6.97

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:48<01:38,  2.18it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:48<01:38,  2.17it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:49<01:37,  2.17it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:49<01:37,  2.18it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:50<01:36,  2.19it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:50<01:35,  2.19it/s]Progress: 33.00%
+---- avg training fps: 7.07

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:50<01:35,  2.19it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:50<01:35,  2.19it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:51<01:35,  2.19it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:51<01:34,  2.19it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:52<01:34,  2.18it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:52<01:34,  2.18it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:53<01:33,  2.18it/s]Progress: 35.00%
+---- avg training fps: 7.16

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:53<01:33,  2.18it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:54<01:32,  2.18it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:54<01:32,  2.18it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:55<01:32,  2.17it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:55<01:32,  2.17it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:55<01:31,  2.18it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:55<01:30,  2.18it/s]Progress: 37.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.23

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:56<01:30,  2.17it/s]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [00:56<01:30,  2.16it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [00:57<01:29,  2.17it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [00:57<01:29,  2.17it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [00:58<01:28,  2.17it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [00:58<01:28,  2.17it/s]Progress: 39.00%
+---- avg training fps: 7.30

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [00:59<01:27,  2.18it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [00:59<01:27,  2.17it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:00<01:27,  2.17it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:00<01:27,  2.17it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:00<01:26,  2.17it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:01<01:26,  2.17it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:01<01:25,  2.17it/s]Progress: 41.00%
+---- avg training fps: 7.36

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:01<01:25,  2.17it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:02<01:24,  2.17it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:02<01:24,  2.17it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:03<01:24,  2.16it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:03<01:23,  2.17it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:04<01:23,  2.16it/s]Progress: 43.00%
+---- avg training fps: 7.42

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:04<01:23,  2.16it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:04<01:22,  2.17it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:05<01:21,  2.18it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:05<01:21,  2.17it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:06<01:21,  2.17it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:06<01:20,  2.18it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:07<01:20,  2.17it/s]Progress: 45.00%
+---- avg training fps: 7.47

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:07<01:19,  2.17it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:07<01:19,  2.16it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:08<01:18,  2.17it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:08<01:18,  2.17it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:09<01:18,  2.17it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:09<01:18,  2.17it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:09<01:17,  2.16it/s]Progress: 47.00%
+---- avg training fps: 7.51

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:10<01:17,  2.16it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:10<01:16,  2.16it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:11<01:16,  2.16it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:11<01:16,  2.16it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:12<01:14,  2.17it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:12<01:14,  2.17it/s]Progress: 49.00%
+---- avg training fps: 7.56

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:13<01:14,  2.16it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:13<01:14,  2.15it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:13<01:14,  2.15it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:13<01:13,  2.17it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:14<01:13,  2.16it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:14<01:12,  2.16it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:15<01:12,  2.15it/s]Progress: 51.00%
+---- avg training fps: 7.60

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:15<01:11,  2.16it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:16<01:11,  2.16it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:16<01:10,  2.16it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:17<01:10,  2.16it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:17<01:09,  2.16it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:18<01:09,  2.16it/s]Progress: 53.00%
+---- avg training fps: 7.63

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:18<01:09,  2.16it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:18<01:09,  2.15it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:22<03:49,  1.55s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:23<02:59,  1.22s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:23<02:25,  1.01it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:24<02:01,  1.20it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:24<01:44,  1.38it/s]Progress: 55.00%
+---- avg training fps: 7.34

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:25<01:32,  1.55it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:25<01:23,  1.70it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:25<01:17,  1.81it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:26<01:13,  1.90it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:26<01:13,  1.90it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:26<01:10,  1.97it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:27<01:08,  2.03it/s]Progress: 57.00%
+---- avg training fps: 7.38

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:27<01:06,  2.06it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:28<01:05,  2.09it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:28<01:03,  2.12it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:29<01:02,  2.13it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:29<01:02,  2.14it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:30<01:01,  2.14it/s]Progress: 59.00%
+---- avg training fps: 7.42

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:30<01:00,  2.15it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:31<01:00,  2.15it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:31<01:00,  2.15it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:31<00:59,  2.15it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:31<00:59,  2.15it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:32<00:58,  2.16it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:32<00:58,  2.16it/s]Progress: 61.00%
+---- avg training fps: 7.46

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:33<00:58,  2.15it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:33<00:57,  2.15it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:34<00:57,  2.16it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:34<00:56,  2.15it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:35<00:56,  2.15it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:35<00:55,  2.15it/s]Progress: 63.00%
+---- avg training fps: 7.49

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:36<00:55,  2.15it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:36<00:55,  2.15it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:36<00:54,  2.15it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:37<00:54,  2.15it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:37<00:53,  2.16it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:37<00:53,  2.15it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:38<00:52,  2.15it/s]Progress: 65.00%
+---- avg training fps: 7.52

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:38<00:52,  2.15it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:39<00:52,  2.15it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:39<00:51,  2.15it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:40<00:51,  2.14it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:40<00:51,  2.14it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:40<00:50,  2.14it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:41<00:50,  2.14it/s]Progress: 67.00%
+---- avg training fps: 7.55

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:41<00:50,  2.13it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:42<00:49,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:42<00:48,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:43<00:48,  2.14it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:43<00:48,  2.14it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:44<00:47,  2.14it/s]Progress: 69.00%
+---- avg training fps: 7.58

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:44<00:47,  2.14it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:44<00:46,  2.15it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:45<00:46,  2.15it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:45<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:49<02:29,  1.53s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:49<01:57,  1.21s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:50<01:34,  1.02it/s]Progress: 71.00%
+---- avg training fps: 7.36

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:50<01:18,  1.21it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:51<01:07,  1.40it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:51<00:59,  1.56it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:52<00:54,  1.70it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:52<00:49,  1.82it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:53<00:47,  1.91it/s]Progress: 73.00%
+---- avg training fps: 7.39

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:53<00:47,  1.91it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:53<00:45,  1.97it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:54<00:43,  2.02it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:54<00:42,  2.06it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:55<00:41,  2.08it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [01:55<00:40,  2.10it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [01:55<00:39,  2.13it/s]Progress: 75.00%
+---- avg training fps: 7.42

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [01:56<00:38,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [01:56<00:38,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [01:57<00:37,  2.14it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [01:57<00:37,  2.15it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [01:58<00:37,  2.15it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [01:58<00:36,  2.15it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [01:58<00:36,  2.15it/s]Progress: 77.00%
+---- avg training fps: 7.45

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [01:59<00:35,  2.15it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [01:59<00:35,  2.15it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:00<00:34,  2.15it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:00<00:34,  2.14it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:01<00:33,  2.15it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:01<00:33,  2.15it/s]Progress: 79.00%
+---- avg training fps: 7.48

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:01<00:32,  2.15it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:02<00:32,  2.15it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:02<00:32,  2.15it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:02<00:31,  2.17it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:03<00:31,  2.17it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:03<00:31,  2.16it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:04<00:30,  2.15it/s]Progress: 81.00%
+---- avg training fps: 7.50

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:04<00:29,  2.17it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:05<00:29,  2.16it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:05<00:29,  2.15it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:06<00:28,  2.15it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:06<00:28,  2.15it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:07<00:27,  2.15it/s]Progress: 83.00%
+---- avg training fps: 7.53

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:07<00:27,  2.15it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:07<00:27,  2.15it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:08<00:26,  2.15it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:08<00:26,  2.16it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:08<00:26,  2.15it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:09<00:25,  2.16it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:09<00:24,  2.17it/s]Progress: 85.00%
+---- avg training fps: 7.55

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:10<00:24,  2.16it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:10<00:24,  2.16it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:11<00:23,  2.16it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:11<00:23,  2.16it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:12<00:23,  2.16it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:12<00:22,  2.16it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:12<00:22,  2.16it/s]Progress: 87.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.57

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:13<00:21,  2.16it/s]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:13<00:21,  2.17it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:14<00:20,  2.16it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:14<00:20,  2.16it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:14<00:19,  2.15it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:15<00:19,  2.16it/s]Progress: 89.00%
+---- avg training fps: 7.59

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:15<00:19,  2.15it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:16<00:18,  2.15it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:16<00:18,  2.15it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:16<00:18,  2.14it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:17<00:17,  2.15it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:17<00:17,  2.15it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:18<00:16,  2.15it/s]Progress: 91.00%
+---- avg training fps: 7.62

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:18<00:16,  2.16it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:19<00:15,  2.16it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:19<00:15,  2.15it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:20<00:14,  2.15it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:20<00:14,  2.16it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:21<00:13,  2.15it/s]Progress: 93.00%
+---- avg training fps: 7.63

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:21<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:21<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:21<00:13,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:22<00:12,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:22<00:12,  2.15it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:23<00:11,  2.15it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:23<00:11,  2.16it/s]Progress: 95.00%
+---- avg training fps: 7.65

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:24<00:10,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:24<00:10,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:25<00:09,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:25<00:10,  1.91it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:26<00:10,  1.91it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:26<00:09,  1.97it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:26<00:08,  2.02it/s]Progress: 97.00%
+---- avg training fps: 7.66

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:27<00:08,  2.06it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:27<00:07,  2.09it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:28<00:07,  2.11it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:28<00:06,  2.12it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:29<00:06,  2.08it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:29<00:05,  2.09it/s]Progress: 99.00%
+---- avg training fps: 7.68

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:30<00:05,  2.09it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:30<00:04,  2.12it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:31<00:04,  2.12it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:31<00:04,  2.13it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:31<00:03,  2.13it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:31<00:03,  2.13it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:32<00:02,  2.14it/s]Progress: 100.00%
+---- avg training fps: 7.69

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:32<00:02,  2.14it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:33<00:01,  2.13it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:33<00:01,  2.14it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:34<00:00,  2.14it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:34<00:00,  2.14it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:35<00:00,  2.15it/s]Progress: 100.00%
+---- avg training fps: 7.71
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [03:55<00:00,  1.27it/s]
+------------------------------------------
+Training done :)
+2024-08-13 03:43:32.328342: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 03:43:32.419573: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 03:43:32.419599: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 03:43:32.438276: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 03:43:32.839855: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:43:32.839937: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:43:32.839946: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23105 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/good_styles/eden_crystals' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.5 freeze_unet_before_completion_f=0.5 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='eden_crystals' output_dir='lora_models/styles_final/eden_crystals_13_03-43-34-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723513414.6742449 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/eden_crystals_13_03-43-34-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.242
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants on a black background.
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants in the dark.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal blending of nature and technology
+
+- In the style of TOK, a potted plant sitting on top of a table next to a lamp.
+- In the style of TOK, a futuristic city in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves on a dark blue background.
+- In the style of TOK, a plant growing out of a circuit board.
+- In the style of TOK, a group of green and red plants on a black background.
+- In the style of TOK, a potted plant sitting on top of a table next to a lamp.
+- In the style of TOK, a futuristic city in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves on a dark blue background.
+- In the style of TOK, a plant growing out of a circuit board.
+- In the style of TOK, a group of green and red plants in the dark.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Surreal blending of nature and technology
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 7602.51it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a potted plant s...
+1     in the style of <s0><s1><s2>, a futuristic cit...
+2     in the style of <s0><s1><s2>, a flower with gr...
+3     in the style of <s0><s1><s2>, a plant growing ...
+4     in the style of <s0><s1><s2>, a group of green...
+5     in the style of <s0><s1><s2>, a potted plant s...
+6     in the style of <s0><s1><s2>, a futuristic cit...
+7     in the style of <s0><s1><s2>, a flower with gr...
+8     in the style of <s0><s1><s2>, a plant growing ...
+9     in the style of <s0><s1><s2>, a group of green...
+10    in the style of <s0><s1><s2>, a potted plant s...
+11    in the style of <s0><s1><s2>, a futuristic cit...
+12    in the style of <s0><s1><s2>, a flower with gr...
+13    in the style of <s0><s1><s2>, a plant growing ...
+14    in the style of <s0><s1><s2>, a group of green...
+15    in the style of <s0><s1><s2>, a potted plant s...
+16    in the style of <s0><s1><s2>, a futuristic cit...
+17    in the style of <s0><s1><s2>, a flower with gr...
+18    in the style of <s0><s1><s2>, a plant growing ...
+19    in the style of <s0><s1><s2>, a group of green...
+20    in the style of <s0><s1><s2>, a potted plant s...
+21    in the style of <s0><s1><s2>, a futuristic cit...
+22    in the style of <s0><s1><s2>, a flower with gr...
+23    in the style of <s0><s1><s2>, a plant growing ...
+24    in the style of <s0><s1><s2>, a group of green...
+25    in the style of <s0><s1><s2>, a potted plant s...
+26    in the style of <s0><s1><s2>, a futuristic cit...
+27    in the style of <s0><s1><s2>, a flower with gr...
+28    in the style of <s0><s1><s2>, a plant growing ...
+29    in the style of <s0><s1><s2>, a group of green...
+30    in the style of <s0><s1><s2>, a potted plant s...
+31    in the style of <s0><s1><s2>, a futuristic cit...
+32    in the style of <s0><s1><s2>, a flower with gr...
+33    in the style of <s0><s1><s2>, a plant growing ...
+34    in the style of <s0><s1><s2>, a group of green...
+35    in the style of <s0><s1><s2>, a potted plant s...
+36    in the style of <s0><s1><s2>, a futuristic cit...
+37    in the style of <s0><s1><s2>, a flower with gr...
+38    in the style of <s0><s1><s2>, a plant growing ...
+39    in the style of <s0><s1><s2>, a group of green...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:08<04:07,  1.19it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.77

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+---- avg training fps: 4.21

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+---- avg training fps: 5.09

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+---- avg training fps: 5.69

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+---- avg training fps: 6.11

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+---- avg training fps: 6.38

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+---- avg training fps: 6.64

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+---- avg training fps: 6.84

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# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:28<01:54,  2.18it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 50/300 [00:28<01:54,  2.18it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 51/300 [00:28<01:54,  2.18it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 52/300 [00:32<05:39,  1.37s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 53/300 [00:32<04:30,  1.09s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:33<03:42,  1.11it/s]Progress: 21.00%
+---- avg training fps: 6.38

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:33<03:08,  1.30it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:34<02:45,  1.48it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 57/300 [00:34<02:28,  1.63it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 58/300 [00:35<02:16,  1.77it/s]
# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:35<02:08,  1.88it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:36<02:02,  1.97it/s]Progress: 23.00%
+---- avg training fps: 6.56

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:36<02:02,  1.97it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:36<01:57,  2.03it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:37<01:54,  2.07it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:37<01:52,  2.11it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:37<01:50,  2.13it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:38<01:49,  2.15it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:38<01:48,  2.16it/s]Progress: 25.00%
+---- avg training fps: 6.71

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:39<01:47,  2.16it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:39<01:46,  2.17it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:40<01:46,  2.16it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:40<01:46,  2.17it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:41<01:46,  2.17it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:41<01:45,  2.17it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:41<01:44,  2.18it/s]Progress: 27.00%
+---- avg training fps: 6.84

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:42<01:44,  2.17it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:42<01:43,  2.18it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:43<01:43,  2.18it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:43<01:55,  1.93it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:44<01:51,  2.00it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:44<01:48,  2.05it/s]Progress: 29.00%
+---- avg training fps: 6.93

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:45<01:45,  2.09it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:45<01:43,  2.12it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:45<01:43,  2.12it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:45<01:42,  2.14it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:46<01:41,  2.14it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:46<01:40,  2.16it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:47<01:39,  2.17it/s]Progress: 31.00%
+---- avg training fps: 7.03

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:47<01:38,  2.18it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:48<01:38,  2.18it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:48<01:37,  2.18it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:49<01:37,  2.17it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:49<01:36,  2.18it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:50<01:36,  2.19it/s]Progress: 33.00%
+---- avg training fps: 7.12

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:50<01:36,  2.19it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:50<01:35,  2.19it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:50<01:35,  2.18it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:51<01:34,  2.18it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:51<01:34,  2.19it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:52<01:33,  2.18it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:52<01:33,  2.18it/s]Progress: 35.00%
+---- avg training fps: 7.21

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:53<01:33,  2.18it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:53<01:32,  2.19it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:54<01:32,  2.18it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:54<01:31,  2.17it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:55<01:31,  2.17it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:55<01:31,  2.18it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:55<01:30,  2.19it/s]Progress: 37.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.28

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:56<01:30,  2.17it/s]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [00:56<01:30,  2.17it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [00:56<01:29,  2.17it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [00:57<01:28,  2.18it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [00:57<01:28,  2.18it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [00:58<01:28,  2.17it/s]Progress: 39.00%
+---- avg training fps: 7.35

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [00:58<01:28,  2.17it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [00:59<01:27,  2.18it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [00:59<01:27,  2.18it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [00:59<01:26,  2.17it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:00<01:26,  2.17it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:00<01:26,  2.16it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:01<01:25,  2.18it/s]Progress: 41.00%
+---- avg training fps: 7.41

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:01<01:24,  2.18it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:02<01:24,  2.17it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:02<01:24,  2.17it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:02<01:23,  2.17it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:03<01:23,  2.18it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:03<01:22,  2.17it/s]Progress: 43.00%
+---- avg training fps: 7.46

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:04<01:22,  2.17it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:04<01:22,  2.17it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:04<01:21,  2.18it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:05<01:21,  2.18it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:05<01:20,  2.18it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:06<01:20,  2.17it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:06<01:19,  2.18it/s]Progress: 45.00%
+---- avg training fps: 7.51

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:07<01:19,  2.18it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:07<01:19,  2.17it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:08<01:19,  2.16it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:08<01:18,  2.17it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:08<01:18,  2.17it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:08<01:17,  2.17it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:09<01:17,  2.17it/s]Progress: 47.00%
+---- avg training fps: 7.56

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:09<01:17,  2.16it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:10<01:16,  2.18it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:10<01:16,  2.17it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:11<01:15,  2.17it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:11<01:15,  2.16it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:12<01:14,  2.17it/s]Progress: 49.00%
+---- avg training fps: 7.60

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:12<01:14,  2.17it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:13<01:14,  2.16it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:13<01:14,  2.16it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:13<01:13,  2.16it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:14<01:12,  2.17it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:14<01:12,  2.18it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:14<01:12,  2.17it/s]Progress: 51.00%
+---- avg training fps: 7.64

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:15<01:11,  2.16it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:15<01:10,  2.17it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:16<01:10,  2.17it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:16<01:10,  2.16it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:17<01:09,  2.17it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:17<01:08,  2.18it/s]Progress: 53.00%
+---- avg training fps: 7.68

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:18<01:08,  2.18it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:18<01:08,  2.18it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:22<03:44,  1.52s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:22<02:56,  1.20s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:23<02:22,  1.02it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:23<01:59,  1.22it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:23<01:42,  1.40it/s]Progress: 55.00%
+---- avg training fps: 7.39

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:24<01:31,  1.57it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:24<01:22,  1.72it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:25<01:16,  1.83it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:25<01:12,  1.93it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:26<01:12,  1.93it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:26<01:09,  1.99it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:26<01:06,  2.06it/s]Progress: 57.00%
+---- avg training fps: 7.43

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:27<01:05,  2.09it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:27<01:04,  2.12it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:28<01:03,  2.14it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:28<01:01,  2.16it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:29<01:01,  2.16it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:29<01:00,  2.17it/s]Progress: 59.00%
+---- avg training fps: 7.47

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:29<01:00,  2.16it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:30<01:00,  2.16it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:30<01:00,  2.16it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:30<00:59,  2.17it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:31<00:58,  2.17it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:31<00:58,  2.16it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:32<00:58,  2.17it/s]Progress: 61.00%
+---- avg training fps: 7.51

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:32<00:57,  2.17it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:33<00:57,  2.17it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:33<00:56,  2.17it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:34<00:56,  2.17it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:34<00:55,  2.17it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:35<00:55,  2.17it/s]Progress: 63.00%
+---- avg training fps: 7.54

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:35<00:55,  2.17it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:35<00:54,  2.17it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:35<00:54,  2.18it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:36<00:53,  2.17it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:36<00:53,  2.17it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:37<00:53,  2.16it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:37<00:52,  2.17it/s]Progress: 65.00%
+---- avg training fps: 7.57

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:38<00:52,  2.17it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:38<00:51,  2.17it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:39<00:51,  2.16it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:39<00:50,  2.17it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:40<00:50,  2.17it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:40<00:50,  2.17it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:40<00:50,  2.15it/s]Progress: 67.00%
+---- avg training fps: 7.60

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:41<00:49,  2.15it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:41<00:48,  2.17it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:41<00:48,  2.15it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:42<00:48,  2.15it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:42<00:47,  2.15it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:43<00:47,  2.16it/s]Progress: 69.00%
+---- avg training fps: 7.63

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:43<00:46,  2.15it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:44<00:46,  2.16it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:44<00:46,  2.16it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:44<00:45,  2.16it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:48<02:24,  1.48s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:49<01:53,  1.17s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:49<01:31,  1.05it/s]Progress: 71.00%
+---- avg training fps: 7.42

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:49<01:16,  1.24it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:50<01:05,  1.43it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:50<00:58,  1.59it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:51<00:52,  1.74it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:51<00:49,  1.86it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:52<00:46,  1.94it/s]Progress: 73.00%
+---- avg training fps: 7.46

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:52<00:46,  1.94it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:52<00:44,  2.00it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:53<00:42,  2.06it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:53<00:41,  2.09it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:54<00:40,  2.11it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [01:54<00:39,  2.13it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [01:54<00:38,  2.16it/s]Progress: 75.00%
+---- avg training fps: 7.49

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [01:55<00:38,  2.17it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [01:55<00:37,  2.16it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [01:56<00:37,  2.16it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [01:56<00:36,  2.16it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [01:57<00:36,  2.16it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [01:57<00:36,  2.16it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [01:57<00:36,  2.16it/s]Progress: 77.00%
+---- avg training fps: 7.51

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [01:58<00:35,  2.16it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [01:58<00:35,  2.16it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [01:59<00:34,  2.16it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [01:59<00:34,  2.16it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:00<00:33,  2.16it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:00<00:33,  2.17it/s]Progress: 79.00%
+---- avg training fps: 7.54

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:00<00:32,  2.16it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:01<00:32,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:01<00:32,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:01<00:31,  2.17it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:02<00:31,  2.18it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:02<00:30,  2.17it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:03<00:30,  2.17it/s]Progress: 81.00%
+---- avg training fps: 7.56

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:03<00:30,  2.16it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:04<00:29,  2.18it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:04<00:29,  2.17it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:05<00:28,  2.16it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:05<00:28,  2.15it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:06<00:27,  2.17it/s]Progress: 83.00%
+---- avg training fps: 7.59

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:06<00:27,  2.17it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:06<00:27,  2.17it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:06<00:26,  2.17it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:07<00:26,  2.15it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:07<00:25,  2.16it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:08<00:25,  2.15it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:08<00:25,  2.15it/s]Progress: 85.00%
+---- avg training fps: 7.61

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:09<00:24,  2.14it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:09<00:24,  2.16it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:10<00:23,  2.16it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:10<00:23,  2.15it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:11<00:23,  2.15it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:11<00:22,  2.16it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:11<00:22,  2.16it/s]Progress: 87.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.63

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:12<00:21,  2.16it/s]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:12<00:21,  2.16it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:12<00:20,  2.17it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:13<00:20,  2.16it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:13<00:19,  2.16it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:14<00:19,  2.16it/s]Progress: 89.00%
+---- avg training fps: 7.65

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:14<00:18,  2.17it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:15<00:18,  2.16it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:15<00:18,  2.16it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:15<00:18,  2.16it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:16<00:17,  2.15it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:16<00:17,  2.16it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:17<00:16,  2.15it/s]Progress: 91.00%
+---- avg training fps: 7.67

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:17<00:16,  2.15it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:18<00:15,  2.15it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:18<00:15,  2.16it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:19<00:14,  2.14it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:19<00:14,  2.13it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:19<00:13,  2.16it/s]Progress: 93.00%
+---- avg training fps: 7.69

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:20<00:13,  2.16it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:20<00:13,  2.16it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:20<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:21<00:12,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:21<00:12,  2.16it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:22<00:11,  2.15it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:22<00:11,  2.15it/s]Progress: 95.00%
+---- avg training fps: 7.71

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:23<00:10,  2.14it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:23<00:10,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:24<00:09,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:24<00:09,  2.14it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:25<00:09,  2.14it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:25<00:08,  2.16it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:25<00:08,  2.15it/s]Progress: 97.00%
+---- avg training fps: 7.73

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:26<00:07,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:26<00:07,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:26<00:06,  2.17it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:27<00:06,  2.16it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:27<00:06,  2.16it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:28<00:05,  2.15it/s]Progress: 99.00%
+---- avg training fps: 7.74

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:28<00:05,  2.16it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:29<00:04,  2.15it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:29<00:04,  2.15it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:29<00:04,  2.16it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:30<00:03,  2.16it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:30<00:03,  2.17it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:31<00:02,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.76

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:31<00:02,  2.16it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:32<00:01,  2.16it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:32<00:01,  1.92it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:33<00:01,  1.98it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:33<00:00,  2.02it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:34<00:00,  2.07it/s]Progress: 100.00%
+---- avg training fps: 7.77
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [03:54<00:00,  1.28it/s]
+------------------------------------------
+Training done :)
+2024-08-13 03:47:58.629349: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 03:47:58.721340: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 03:47:58.721369: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 03:47:58.740258: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 03:47:59.142726: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:47:59.142809: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:47:59.142819: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23109 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/journey_small' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.5 freeze_unet_before_completion_f=0.0 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='journey_small' output_dir='lora_models/styles_final/journey_small_13_03-48-00-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723513680.9630408 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/journey_small_13_03-48-00-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 0.812
+New train_img_size: [448, 576]
+Validation_img_size was set to: [896, 1152]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red cloak standing in front of a building.
+- A person standing on top of a body of water.
+- A person walking in the desert with a camel in front of a mountain.
+- A man standing on top of a sandy beach next to the ocean.
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red dress is walking through an archway.
+- A person standing on top of a body of water.
+- A person walking in the desert with a dog on a leash.
+- A person walking on a beach next to a body of water.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal and dreamlike, focus on vastness.
+
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red cloak stands in front of a building.
+- In the style of TOK, a person stands on top of a body of water.
+- In the style of TOK, a person walks in the desert with a camel in front of a mountain.
+- In the style of TOK, a man stands on top of a sandy beach next to the ocean.
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red dress walks through an archway.
+- In the style of TOK, a person stands on top of a body of water.
+- In the style of TOK, a person walks in the desert with a dog on a leash.
+- In the style of TOK, a person walks on a beach next to a body of water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Surreal and dreamlike, focus on vastness.
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 10099.46it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a sunset over a ...
+1     in the style of <s0><s1><s2>, a woman in a red...
+2     in the style of <s0><s1><s2>, a person stands ...
+3     in the style of <s0><s1><s2>, a person walks i...
+4     in the style of <s0><s1><s2>, a man stands on ...
+5     in the style of <s0><s1><s2>, a sunset over a ...
+6     in the style of <s0><s1><s2>, a woman in a red...
+7     in the style of <s0><s1><s2>, a person stands ...
+8     in the style of <s0><s1><s2>, a person walks i...
+9     in the style of <s0><s1><s2>, a person walks o...
+10    in the style of <s0><s1><s2>, a sunset over a ...
+11    in the style of <s0><s1><s2>, a woman in a red...
+12    in the style of <s0><s1><s2>, a person stands ...
+13    in the style of <s0><s1><s2>, a person walks i...
+14    in the style of <s0><s1><s2>, a man stands on ...
+15    in the style of <s0><s1><s2>, a sunset over a ...
+16    in the style of <s0><s1><s2>, a woman in a red...
+17    in the style of <s0><s1><s2>, a person stands ...
+18    in the style of <s0><s1><s2>, a person walks i...
+19    in the style of <s0><s1><s2>, a person walks o...
+20    in the style of <s0><s1><s2>, a sunset over a ...
+21    in the style of <s0><s1><s2>, a woman in a red...
+22    in the style of <s0><s1><s2>, a person stands ...
+23    in the style of <s0><s1><s2>, a person walks i...
+24    in the style of <s0><s1><s2>, a man stands on ...
+25    in the style of <s0><s1><s2>, a sunset over a ...
+26    in the style of <s0><s1><s2>, a woman in a red...
+27    in the style of <s0><s1><s2>, a person stands ...
+28    in the style of <s0><s1><s2>, a person walks i...
+29    in the style of <s0><s1><s2>, a person walks o...
+30    in the style of <s0><s1><s2>, a sunset over a ...
+31    in the style of <s0><s1><s2>, a woman in a red...
+32    in the style of <s0><s1><s2>, a person stands ...
+33    in the style of <s0><s1><s2>, a person walks i...
+34    in the style of <s0><s1><s2>, a man stands on ...
+35    in the style of <s0><s1><s2>, a sunset over a ...
+36    in the style of <s0><s1><s2>, a woman in a red...
+37    in the style of <s0><s1><s2>, a person stands ...
+38    in the style of <s0><s1><s2>, a person walks i...
+39    in the style of <s0><s1><s2>, a person walks o...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:09<04:37,  1.06it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.42

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+---- avg training fps: 3.72

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+---- avg training fps: 4.60

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+---- avg training fps: 5.21

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+---- avg training fps: 5.66

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+---- avg training fps: 5.99

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+---- avg training fps: 6.26

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+---- avg training fps: 6.48

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+---- avg training fps: 5.96

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+---- avg training fps: 6.15

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+---- avg training fps: 6.31

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# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:43<01:47,  2.13it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:44<01:46,  2.13it/s]Progress: 27.00%
+---- avg training fps: 6.45

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:44<01:46,  2.13it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:45<01:45,  2.14it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:45<01:45,  2.13it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:46<01:44,  2.14it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:46<01:44,  2.14it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:46<01:43,  2.14it/s]Progress: 29.00%
+---- avg training fps: 6.58

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:47<01:43,  2.13it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:47<01:43,  2.14it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:48<01:43,  2.14it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:48<01:42,  2.14it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:48<01:41,  2.14it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:49<01:41,  2.14it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:49<01:41,  2.14it/s]Progress: 31.00%
+---- avg training fps: 6.69

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:50<01:40,  2.14it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:50<01:40,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:51<01:39,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:51<01:39,  2.14it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:52<01:38,  2.14it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:52<01:38,  2.14it/s]Progress: 33.00%
+---- avg training fps: 6.78

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:53<01:38,  2.14it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:53<01:37,  2.13it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:53<01:37,  2.13it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:54<01:37,  2.13it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:54<01:36,  2.13it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:54<01:35,  2.14it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:55<01:35,  2.13it/s]Progress: 35.00%
+---- avg training fps: 6.87

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:55<01:35,  2.13it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:56<01:34,  2.14it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:56<01:34,  2.14it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:57<01:33,  2.14it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:57<01:33,  2.14it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:57<01:33,  2.13it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [01:02<05:42,  1.73s/it]Progress: 37.00%
+---- avg training fps: 6.49

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [01:02<04:26,  1.35s/it]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [01:03<03:32,  1.09s/it]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [01:03<02:55,  1.11it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [01:04<02:29,  1.30it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:04<02:11,  1.47it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:05<01:58,  1.62it/s]Progress: 39.00%
+---- avg training fps: 6.58

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:05<01:49,  1.75it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:06<01:42,  1.85it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:06<01:42,  1.85it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:06<01:38,  1.92it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:07<01:34,  1.98it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:07<01:32,  2.02it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:08<01:30,  2.05it/s]Progress: 41.00%
+---- avg training fps: 6.66

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:08<01:29,  2.07it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:08<01:28,  2.09it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:09<01:26,  2.11it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:09<01:26,  2.12it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:10<01:25,  2.12it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:10<01:24,  2.12it/s]Progress: 43.00%
+---- avg training fps: 6.73

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:11<01:24,  2.12it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:11<01:24,  2.13it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:11<01:23,  2.13it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:12<01:23,  2.13it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:12<01:22,  2.14it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:13<01:22,  2.13it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:13<01:21,  2.13it/s]Progress: 45.00%
+---- avg training fps: 6.80

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:14<01:21,  2.13it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:14<01:21,  2.12it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:15<01:20,  2.12it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:15<01:20,  2.12it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:16<01:20,  2.12it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:16<01:19,  2.13it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:16<01:19,  2.12it/s]Progress: 47.00%
+---- avg training fps: 6.86

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:16<01:18,  2.12it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:17<01:17,  2.13it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:17<01:17,  2.12it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:18<01:17,  2.12it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:18<01:16,  2.12it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:19<01:16,  2.12it/s]Progress: 49.00%
+---- avg training fps: 6.92

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:19<01:16,  2.12it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:20<01:15,  2.11it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:20<01:15,  2.11it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:20<01:15,  2.12it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:21<01:14,  2.12it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:21<01:14,  2.11it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:22<01:13,  2.12it/s]Progress: 51.00%
+---- avg training fps: 6.97

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:22<01:13,  2.11it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:23<01:12,  2.11it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:23<01:12,  2.12it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:24<01:11,  2.11it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:24<01:11,  2.11it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:25<01:10,  2.12it/s]Progress: 53.00%
+---- avg training fps: 7.02

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:25<01:10,  2.12it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:25<01:10,  2.12it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:30<04:14,  1.72s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:30<03:17,  1.34s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:31<02:37,  1.08s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:31<02:10,  1.11it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:31<01:50,  1.30it/s]Progress: 55.00%
+---- avg training fps: 6.75

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:32<01:36,  1.48it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:32<01:27,  1.63it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:33<01:20,  1.75it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:33<01:15,  1.85it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:34<01:15,  1.85it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:34<01:11,  1.93it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:34<01:09,  1.99it/s]Progress: 57.00%
+---- avg training fps: 6.80

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:35<01:07,  2.03it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:35<01:06,  2.06it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:36<01:04,  2.08it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:36<01:04,  2.09it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:37<01:03,  2.10it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:37<01:02,  2.11it/s]Progress: 59.00%
+---- avg training fps: 6.85

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:38<01:01,  2.11it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:38<01:00,  2.13it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:39<01:00,  2.13it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:39<01:00,  2.13it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:39<01:00,  2.13it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:39<00:59,  2.13it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:40<00:59,  2.13it/s]Progress: 61.00%
+---- avg training fps: 6.90

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:40<00:58,  2.13it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:41<00:58,  2.12it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:41<00:57,  2.13it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:42<00:57,  2.13it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:42<00:57,  2.12it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:43<00:56,  2.13it/s]Progress: 63.00%
+---- avg training fps: 6.94

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:43<00:56,  2.13it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:43<00:55,  2.13it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:44<00:55,  2.13it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:44<00:55,  2.13it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:45<00:54,  2.13it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:45<00:54,  2.12it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:46<00:53,  2.12it/s]Progress: 65.00%
+---- avg training fps: 6.98

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:46<00:52,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:47<00:52,  2.13it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:47<00:52,  2.12it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:47<00:51,  2.12it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:48<00:51,  2.12it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:48<00:51,  2.12it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:48<00:50,  2.12it/s]Progress: 67.00%
+---- avg training fps: 7.02

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:49<00:50,  2.12it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:49<00:49,  2.12it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:50<00:49,  2.12it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:50<00:49,  2.12it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:51<00:48,  2.13it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:51<00:47,  2.13it/s]Progress: 69.00%
+---- avg training fps: 7.06

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:52<00:47,  2.12it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:52<00:47,  2.12it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:53<00:47,  2.12it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:53<00:46,  2.12it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:57<02:51,  1.75s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:58<02:12,  1.36s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:58<01:45,  1.09s/it]Progress: 71.00%
+---- avg training fps: 6.84

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:59<01:26,  1.10it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:59<01:12,  1.29it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [02:00<01:03,  1.47it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [02:00<00:56,  1.62it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [02:01<00:52,  1.75it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [02:01<00:48,  1.85it/s]Progress: 73.00%
+---- avg training fps: 6.88

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [02:02<00:48,  1.85it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [02:02<00:46,  1.92it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [02:02<00:44,  1.98it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [02:02<00:42,  2.03it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [02:03<00:41,  2.05it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [02:03<00:40,  2.08it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [02:04<00:40,  2.09it/s]Progress: 75.00%
+---- avg training fps: 6.92

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [02:04<00:39,  2.10it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [02:05<00:38,  2.11it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:05<00:38,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:06<00:37,  2.12it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:06<00:37,  2.12it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:06<00:37,  2.12it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:07<00:36,  2.13it/s]Progress: 77.00%
+---- avg training fps: 6.95

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:07<00:36,  2.12it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:08<00:35,  2.12it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:08<00:35,  2.12it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:09<00:34,  2.13it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:09<00:34,  2.12it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:10<00:33,  2.12it/s]Progress: 79.00%
+---- avg training fps: 6.99

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:10<00:33,  2.12it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:10<00:32,  2.12it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:11<00:32,  2.12it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:11<00:32,  2.12it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:11<00:31,  2.14it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:12<00:31,  2.12it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:12<00:31,  2.12it/s]Progress: 81.00%
+---- avg training fps: 7.02

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:13<00:30,  2.12it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:13<00:30,  2.12it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:14<00:29,  2.12it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:14<00:29,  2.12it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:15<00:28,  2.13it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:15<00:28,  2.12it/s]Progress: 83.00%
+---- avg training fps: 7.05

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:16<00:28,  2.12it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:16<00:27,  2.12it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:16<00:27,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:17<00:26,  2.12it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:17<00:26,  2.12it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:18<00:25,  2.13it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:18<00:25,  2.13it/s]Progress: 85.00%
+---- avg training fps: 7.08

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:18<00:24,  2.12it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:19<00:24,  2.12it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:19<00:23,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:20<00:23,  2.12it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:20<00:23,  2.12it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:20<00:23,  2.12it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:25<01:23,  1.73s/it]Progress: 87.00%
+---- avg training fps: 6.90

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:26<01:03,  1.35s/it]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:26<00:49,  1.08s/it]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:26<00:40,  1.11it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:27<00:33,  1.30it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:27<00:29,  1.48it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:28<00:25,  1.64it/s]Progress: 89.00%
+---- avg training fps: 6.94

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:28<00:23,  1.76it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:29<00:21,  1.86it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:29<00:21,  1.86it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:29<00:20,  1.93it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:30<00:19,  1.99it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:30<00:18,  2.04it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:31<00:17,  2.06it/s]Progress: 91.00%
+---- avg training fps: 6.97

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:31<00:16,  2.09it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:32<00:16,  2.10it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:32<00:15,  2.11it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:32<00:15,  2.12it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:33<00:14,  2.13it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:33<00:14,  2.13it/s]Progress: 93.00%
+---- avg training fps: 6.99

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:34<00:14,  2.13it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:34<00:13,  2.13it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:34<00:13,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:35<00:12,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:35<00:12,  2.13it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:36<00:11,  2.13it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:36<00:11,  2.13it/s]Progress: 95.00%
+---- avg training fps: 7.02

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:37<00:10,  2.13it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:37<00:10,  2.13it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:38<00:09,  2.14it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:38<00:09,  2.13it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:39<00:09,  2.13it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:39<00:08,  2.13it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:39<00:08,  2.13it/s]Progress: 97.00%
+---- avg training fps: 7.05

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:40<00:07,  2.13it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:40<00:07,  2.13it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:40<00:07,  2.13it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:41<00:06,  2.13it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:41<00:06,  2.13it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:42<00:05,  2.12it/s]Progress: 99.00%
+---- avg training fps: 7.07

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:42<00:05,  2.14it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:43<00:04,  2.13it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:43<00:04,  2.13it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:43<00:04,  2.13it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:44<00:03,  2.13it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:44<00:03,  2.13it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:45<00:02,  2.13it/s]Progress: 100.00%
+---- avg training fps: 7.10

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:45<00:02,  2.12it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:46<00:01,  2.14it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:46<00:01,  2.13it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:47<00:00,  2.13it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:47<00:00,  2.12it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:48<00:00,  2.13it/s]Progress: 100.00%
+---- avg training fps: 7.12
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [04:08<00:00,  1.21it/s]
+------------------------------------------
+Training done :)
+2024-08-13 03:52:35.226685: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 03:52:35.317511: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 03:52:35.317542: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 03:52:35.336012: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 03:52:35.735174: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:52:35.735254: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:52:35.735263: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23128 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/journey_small' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.75 freeze_unet_before_completion_f=0.25 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='journey_small' output_dir='lora_models/styles_final/journey_small_13_03-52-37-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723513957.53574 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/journey_small_13_03-52-37-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 0.812
+New train_img_size: [448, 576]
+Validation_img_size was set to: [896, 1152]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red cloak standing in front of a building.
+- A person standing on top of a body of water.
+- A person walking in the desert with a camel in front of a mountain.
+- A man standing on top of a sandy beach next to the ocean.
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red dress is walking through an archway.
+- A person standing on top of a body of water.
+- A person walking in the desert with a dog on a leash.
+- A person walking on a beach next to a body of water.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal and minimalist dreamscapes
+
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red cloak standing in front of a building.
+- In the style of TOK, a person standing on top of a body of water.
+- In the style of TOK, a person walking in the desert with a camel in front of a mountain.
+- In the style of TOK, a man standing on top of a sandy beach next to the ocean.
+- In the style of TOK, a woman in a red dress walking through an archway.
+- In the style of TOK, a person walking in the desert with a dog on a leash.
+- In the style of TOK, a person walking on a beach next to a body of water.
+--------------------------
+GPT-4 returned the wrong number of prompts 8 instead of 10, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red cloak standing in front of a building.
+- A person standing on top of a body of water.
+- A person walking in the desert with a camel in front of a mountain.
+- A man standing on top of a sandy beach next to the ocean.
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red dress is walking through an archway.
+- A person standing on top of a body of water.
+- A person walking in the desert with a dog on a leash.
+- A person walking on a beach next to a body of water.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 1...
+----- GPT response: -----
+Style Description: Dreamy, surreal landscape with vivid colors.
+
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red cloak stands in front of a building.
+- In the style of TOK, a person stands on top of a body of water.
+- In the style of TOK, a person walks in the desert with a camel in front of a mountain.
+- In the style of TOK, a man stands on top of a sandy beach next to the ocean.
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red dress walks through an archway.
+- In the style of TOK, a person stands on top of a body of water.
+- In the style of TOK, a person walks in the desert with a dog on a leash.
+- In the style of TOK, a person walks on a beach next to a body of water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Dreamy, surreal landscape with vivid colors.
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 9739.47it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a sunset over a ...
+1     in the style of <s0><s1><s2>, a woman in a red...
+2     in the style of <s0><s1><s2>, a person stands ...
+3     in the style of <s0><s1><s2>, a person walks i...
+4     in the style of <s0><s1><s2>, a man stands on ...
+5     in the style of <s0><s1><s2>, a sunset over a ...
+6     in the style of <s0><s1><s2>, a woman in a red...
+7     in the style of <s0><s1><s2>, a person stands ...
+8     in the style of <s0><s1><s2>, a person walks i...
+9     in the style of <s0><s1><s2>, a person walks o...
+10    in the style of <s0><s1><s2>, a sunset over a ...
+11    in the style of <s0><s1><s2>, a woman in a red...
+12    in the style of <s0><s1><s2>, a person stands ...
+13    in the style of <s0><s1><s2>, a person walks i...
+14    in the style of <s0><s1><s2>, a man stands on ...
+15    in the style of <s0><s1><s2>, a sunset over a ...
+16    in the style of <s0><s1><s2>, a woman in a red...
+17    in the style of <s0><s1><s2>, a person stands ...
+18    in the style of <s0><s1><s2>, a person walks i...
+19    in the style of <s0><s1><s2>, a person walks o...
+20    in the style of <s0><s1><s2>, a sunset over a ...
+21    in the style of <s0><s1><s2>, a woman in a red...
+22    in the style of <s0><s1><s2>, a person stands ...
+23    in the style of <s0><s1><s2>, a person walks i...
+24    in the style of <s0><s1><s2>, a man stands on ...
+25    in the style of <s0><s1><s2>, a sunset over a ...
+26    in the style of <s0><s1><s2>, a woman in a red...
+27    in the style of <s0><s1><s2>, a person stands ...
+28    in the style of <s0><s1><s2>, a person walks i...
+29    in the style of <s0><s1><s2>, a person walks o...
+30    in the style of <s0><s1><s2>, a sunset over a ...
+31    in the style of <s0><s1><s2>, a woman in a red...
+32    in the style of <s0><s1><s2>, a person stands ...
+33    in the style of <s0><s1><s2>, a person walks i...
+34    in the style of <s0><s1><s2>, a man stands on ...
+35    in the style of <s0><s1><s2>, a sunset over a ...
+36    in the style of <s0><s1><s2>, a woman in a red...
+37    in the style of <s0><s1><s2>, a person stands ...
+38    in the style of <s0><s1><s2>, a person walks i...
+39    in the style of <s0><s1><s2>, a person walks o...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:09<04:43,  1.04it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.35

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+---- avg training fps: 3.64

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+---- avg training fps: 4.51

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+---- avg training fps: 5.11

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+---- avg training fps: 5.55

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+---- avg training fps: 5.90

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+---- avg training fps: 6.18

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+---- avg training fps: 6.40

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+---- avg training fps: 5.91

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+---- avg training fps: 6.10

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# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:41<01:51,  2.10it/s]Progress: 25.00%
+---- avg training fps: 6.26

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:42<01:50,  2.11it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:42<01:49,  2.12it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:43<01:48,  2.13it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:43<01:48,  2.12it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:44<01:48,  2.12it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:44<01:47,  2.12it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:44<01:47,  2.13it/s]Progress: 27.00%
+---- avg training fps: 6.40

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:44<01:46,  2.13it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:45<01:46,  2.13it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:45<01:45,  2.13it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:46<01:44,  2.14it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:46<01:44,  2.13it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:47<01:44,  2.13it/s]Progress: 29.00%
+---- avg training fps: 6.53

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:47<01:43,  2.13it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:48<01:43,  2.13it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:48<01:43,  2.13it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:48<01:43,  2.12it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:49<01:42,  2.13it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:49<01:41,  2.14it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:50<01:40,  2.14it/s]Progress: 31.00%
+---- avg training fps: 6.64

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:50<01:40,  2.14it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:51<01:40,  2.14it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:51<01:39,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:52<01:39,  2.13it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:52<01:38,  2.13it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:52<01:37,  2.15it/s]Progress: 33.00%
+---- avg training fps: 6.74

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:53<01:37,  2.15it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:53<01:37,  2.14it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:53<01:37,  2.13it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:54<01:37,  2.13it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:54<01:36,  2.13it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:55<01:36,  2.13it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:55<01:36,  2.12it/s]Progress: 35.00%
+---- avg training fps: 6.83

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:56<01:35,  2.14it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:56<01:34,  2.13it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:57<01:34,  2.13it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:57<01:33,  2.13it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:58<01:33,  2.13it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:58<01:33,  2.13it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:58<01:33,  2.13it/s]Progress: 37.00%
Failed to plot token attention loss
+
+---- avg training fps: 6.91

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:59<01:32,  2.12it/s]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [00:59<01:32,  2.12it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [01:00<01:32,  2.12it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [01:00<01:31,  2.12it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:00<01:31,  2.12it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:01<01:30,  2.12it/s]Progress: 39.00%
+---- avg training fps: 6.98

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:01<01:30,  2.11it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:02<01:29,  2.13it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:02<01:29,  2.13it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:02<01:29,  2.12it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:03<01:28,  2.12it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:03<01:28,  2.11it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:04<01:27,  2.11it/s]Progress: 41.00%
+---- avg training fps: 7.04

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:04<01:27,  2.11it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:05<01:27,  2.11it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:05<01:26,  2.12it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:06<01:26,  2.12it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:06<01:25,  2.12it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:07<01:24,  2.12it/s]Progress: 43.00%
+---- avg training fps: 7.10

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:07<01:24,  2.12it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:07<01:24,  2.12it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:08<01:24,  2.12it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:08<01:23,  2.13it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:08<01:22,  2.12it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:09<01:22,  2.11it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:09<01:22,  2.11it/s]Progress: 45.00%
+---- avg training fps: 7.16

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:10<01:21,  2.12it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:10<01:21,  2.11it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:11<01:20,  2.11it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:11<01:20,  2.12it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:12<01:20,  2.12it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:12<01:19,  2.12it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:12<01:19,  2.11it/s]Progress: 47.00%
+---- avg training fps: 7.21

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:13<01:19,  2.11it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:13<01:18,  2.11it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:14<01:18,  2.10it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:14<01:17,  2.12it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:15<01:16,  2.12it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:15<01:16,  2.11it/s]Progress: 49.00%
+---- avg training fps: 7.26

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:16<01:16,  2.12it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:16<01:15,  2.11it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:17<01:15,  2.11it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:17<01:15,  2.10it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:17<01:14,  2.11it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:17<01:14,  2.11it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:18<01:13,  2.11it/s]Progress: 51.00%
+---- avg training fps: 7.30

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:18<01:13,  2.10it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:19<01:13,  2.11it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:19<01:12,  2.11it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:20<01:12,  2.11it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:20<01:11,  2.11it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:21<01:11,  2.11it/s]Progress: 53.00%
+---- avg training fps: 7.34

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:21<01:11,  2.11it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:21<01:10,  2.10it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:26<04:18,  1.74s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:26<03:20,  1.36s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:27<02:39,  1.10s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:27<02:11,  1.10it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:28<01:51,  1.29it/s]Progress: 55.00%
+---- avg training fps: 7.02

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:28<01:37,  1.46it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:29<01:27,  1.62it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:29<01:20,  1.74it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:30<01:15,  1.85it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:30<01:15,  1.85it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:30<01:12,  1.92it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:31<01:09,  1.98it/s]Progress: 57.00%
+---- avg training fps: 7.07

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:31<01:07,  2.02it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:32<01:06,  2.05it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:32<01:05,  2.07it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:33<01:04,  2.09it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:33<01:03,  2.10it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:34<01:02,  2.10it/s]Progress: 59.00%
+---- avg training fps: 7.11

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:34<01:02,  2.10it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:34<01:01,  2.11it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:35<01:01,  2.11it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:35<01:01,  2.11it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:35<01:00,  2.11it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:36<00:59,  2.12it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:36<00:59,  2.11it/s]Progress: 61.00%
+---- avg training fps: 7.15

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:37<00:59,  2.11it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:37<00:58,  2.12it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:38<00:57,  2.12it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:38<00:57,  2.12it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:39<00:57,  2.11it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:39<00:56,  2.12it/s]Progress: 63.00%
+---- avg training fps: 7.19

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:40<00:56,  2.12it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:40<00:56,  2.12it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:40<00:55,  2.11it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:41<00:55,  2.12it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:41<00:54,  2.11it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:42<00:54,  2.11it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:42<00:53,  2.12it/s]Progress: 65.00%
+---- avg training fps: 7.22

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:42<00:53,  2.11it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:43<00:53,  2.11it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:43<00:52,  2.12it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:44<00:52,  2.11it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:44<00:52,  2.11it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:44<00:51,  2.11it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:45<00:51,  2.11it/s]Progress: 67.00%
+---- avg training fps: 7.26

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:45<00:50,  2.11it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:46<00:50,  2.11it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:46<00:49,  2.10it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:47<00:49,  2.11it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:47<00:48,  2.11it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:48<00:48,  2.10it/s]Progress: 69.00%
+---- avg training fps: 7.29

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:48<00:47,  2.11it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:49<00:47,  2.11it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:49<00:47,  2.11it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:49<00:46,  2.11it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:54<02:54,  1.78s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:54<02:14,  1.39s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:55<01:46,  1.11s/it]Progress: 71.00%
+---- avg training fps: 7.04

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:55<01:27,  1.09it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:56<01:13,  1.28it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:56<01:03,  1.45it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:57<00:57,  1.61it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:57<00:52,  1.73it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:58<00:48,  1.84it/s]Progress: 73.00%
+---- avg training fps: 7.08

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:58<00:48,  1.84it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:58<00:46,  1.91it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:59<00:44,  1.96it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:59<00:43,  2.01it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [02:00<00:42,  2.04it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [02:00<00:41,  2.06it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [02:01<00:40,  2.08it/s]Progress: 75.00%
+---- avg training fps: 7.11

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [02:01<00:39,  2.09it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [02:01<00:39,  2.10it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:02<00:38,  2.10it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:02<00:37,  2.11it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:03<00:37,  2.11it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:03<00:37,  2.12it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:03<00:36,  2.11it/s]Progress: 77.00%
+---- avg training fps: 7.14

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:04<00:36,  2.12it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:04<00:35,  2.11it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:05<00:35,  2.11it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:05<00:35,  2.11it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:06<00:34,  2.11it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:06<00:33,  2.12it/s]Progress: 79.00%
+---- avg training fps: 7.17

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:07<00:33,  2.12it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:07<00:33,  2.11it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:08<00:33,  2.11it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:08<00:32,  2.13it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:08<00:32,  2.12it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:09<00:31,  2.12it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:09<00:31,  2.11it/s]Progress: 81.00%
+---- avg training fps: 7.20

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:10<00:30,  2.12it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:10<00:30,  2.12it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:10<00:29,  2.11it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:11<00:29,  2.12it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:11<00:28,  2.12it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:12<00:28,  2.12it/s]Progress: 83.00%
+---- avg training fps: 7.23

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:12<00:28,  2.12it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:12<00:27,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:13<00:27,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:13<00:26,  2.12it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:14<00:26,  2.14it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:14<00:25,  2.13it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:15<00:25,  2.13it/s]Progress: 85.00%
+---- avg training fps: 7.25

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:15<00:25,  2.12it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:16<00:24,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:16<00:24,  2.12it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:17<00:23,  2.12it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:17<00:23,  2.12it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:17<00:23,  2.12it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:18<00:22,  2.12it/s]Progress: 87.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.28

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:18<00:22,  2.12it/s]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:18<00:21,  2.13it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:19<00:21,  2.12it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:19<00:20,  2.12it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:20<00:20,  2.12it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:20<00:19,  2.12it/s]Progress: 89.00%
+---- avg training fps: 7.30

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:21<00:19,  2.12it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:21<00:18,  2.12it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:22<00:18,  2.12it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:22<00:18,  2.12it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:22<00:17,  2.12it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:23<00:17,  2.11it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:23<00:16,  2.13it/s]Progress: 91.00%
+---- avg training fps: 7.33

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:24<00:16,  2.12it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:24<00:16,  2.11it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:25<00:15,  2.13it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:25<00:15,  2.13it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:26<00:14,  2.13it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:26<00:14,  2.13it/s]Progress: 93.00%
+---- avg training fps: 7.35

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:26<00:14,  2.13it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:26<00:13,  2.13it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:27<00:13,  2.12it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:27<00:12,  2.12it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:28<00:12,  2.14it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:28<00:11,  2.13it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:29<00:11,  2.13it/s]Progress: 95.00%
+---- avg training fps: 7.37

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:29<00:10,  2.12it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:30<00:10,  2.12it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:30<00:09,  2.12it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:31<00:09,  2.12it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:31<00:09,  2.12it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:31<00:08,  2.12it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:32<00:08,  2.12it/s]Progress: 97.00%
+---- avg training fps: 7.39

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:32<00:08,  2.12it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:33<00:08,  1.88it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:33<00:07,  1.94it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:34<00:07,  1.99it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:34<00:06,  2.03it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:35<00:05,  2.06it/s]Progress: 99.00%
+---- avg training fps: 7.40

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:35<00:05,  2.08it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:36<00:04,  2.10it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:36<00:04,  2.10it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:36<00:04,  2.12it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:37<00:03,  2.12it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:37<00:03,  2.12it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:37<00:02,  2.14it/s]Progress: 100.00%
+---- avg training fps: 7.42

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:38<00:02,  2.14it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:38<00:01,  2.13it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:39<00:01,  2.13it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:39<00:00,  2.14it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:40<00:00,  2.13it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:40<00:00,  2.13it/s]Progress: 100.00%
+---- avg training fps: 7.44
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [04:01<00:00,  1.24it/s]
+------------------------------------------
+Training done :)
+2024-08-13 03:57:05.831434: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 03:57:05.922806: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 03:57:05.922834: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 03:57:05.942170: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 03:57:06.346188: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:57:06.346267: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 03:57:06.346276: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23112 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.5 freeze_unet_before_completion_f=0.5 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='clipx.zip' output_dir='lora_models/styles_final/clipx.zip_13_03-57-08-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723514228.109371 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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Fetching 17 files: 100%|██████████| 17/17 [00:00<00:00, 55705.60it/s]
+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Downloading training data...
+Downloading https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip to lora_models/styles_final/clipx.zip_13_03-57-08-style_512_florence_300/images_in/clipx.zip...
+Succesfully prepped 54 .jpg images in lora_models/styles_final/clipx.zip_13_03-57-08-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.314
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 27 images, 27 of which have captions.
+upscaling imgs..
+
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100%|██████████| 27/27 [00:00<00:00, 308572.77it/s]
+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 27 to 54)
+Generating 54 captions using mode: style...
+
  0%|          | 0/54 [00:00<?, ?it/s]
100%|██████████| 54/54 [00:00<00:00, 1665385.41it/s]
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal, whimsical, detailed, and quirky scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene is Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase describes the best and worst of times.
+- In the style of TOK, a blockchain is depicted using bees.
+- In the style of TOK, a gorgon made of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset shows Venus and Earth circling the sun.
+- In the style of TOK, there is an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is portrayed.
+- In the style of TOK, the phrase exemplifies the age of wisdom and foolishness.
+- In the style of TOK, two characters express a desire to quit their boring corporation job together.
+- In the style of TOK, petroglyphs are presented.
+- In the style of TOK, a room and space exchange based on first and second-degree connections is depicted.
+- In the style of TOK, the fear known as trypophobia is illustrated.
+- In the style of TOK, there is a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers is highlighted.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam in a Hawaiian-shirt pattern.
+- In the style of TOK, a brigade of beavers is quickly building a dam as envisioned by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is depicted in M. C. Escher's style.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models and egrets seated in the audience.
+- In the style of TOK, there is a hyper-realistic painting of lemon-lime architecture.
+--------------------------
+GPT-4 returned the wrong number of prompts 26 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 1...
+----- GPT response: -----
+Style Description: Surrealistic, intricate, and fantastical scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is embodied in a visual representation.
+- In the style of TOK, a blockchain is composed of bees.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun is depicted.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is presented.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is embodied in a visual representation.
+- In the style of TOK, two individuals are depicted quitting their boring corporation job together.
+- In the style of TOK, petroglyphs are portrayed.
+- In the style of TOK, a room and space exchange is based on first and second-degree connections.
+- In the style of TOK, trypophobia is visually expressed.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of dancing, singing beavers is featured with a nod to Jim Henson's Muppets.
+- In the style of TOK, a beaver is shown chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a pattern like a Hawaiian shirt.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is depicted, reminiscent of M. C. Escher's style.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show is held with elephant seals as the runway models and egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture is displayed.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash is swimming up through the sea to take a peek above the water.
+- In the style of TOK, a mermaid made out of fishnets and other sea trash is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge, with a nod to C. G. Jung's "Liber Novus."
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is visually represented.
+- In the style of TOK, a blockchain composed of bees is shown.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun is depicted.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are shown pooping in zero-gravity.
+- In the style of TOK, a goblin goat is depicted.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is visually represented.
+- In the style of TOK, two individuals are depicted quitting their boring corporation job together.
+- In the style of TOK, petroglyphs are portrayed.
+- In the style of TOK, a room and space exchange is based on first and second-degree connections.
+- In the style of TOK, trypophobia is visually expressed.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of dancing, singing beavers is featured with a nod to Jim Henson's Muppets.
+- In the style of TOK, a beaver is shown chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a pattern like a Hawaiian shirt.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is depicted, reminiscent of M. C. Escher's style.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show is held with elephant seals as the runway models and egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture is displayed.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash is swimming up through the sea to take a peek above the water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Using GPT concept name as CLIP-segmentation prompt: Surrealistic, intricate, and fantastical scenes
+Generating 54 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
54it [00:00, 15387.76it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 54 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a mermaid made o...
+1     in the style of <s0><s1><s2>, a throng of lill...
+2     in the style of <s0><s1><s2>, fort kochi in ke...
+3     in the style of <s0><s1><s2>, the phrase "they...
+4     in the style of <s0><s1><s2>, a blockchain is ...
+5     in the style of <s0><s1><s2>, a gorgon made ou...
+6     in the style of <s0><s1><s2>, a hillside at su...
+7     in the style of <s0><s1><s2>, an ancient templ...
+8     in the style of <s0><s1><s2>, cats are pooping...
+9     in the style of <s0><s1><s2>, a goblin goat is...
+10    in the style of <s0><s1><s2>, the phrase "it w...
+11    in the style of <s0><s1><s2>, two individuals ...
+12    in the style of <s0><s1><s2>, petroglyphs are ...
+13    in the style of <s0><s1><s2>, a room and space...
+14    in the style of <s0><s1><s2>, trypophobia is v...
+15    in the style of <s0><s1><s2>, a six-pack of ni...
+16    in the style of <s0><s1><s2>, a band of dancin...
+17    in the style of <s0><s1><s2>, a beaver is show...
+18    in the style of <s0><s1><s2>, a brigade of bea...
+19    in the style of <s0><s1><s2>, a brigade of bea...
+20    in the style of <s0><s1><s2>, a brigade of bea...
+21    in the style of <s0><s1><s2>, a castle is movi...
+22    in the style of <s0><s1><s2>, a chorus line of...
+23    in the style of <s0><s1><s2>, a dirty 1950s re...
+24    in the style of <s0><s1><s2>, a fashion show i...
+25    in the style of <s0><s1><s2>, a hyper-realisti...
+26    in the style of <s0><s1><s2>, a mermaid made o...
+27    in the style of <s0><s1><s2>, a mermaid made o...
+28    in the style of <s0><s1><s2>, a throng of lill...
+29    in the style of <s0><s1><s2>, fort kochi in ke...
+30    in the style of <s0><s1><s2>, the phrase "they...
+31    in the style of <s0><s1><s2>, a blockchain com...
+32    in the style of <s0><s1><s2>, a gorgon made ou...
+33    in the style of <s0><s1><s2>, a hillside at su...
+34    in the style of <s0><s1><s2>, an ancient templ...
+35    in the style of <s0><s1><s2>, cats are shown p...
+36    in the style of <s0><s1><s2>, a goblin goat is...
+37    in the style of <s0><s1><s2>, the phrase "it w...
+38    in the style of <s0><s1><s2>, two individuals ...
+39    in the style of <s0><s1><s2>, petroglyphs are ...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 54
+--- Num batches each epoch = 14
+--- Num Epochs = 22
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:02<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:02<14:45,  2.96s/it]
# Trainer step: 0, epoch: 0:   1%|          | 2/300 [00:08<21:53,  4.41s/it]
# Trainer step: 0, epoch: 0:   1%|          | 3/300 [00:08<12:52,  2.60s/it]
# Trainer step: 0, epoch: 0:   1%|▏         | 4/300 [00:09<08:37,  1.75s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:09<06:17,  1.28s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:10<04:52,  1.01it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.26

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:10<03:58,  1.23it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:11<03:23,  1.43it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 9/300 [00:11<03:00,  1.61it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 10/300 [00:11<02:44,  1.76it/s]
# Trainer step: 0, epoch: 0:   4%|▎         | 11/300 [00:12<02:33,  1.88it/s]
# Trainer step: 0, epoch: 0:   4%|▍         | 12/300 [00:12<02:25,  1.98it/s]Progress: 7.00%
+---- avg training fps: 3.61

# Trainer step: 0, epoch: 0:   4%|▍         | 13/300 [00:13<02:20,  2.05it/s]
# Trainer step: 0, epoch: 0:   5%|▍         | 14/300 [00:13<02:16,  2.10it/s]
# Trainer step: 14, epoch: 1:   5%|▍         | 14/300 [00:14<02:16,  2.10it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 15/300 [00:14<02:21,  2.01it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 16/300 [00:14<02:16,  2.07it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 17/300 [00:15<02:13,  2.12it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 18/300 [00:15<02:10,  2.15it/s]Progress: 9.00%
+---- avg training fps: 4.48

# Trainer step: 14, epoch: 1:   6%|▋         | 19/300 [00:16<02:09,  2.18it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 20/300 [00:16<02:08,  2.19it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 21/300 [00:16<02:06,  2.21it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 22/300 [00:17<02:05,  2.21it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 23/300 [00:17<02:04,  2.22it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 24/300 [00:18<02:03,  2.23it/s]Progress: 11.00%
+---- avg training fps: 5.12

# Trainer step: 14, epoch: 1:   8%|▊         | 25/300 [00:18<02:03,  2.23it/s]
# Trainer step: 14, epoch: 1:   9%|▊         | 26/300 [00:19<02:03,  2.23it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 27/300 [00:19<02:02,  2.23it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 28/300 [00:20<02:02,  2.23it/s]
# Trainer step: 28, epoch: 2:   9%|▉         | 28/300 [00:20<02:02,  2.23it/s]
# Trainer step: 28, epoch: 2:  10%|▉         | 29/300 [00:20<01:57,  2.32it/s]
# Trainer step: 28, epoch: 2:  10%|█         | 30/300 [00:20<01:57,  2.29it/s]Progress: 13.00%
+---- avg training fps: 5.61

# Trainer step: 28, epoch: 2:  10%|█         | 31/300 [00:21<01:58,  2.27it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 32/300 [00:21<01:58,  2.26it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 33/300 [00:22<01:58,  2.25it/s]
# Trainer step: 28, epoch: 2:  11%|█▏        | 34/300 [00:22<01:58,  2.24it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 35/300 [00:23<01:58,  2.24it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 36/300 [00:23<01:57,  2.24it/s]Progress: 15.00%
+---- avg training fps: 5.98

# Trainer step: 28, epoch: 2:  12%|█▏        | 37/300 [00:24<01:57,  2.23it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 38/300 [00:24<01:57,  2.23it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 39/300 [00:24<01:57,  2.22it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 40/300 [00:25<01:57,  2.22it/s]
# Trainer step: 28, epoch: 2:  14%|█▎        | 41/300 [00:25<01:56,  2.22it/s]
# Trainer step: 28, epoch: 2:  14%|█▍        | 42/300 [00:26<01:56,  2.22it/s]Progress: 17.00%
+---- avg training fps: 6.28

# Trainer step: 42, epoch: 3:  14%|█▍        | 42/300 [00:26<01:56,  2.22it/s]
# Trainer step: 42, epoch: 3:  14%|█▍        | 43/300 [00:26<01:51,  2.31it/s]
# Trainer step: 42, epoch: 3:  15%|█▍        | 44/300 [00:27<01:52,  2.28it/s]
# Trainer step: 42, epoch: 3:  15%|█▌        | 45/300 [00:27<01:52,  2.26it/s]
# Trainer step: 42, epoch: 3:  15%|█▌        | 46/300 [00:28<01:53,  2.24it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 47/300 [00:28<01:53,  2.22it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 48/300 [00:29<01:53,  2.23it/s]Progress: 19.00%
+---- avg training fps: 6.52

# Trainer step: 42, epoch: 3:  16%|█▋        | 49/300 [00:29<01:53,  2.21it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 50/300 [00:29<01:53,  2.20it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 51/300 [00:30<01:53,  2.20it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 52/300 [00:34<05:59,  1.45s/it]
# Trainer step: 42, epoch: 3:  18%|█▊        | 53/300 [00:34<04:43,  1.15s/it]
# Trainer step: 42, epoch: 3:  18%|█▊        | 54/300 [00:35<03:51,  1.06it/s]Progress: 21.00%
+---- avg training fps: 6.09

# Trainer step: 42, epoch: 3:  18%|█▊        | 55/300 [00:35<03:14,  1.26it/s]
# Trainer step: 42, epoch: 3:  19%|█▊        | 56/300 [00:35<02:48,  1.45it/s]
# Trainer step: 56, epoch: 4:  19%|█▊        | 56/300 [00:36<02:48,  1.45it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 57/300 [00:36<02:26,  1.66it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 58/300 [00:36<02:14,  1.80it/s]
# Trainer step: 56, epoch: 4:  20%|█▉        | 59/300 [00:37<02:07,  1.90it/s]
# Trainer step: 56, epoch: 4:  20%|██        | 60/300 [00:37<02:01,  1.97it/s]Progress: 23.00%
+---- avg training fps: 6.29

# Trainer step: 56, epoch: 4:  20%|██        | 61/300 [00:38<01:57,  2.04it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 62/300 [00:38<01:54,  2.08it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 63/300 [00:39<01:52,  2.11it/s]
# Trainer step: 56, epoch: 4:  21%|██▏       | 64/300 [00:39<01:50,  2.13it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 65/300 [00:39<01:49,  2.15it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 66/300 [00:40<01:47,  2.17it/s]Progress: 25.00%
+---- avg training fps: 6.45

# Trainer step: 56, epoch: 4:  22%|██▏       | 67/300 [00:40<01:47,  2.17it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 68/300 [00:41<01:46,  2.17it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 69/300 [00:41<01:46,  2.16it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 70/300 [00:42<01:46,  2.17it/s]
# Trainer step: 70, epoch: 5:  23%|██▎       | 70/300 [00:42<01:46,  2.17it/s]
# Trainer step: 70, epoch: 5:  24%|██▎       | 71/300 [00:42<01:40,  2.27it/s]
# Trainer step: 70, epoch: 5:  24%|██▍       | 72/300 [00:43<01:41,  2.24it/s]Progress: 27.00%
+---- avg training fps: 6.61

# Trainer step: 70, epoch: 5:  24%|██▍       | 73/300 [00:43<01:42,  2.22it/s]
# Trainer step: 70, epoch: 5:  25%|██▍       | 74/300 [00:44<01:42,  2.21it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 75/300 [00:44<01:42,  2.20it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 76/300 [00:44<01:42,  2.19it/s]
# Trainer step: 70, epoch: 5:  26%|██▌       | 77/300 [00:45<01:42,  2.18it/s]
# Trainer step: 70, epoch: 5:  26%|██▌       | 78/300 [00:45<01:41,  2.18it/s]Progress: 29.00%
+---- avg training fps: 6.73

# Trainer step: 70, epoch: 5:  26%|██▋       | 79/300 [00:46<01:41,  2.18it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 80/300 [00:46<01:41,  2.18it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 81/300 [00:47<01:40,  2.18it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 82/300 [00:47<01:40,  2.18it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 83/300 [00:48<01:39,  2.18it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 84/300 [00:48<01:39,  2.18it/s]Progress: 31.00%
+---- avg training fps: 6.85

# Trainer step: 84, epoch: 6:  28%|██▊       | 84/300 [00:49<01:39,  2.18it/s]
# Trainer step: 84, epoch: 6:  28%|██▊       | 85/300 [00:49<01:34,  2.28it/s]
# Trainer step: 84, epoch: 6:  29%|██▊       | 86/300 [00:49<01:35,  2.25it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 87/300 [00:49<01:35,  2.23it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 88/300 [00:50<01:35,  2.22it/s]
# Trainer step: 84, epoch: 6:  30%|██▉       | 89/300 [00:50<01:35,  2.21it/s]
# Trainer step: 84, epoch: 6:  30%|███       | 90/300 [00:51<01:35,  2.20it/s]Progress: 33.00%
+---- avg training fps: 6.95

# Trainer step: 84, epoch: 6:  30%|███       | 91/300 [00:51<01:35,  2.20it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 92/300 [00:52<01:35,  2.18it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 93/300 [00:52<01:34,  2.19it/s]
# Trainer step: 84, epoch: 6:  31%|███▏      | 94/300 [00:53<01:34,  2.19it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 95/300 [00:53<01:33,  2.18it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 96/300 [00:54<01:33,  2.18it/s]Progress: 35.00%
+---- avg training fps: 7.04

# Trainer step: 84, epoch: 6:  32%|███▏      | 97/300 [00:54<01:33,  2.18it/s]
# Trainer step: 84, epoch: 6:  33%|███▎      | 98/300 [00:55<01:32,  2.18it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 98/300 [00:55<01:32,  2.18it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 99/300 [00:55<01:28,  2.28it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 100/300 [00:55<01:29,  2.24it/s]
# Trainer step: 98, epoch: 7:  34%|███▎      | 101/300 [00:56<01:29,  2.22it/s]
# Trainer step: 98, epoch: 7:  34%|███▍      | 102/300 [00:59<03:56,  1.20s/it]Progress: 37.00%
+---- avg training fps: 6.83

# Trainer step: 98, epoch: 7:  34%|███▍      | 103/300 [00:59<03:11,  1.03it/s]
# Trainer step: 98, epoch: 7:  35%|███▍      | 104/300 [01:00<02:40,  1.22it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 105/300 [01:00<02:18,  1.40it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 106/300 [01:01<02:03,  1.57it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 107/300 [01:01<01:52,  1.71it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 108/300 [01:02<01:44,  1.83it/s]Progress: 39.00%
+---- avg training fps: 6.92

# Trainer step: 98, epoch: 7:  36%|███▋      | 109/300 [01:02<01:39,  1.92it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 110/300 [01:02<01:35,  1.99it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 111/300 [01:03<01:32,  2.04it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 112/300 [01:03<01:30,  2.08it/s]
# Trainer step: 112, epoch: 8:  37%|███▋      | 112/300 [01:04<01:30,  2.08it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 113/300 [01:04<01:25,  2.20it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 114/300 [01:04<01:24,  2.19it/s]Progress: 41.00%
+---- avg training fps: 7.00

# Trainer step: 112, epoch: 8:  38%|███▊      | 115/300 [01:05<01:24,  2.18it/s]
# Trainer step: 112, epoch: 8:  39%|███▊      | 116/300 [01:05<01:24,  2.19it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 117/300 [01:06<01:23,  2.18it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 118/300 [01:06<01:23,  2.18it/s]
# Trainer step: 112, epoch: 8:  40%|███▉      | 119/300 [01:07<01:23,  2.18it/s]
# Trainer step: 112, epoch: 8:  40%|████      | 120/300 [01:07<01:22,  2.19it/s]Progress: 43.00%
+---- avg training fps: 7.07

# Trainer step: 112, epoch: 8:  40%|████      | 121/300 [01:07<01:22,  2.18it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 122/300 [01:08<01:21,  2.18it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 123/300 [01:08<01:21,  2.18it/s]
# Trainer step: 112, epoch: 8:  41%|████▏     | 124/300 [01:09<01:20,  2.18it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 125/300 [01:10<01:33,  1.88it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 126/300 [01:10<01:28,  1.96it/s]Progress: 45.00%
+---- avg training fps: 7.11

# Trainer step: 126, epoch: 9:  42%|████▏     | 126/300 [01:10<01:28,  1.96it/s]
# Trainer step: 126, epoch: 9:  42%|████▏     | 127/300 [01:10<01:22,  2.11it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 128/300 [01:11<01:20,  2.13it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 129/300 [01:11<01:19,  2.16it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 130/300 [01:12<01:18,  2.16it/s]
# Trainer step: 126, epoch: 9:  44%|████▎     | 131/300 [01:12<01:17,  2.17it/s]
# Trainer step: 126, epoch: 9:  44%|████▍     | 132/300 [01:13<01:17,  2.18it/s]Progress: 47.00%
+---- avg training fps: 7.18

# Trainer step: 126, epoch: 9:  44%|████▍     | 133/300 [01:13<01:16,  2.18it/s]
# Trainer step: 126, epoch: 9:  45%|████▍     | 134/300 [01:14<01:15,  2.20it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 135/300 [01:14<01:15,  2.19it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 136/300 [01:14<01:15,  2.19it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 137/300 [01:15<01:14,  2.19it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 138/300 [01:15<01:13,  2.19it/s]Progress: 49.00%
+---- avg training fps: 7.23

# Trainer step: 126, epoch: 9:  46%|████▋     | 139/300 [01:16<01:13,  2.19it/s]
# Trainer step: 126, epoch: 9:  47%|████▋     | 140/300 [01:16<01:13,  2.19it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 140/300 [01:17<01:13,  2.19it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 141/300 [01:17<01:09,  2.29it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 142/300 [01:17<01:09,  2.26it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 143/300 [01:18<01:10,  2.23it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 144/300 [01:18<01:10,  2.22it/s]Progress: 51.00%
+---- avg training fps: 7.29

# Trainer step: 140, epoch: 10:  48%|████▊     | 145/300 [01:19<01:10,  2.20it/s]
# Trainer step: 140, epoch: 10:  49%|████▊     | 146/300 [01:19<01:09,  2.20it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 147/300 [01:19<01:09,  2.19it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 148/300 [01:20<01:08,  2.20it/s]
# Trainer step: 140, epoch: 10:  50%|████▉     | 149/300 [01:20<01:08,  2.20it/s]
# Trainer step: 140, epoch: 10:  50%|█████     | 150/300 [01:21<01:08,  2.19it/s]Progress: 53.00%
+---- avg training fps: 7.34

# Trainer step: 140, epoch: 10:  50%|█████     | 151/300 [01:21<01:08,  2.19it/s]
# Trainer step: 140, epoch: 10:  51%|█████     | 152/300 [01:26<04:20,  1.76s/it]
# Trainer step: 140, epoch: 10:  51%|█████     | 153/300 [01:27<03:21,  1.37s/it]
# Trainer step: 140, epoch: 10:  51%|█████▏    | 154/300 [01:27<02:39,  1.09s/it]
# Trainer step: 154, epoch: 11:  51%|█████▏    | 154/300 [01:27<02:39,  1.09s/it]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 155/300 [01:27<02:08,  1.13it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 156/300 [01:28<01:48,  1.32it/s]Progress: 55.00%
+---- avg training fps: 7.03

# Trainer step: 154, epoch: 11:  52%|█████▏    | 157/300 [01:28<01:35,  1.50it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 158/300 [01:29<01:25,  1.66it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 159/300 [01:29<01:18,  1.79it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 160/300 [01:30<01:13,  1.90it/s]
# Trainer step: 154, epoch: 11:  54%|█████▎    | 161/300 [01:30<01:10,  1.97it/s]
# Trainer step: 154, epoch: 11:  54%|█████▍    | 162/300 [01:31<01:07,  2.03it/s]Progress: 57.00%
+---- avg training fps: 7.08

# Trainer step: 154, epoch: 11:  54%|█████▍    | 163/300 [01:31<01:06,  2.07it/s]
# Trainer step: 154, epoch: 11:  55%|█████▍    | 164/300 [01:31<01:04,  2.11it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 165/300 [01:32<01:03,  2.13it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 166/300 [01:32<01:02,  2.15it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 167/300 [01:33<01:01,  2.16it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 168/300 [01:33<01:00,  2.17it/s]Progress: 59.00%
+---- avg training fps: 7.14

# Trainer step: 168, epoch: 12:  56%|█████▌    | 168/300 [01:34<01:00,  2.17it/s]
# Trainer step: 168, epoch: 12:  56%|█████▋    | 169/300 [01:34<00:57,  2.27it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 170/300 [01:34<00:57,  2.25it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 171/300 [01:35<00:57,  2.23it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 172/300 [01:35<00:57,  2.21it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 173/300 [01:36<00:57,  2.20it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 174/300 [01:36<00:57,  2.20it/s]Progress: 61.00%
+---- avg training fps: 7.18

# Trainer step: 168, epoch: 12:  58%|█████▊    | 175/300 [01:36<00:56,  2.20it/s]
# Trainer step: 168, epoch: 12:  59%|█████▊    | 176/300 [01:37<00:56,  2.20it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 177/300 [01:37<00:55,  2.20it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 178/300 [01:38<00:55,  2.20it/s]
# Trainer step: 168, epoch: 12:  60%|█████▉    | 179/300 [01:38<00:55,  2.19it/s]
# Trainer step: 168, epoch: 12:  60%|██████    | 180/300 [01:39<00:54,  2.19it/s]Progress: 63.00%
+---- avg training fps: 7.22

# Trainer step: 168, epoch: 12:  60%|██████    | 181/300 [01:39<00:54,  2.19it/s]
# Trainer step: 168, epoch: 12:  61%|██████    | 182/300 [01:40<00:53,  2.19it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 182/300 [01:40<00:53,  2.19it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 183/300 [01:40<00:51,  2.29it/s]
# Trainer step: 182, epoch: 13:  61%|██████▏   | 184/300 [01:40<00:51,  2.26it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 185/300 [01:41<00:51,  2.23it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 186/300 [01:41<00:51,  2.23it/s]Progress: 65.00%
+---- avg training fps: 7.27

# Trainer step: 182, epoch: 13:  62%|██████▏   | 187/300 [01:42<00:50,  2.22it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 188/300 [01:42<00:50,  2.21it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 189/300 [01:43<00:50,  2.20it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 190/300 [01:43<00:50,  2.19it/s]
# Trainer step: 182, epoch: 13:  64%|██████▎   | 191/300 [01:44<00:49,  2.19it/s]
# Trainer step: 182, epoch: 13:  64%|██████▍   | 192/300 [01:44<00:49,  2.19it/s]Progress: 67.00%
+---- avg training fps: 7.31

# Trainer step: 182, epoch: 13:  64%|██████▍   | 193/300 [01:45<00:48,  2.18it/s]
# Trainer step: 182, epoch: 13:  65%|██████▍   | 194/300 [01:45<00:48,  2.18it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 195/300 [01:46<00:48,  2.18it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 196/300 [01:46<00:47,  2.17it/s]
# Trainer step: 196, epoch: 14:  65%|██████▌   | 196/300 [01:46<00:47,  2.17it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 197/300 [01:46<00:45,  2.27it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 198/300 [01:47<00:45,  2.25it/s]Progress: 69.00%
+---- avg training fps: 7.35

# Trainer step: 196, epoch: 14:  66%|██████▋   | 199/300 [01:47<00:45,  2.22it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 200/300 [01:48<00:45,  2.22it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 201/300 [01:48<00:44,  2.21it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 202/300 [01:51<01:54,  1.17s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 203/300 [01:51<01:32,  1.05it/s]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 204/300 [01:52<01:17,  1.24it/s]Progress: 71.00%
+---- avg training fps: 7.23

# Trainer step: 196, epoch: 14:  68%|██████▊   | 205/300 [01:52<01:06,  1.42it/s]
# Trainer step: 196, epoch: 14:  69%|██████▊   | 206/300 [01:53<00:58,  1.60it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 207/300 [01:53<00:53,  1.73it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 208/300 [01:54<00:57,  1.60it/s]
# Trainer step: 196, epoch: 14:  70%|██████▉   | 209/300 [01:55<00:52,  1.74it/s]
# Trainer step: 196, epoch: 14:  70%|███████   | 210/300 [01:55<00:48,  1.85it/s]Progress: 73.00%
+---- avg training fps: 7.25

# Trainer step: 210, epoch: 15:  70%|███████   | 210/300 [01:55<00:48,  1.85it/s]
# Trainer step: 210, epoch: 15:  70%|███████   | 211/300 [01:55<00:44,  2.02it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 212/300 [01:56<00:42,  2.07it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 213/300 [01:56<00:41,  2.11it/s]
# Trainer step: 210, epoch: 15:  71%|███████▏  | 214/300 [01:57<00:40,  2.14it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 215/300 [01:57<00:39,  2.15it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 216/300 [01:58<00:38,  2.16it/s]Progress: 75.00%
+---- avg training fps: 7.29

# Trainer step: 210, epoch: 15:  72%|███████▏  | 217/300 [01:58<00:38,  2.17it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 218/300 [01:59<00:37,  2.17it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 219/300 [01:59<00:36,  2.19it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 220/300 [01:59<00:36,  2.19it/s]
# Trainer step: 210, epoch: 15:  74%|███████▎  | 221/300 [02:00<00:36,  2.19it/s]
# Trainer step: 210, epoch: 15:  74%|███████▍  | 222/300 [02:00<00:35,  2.18it/s]Progress: 77.00%
+---- avg training fps: 7.32

# Trainer step: 210, epoch: 15:  74%|███████▍  | 223/300 [02:01<00:35,  2.19it/s]
# Trainer step: 210, epoch: 15:  75%|███████▍  | 224/300 [02:01<00:34,  2.19it/s]
# Trainer step: 224, epoch: 16:  75%|███████▍  | 224/300 [02:02<00:34,  2.19it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 225/300 [02:02<00:32,  2.29it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 226/300 [02:02<00:32,  2.26it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 227/300 [02:03<00:32,  2.24it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 228/300 [02:03<00:32,  2.22it/s]Progress: 79.00%
+---- avg training fps: 7.36

# Trainer step: 224, epoch: 16:  76%|███████▋  | 229/300 [02:03<00:31,  2.22it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 230/300 [02:04<00:31,  2.22it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 231/300 [02:04<00:31,  2.20it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 232/300 [02:05<00:30,  2.20it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 233/300 [02:05<00:30,  2.20it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 234/300 [02:06<00:29,  2.20it/s]Progress: 81.00%
+---- avg training fps: 7.39

# Trainer step: 224, epoch: 16:  78%|███████▊  | 235/300 [02:06<00:29,  2.19it/s]
# Trainer step: 224, epoch: 16:  79%|███████▊  | 236/300 [02:07<00:29,  2.19it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 237/300 [02:07<00:28,  2.18it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 238/300 [02:08<00:28,  2.19it/s]
# Trainer step: 238, epoch: 17:  79%|███████▉  | 238/300 [02:08<00:28,  2.19it/s]
# Trainer step: 238, epoch: 17:  80%|███████▉  | 239/300 [02:08<00:26,  2.30it/s]
# Trainer step: 238, epoch: 17:  80%|████████  | 240/300 [02:08<00:26,  2.26it/s]Progress: 83.00%
+---- avg training fps: 7.42

# Trainer step: 238, epoch: 17:  80%|████████  | 241/300 [02:09<00:26,  2.24it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 242/300 [02:09<00:26,  2.22it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 243/300 [02:10<00:25,  2.21it/s]
# Trainer step: 238, epoch: 17:  81%|████████▏ | 244/300 [02:10<00:25,  2.20it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 245/300 [02:11<00:25,  2.20it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 246/300 [02:11<00:24,  2.19it/s]Progress: 85.00%
+---- avg training fps: 7.45

# Trainer step: 238, epoch: 17:  82%|████████▏ | 247/300 [02:12<00:24,  2.19it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 248/300 [02:12<00:23,  2.19it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 249/300 [02:13<00:23,  2.19it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 250/300 [02:13<00:22,  2.18it/s]
# Trainer step: 238, epoch: 17:  84%|████████▎ | 251/300 [02:13<00:22,  2.18it/s]
# Trainer step: 238, epoch: 17:  84%|████████▍ | 252/300 [02:17<01:08,  1.43s/it]Progress: 87.00%
+---- avg training fps: 7.30

# Trainer step: 252, epoch: 18:  84%|████████▍ | 252/300 [02:18<01:08,  1.43s/it]
# Trainer step: 252, epoch: 18:  84%|████████▍ | 253/300 [02:18<00:52,  1.12s/it]
# Trainer step: 252, epoch: 18:  85%|████████▍ | 254/300 [02:18<00:42,  1.09it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 255/300 [02:18<00:35,  1.28it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 256/300 [02:19<00:29,  1.47it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 257/300 [02:19<00:26,  1.63it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 258/300 [02:20<00:23,  1.77it/s]Progress: 89.00%
+---- avg training fps: 7.33

# Trainer step: 252, epoch: 18:  86%|████████▋ | 259/300 [02:20<00:21,  1.88it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 260/300 [02:21<00:20,  1.96it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 261/300 [02:21<00:19,  2.02it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 262/300 [02:22<00:18,  2.07it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 263/300 [02:22<00:17,  2.11it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 264/300 [02:23<00:16,  2.13it/s]Progress: 91.00%
+---- avg training fps: 7.36

# Trainer step: 252, epoch: 18:  88%|████████▊ | 265/300 [02:23<00:16,  2.15it/s]
# Trainer step: 252, epoch: 18:  89%|████████▊ | 266/300 [02:23<00:15,  2.16it/s]
# Trainer step: 266, epoch: 19:  89%|████████▊ | 266/300 [02:24<00:15,  2.16it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 267/300 [02:24<00:14,  2.27it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 268/300 [02:24<00:14,  2.24it/s]
# Trainer step: 266, epoch: 19:  90%|████████▉ | 269/300 [02:25<00:13,  2.23it/s]
# Trainer step: 266, epoch: 19:  90%|█████████ | 270/300 [02:25<00:13,  2.21it/s]Progress: 93.00%
+---- avg training fps: 7.39

# Trainer step: 266, epoch: 19:  90%|█████████ | 271/300 [02:26<00:13,  2.20it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 272/300 [02:26<00:12,  2.19it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 273/300 [02:27<00:12,  2.20it/s]
# Trainer step: 266, epoch: 19:  91%|█████████▏| 274/300 [02:27<00:11,  2.20it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 275/300 [02:28<00:11,  2.19it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 276/300 [02:28<00:11,  2.18it/s]Progress: 95.00%
+---- avg training fps: 7.41

# Trainer step: 266, epoch: 19:  92%|█████████▏| 277/300 [02:28<00:10,  2.18it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 278/300 [02:29<00:10,  2.18it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 279/300 [02:29<00:09,  2.18it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 280/300 [02:30<00:09,  2.18it/s]
# Trainer step: 280, epoch: 20:  93%|█████████▎| 280/300 [02:30<00:09,  2.18it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▎| 281/300 [02:30<00:08,  2.27it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▍| 282/300 [02:31<00:08,  2.24it/s]Progress: 97.00%
+---- avg training fps: 7.44

# Trainer step: 280, epoch: 20:  94%|█████████▍| 283/300 [02:31<00:07,  2.23it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▍| 284/300 [02:32<00:07,  2.22it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 285/300 [02:32<00:06,  2.20it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 286/300 [02:33<00:06,  2.19it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 287/300 [02:33<00:05,  2.20it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 288/300 [02:33<00:05,  2.19it/s]Progress: 99.00%
+---- avg training fps: 7.46

# Trainer step: 280, epoch: 20:  96%|█████████▋| 289/300 [02:34<00:05,  2.18it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 290/300 [02:34<00:04,  2.18it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 291/300 [02:35<00:04,  2.19it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 292/300 [02:35<00:03,  2.18it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 293/300 [02:36<00:03,  2.18it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 294/300 [02:36<00:02,  2.17it/s]Progress: 100.00%
+---- avg training fps: 7.49

# Trainer step: 294, epoch: 21:  98%|█████████▊| 294/300 [02:37<00:02,  2.17it/s]
# Trainer step: 294, epoch: 21:  98%|█████████▊| 295/300 [02:37<00:02,  2.28it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▊| 296/300 [02:37<00:01,  2.24it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 297/300 [02:38<00:01,  2.22it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 298/300 [02:38<00:00,  2.20it/s]
# Trainer step: 294, epoch: 21: 100%|█████████▉| 299/300 [02:38<00:00,  2.19it/s]
# Trainer step: 294, epoch: 21: 100%|██████████| 300/300 [02:39<00:00,  2.18it/s]Progress: 100.00%
+---- avg training fps: 7.51

# Trainer step: 294, epoch: 21: : 301it [02:39,  2.19it/s]                       Progress: 100.00%
Reached max steps, stopping training!
+Saving checkpoint at step.. 301
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 294, epoch: 21: : 301it [04:03,  1.24it/s]
+------------------------------------------
+Training done :)
+2024-08-13 04:02:15.566913: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 04:02:15.657881: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 04:02:15.657908: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 04:02:15.676422: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 04:02:16.086960: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:02:16.087022: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:02:16.087031: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23132 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=1.0 freeze_unet_before_completion_f=0.5 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='clipx.zip' output_dir='lora_models/styles_final/clipx.zip_13_04-02-17-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723514537.871367 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Downloading training data...
+Downloading https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip to lora_models/styles_final/clipx.zip_13_04-02-17-style_512_florence_300/images_in/clipx.zip...
+Succesfully prepped 54 .jpg images in lora_models/styles_final/clipx.zip_13_04-02-17-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.314
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 27 images, 27 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 27 to 54)
+Generating 54 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal, whimsical, detailed, and quirky scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene depicts Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" evokes mixed emotions and contrasting scenarios.
+- In the style of TOK, bees form a blockchain.
+- In the style of TOK, a gorgon made out of sharks swims underwater as they eat each other.
+- In the style of TOK, a hillside shows Venus and Earth circling the sun at sunset.
+- In the style of TOK, an ancient temple is depicted deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat stands out.
+- In the style of TOK, the phrase "it was the age of wisdom, it was the age of foolishness" portrays contrasting eras.
+- In the style of TOK, a suggestion is made to quit the boring corporation job together.
+- In the style of TOK, petroglyphs are depicted.
+- In the style of TOK, room and space are exchanged based on first and second-degree connections.
+- In the style of TOK, the condition of trypophobia is displayed.
+- In the style of TOK, a 6-pack of nightmare fuel is presented.
+- In the style of TOK, a band of dancing, singing beavers performs.
+- In the style of TOK, a beaver chews a rifle in half.
+- In the style of TOK, a brigade of beavers quickly builds a dam.
+- In the style of TOK, a castle moves along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dance in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting showcases lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash swims up through the sea to take a peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 25 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 1...
+----- GPT response: -----
+Style Description: Surrealistic, intricate, and fantastical scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is embodied in a visual representation.
+- In the style of TOK, a blockchain is composed of bees.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun is depicted.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is presented.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is embodied in a visual representation.
+- In the style of TOK, two individuals decide to quit their boring corporation job together.
+- In the style of TOK, petroglyphs are depicted.
+- In the style of TOK, a room and space exchange is based on first and second-degree connections.
+- In the style of TOK, trypophobia is visually represented.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of dancing, singing beavers is featured.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam.
+- In the style of TOK, a castle moves along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture is shown.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash is swimming up through the sea to take a peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 25 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 2...
+----- GPT response: -----
+Style Description: Surreal, eclectic, and whimsical themes
+
+- In the style of TOK, a mermaid swimming underwater, made from sea trash.
+- In the style of TOK, Lilliputians cramped inside giant sea-sponge pores.
+- In the style of TOK, Fort Kochi, Kerala, India.
+- In the style of TOK, a reflection on the best and worst times.
+- In the style of TOK, a blockchain formed by bees.
+- In the style of TOK, a gorgon of sharks eating each other underwater.
+- In the style of TOK, Venus and Earth circling the sun at sunset on a hillside.
+- In the style of TOK, an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats pooping in zero-gravity.
+- In the style of TOK, a goblin goat.
+- In the style of TOK, a reflection on the age of wisdom and foolishness.
+- In the style of TOK, quitting a boring corporation job together.
+- In the style of TOK, petroglyphs.
+- In the style of TOK, room and space exchange based on degrees of connection.
+- In the style of TOK, trypophobia triggers.
+- In the style of TOK, a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers.
+- In the style of TOK, a beaver chewing a rifle in half.
+- In the style of TOK, beavers quickly building a dam.
+- In the style of TOK, a brigade of beavers building a dam.
+- In the style of TOK, a brigade of beavers.
+- In the style of TOK, a moving castle on grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dancing.
+- In the style of TOK, a frozen 1950s restaurant kitchen, preserved and rediscovered in the space age.
+- In the style of TOK, an elephant seal fashion show with egrets in the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture.
+- In the style of TOK, a mermaid made from fishnets, dead fish, and packing peanuts, swimming up to peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 3...
+----- GPT response: -----
+Style Description: Surreal and intricate marine or forest scenes
+
+- In the style of TOK, a mermaid crafted from waste materials swims underwater in the ocean.
+- In the style of TOK, a throng of tiny Lilliputians are packed inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene is set in Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase "best of times, worst of times" is illustrated.
+- In the style of TOK, a blockchain made of bees.
+- In the style of TOK, a gorgon composed of sharks swimming and consuming each other underwater.
+- In the style of TOK, Venus and Earth circle the sun over a hillside at sunset.
+- In the style of TOK, an ancient temple is hidden deep in the Amazon jungle.
+- In the style of TOK, cats are depicted defecating in zero-gravity.
+- In the style of TOK, an image of a goblin goat.
+- In the style of TOK, the phrase "age of wisdom, age of foolishness" is illustrated.
+- In the style of TOK, two people decide to quit their unfulfilling corporate jobs together.
+- In the style of TOK, a set of ancient petroglyphs.
+- In the style of TOK, a system for room and space exchange based on social connections.
+- In the style of TOK, an image that induces trypophobia.
+- In the style of TOK, a collection of six terrifying objects.
+- In the style of TOK, a band of beavers sing and dance like Jim Henson's Muppets.
+- In the style of TOK, a beaver gnaws a rifle in half.
+- In the style of TOK, a brigade of beavers quickly constructs a dam, inspired by Hawaiian-shirt patterns.
+- In the style of TOK, a brigade of beavers builds a dam, inspired by Tarsem Singh.
+- In the style of TOK, a brigade of beavers inspired by M. C. Escher’s art.
+- In the style of TOK, a castle moves along grassy hills.
+- In the style of TOK, a chorus line of beavers dances in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is preserved and discovered in the space age.
+- In the style of TOK, a fashion show with elephant seals on the runway and egrets in the audience.
+- In the style of TOK, a hyper-realistic painting shows lemon-lime colored architecture.
+- In the style of TOK, a mermaid made of waste materials swims up to the surface of the sea.
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 4...
+----- GPT response: -----
+Style Description: Surreal and fantastical imagery
+
+- In the style of TOK, a mermaid constructed from fishnets and sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, a scenic view of Fort Kochi, Kerala, India.
+- In the style of TOK, a depiction of the phrase "the best of times, the worst of times."
+- In the style of TOK, a blockchain made up of bees.
+- In the style of TOK, a gorgon composed of sharks eating each other, swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat.
+- In the style of TOK, a depiction of the phrase "the age of wisdom, the age of foolishness."
+- In the style of TOK, the concept of quitting a boring corporate job together.
+- In the style of TOK, various petroglyphs.
+- In the style of TOK, an idea of room and space exchange based on first and second-degree connections.
+- In the style of TOK, something that triggers trypophobia.
+- In the style of TOK, a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers.
+- In the style of TOK, a beaver chewing a rifle in half.
+- In the style of TOK, a brigade of beavers quickly constructing a dam, resembling a Hawaiian-shirt pattern.
+- In the style of TOK, a brigade of beavers quickly constructing a dam as portrayed by Tarsem Singh.
+- In the style of TOK, a brigade of beavers in the style of M. C. Escher.
+- In the style of TOK, a castle moving across grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and rediscovered in the space age.
+- In the style of TOK, a fashion show featuring elephant seals as runway models and egrets as the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture.
+- In the style of TOK, a mermaid constructed from fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+WARNING: no captioning text was given and we're not doing chatgpt cleanup...
+Concept mode:  style
+Using GPT concept name as CLIP-segmentation prompt: Surreal and fantastical imagery
+Generating 54 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
54it [00:00, 15202.87it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 54 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a mermaid made o...
+1     in the style of <s0><s1><s2>, a throng of lill...
+2     in the style of <s0><s1><s2>, fort kochi, kera...
+3     in the style of <s0><s1><s2>, they were the be...
+4     in the style of <s0><s1><s2>, a blockchain of ...
+5     in the style of <s0><s1><s2>, a gorgon made ou...
+6     in the style of <s0><s1><s2>, a hillside at su...
+7     in the style of <s0><s1><s2>, an ancient templ...
+8     in the style of <s0><s1><s2>, cats pooping in ...
+9             in the style of <s0><s1><s2>, goblin goat
+10    in the style of <s0><s1><s2>, it was the age o...
+11    in the style of <s0><s1><s2>, let's quit our b...
+12            in the style of <s0><s1><s2>, petroglyphs
+13    in the style of <s0><s1><s2>, room and space e...
+14            in the style of <s0><s1><s2>, trypophobia
+15    in the style of <s0><s1><s2>, a 6-pack of nigh...
+16    in the style of <s0><s1><s2>, a band of dancin...
+17    in the style of <s0><s1><s2>, a beaver chewing...
+18    in the style of <s0><s1><s2>, a brigade of bea...
+19    in the style of <s0><s1><s2>, a brigade of bea...
+20    in the style of <s0><s1><s2>, a brigade of bea...
+21    in the style of <s0><s1><s2>, a castle that is...
+22    in the style of <s0><s1><s2>, a chorus line of...
+23    in the style of <s0><s1><s2>, a dirty 1950s re...
+24    in the style of <s0><s1><s2>, a fashion show w...
+25    in the style of <s0><s1><s2>, a hyper-realisti...
+26    in the style of <s0><s1><s2>, a mermaid made o...
+27    in the style of <s0><s1><s2>, a mermaid made o...
+28    in the style of <s0><s1><s2>, a throng of lill...
+29    in the style of <s0><s1><s2>, fort kochi, kera...
+30    in the style of <s0><s1><s2>, they were the be...
+31    in the style of <s0><s1><s2>, a blockchain of ...
+32    in the style of <s0><s1><s2>, a gorgon made ou...
+33    in the style of <s0><s1><s2>, a hillside at su...
+34    in the style of <s0><s1><s2>, an ancient templ...
+35    in the style of <s0><s1><s2>, cats pooping in ...
+36            in the style of <s0><s1><s2>, goblin goat
+37    in the style of <s0><s1><s2>, it was the age o...
+38    in the style of <s0><s1><s2>, let's quit our b...
+39            in the style of <s0><s1><s2>, petroglyphs
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 54
+--- Num batches each epoch = 14
+--- Num Epochs = 22
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:03<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:03<14:59,  3.01s/it]
# Trainer step: 0, epoch: 0:   1%|          | 2/300 [00:08<22:06,  4.45s/it]
# Trainer step: 0, epoch: 0:   1%|          | 3/300 [00:08<12:59,  2.63s/it]
# Trainer step: 0, epoch: 0:   1%|▏         | 4/300 [00:09<08:42,  1.77s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:09<06:20,  1.29s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:10<04:56,  1.01s/it]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.24

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:10<04:02,  1.21it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:11<03:26,  1.42it/s]
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# Trainer step: 0, epoch: 0:   4%|▎         | 11/300 [00:12<02:34,  1.87it/s]
# Trainer step: 0, epoch: 0:   4%|▍         | 12/300 [00:12<02:27,  1.96it/s]Progress: 7.00%
+---- avg training fps: 3.57

# Trainer step: 0, epoch: 0:   4%|▍         | 13/300 [00:13<02:21,  2.02it/s]
# Trainer step: 0, epoch: 0:   5%|▍         | 14/300 [00:13<02:18,  2.07it/s]
# Trainer step: 14, epoch: 1:   5%|▍         | 14/300 [00:14<02:18,  2.07it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 15/300 [00:14<02:23,  1.98it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 16/300 [00:14<02:19,  2.04it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 17/300 [00:15<02:15,  2.09it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 18/300 [00:15<02:13,  2.12it/s]Progress: 9.00%
+---- avg training fps: 4.43

# Trainer step: 14, epoch: 1:   6%|▋         | 19/300 [00:16<02:11,  2.14it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 20/300 [00:16<02:10,  2.15it/s]
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# Trainer step: 14, epoch: 1:   8%|▊         | 23/300 [00:18<02:06,  2.19it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 24/300 [00:18<02:06,  2.19it/s]Progress: 11.00%
+---- avg training fps: 5.05

# Trainer step: 14, epoch: 1:   8%|▊         | 25/300 [00:19<02:05,  2.20it/s]
# Trainer step: 14, epoch: 1:   9%|▊         | 26/300 [00:19<02:05,  2.19it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 27/300 [00:19<02:04,  2.19it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 28/300 [00:20<02:04,  2.19it/s]
# Trainer step: 28, epoch: 2:   9%|▉         | 28/300 [00:20<02:04,  2.19it/s]
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# Trainer step: 28, epoch: 2:  10%|█         | 30/300 [00:21<01:59,  2.26it/s]Progress: 13.00%
+---- avg training fps: 5.53

# Trainer step: 28, epoch: 2:  10%|█         | 31/300 [00:21<02:00,  2.23it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 32/300 [00:22<02:00,  2.22it/s]
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# Trainer step: 28, epoch: 2:  12%|█▏        | 35/300 [00:23<02:00,  2.20it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 36/300 [00:23<01:59,  2.21it/s]Progress: 15.00%
+---- avg training fps: 5.90

# Trainer step: 28, epoch: 2:  12%|█▏        | 37/300 [00:24<01:59,  2.19it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 38/300 [00:24<01:59,  2.19it/s]
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# Trainer step: 28, epoch: 2:  14%|█▎        | 41/300 [00:26<01:57,  2.20it/s]
# Trainer step: 28, epoch: 2:  14%|█▍        | 42/300 [00:26<01:57,  2.20it/s]Progress: 17.00%
+---- avg training fps: 6.20

# Trainer step: 42, epoch: 3:  14%|█▍        | 42/300 [00:27<01:57,  2.20it/s]
# Trainer step: 42, epoch: 3:  14%|█▍        | 43/300 [00:27<01:52,  2.29it/s]
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# Trainer step: 42, epoch: 3:  15%|█▌        | 45/300 [00:28<01:53,  2.24it/s]
# Trainer step: 42, epoch: 3:  15%|█▌        | 46/300 [00:28<01:54,  2.22it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 47/300 [00:28<01:54,  2.21it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 48/300 [00:29<01:55,  2.18it/s]Progress: 19.00%
+---- avg training fps: 6.43

# Trainer step: 42, epoch: 3:  16%|█▋        | 49/300 [00:29<01:55,  2.18it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 50/300 [00:30<01:54,  2.18it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 51/300 [00:30<01:53,  2.19it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 52/300 [00:34<05:23,  1.30s/it]
# Trainer step: 42, epoch: 3:  18%|█▊        | 53/300 [00:34<04:19,  1.05s/it]
# Trainer step: 42, epoch: 3:  18%|█▊        | 54/300 [00:34<03:34,  1.15it/s]Progress: 21.00%
+---- avg training fps: 6.10

# Trainer step: 42, epoch: 3:  18%|█▊        | 55/300 [00:35<03:03,  1.33it/s]
# Trainer step: 42, epoch: 3:  19%|█▊        | 56/300 [00:35<02:41,  1.51it/s]
# Trainer step: 56, epoch: 4:  19%|█▊        | 56/300 [00:36<02:41,  1.51it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 57/300 [00:36<02:21,  1.72it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 58/300 [00:36<02:11,  1.84it/s]
# Trainer step: 56, epoch: 4:  20%|█▉        | 59/300 [00:37<02:05,  1.92it/s]
# Trainer step: 56, epoch: 4:  20%|██        | 60/300 [00:37<02:00,  1.99it/s]Progress: 23.00%
+---- avg training fps: 6.30

# Trainer step: 56, epoch: 4:  20%|██        | 61/300 [00:38<01:56,  2.05it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 62/300 [00:38<01:54,  2.08it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 63/300 [00:39<01:52,  2.10it/s]
# Trainer step: 56, epoch: 4:  21%|██▏       | 64/300 [00:39<01:51,  2.12it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 65/300 [00:39<01:49,  2.15it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 66/300 [00:40<01:49,  2.14it/s]Progress: 25.00%
+---- avg training fps: 6.46

# Trainer step: 56, epoch: 4:  22%|██▏       | 67/300 [00:40<01:48,  2.14it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 68/300 [00:41<01:47,  2.15it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 69/300 [00:41<01:47,  2.15it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 70/300 [00:42<01:46,  2.15it/s]
# Trainer step: 70, epoch: 5:  23%|██▎       | 70/300 [00:42<01:46,  2.15it/s]
# Trainer step: 70, epoch: 5:  24%|██▎       | 71/300 [00:42<01:42,  2.24it/s]
# Trainer step: 70, epoch: 5:  24%|██▍       | 72/300 [00:43<01:42,  2.22it/s]Progress: 27.00%
+---- avg training fps: 6.56

# Trainer step: 70, epoch: 5:  24%|██▍       | 73/300 [00:43<02:01,  1.87it/s]
# Trainer step: 70, epoch: 5:  25%|██▍       | 74/300 [00:44<01:56,  1.95it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 75/300 [00:44<01:52,  2.01it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 76/300 [00:45<01:49,  2.05it/s]
# Trainer step: 70, epoch: 5:  26%|██▌       | 77/300 [00:45<01:47,  2.08it/s]
# Trainer step: 70, epoch: 5:  26%|██▌       | 78/300 [00:46<01:45,  2.10it/s]Progress: 29.00%
+---- avg training fps: 6.69

# Trainer step: 70, epoch: 5:  26%|██▋       | 79/300 [00:46<01:44,  2.12it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 80/300 [00:47<01:42,  2.14it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 81/300 [00:47<01:42,  2.14it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 82/300 [00:48<01:41,  2.14it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 83/300 [00:48<01:41,  2.14it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 84/300 [00:48<01:41,  2.14it/s]Progress: 31.00%
+---- avg training fps: 6.80

# Trainer step: 84, epoch: 6:  28%|██▊       | 84/300 [00:49<01:41,  2.14it/s]
# Trainer step: 84, epoch: 6:  28%|██▊       | 85/300 [00:49<01:36,  2.23it/s]
# Trainer step: 84, epoch: 6:  29%|██▊       | 86/300 [00:49<01:36,  2.21it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 87/300 [00:50<01:36,  2.20it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 88/300 [00:50<01:36,  2.19it/s]
# Trainer step: 84, epoch: 6:  30%|██▉       | 89/300 [00:51<01:37,  2.18it/s]
# Trainer step: 84, epoch: 6:  30%|███       | 90/300 [00:51<01:36,  2.17it/s]Progress: 33.00%
+---- avg training fps: 6.90

# Trainer step: 84, epoch: 6:  30%|███       | 91/300 [00:52<01:36,  2.17it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 92/300 [00:52<01:35,  2.18it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 93/300 [00:53<01:35,  2.18it/s]
# Trainer step: 84, epoch: 6:  31%|███▏      | 94/300 [00:53<01:34,  2.17it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 95/300 [00:54<01:34,  2.17it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 96/300 [00:54<01:34,  2.17it/s]Progress: 35.00%
+---- avg training fps: 6.99

# Trainer step: 84, epoch: 6:  32%|███▏      | 97/300 [00:54<01:33,  2.16it/s]
# Trainer step: 84, epoch: 6:  33%|███▎      | 98/300 [00:55<01:33,  2.16it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 98/300 [00:55<01:33,  2.16it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 99/300 [00:55<01:29,  2.25it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 100/300 [00:56<01:29,  2.23it/s]
# Trainer step: 98, epoch: 7:  34%|███▎      | 101/300 [00:56<01:29,  2.21it/s]
# Trainer step: 98, epoch: 7:  34%|███▍      | 102/300 [00:59<03:29,  1.06s/it]Progress: 37.00%
+---- avg training fps: 6.84

# Trainer step: 98, epoch: 7:  34%|███▍      | 103/300 [00:59<02:53,  1.14it/s]
# Trainer step: 98, epoch: 7:  35%|███▍      | 104/300 [01:00<02:27,  1.32it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 105/300 [01:00<02:10,  1.50it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 106/300 [01:01<01:57,  1.65it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 107/300 [01:01<01:49,  1.77it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 108/300 [01:01<01:42,  1.87it/s]Progress: 39.00%
+---- avg training fps: 6.92

# Trainer step: 98, epoch: 7:  36%|███▋      | 109/300 [01:02<01:38,  1.95it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 110/300 [01:02<01:34,  2.00it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 111/300 [01:03<01:32,  2.04it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 112/300 [01:03<01:30,  2.07it/s]
# Trainer step: 112, epoch: 8:  37%|███▋      | 112/300 [01:04<01:30,  2.07it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 113/300 [01:04<01:26,  2.17it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 114/300 [01:04<01:25,  2.17it/s]Progress: 41.00%
+---- avg training fps: 6.99

# Trainer step: 112, epoch: 8:  38%|███▊      | 115/300 [01:05<01:26,  2.15it/s]
# Trainer step: 112, epoch: 8:  39%|███▊      | 116/300 [01:05<01:25,  2.15it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 117/300 [01:06<01:25,  2.15it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 118/300 [01:06<01:24,  2.14it/s]
# Trainer step: 112, epoch: 8:  40%|███▉      | 119/300 [01:07<01:24,  2.15it/s]
# Trainer step: 112, epoch: 8:  40%|████      | 120/300 [01:07<01:23,  2.15it/s]Progress: 43.00%
+---- avg training fps: 7.06

# Trainer step: 112, epoch: 8:  40%|████      | 121/300 [01:07<01:23,  2.16it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 122/300 [01:08<01:23,  2.14it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 123/300 [01:08<01:22,  2.15it/s]
# Trainer step: 112, epoch: 8:  41%|████▏     | 124/300 [01:09<01:22,  2.14it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 125/300 [01:09<01:21,  2.15it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 126/300 [01:10<01:21,  2.14it/s]Progress: 45.00%
+---- avg training fps: 7.13

# Trainer step: 126, epoch: 9:  42%|████▏     | 126/300 [01:10<01:21,  2.14it/s]
# Trainer step: 126, epoch: 9:  42%|████▏     | 127/300 [01:10<01:17,  2.24it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 128/300 [01:11<01:18,  2.21it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 129/300 [01:11<01:18,  2.19it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 130/300 [01:12<01:18,  2.18it/s]
# Trainer step: 126, epoch: 9:  44%|████▎     | 131/300 [01:12<01:18,  2.17it/s]
# Trainer step: 126, epoch: 9:  44%|████▍     | 132/300 [01:13<01:17,  2.16it/s]Progress: 47.00%
+---- avg training fps: 7.18

# Trainer step: 126, epoch: 9:  44%|████▍     | 133/300 [01:13<01:17,  2.16it/s]
# Trainer step: 126, epoch: 9:  45%|████▍     | 134/300 [01:13<01:17,  2.15it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 135/300 [01:14<01:16,  2.15it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 136/300 [01:14<01:16,  2.15it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 137/300 [01:15<01:15,  2.16it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 138/300 [01:15<01:15,  2.16it/s]Progress: 49.00%
+---- avg training fps: 7.23

# Trainer step: 126, epoch: 9:  46%|████▋     | 139/300 [01:16<01:14,  2.15it/s]
# Trainer step: 126, epoch: 9:  47%|████▋     | 140/300 [01:16<01:14,  2.14it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 140/300 [01:17<01:14,  2.14it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 141/300 [01:17<01:11,  2.23it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 142/300 [01:17<01:11,  2.20it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 143/300 [01:18<01:12,  2.18it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 144/300 [01:18<01:12,  2.17it/s]Progress: 51.00%
+---- avg training fps: 7.29

# Trainer step: 140, epoch: 10:  48%|████▊     | 145/300 [01:19<01:11,  2.17it/s]
# Trainer step: 140, epoch: 10:  49%|████▊     | 146/300 [01:19<01:19,  1.93it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 147/300 [01:20<01:16,  2.00it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 148/300 [01:20<01:14,  2.04it/s]
# Trainer step: 140, epoch: 10:  50%|████▉     | 149/300 [01:21<01:12,  2.08it/s]
# Trainer step: 140, epoch: 10:  50%|█████     | 150/300 [01:21<01:11,  2.10it/s]Progress: 53.00%
+---- avg training fps: 7.32

# Trainer step: 140, epoch: 10:  50%|█████     | 151/300 [01:22<01:10,  2.12it/s]
# Trainer step: 140, epoch: 10:  51%|█████     | 152/300 [01:25<03:24,  1.38s/it]
# Trainer step: 140, epoch: 10:  51%|█████     | 153/300 [01:25<02:42,  1.11s/it]
# Trainer step: 140, epoch: 10:  51%|█████▏    | 154/300 [01:26<02:13,  1.09it/s]
# Trainer step: 154, epoch: 11:  51%|█████▏    | 154/300 [01:26<02:13,  1.09it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 155/300 [01:26<01:50,  1.31it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 156/300 [01:27<01:37,  1.48it/s]Progress: 55.00%
+---- avg training fps: 7.11

# Trainer step: 154, epoch: 11:  52%|█████▏    | 157/300 [01:27<01:27,  1.63it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 158/300 [01:28<01:20,  1.76it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 159/300 [01:28<01:15,  1.86it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 160/300 [01:29<01:12,  1.94it/s]
# Trainer step: 154, epoch: 11:  54%|█████▎    | 161/300 [01:29<01:09,  2.00it/s]
# Trainer step: 154, epoch: 11:  54%|█████▍    | 162/300 [01:30<01:07,  2.06it/s]Progress: 57.00%
+---- avg training fps: 7.16

# Trainer step: 154, epoch: 11:  54%|█████▍    | 163/300 [01:30<01:05,  2.09it/s]
# Trainer step: 154, epoch: 11:  55%|█████▍    | 164/300 [01:31<01:04,  2.12it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 165/300 [01:31<01:03,  2.13it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 166/300 [01:31<01:02,  2.15it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 167/300 [01:32<01:01,  2.15it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 168/300 [01:32<01:00,  2.16it/s]Progress: 59.00%
+---- avg training fps: 7.21

# Trainer step: 168, epoch: 12:  56%|█████▌    | 168/300 [01:33<01:00,  2.16it/s]
# Trainer step: 168, epoch: 12:  56%|█████▋    | 169/300 [01:33<00:57,  2.26it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 170/300 [01:33<00:58,  2.23it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 171/300 [01:34<00:58,  2.22it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 172/300 [01:34<00:58,  2.20it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 173/300 [01:35<00:57,  2.20it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 174/300 [01:35<00:57,  2.19it/s]Progress: 61.00%
+---- avg training fps: 7.25

# Trainer step: 168, epoch: 12:  58%|█████▊    | 175/300 [01:36<00:57,  2.18it/s]
# Trainer step: 168, epoch: 12:  59%|█████▊    | 176/300 [01:36<00:57,  2.17it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 177/300 [01:36<00:56,  2.17it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 178/300 [01:37<00:56,  2.17it/s]
# Trainer step: 168, epoch: 12:  60%|█████▉    | 179/300 [01:37<00:55,  2.18it/s]
# Trainer step: 168, epoch: 12:  60%|██████    | 180/300 [01:38<00:55,  2.17it/s]Progress: 63.00%
+---- avg training fps: 7.29

# Trainer step: 168, epoch: 12:  60%|██████    | 181/300 [01:38<00:54,  2.17it/s]
# Trainer step: 168, epoch: 12:  61%|██████    | 182/300 [01:39<00:54,  2.17it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 182/300 [01:39<00:54,  2.17it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 183/300 [01:39<00:51,  2.27it/s]
# Trainer step: 182, epoch: 13:  61%|██████▏   | 184/300 [01:40<00:58,  1.99it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 185/300 [01:40<00:56,  2.05it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 186/300 [01:41<00:54,  2.09it/s]Progress: 65.00%
+---- avg training fps: 7.32

# Trainer step: 182, epoch: 13:  62%|██████▏   | 187/300 [01:41<00:53,  2.12it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 188/300 [01:42<00:52,  2.13it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 189/300 [01:42<00:51,  2.15it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 190/300 [01:43<00:50,  2.16it/s]
# Trainer step: 182, epoch: 13:  64%|██████▎   | 191/300 [01:43<00:50,  2.17it/s]
# Trainer step: 182, epoch: 13:  64%|██████▍   | 192/300 [01:43<00:49,  2.17it/s]Progress: 67.00%
+---- avg training fps: 7.36

# Trainer step: 182, epoch: 13:  64%|██████▍   | 193/300 [01:44<00:49,  2.17it/s]
# Trainer step: 182, epoch: 13:  65%|██████▍   | 194/300 [01:44<00:48,  2.18it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 195/300 [01:45<00:48,  2.18it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 196/300 [01:45<00:47,  2.17it/s]
# Trainer step: 196, epoch: 14:  65%|██████▌   | 196/300 [01:46<00:47,  2.17it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 197/300 [01:46<00:45,  2.27it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 198/300 [01:46<00:45,  2.24it/s]Progress: 69.00%
+---- avg training fps: 7.40

# Trainer step: 196, epoch: 14:  66%|██████▋   | 199/300 [01:47<00:45,  2.23it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 200/300 [01:47<00:45,  2.21it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 201/300 [01:48<00:45,  2.20it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 202/300 [01:50<01:39,  1.01s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 203/300 [01:50<01:22,  1.18it/s]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 204/300 [01:51<01:10,  1.37it/s]Progress: 71.00%
+---- avg training fps: 7.30

# Trainer step: 196, epoch: 14:  68%|██████▊   | 205/300 [01:51<01:01,  1.54it/s]
# Trainer step: 196, epoch: 14:  69%|██████▊   | 206/300 [01:52<00:55,  1.69it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 207/300 [01:52<00:51,  1.82it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 208/300 [01:53<00:48,  1.91it/s]
# Trainer step: 196, epoch: 14:  70%|██████▉   | 209/300 [01:53<00:45,  1.98it/s]
# Trainer step: 196, epoch: 14:  70%|███████   | 210/300 [01:54<00:44,  2.04it/s]Progress: 73.00%
+---- avg training fps: 7.34

# Trainer step: 210, epoch: 15:  70%|███████   | 210/300 [01:54<00:44,  2.04it/s]
# Trainer step: 210, epoch: 15:  70%|███████   | 211/300 [01:54<00:41,  2.17it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 212/300 [01:54<00:40,  2.17it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 213/300 [01:55<00:39,  2.18it/s]
# Trainer step: 210, epoch: 15:  71%|███████▏  | 214/300 [01:55<00:39,  2.17it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 215/300 [01:56<00:39,  2.17it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 216/300 [01:56<00:38,  2.17it/s]Progress: 75.00%
+---- avg training fps: 7.37

# Trainer step: 210, epoch: 15:  72%|███████▏  | 217/300 [01:57<00:38,  2.18it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 218/300 [01:57<00:37,  2.17it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 219/300 [01:58<00:37,  2.16it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 220/300 [01:58<00:37,  2.16it/s]
# Trainer step: 210, epoch: 15:  74%|███████▎  | 221/300 [01:59<00:36,  2.17it/s]
# Trainer step: 210, epoch: 15:  74%|███████▍  | 222/300 [01:59<00:36,  2.17it/s]Progress: 77.00%
+---- avg training fps: 7.40

# Trainer step: 210, epoch: 15:  74%|███████▍  | 223/300 [01:59<00:35,  2.16it/s]
# Trainer step: 210, epoch: 15:  75%|███████▍  | 224/300 [02:00<00:35,  2.16it/s]
# Trainer step: 224, epoch: 16:  75%|███████▍  | 224/300 [02:00<00:35,  2.16it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 225/300 [02:00<00:33,  2.27it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 226/300 [02:01<00:33,  2.24it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 227/300 [02:01<00:32,  2.21it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 228/300 [02:02<00:32,  2.20it/s]Progress: 79.00%
+---- avg training fps: 7.44

# Trainer step: 224, epoch: 16:  76%|███████▋  | 229/300 [02:02<00:32,  2.19it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 230/300 [02:03<00:31,  2.19it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 231/300 [02:03<00:31,  2.18it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 232/300 [02:04<00:31,  2.18it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 233/300 [02:04<00:31,  2.15it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 234/300 [02:04<00:30,  2.16it/s]Progress: 81.00%
+---- avg training fps: 7.46

# Trainer step: 224, epoch: 16:  78%|███████▊  | 235/300 [02:05<00:30,  2.16it/s]
# Trainer step: 224, epoch: 16:  79%|███████▊  | 236/300 [02:05<00:29,  2.16it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 237/300 [02:06<00:29,  2.16it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 238/300 [02:06<00:28,  2.16it/s]
# Trainer step: 238, epoch: 17:  79%|███████▉  | 238/300 [02:07<00:28,  2.16it/s]
# Trainer step: 238, epoch: 17:  80%|███████▉  | 239/300 [02:07<00:26,  2.30it/s]
# Trainer step: 238, epoch: 17:  80%|████████  | 240/300 [02:07<00:26,  2.25it/s]Progress: 83.00%
+---- avg training fps: 7.49

# Trainer step: 238, epoch: 17:  80%|████████  | 241/300 [02:08<00:26,  2.22it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 242/300 [02:08<00:26,  2.20it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 243/300 [02:09<00:25,  2.20it/s]
# Trainer step: 238, epoch: 17:  81%|████████▏ | 244/300 [02:09<00:25,  2.19it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 245/300 [02:09<00:25,  2.18it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 246/300 [02:10<00:24,  2.17it/s]Progress: 85.00%
+---- avg training fps: 7.52

# Trainer step: 238, epoch: 17:  82%|████████▏ | 247/300 [02:10<00:24,  2.17it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 248/300 [02:11<00:24,  2.16it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 249/300 [02:11<00:23,  2.16it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 250/300 [02:12<00:23,  2.17it/s]
# Trainer step: 238, epoch: 17:  84%|████████▎ | 251/300 [02:12<00:22,  2.17it/s]
# Trainer step: 238, epoch: 17:  84%|████████▍ | 252/300 [02:16<01:05,  1.36s/it]Progress: 87.00%
+---- avg training fps: 7.38

# Trainer step: 252, epoch: 18:  84%|████████▍ | 252/300 [02:16<01:05,  1.36s/it]
# Trainer step: 252, epoch: 18:  84%|████████▍ | 253/300 [02:16<00:50,  1.07s/it]
# Trainer step: 252, epoch: 18:  85%|████████▍ | 254/300 [02:17<00:40,  1.13it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 255/300 [02:17<00:34,  1.32it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 256/300 [02:17<00:29,  1.49it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 257/300 [02:18<00:26,  1.65it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 258/300 [02:18<00:23,  1.79it/s]Progress: 89.00%
+---- avg training fps: 7.41

# Trainer step: 252, epoch: 18:  86%|████████▋ | 259/300 [02:19<00:21,  1.88it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 260/300 [02:19<00:20,  1.96it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 261/300 [02:20<00:19,  2.02it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 262/300 [02:20<00:18,  2.07it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 263/300 [02:21<00:17,  2.10it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 264/300 [02:21<00:17,  2.11it/s]Progress: 91.00%
+---- avg training fps: 7.43

# Trainer step: 252, epoch: 18:  88%|████████▊ | 265/300 [02:22<00:16,  2.12it/s]
# Trainer step: 252, epoch: 18:  89%|████████▊ | 266/300 [02:22<00:15,  2.15it/s]
# Trainer step: 266, epoch: 19:  89%|████████▊ | 266/300 [02:22<00:15,  2.15it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 267/300 [02:22<00:14,  2.26it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 268/300 [02:23<00:14,  2.22it/s]
# Trainer step: 266, epoch: 19:  90%|████████▉ | 269/300 [02:23<00:14,  2.20it/s]
# Trainer step: 266, epoch: 19:  90%|█████████ | 270/300 [02:24<00:13,  2.19it/s]Progress: 93.00%
+---- avg training fps: 7.46

# Trainer step: 266, epoch: 19:  90%|█████████ | 271/300 [02:24<00:13,  2.19it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 272/300 [02:25<00:12,  2.19it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 273/300 [02:25<00:12,  2.18it/s]
# Trainer step: 266, epoch: 19:  91%|█████████▏| 274/300 [02:26<00:11,  2.18it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 275/300 [02:26<00:11,  2.18it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 276/300 [02:27<00:11,  2.18it/s]Progress: 95.00%
+---- avg training fps: 7.48

# Trainer step: 266, epoch: 19:  92%|█████████▏| 277/300 [02:27<00:10,  2.17it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 278/300 [02:28<00:10,  2.17it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 279/300 [02:28<00:09,  2.17it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 280/300 [02:28<00:09,  2.16it/s]
# Trainer step: 280, epoch: 20:  93%|█████████▎| 280/300 [02:29<00:09,  2.16it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▎| 281/300 [02:29<00:08,  2.26it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▍| 282/300 [02:29<00:08,  2.23it/s]Progress: 97.00%
+---- avg training fps: 7.51

# Trainer step: 280, epoch: 20:  94%|█████████▍| 283/300 [02:30<00:07,  2.21it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▍| 284/300 [02:30<00:07,  2.20it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 285/300 [02:31<00:06,  2.20it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 286/300 [02:31<00:06,  2.19it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 287/300 [02:32<00:05,  2.17it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 288/300 [02:32<00:05,  2.16it/s]Progress: 99.00%
+---- avg training fps: 7.53

# Trainer step: 280, epoch: 20:  96%|█████████▋| 289/300 [02:33<00:05,  2.17it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 290/300 [02:33<00:04,  2.16it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 291/300 [02:33<00:04,  2.16it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 292/300 [02:34<00:03,  2.17it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 293/300 [02:34<00:03,  2.17it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 294/300 [02:35<00:02,  2.17it/s]Progress: 100.00%
+---- avg training fps: 7.55

# Trainer step: 294, epoch: 21:  98%|█████████▊| 294/300 [02:35<00:02,  2.17it/s]
# Trainer step: 294, epoch: 21:  98%|█████████▊| 295/300 [02:35<00:02,  2.27it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▊| 296/300 [02:36<00:01,  2.23it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 297/300 [02:36<00:01,  2.21it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 298/300 [02:37<00:00,  2.21it/s]
# Trainer step: 294, epoch: 21: 100%|█████████▉| 299/300 [02:37<00:00,  2.18it/s]
# Trainer step: 294, epoch: 21: 100%|██████████| 300/300 [02:38<00:00,  2.17it/s]Progress: 100.00%
+---- avg training fps: 7.57

# Trainer step: 294, epoch: 21: : 301it [02:38,  2.17it/s]                       Progress: 100.00%
Failed to plot token attention loss
+Reached max steps, stopping training!
+Saving checkpoint at step.. 301
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 294, epoch: 21: : 301it [03:58,  1.26it/s]
+------------------------------------------
+Training done :)
+2024-08-13 04:07:27.940317: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 04:07:28.033783: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 04:07:28.033809: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 04:07:28.052828: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 04:07:28.454170: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:07:28.454246: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:07:28.454254: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23130 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/journey_small' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=1.0 freeze_unet_before_completion_f=0.0 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='journey_small' output_dir='lora_models/styles_final/journey_small_13_04-07-30-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723514850.2719877 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/journey_small_13_04-07-30-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 0.812
+New train_img_size: [448, 576]
+Validation_img_size was set to: [896, 1152]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red cloak standing in front of a building.
+- A person standing on top of a body of water.
+- A person walking in the desert with a camel in front of a mountain.
+- A man standing on top of a sandy beach next to the ocean.
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red dress is walking through an archway.
+- A person standing on top of a body of water.
+- A person walking in the desert with a dog on a leash.
+- A person walking on a beach next to a body of water.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal and dreamlike, bold compositions
+
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red cloak standing in front of a building.
+- In the style of TOK, a person standing on top of a body of water.
+- In the style of TOK, a person walking in the desert with a camel in front of a mountain.
+- In the style of TOK, a man standing on top of a sandy beach next to the ocean.
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red dress walking through an archway.
+- In the style of TOK, a person standing on top of a body of water.
+- In the style of TOK, a person walking in the desert with a dog on a leash.
+- In the style of TOK, a person walking on a beach next to a body of water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Surreal and dreamlike, bold compositions
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 11091.64it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a sunset over a ...
+1     in the style of <s0><s1><s2>, a woman in a red...
+2     in the style of <s0><s1><s2>, a person standin...
+3     in the style of <s0><s1><s2>, a person walking...
+4     in the style of <s0><s1><s2>, a man standing o...
+5     in the style of <s0><s1><s2>, a sunset over a ...
+6     in the style of <s0><s1><s2>, a woman in a red...
+7     in the style of <s0><s1><s2>, a person standin...
+8     in the style of <s0><s1><s2>, a person walking...
+9     in the style of <s0><s1><s2>, a person walking...
+10    in the style of <s0><s1><s2>, a sunset over a ...
+11    in the style of <s0><s1><s2>, a woman in a red...
+12    in the style of <s0><s1><s2>, a person standin...
+13    in the style of <s0><s1><s2>, a person walking...
+14    in the style of <s0><s1><s2>, a man standing o...
+15    in the style of <s0><s1><s2>, a sunset over a ...
+16    in the style of <s0><s1><s2>, a woman in a red...
+17    in the style of <s0><s1><s2>, a person standin...
+18    in the style of <s0><s1><s2>, a person walking...
+19    in the style of <s0><s1><s2>, a person walking...
+20    in the style of <s0><s1><s2>, a sunset over a ...
+21    in the style of <s0><s1><s2>, a woman in a red...
+22    in the style of <s0><s1><s2>, a person standin...
+23    in the style of <s0><s1><s2>, a person walking...
+24    in the style of <s0><s1><s2>, a man standing o...
+25    in the style of <s0><s1><s2>, a sunset over a ...
+26    in the style of <s0><s1><s2>, a woman in a red...
+27    in the style of <s0><s1><s2>, a person standin...
+28    in the style of <s0><s1><s2>, a person walking...
+29    in the style of <s0><s1><s2>, a person walking...
+30    in the style of <s0><s1><s2>, a sunset over a ...
+31    in the style of <s0><s1><s2>, a woman in a red...
+32    in the style of <s0><s1><s2>, a person standin...
+33    in the style of <s0><s1><s2>, a person walking...
+34    in the style of <s0><s1><s2>, a man standing o...
+35    in the style of <s0><s1><s2>, a sunset over a ...
+36    in the style of <s0><s1><s2>, a woman in a red...
+37    in the style of <s0><s1><s2>, a person standin...
+38    in the style of <s0><s1><s2>, a person walking...
+39    in the style of <s0><s1><s2>, a person walking...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:09<04:36,  1.06it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.43

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+---- avg training fps: 3.75

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+---- avg training fps: 4.63

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+---- avg training fps: 5.25

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+---- avg training fps: 5.70

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+---- avg training fps: 6.04

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+---- avg training fps: 6.32

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+---- avg training fps: 6.54

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+---- avg training fps: 6.04

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+---- avg training fps: 6.23

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+---- avg training fps: 6.39

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+---- avg training fps: 6.53

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:44<01:45,  2.14it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:44<01:45,  2.15it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:45<01:44,  2.15it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:45<01:44,  2.15it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:45<01:43,  2.15it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:46<01:43,  2.15it/s]Progress: 29.00%
+---- avg training fps: 6.65

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:46<01:43,  2.15it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:47<01:42,  2.15it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:47<01:42,  2.15it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:47<01:41,  2.15it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:48<01:41,  2.15it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:48<01:40,  2.15it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:49<01:40,  2.15it/s]Progress: 31.00%
+---- avg training fps: 6.76

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:49<01:39,  2.15it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:50<01:39,  2.16it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:50<01:38,  2.15it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:51<01:38,  2.15it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:51<01:38,  2.15it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:52<01:37,  2.16it/s]Progress: 33.00%
+---- avg training fps: 6.86

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:52<01:37,  2.16it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:52<01:36,  2.16it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:52<01:36,  2.16it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:53<01:35,  2.16it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:53<01:35,  2.16it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:54<01:35,  2.16it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:54<01:34,  2.16it/s]Progress: 35.00%
+---- avg training fps: 6.95

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:55<01:33,  2.17it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:55<01:33,  2.16it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:56<01:33,  2.16it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:56<01:32,  2.15it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:57<01:32,  2.15it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:57<01:31,  2.17it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:57<01:31,  2.16it/s]Progress: 37.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.03

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:58<01:31,  2.16it/s]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [00:58<01:31,  2.15it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [00:58<01:30,  2.14it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [00:59<01:30,  2.14it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [00:59<01:30,  2.14it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:00<01:29,  2.14it/s]Progress: 39.00%
+---- avg training fps: 7.10

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:00<01:29,  2.14it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:01<01:28,  2.14it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:01<01:28,  2.14it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:01<01:28,  2.14it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:02<01:27,  2.15it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:02<01:27,  2.15it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:03<01:27,  2.14it/s]Progress: 41.00%
+---- avg training fps: 7.16

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:03<01:26,  2.14it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:04<01:26,  2.13it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:04<01:25,  2.13it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:05<01:25,  2.14it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:05<01:24,  2.15it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:05<01:23,  2.15it/s]Progress: 43.00%
+---- avg training fps: 7.22

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:06<01:23,  2.15it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:06<01:23,  2.14it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:06<01:23,  2.14it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:07<01:22,  2.15it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:07<01:22,  2.14it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:08<01:21,  2.15it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:08<01:21,  2.15it/s]Progress: 45.00%
+---- avg training fps: 7.28

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:09<01:20,  2.15it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:09<01:20,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:10<01:19,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:10<01:18,  2.15it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:11<01:18,  2.15it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:11<01:18,  2.15it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:11<01:18,  2.15it/s]Progress: 47.00%
+---- avg training fps: 7.33

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:12<01:17,  2.15it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:12<01:17,  2.15it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:12<01:16,  2.15it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:13<01:16,  2.15it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:13<01:16,  2.14it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:14<01:15,  2.14it/s]Progress: 49.00%
+---- avg training fps: 7.38

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:14<01:15,  2.14it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:15<01:14,  2.13it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:15<01:14,  2.13it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:15<01:14,  2.14it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:16<01:13,  2.14it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:16<01:13,  2.14it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:17<01:12,  2.14it/s]Progress: 51.00%
+---- avg training fps: 7.42

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:17<01:12,  2.14it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:18<01:11,  2.14it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:18<01:11,  2.14it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:19<01:10,  2.15it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:19<01:10,  2.15it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:19<01:10,  2.14it/s]Progress: 53.00%
+---- avg training fps: 7.46

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:20<01:10,  2.14it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:20<01:09,  2.14it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:25<04:13,  1.71s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:25<03:16,  1.34s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:25<02:37,  1.08s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:26<02:09,  1.12it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:26<01:50,  1.31it/s]Progress: 55.00%
+---- avg training fps: 7.14

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:27<01:36,  1.48it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:27<01:26,  1.63it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:28<01:20,  1.76it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:28<01:15,  1.86it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:29<01:15,  1.86it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:29<01:11,  1.93it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:29<01:09,  1.99it/s]Progress: 57.00%
+---- avg training fps: 7.19

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:30<01:07,  2.03it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:30<01:06,  2.06it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:31<01:04,  2.10it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:31<01:03,  2.11it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:32<01:02,  2.13it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:32<01:01,  2.14it/s]Progress: 59.00%
+---- avg training fps: 7.23

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:32<01:01,  2.14it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:33<01:00,  2.14it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:33<01:00,  2.14it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:33<00:59,  2.15it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:34<00:59,  2.15it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:34<00:58,  2.15it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:35<00:58,  2.15it/s]Progress: 61.00%
+---- avg training fps: 7.27

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:35<00:58,  2.14it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:36<00:57,  2.15it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:36<00:57,  2.16it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:37<00:56,  2.15it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:37<00:56,  2.15it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:38<00:55,  2.16it/s]Progress: 63.00%
+---- avg training fps: 7.31

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:38<00:55,  2.16it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:38<00:54,  2.17it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:38<00:54,  2.16it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:39<00:54,  2.16it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:39<00:53,  2.17it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:40<00:53,  2.16it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:40<00:52,  2.16it/s]Progress: 65.00%
+---- avg training fps: 7.34

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:41<00:52,  2.15it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:41<00:51,  2.16it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:42<00:51,  2.16it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:42<00:51,  2.15it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:43<00:51,  2.15it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:43<00:50,  2.14it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:43<00:50,  2.15it/s]Progress: 67.00%
+---- avg training fps: 7.38

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:44<00:49,  2.15it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:44<00:49,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:45<00:48,  2.15it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:45<00:48,  2.15it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:45<00:47,  2.15it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:46<00:47,  2.15it/s]Progress: 69.00%
+---- avg training fps: 7.41

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:46<00:46,  2.16it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:47<00:46,  2.16it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:47<00:46,  2.16it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:47<00:45,  2.15it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:52<02:48,  1.72s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:52<02:10,  1.35s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:53<01:43,  1.08s/it]Progress: 71.00%
+---- avg training fps: 7.17

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:53<01:24,  1.12it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:54<01:11,  1.31it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:54<01:02,  1.49it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:55<00:56,  1.64it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:55<00:51,  1.78it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:56<00:47,  1.88it/s]Progress: 73.00%
+---- avg training fps: 7.20

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:56<00:47,  1.88it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:56<00:45,  1.95it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:57<00:43,  2.01it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:57<00:42,  2.05it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:58<00:41,  2.09it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [01:58<00:40,  2.11it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [01:58<00:39,  2.12it/s]Progress: 75.00%
+---- avg training fps: 7.24

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [01:59<00:38,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [01:59<00:38,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:00<00:37,  2.14it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:00<00:37,  2.15it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:01<00:37,  2.15it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:01<00:36,  2.15it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:01<00:36,  2.15it/s]Progress: 77.00%
+---- avg training fps: 7.27

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:02<00:35,  2.16it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:02<00:35,  2.17it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:03<00:34,  2.16it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:03<00:34,  2.16it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:04<00:33,  2.16it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:04<00:33,  2.16it/s]Progress: 79.00%
+---- avg training fps: 7.30

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:04<00:32,  2.15it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:05<00:32,  2.15it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:05<00:32,  2.15it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:05<00:32,  2.15it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:06<00:31,  2.16it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:06<00:31,  2.15it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:07<00:30,  2.14it/s]Progress: 81.00%
+---- avg training fps: 7.33

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:07<00:30,  2.15it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:08<00:29,  2.15it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:08<00:29,  2.14it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:09<00:28,  2.14it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:09<00:28,  2.16it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:10<00:27,  2.15it/s]Progress: 83.00%
+---- avg training fps: 7.35

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:10<00:27,  2.15it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:10<00:27,  2.15it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:11<00:26,  2.15it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:11<00:26,  2.15it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:11<00:25,  2.16it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:12<00:25,  2.16it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:12<00:25,  2.15it/s]Progress: 85.00%
+---- avg training fps: 7.38

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:13<00:24,  2.14it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:13<00:24,  2.15it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:14<00:23,  2.15it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:14<00:23,  2.16it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:15<00:23,  2.16it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:15<00:22,  2.15it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:15<00:22,  2.15it/s]Progress: 87.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.40

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:16<00:21,  2.15it/s]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:16<00:21,  2.14it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:17<00:21,  2.13it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:17<00:20,  2.13it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:18<00:20,  2.14it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:18<00:19,  2.14it/s]Progress: 89.00%
+---- avg training fps: 7.43

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:18<00:19,  2.14it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:19<00:18,  2.14it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:19<00:18,  2.14it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:19<00:18,  2.16it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:20<00:17,  2.16it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:20<00:17,  2.15it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:21<00:16,  2.15it/s]Progress: 91.00%
+---- avg training fps: 7.45

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:21<00:16,  2.16it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:22<00:15,  2.15it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:22<00:15,  2.15it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:23<00:14,  2.15it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:23<00:14,  2.15it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:24<00:13,  2.15it/s]Progress: 93.00%
+---- avg training fps: 7.47

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:24<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:24<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:24<00:12,  2.16it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:25<00:12,  2.16it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:25<00:12,  2.15it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:26<00:11,  2.15it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:26<00:11,  2.15it/s]Progress: 95.00%
+---- avg training fps: 7.50

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:27<00:10,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:27<00:10,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:28<00:09,  2.14it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:28<00:09,  2.14it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:29<00:09,  2.14it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:29<00:08,  2.14it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:29<00:08,  2.14it/s]Progress: 97.00%
+---- avg training fps: 7.52

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:30<00:07,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:30<00:08,  1.90it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:31<00:07,  1.97it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:31<00:06,  2.02it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:32<00:06,  2.07it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:32<00:05,  2.09it/s]Progress: 99.00%
+---- avg training fps: 7.53

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:33<00:05,  2.10it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:33<00:04,  2.12it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:34<00:04,  2.12it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:34<00:04,  2.14it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:34<00:03,  2.15it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:34<00:03,  2.15it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:35<00:02,  2.15it/s]Progress: 100.00%
+---- avg training fps: 7.55

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:35<00:02,  2.16it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:36<00:01,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:36<00:01,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:37<00:00,  2.15it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:37<00:00,  2.16it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:38<00:00,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.56
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [03:57<00:00,  1.26it/s]
+------------------------------------------
+Training done :)
+2024-08-13 04:11:57.346856: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 04:11:57.439928: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 04:11:57.439954: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 04:11:57.458681: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 04:11:57.861335: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:11:57.861418: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:11:57.861427: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23106 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/journey_small' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.5 freeze_unet_before_completion_f=0.25 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='journey_small' output_dir='lora_models/styles_final/journey_small_13_04-11-59-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723515119.6881795 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/journey_small_13_04-11-59-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 0.812
+New train_img_size: [448, 576]
+Validation_img_size was set to: [896, 1152]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red cloak standing in front of a building.
+- A person standing on top of a body of water.
+- A person walking in the desert with a camel in front of a mountain.
+- A man standing on top of a sandy beach next to the ocean.
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red dress is walking through an archway.
+- A person standing on top of a body of water.
+- A person walking in the desert with a dog on a leash.
+- A person walking on a beach next to a body of water.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal and dreamlike landscapes.
+
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red cloak standing in front of a building.
+- In the style of TOK, a person standing on top of a body of water.
+- In the style of TOK, a person walking in the desert with a camel in front of a mountain.
+- In the style of TOK, a man standing on top of a sandy beach next to the ocean.
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red dress is walking through an archway.
+- In the style of TOK, a person standing on top of a body of water.
+- In the style of TOK, a person walking in the desert with a dog on a leash.
+- In the style of TOK, a person walking on a beach next to a body of water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Surreal and dreamlike landscapes.
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 11180.34it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a sunset over a ...
+1     in the style of <s0><s1><s2>, a woman in a red...
+2     in the style of <s0><s1><s2>, a person standin...
+3     in the style of <s0><s1><s2>, a person walking...
+4     in the style of <s0><s1><s2>, a man standing o...
+5     in the style of <s0><s1><s2>, a sunset over a ...
+6     in the style of <s0><s1><s2>, a woman in a red...
+7     in the style of <s0><s1><s2>, a person standin...
+8     in the style of <s0><s1><s2>, a person walking...
+9     in the style of <s0><s1><s2>, a person walking...
+10    in the style of <s0><s1><s2>, a sunset over a ...
+11    in the style of <s0><s1><s2>, a woman in a red...
+12    in the style of <s0><s1><s2>, a person standin...
+13    in the style of <s0><s1><s2>, a person walking...
+14    in the style of <s0><s1><s2>, a man standing o...
+15    in the style of <s0><s1><s2>, a sunset over a ...
+16    in the style of <s0><s1><s2>, a woman in a red...
+17    in the style of <s0><s1><s2>, a person standin...
+18    in the style of <s0><s1><s2>, a person walking...
+19    in the style of <s0><s1><s2>, a person walking...
+20    in the style of <s0><s1><s2>, a sunset over a ...
+21    in the style of <s0><s1><s2>, a woman in a red...
+22    in the style of <s0><s1><s2>, a person standin...
+23    in the style of <s0><s1><s2>, a person walking...
+24    in the style of <s0><s1><s2>, a man standing o...
+25    in the style of <s0><s1><s2>, a sunset over a ...
+26    in the style of <s0><s1><s2>, a woman in a red...
+27    in the style of <s0><s1><s2>, a person standin...
+28    in the style of <s0><s1><s2>, a person walking...
+29    in the style of <s0><s1><s2>, a person walking...
+30    in the style of <s0><s1><s2>, a sunset over a ...
+31    in the style of <s0><s1><s2>, a woman in a red...
+32    in the style of <s0><s1><s2>, a person standin...
+33    in the style of <s0><s1><s2>, a person walking...
+34    in the style of <s0><s1><s2>, a man standing o...
+35    in the style of <s0><s1><s2>, a sunset over a ...
+36    in the style of <s0><s1><s2>, a woman in a red...
+37    in the style of <s0><s1><s2>, a person standin...
+38    in the style of <s0><s1><s2>, a person walking...
+39    in the style of <s0><s1><s2>, a person walking...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:09<04:40,  1.05it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.38

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+---- avg training fps: 3.68

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+---- avg training fps: 4.56

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+---- avg training fps: 5.17

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+---- avg training fps: 5.62

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+---- avg training fps: 5.96

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+---- avg training fps: 6.24

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+---- avg training fps: 6.46

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+---- avg training fps: 5.96

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+---- avg training fps: 6.14

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+---- avg training fps: 6.31

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+---- avg training fps: 6.45

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+---- avg training fps: 6.57

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+---- avg training fps: 6.68

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+---- avg training fps: 6.78

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# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:54<01:36,  2.15it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:54<01:35,  2.15it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:54<01:35,  2.15it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:55<01:35,  2.14it/s]Progress: 35.00%
+---- avg training fps: 6.87

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:55<01:34,  2.14it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:56<01:34,  2.13it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:56<01:34,  2.14it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:57<01:33,  2.14it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:57<01:33,  2.14it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:57<01:32,  2.15it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:58<01:32,  2.15it/s]Progress: 37.00%
Failed to plot token attention loss
+
+---- avg training fps: 6.95

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:58<01:31,  2.15it/s]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [00:59<01:31,  2.14it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [00:59<01:31,  2.14it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [01:00<01:30,  2.14it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:00<01:30,  2.14it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:01<01:29,  2.15it/s]Progress: 39.00%
+---- avg training fps: 7.03

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:01<01:29,  2.14it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:01<01:28,  2.14it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:02<01:28,  2.14it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:02<01:28,  2.14it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:02<01:27,  2.14it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:03<01:27,  2.14it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:03<01:27,  2.14it/s]Progress: 41.00%
+---- avg training fps: 7.09

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:04<01:26,  2.14it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:04<01:26,  2.14it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:05<01:25,  2.14it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:05<01:25,  2.13it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:06<01:24,  2.14it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:06<01:24,  2.14it/s]Progress: 43.00%
+---- avg training fps: 7.15

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:07<01:24,  2.14it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:07<01:23,  2.14it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:07<01:23,  2.14it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:08<01:22,  2.14it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:08<01:22,  2.14it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:08<01:21,  2.13it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:09<01:21,  2.14it/s]Progress: 45.00%
+---- avg training fps: 7.21

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:09<01:20,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:10<01:20,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:10<01:19,  2.15it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:11<01:19,  2.14it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:11<01:19,  2.14it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:11<01:18,  2.14it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:12<01:18,  2.13it/s]Progress: 47.00%
+---- avg training fps: 7.26

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:12<01:18,  2.13it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:13<01:17,  2.13it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:13<01:17,  2.13it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:14<01:16,  2.14it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:14<01:16,  2.14it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:15<01:15,  2.13it/s]Progress: 49.00%
+---- avg training fps: 7.31

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:15<01:15,  2.13it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:15<01:14,  2.14it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:16<01:14,  2.14it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:16<01:14,  2.13it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:16<01:14,  2.13it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:17<01:13,  2.15it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:17<01:12,  2.14it/s]Progress: 51.00%
+---- avg training fps: 7.35

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:18<01:12,  2.14it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:18<01:12,  2.13it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:19<01:11,  2.13it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:19<01:11,  2.13it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:20<01:11,  2.12it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:20<01:10,  2.13it/s]Progress: 53.00%
+---- avg training fps: 7.39

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:21<01:10,  2.13it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:21<01:10,  2.12it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:25<04:14,  1.72s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:26<03:17,  1.34s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:26<02:37,  1.08s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:27<02:09,  1.12it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:27<01:49,  1.31it/s]Progress: 55.00%
+---- avg training fps: 7.08

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:28<01:36,  1.49it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:28<01:26,  1.64it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:29<01:19,  1.77it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:29<01:14,  1.87it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:29<01:14,  1.87it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:29<01:11,  1.95it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:30<01:08,  2.01it/s]Progress: 57.00%
+---- avg training fps: 7.13

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:30<01:06,  2.05it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:31<01:05,  2.09it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:31<01:04,  2.11it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:32<01:03,  2.12it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:32<01:02,  2.13it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:33<01:01,  2.14it/s]Progress: 59.00%
+---- avg training fps: 7.18

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:33<01:01,  2.14it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:34<01:00,  2.13it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:34<01:00,  2.13it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:34<01:00,  2.14it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:35<00:59,  2.14it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:35<00:59,  2.15it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:35<00:58,  2.14it/s]Progress: 61.00%
+---- avg training fps: 7.22

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:36<00:58,  2.14it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:36<00:58,  2.14it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:37<00:57,  2.14it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:37<00:57,  2.14it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:38<00:56,  2.14it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:38<00:55,  2.14it/s]Progress: 63.00%
+---- avg training fps: 7.25

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:39<00:55,  2.14it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:39<00:55,  2.14it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:39<00:54,  2.15it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:40<00:54,  2.15it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:40<00:54,  2.15it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:41<00:53,  2.14it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:41<00:53,  2.14it/s]Progress: 65.00%
+---- avg training fps: 7.29

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:42<00:52,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:42<00:52,  2.13it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:42<00:51,  2.15it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:43<00:51,  2.14it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:43<00:51,  2.14it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:43<00:50,  2.14it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:44<00:50,  2.14it/s]Progress: 67.00%
+---- avg training fps: 7.32

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:44<00:49,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:45<00:49,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:45<00:49,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:46<00:48,  2.14it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:46<00:48,  2.14it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:47<00:47,  2.13it/s]Progress: 69.00%
+---- avg training fps: 7.36

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:47<00:47,  2.13it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:48<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:48<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:48<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:53<02:52,  1.76s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:53<02:13,  1.37s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:54<01:45,  1.10s/it]Progress: 71.00%
+---- avg training fps: 7.11

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:54<01:26,  1.10it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:55<01:13,  1.29it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:55<01:03,  1.46it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:56<00:56,  1.62it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:56<00:51,  1.75it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:57<00:48,  1.86it/s]Progress: 73.00%
+---- avg training fps: 7.14

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:57<00:48,  1.86it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:57<00:46,  1.93it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:58<00:44,  1.98it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:58<00:42,  2.03it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:58<00:41,  2.07it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [01:59<00:40,  2.09it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [01:59<00:39,  2.11it/s]Progress: 75.00%
+---- avg training fps: 7.18

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [02:00<00:39,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [02:00<00:38,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:01<00:37,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:01<00:37,  2.14it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:02<00:37,  2.14it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:02<00:36,  2.14it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:02<00:36,  2.14it/s]Progress: 77.00%
+---- avg training fps: 7.21

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:03<00:35,  2.14it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:03<00:35,  2.14it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:04<00:34,  2.14it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:04<00:34,  2.14it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:05<00:34,  2.14it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:05<00:33,  2.14it/s]Progress: 79.00%
+---- avg training fps: 7.24

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:05<00:33,  2.14it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:06<00:32,  2.14it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:06<00:32,  2.14it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:06<00:32,  2.14it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:07<00:31,  2.15it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:07<00:31,  2.14it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:08<00:30,  2.14it/s]Progress: 81.00%
+---- avg training fps: 7.27

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:08<00:30,  2.14it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:09<00:29,  2.14it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:09<00:29,  2.14it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:10<00:28,  2.14it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:10<00:28,  2.15it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:11<00:27,  2.15it/s]Progress: 83.00%
+---- avg training fps: 7.30

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:11<00:27,  2.15it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:11<00:27,  2.15it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:12<00:27,  2.14it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:12<00:26,  2.14it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:12<00:26,  2.14it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:13<00:25,  2.14it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:13<00:25,  2.14it/s]Progress: 85.00%
+---- avg training fps: 7.32

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:14<00:24,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:14<00:24,  2.14it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:15<00:23,  2.14it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:15<00:23,  2.14it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:16<00:23,  2.14it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:16<00:22,  2.14it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:16<00:22,  2.14it/s]Progress: 87.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.35

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:17<00:21,  2.14it/s]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:17<00:21,  2.14it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:18<00:21,  2.14it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:18<00:20,  2.14it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:19<00:19,  2.15it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:19<00:19,  2.14it/s]Progress: 89.00%
+---- avg training fps: 7.37

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:19<00:19,  2.14it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:20<00:18,  2.13it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:20<00:18,  2.13it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:20<00:18,  2.14it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:21<00:17,  2.14it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:21<00:17,  2.13it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:22<00:16,  2.15it/s]Progress: 91.00%
+---- avg training fps: 7.40

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:22<00:16,  2.13it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:23<00:15,  2.13it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:23<00:15,  2.13it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:24<00:14,  2.14it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:24<00:14,  2.14it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:25<00:14,  2.14it/s]Progress: 93.00%
+---- avg training fps: 7.42

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:25<00:14,  2.14it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:25<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:26<00:13,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:26<00:12,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:27<00:12,  2.14it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:27<00:11,  2.14it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:27<00:11,  2.14it/s]Progress: 95.00%
+---- avg training fps: 7.44

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:28<00:10,  2.13it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:28<00:10,  2.13it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:29<00:09,  2.13it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:29<00:09,  2.12it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:30<00:09,  2.12it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:30<00:08,  2.13it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:30<00:08,  2.13it/s]Progress: 97.00%
+---- avg training fps: 7.46

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:31<00:07,  2.13it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:31<00:07,  2.13it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:32<00:07,  2.14it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:32<00:06,  2.13it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:33<00:06,  2.13it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:33<00:06,  1.86it/s]Progress: 99.00%
+---- avg training fps: 7.47

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:34<00:05,  1.94it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:34<00:05,  1.99it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:35<00:05,  1.99it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:35<00:04,  2.03it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:35<00:03,  2.06it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:36<00:03,  2.09it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:36<00:02,  2.10it/s]Progress: 100.00%
+---- avg training fps: 7.49

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:37<00:02,  2.12it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:37<00:01,  2.13it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:38<00:01,  2.13it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:38<00:00,  2.13it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:38<00:00,  2.12it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:39<00:00,  2.13it/s]Progress: 100.00%
+---- avg training fps: 7.51
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [03:59<00:00,  1.25it/s]
+------------------------------------------
+Training done :)
+2024-08-13 04:16:23.494439: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 04:16:23.585720: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 04:16:23.585750: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 04:16:23.604492: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 04:16:24.007490: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:16:24.007571: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:16:24.007580: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23115 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=1.0 freeze_unet_before_completion_f=0.25 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='clipx.zip' output_dir='lora_models/styles_final/clipx.zip_13_04-16-25-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723515385.7808163 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Downloading training data...
+Downloading https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip to lora_models/styles_final/clipx.zip_13_04-16-25-style_512_florence_300/images_in/clipx.zip...
+Succesfully prepped 54 .jpg images in lora_models/styles_final/clipx.zip_13_04-16-25-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.314
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 27 images, 27 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 27 to 54)
+Generating 54 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal, whimsical, detailed, and quirky scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene depicts Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is featured.
+- In the style of TOK, a blockchain formed by bees is shown.
+- In the style of TOK, a gorgon made out of sharks swims underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple is hidden deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is depicted.
+- In the style of TOK, the phrase "it was the age of wisdom, it was the age of foolishness" is featured.
+- In the style of TOK, someone suggests quitting their boring corporation job together.
+- In the style of TOK, petroglyphs are shown.
+- In the style of TOK, a room and space exchange is based on first and second degree connections.
+- In the style of TOK, a 6-pack of nightmare fuel is portrayed.
+- In the style of TOK, a band of dancing, singing beavers is shown.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show has elephant seals as runway models and egrets in the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture is featured.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash is swimming up through the sea to take a peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 24 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 1...
+----- GPT response: -----
+Style Description: Surrealistic, intricate, and fantastical scenes
+
+- In the style of TOK, a mermaid swims underwater in the ocean, crafted from fishnets and sea trash.
+- In the style of TOK, a throng of Lilliputians are cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi, Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is illustrated.
+- In the style of TOK, a blockchain of bees is depicted.
+- In the style of TOK, a gorgon made out of sharks is depicted swimming underwater, the sharks eating each other.
+- In the style of TOK, a hillside at sunset is shown with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple deep in the Amazon jungle is illustrated.
+- In the style of TOK, cats are shown pooping in zero-gravity.
+- In the style of TOK, a goblin goat is depicted.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is illustrated.
+- In the style of TOK, the phrase "Let's quit our boring corporation job together" is illustrated.
+- In the style of TOK, petroglyphs are depicted.
+- In the style of TOK, room and space exchange based on first and second-degree connection is depicted.
+- In the style of TOK, trypophobia is illustrated.
+- In the style of TOK, a six-pack of nightmare fuel is depicted.
+- In the style of TOK, a band of dancing, singing beavers is shown.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam in a Hawaiian-shirt pattern.
+- In the style of TOK, a brigade of beavers is quickly building a dam as depicted by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is illustrated in the manner of M. C. Escher.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is shown, frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture is depicted.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, and packing peanuts is swimming up through the sea to peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 2...
+----- GPT response: -----
+Style Description: Surreal, eclectic, and whimsical themes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi, Kerala, India.
+- In the style of TOK, "They were the best of times, they were the worst of times."
+- In the style of TOK, a blockchain of bees.
+- In the style of TOK, a gorgon made out of sharks eating each other swims underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple sits deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat.
+- In the style of TOK, "It was the age of wisdom, it was the age of foolishness."
+- In the style of TOK, let's quit our boring corporation job together.
+- In the style of TOK, petroglyphs.
+- In the style of TOK, room and space exchange based on first and second-degree connections.
+- In the style of TOK, trypophobia.
+- In the style of TOK, a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers.
+- In the style of TOK, a beaver chews a rifle in half.
+- In the style of TOK, a brigade of beavers quickly builds a dam.
+- In the style of TOK, a brigade of beavers quickly builds a dam.
+- In the style of TOK, a brigade of beavers.
+- In the style of TOK, a castle moves along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dances in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show with elephant seals as runway models and egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash swims up through the sea to take a peek above the water.
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea sponge.
+- In the style of TOK, Fort Kochi, Kerala, India.
+- In the style of TOK, "They were the best of times, they were the worst of times."
+- In the style of TOK, a blockchain of bees.
+- In the style of TOK, a gorgon made out of sharks eating each other swims underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple sits deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat.
+- In the style of TOK, "It was the age of wisdom, it was the age of foolishness."
+- In the style of TOK, let's quit our boring corporation job together.
+- In the style of TOK, petroglyphs.
+- In the style of TOK, room and space exchange based on first and second-degree connections.
+- In the style of TOK, trypophobia.
+- In the style of TOK, a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers.
+- In the style of TOK, a beaver chews a rifle in half.
+- In the style of TOK, a brigade of beavers quickly builds a dam.
+- In the style of TOK, a brigade of beavers quickly builds a dam.
+- In the style of TOK, a brigade of beavers.
+- In the style of TOK, a castle moves along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dances in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show with elephant seals as runway models and egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture.
+
+--------------------------
+GPT-4 returned the wrong number of prompts 53 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 3...
+----- GPT response: -----
+Style Description: Surreal and intricate marine or nature scenes
+
+- In the style of TOK, a mermaid crafted from fishnets and sea trash swims underwater in the ocean.
+- In the style of TOK, Lilliputians are cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi, Kerala, India.
+- In the style of TOK, narrating the best and worst of times.
+- In the style of TOK, a blockchain composed of bees.
+- In the style of TOK, a gorgon formed from sharks cannibalizing each other, swimming underwater.
+- In the style of TOK, a sunset over a hillside with Venus and Earth orbiting the sun.
+- In the style of TOK, an ancient temple nestled in the Amazon jungle.
+- In the style of TOK, cats defecating in zero-gravity.
+- In the style of TOK, a goblin goat.
+- In the style of TOK, it was the age of wisdom and foolishness.
+- In the style of TOK, two people planning to leave their corporate jobs together.
+- In the style of TOK, ancient rock carvings.
+- In the style of TOK, room and space exchange based on connections.
+- In the style of TOK, a pattern inducing trypophobia.
+- In the style of TOK, a six-pack of nightmare fuel.
+- In the style of TOK, dancing and singing beavers, reminiscent of Jim Henson's Muppets.
+- In the style of TOK, a beaver chewing a rifle in half.
+- In the style of TOK, a brigade of beavers rapidly constructing a dam, akin to a Hawaiian-shirt pattern.
+- In the style of TOK, a brigade of beavers rapidly constructing a dam, similar to Tarsem Singh's visuals.
+- In the style of TOK, a brigade of beavers in an M.C. Escher-like scene.
+- In the style of TOK, a moving castle traversing grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dancing together.
+- In the style of TOK, a preserved, frozen 1950s restaurant kitchen rediscovered in the space age.
+- In the style of TOK, elephant seals strutting down the runway with egrets in the audience at a fashion show.
+- In the style of TOK, a hyper-realistic painting featuring lemon-lime architecture.
+- In the style of TOK, a mermaid made from fishnets, dead fish, packing peanuts, and sea trash, swimming to peek above the water.
+- In the style of TOK, a mermaid fashioned from fishnets and sea trash, swimming underwater in the ocean.
+- In the style of TOK, Lilliputians crammed into the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India.
+- In the style of TOK, the best and worst of times described.
+- In the style of TOK, bees forming a blockchain.
+- In the style of TOK, a gorgon created from sharks eating each other, swimming underwater.
+- In the style of TOK, a sunset with Venus and Earth orbiting the sun over a hillside.
+- In the style of TOK, an ancient temple deep within the Amazon jungle.
+- In the style of TOK, zero-gravity cats pooping.
+- In the style of TOK, a goblin goat depiction.
+- In the style of TOK, an age of wisdom and foolishness.
+- In the style of TOK, let's quit our boring corporate jobs.
+- In the style of TOK, ancient petroglyphs.
+- In the style of TOK, room and space swap based on connections.
+- In the style of TOK, a trypophobia-provoking pattern.
+- In the style of TOK, a six-pack of nightmare fuel.
+- In the style of TOK, singing and dancing beavers like Jim Henson's Muppets.
+- In the style of TOK, a beaver gnawing a rifle in two.
+- In the style of TOK, a brigade of beavers building a dam quickly, evoking a Hawaiian-shirt pattern.
+- In the style of TOK, a brigade of beavers constructing a dam rapidly, reminiscent of Tarsem Singh's work.
+- In the style of TOK, a brigade of beavers in an M.C. Escher-inspired scene.
+- In the style of TOK, a mobile castle moving along grassy hills.
+- In the style of TOK, a synchronized chorus line of adorable beavers.
+- In the style of TOK, a frozen, preserved 1950s restaurant kitchen rediscovered in the space age.
+- In the style of TOK, a fashion show featuring elephant seals on the runway and egrets in the audience.
+- In the style of TOK, an ultra-realistic painting with lemon-lime architectural details.
+- In the style of TOK, a mermaid made from fishnets, dead fish, packing peanuts, and other sea trash ascending to peek above the ocean surface.
+--------------------------
+Making sure TOK is added to every training prompt...
+Using GPT concept name as CLIP-segmentation prompt: Surreal and intricate marine or nature scenes
+Generating 54 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
54it [00:00, 15730.82it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 54 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a mermaid crafte...
+1     in the style of <s0><s1><s2>, lilliputians are...
+2     in the style of <s0><s1><s2>, fort kochi, kera...
+3     in the style of <s0><s1><s2>, narrating the be...
+4     in the style of <s0><s1><s2>, a blockchain com...
+5     in the style of <s0><s1><s2>, a gorgon formed ...
+6     in the style of <s0><s1><s2>, a sunset over a ...
+7     in the style of <s0><s1><s2>, an ancient templ...
+8     in the style of <s0><s1><s2>, cats defecating ...
+9          in the style of <s0><s1><s2>, a goblin goat.
+10    in the style of <s0><s1><s2>, it was the age o...
+11    in the style of <s0><s1><s2>, two people plann...
+12    in the style of <s0><s1><s2>, ancient rock car...
+13    in the style of <s0><s1><s2>, room and space e...
+14    in the style of <s0><s1><s2>, a pattern induci...
+15    in the style of <s0><s1><s2>, a six-pack of ni...
+16    in the style of <s0><s1><s2>, dancing and sing...
+17    in the style of <s0><s1><s2>, a beaver chewing...
+18    in the style of <s0><s1><s2>, a brigade of bea...
+19    in the style of <s0><s1><s2>, a brigade of bea...
+20    in the style of <s0><s1><s2>, a brigade of bea...
+21    in the style of <s0><s1><s2>, a moving castle ...
+22    in the style of <s0><s1><s2>, a chorus line of...
+23    in the style of <s0><s1><s2>, a preserved, fro...
+24    in the style of <s0><s1><s2>, elephant seals s...
+25    in the style of <s0><s1><s2>, a hyper-realisti...
+26    in the style of <s0><s1><s2>, a mermaid made f...
+27    in the style of <s0><s1><s2>, a mermaid fashio...
+28    in the style of <s0><s1><s2>, lilliputians cra...
+29    in the style of <s0><s1><s2>, fort kochi in ke...
+30    in the style of <s0><s1><s2>, the best and wor...
+31    in the style of <s0><s1><s2>, bees forming a b...
+32    in the style of <s0><s1><s2>, a gorgon created...
+33    in the style of <s0><s1><s2>, a sunset with ve...
+34    in the style of <s0><s1><s2>, an ancient templ...
+35    in the style of <s0><s1><s2>, zero-gravity cat...
+36    in the style of <s0><s1><s2>, a goblin goat de...
+37    in the style of <s0><s1><s2>, an age of wisdom...
+38    in the style of <s0><s1><s2>, let's quit our b...
+39    in the style of <s0><s1><s2>, ancient petrogly...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 54
+--- Num batches each epoch = 14
+--- Num Epochs = 22
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
  0%|          | 0/300 [00:00<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:02<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:02<14:35,  2.93s/it]
# Trainer step: 0, epoch: 0:   1%|          | 2/300 [00:08<20:56,  4.22s/it]
# Trainer step: 0, epoch: 0:   1%|          | 3/300 [00:08<12:20,  2.49s/it]
# Trainer step: 0, epoch: 0:   1%|▏         | 4/300 [00:08<08:18,  1.69s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:09<06:05,  1.24s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:09<04:45,  1.03it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.34

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:10<03:53,  1.25it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:10<03:20,  1.45it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 9/300 [00:11<02:58,  1.63it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 10/300 [00:11<02:42,  1.78it/s]
# Trainer step: 0, epoch: 0:   4%|▎         | 11/300 [00:12<02:32,  1.89it/s]
# Trainer step: 0, epoch: 0:   4%|▍         | 12/300 [00:12<02:25,  1.98it/s]Progress: 7.00%
+---- avg training fps: 3.70

# Trainer step: 0, epoch: 0:   4%|▍         | 13/300 [00:12<02:19,  2.05it/s]
# Trainer step: 0, epoch: 0:   5%|▍         | 14/300 [00:13<02:16,  2.10it/s]
# Trainer step: 14, epoch: 1:   5%|▍         | 14/300 [00:13<02:16,  2.10it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 15/300 [00:13<02:22,  2.00it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 16/300 [00:14<02:17,  2.06it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 17/300 [00:14<02:14,  2.10it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 18/300 [00:15<02:11,  2.14it/s]Progress: 9.00%
+---- avg training fps: 4.56

# Trainer step: 14, epoch: 1:   6%|▋         | 19/300 [00:15<02:09,  2.16it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 20/300 [00:16<02:08,  2.18it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 21/300 [00:16<02:07,  2.20it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 22/300 [00:17<02:06,  2.20it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 23/300 [00:17<02:05,  2.21it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 24/300 [00:18<02:04,  2.21it/s]Progress: 11.00%
+---- avg training fps: 5.19

# Trainer step: 14, epoch: 1:   8%|▊         | 25/300 [00:18<02:04,  2.20it/s]
# Trainer step: 14, epoch: 1:   9%|▊         | 26/300 [00:18<02:04,  2.21it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 27/300 [00:19<02:03,  2.21it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 28/300 [00:19<02:02,  2.21it/s]
# Trainer step: 28, epoch: 2:   9%|▉         | 28/300 [00:20<02:02,  2.21it/s]
# Trainer step: 28, epoch: 2:  10%|▉         | 29/300 [00:20<01:57,  2.30it/s]
# Trainer step: 28, epoch: 2:  10%|█         | 30/300 [00:20<01:59,  2.27it/s]Progress: 13.00%
+---- avg training fps: 5.68

# Trainer step: 28, epoch: 2:  10%|█         | 31/300 [00:21<01:59,  2.25it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 32/300 [00:21<01:59,  2.24it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 33/300 [00:22<01:59,  2.23it/s]
# Trainer step: 28, epoch: 2:  11%|█▏        | 34/300 [00:22<01:59,  2.23it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 35/300 [00:22<01:59,  2.23it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 36/300 [00:23<01:58,  2.22it/s]Progress: 15.00%
+---- avg training fps: 6.04

# Trainer step: 28, epoch: 2:  12%|█▏        | 37/300 [00:23<01:58,  2.22it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 38/300 [00:24<01:58,  2.22it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 39/300 [00:24<01:57,  2.22it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 40/300 [00:25<01:57,  2.22it/s]
# Trainer step: 28, epoch: 2:  14%|█▎        | 41/300 [00:25<01:56,  2.22it/s]
# Trainer step: 28, epoch: 2:  14%|█▍        | 42/300 [00:26<01:56,  2.22it/s]Progress: 17.00%
+---- avg training fps: 6.34

# Trainer step: 42, epoch: 3:  14%|█▍        | 42/300 [00:26<01:56,  2.22it/s]
# Trainer step: 42, epoch: 3:  14%|█▍        | 43/300 [00:26<01:51,  2.30it/s]
# Trainer step: 42, epoch: 3:  15%|█▍        | 44/300 [00:26<01:52,  2.28it/s]
# Trainer step: 42, epoch: 3:  15%|█▌        | 45/300 [00:27<01:52,  2.26it/s]
# Trainer step: 42, epoch: 3:  15%|█▌        | 46/300 [00:27<01:52,  2.25it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 47/300 [00:28<01:52,  2.24it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 48/300 [00:28<01:53,  2.23it/s]Progress: 19.00%
+---- avg training fps: 6.58

# Trainer step: 42, epoch: 3:  16%|█▋        | 49/300 [00:29<01:52,  2.23it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 50/300 [00:29<01:52,  2.23it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 51/300 [00:30<01:52,  2.22it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 52/300 [00:33<05:06,  1.23s/it]
# Trainer step: 42, epoch: 3:  18%|█▊        | 53/300 [00:33<04:07,  1.00s/it]
# Trainer step: 42, epoch: 3:  18%|█▊        | 54/300 [00:34<03:26,  1.19it/s]Progress: 21.00%
+---- avg training fps: 6.26

# Trainer step: 42, epoch: 3:  18%|█▊        | 55/300 [00:34<02:56,  1.38it/s]
# Trainer step: 42, epoch: 3:  19%|█▊        | 56/300 [00:34<02:36,  1.56it/s]
# Trainer step: 56, epoch: 4:  19%|█▊        | 56/300 [00:35<02:36,  1.56it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 57/300 [00:35<02:18,  1.76it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 58/300 [00:35<02:09,  1.87it/s]
# Trainer step: 56, epoch: 4:  20%|█▉        | 59/300 [00:36<02:03,  1.96it/s]
# Trainer step: 56, epoch: 4:  20%|██        | 60/300 [00:36<01:58,  2.03it/s]Progress: 23.00%
+---- avg training fps: 6.45

# Trainer step: 56, epoch: 4:  20%|██        | 61/300 [00:37<01:54,  2.08it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 62/300 [00:37<01:52,  2.11it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 63/300 [00:38<01:51,  2.13it/s]
# Trainer step: 56, epoch: 4:  21%|██▏       | 64/300 [00:38<01:50,  2.14it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 65/300 [00:39<01:48,  2.16it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 66/300 [00:39<01:47,  2.18it/s]Progress: 25.00%
+---- avg training fps: 6.61

# Trainer step: 56, epoch: 4:  22%|██▏       | 67/300 [00:39<01:46,  2.18it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 68/300 [00:40<01:46,  2.17it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 69/300 [00:40<01:45,  2.18it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 70/300 [00:41<01:45,  2.18it/s]
# Trainer step: 70, epoch: 5:  23%|██▎       | 70/300 [00:41<01:45,  2.18it/s]
# Trainer step: 70, epoch: 5:  24%|██▎       | 71/300 [00:41<01:40,  2.27it/s]
# Trainer step: 70, epoch: 5:  24%|██▍       | 72/300 [00:42<01:41,  2.25it/s]Progress: 27.00%
+---- avg training fps: 6.76

# Trainer step: 70, epoch: 5:  24%|██▍       | 73/300 [00:42<01:41,  2.23it/s]
# Trainer step: 70, epoch: 5:  25%|██▍       | 74/300 [00:43<01:42,  2.21it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 75/300 [00:43<01:42,  2.20it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 76/300 [00:44<01:42,  2.19it/s]
# Trainer step: 70, epoch: 5:  26%|██▌       | 77/300 [00:44<01:42,  2.18it/s]
# Trainer step: 70, epoch: 5:  26%|██▌       | 78/300 [00:44<01:41,  2.19it/s]Progress: 29.00%
+---- avg training fps: 6.87

# Trainer step: 70, epoch: 5:  26%|██▋       | 79/300 [00:45<01:41,  2.18it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 80/300 [00:45<01:41,  2.16it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 81/300 [00:46<01:41,  2.16it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 82/300 [00:47<01:58,  1.84it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 83/300 [00:47<01:52,  1.92it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 84/300 [00:47<01:48,  1.99it/s]Progress: 31.00%
+---- avg training fps: 6.95

# Trainer step: 84, epoch: 6:  28%|██▊       | 84/300 [00:48<01:48,  1.99it/s]
# Trainer step: 84, epoch: 6:  28%|██▊       | 85/300 [00:48<01:41,  2.12it/s]
# Trainer step: 84, epoch: 6:  29%|██▊       | 86/300 [00:48<01:39,  2.14it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 87/300 [00:49<01:38,  2.15it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 88/300 [00:49<01:38,  2.16it/s]
# Trainer step: 84, epoch: 6:  30%|██▉       | 89/300 [00:50<01:37,  2.17it/s]
# Trainer step: 84, epoch: 6:  30%|███       | 90/300 [00:50<01:36,  2.17it/s]Progress: 33.00%
+---- avg training fps: 7.04

# Trainer step: 84, epoch: 6:  30%|███       | 91/300 [00:51<01:36,  2.17it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 92/300 [00:51<01:35,  2.17it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 93/300 [00:52<01:35,  2.17it/s]
# Trainer step: 84, epoch: 6:  31%|███▏      | 94/300 [00:52<01:34,  2.18it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 95/300 [00:52<01:34,  2.18it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 96/300 [00:53<01:33,  2.18it/s]Progress: 35.00%
+---- avg training fps: 7.13

# Trainer step: 84, epoch: 6:  32%|███▏      | 97/300 [00:53<01:33,  2.17it/s]
# Trainer step: 84, epoch: 6:  33%|███▎      | 98/300 [00:54<01:33,  2.17it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 98/300 [00:54<01:33,  2.17it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 99/300 [00:54<01:28,  2.26it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 100/300 [00:55<01:29,  2.24it/s]
# Trainer step: 98, epoch: 7:  34%|███▎      | 101/300 [00:55<01:29,  2.22it/s]
# Trainer step: 98, epoch: 7:  34%|███▍      | 102/300 [00:58<03:42,  1.12s/it]Progress: 37.00%
+---- avg training fps: 6.94

# Trainer step: 98, epoch: 7:  34%|███▍      | 103/300 [00:58<03:02,  1.08it/s]
# Trainer step: 98, epoch: 7:  35%|███▍      | 104/300 [00:59<02:33,  1.27it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 105/300 [00:59<02:13,  1.46it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 106/300 [01:00<02:00,  1.62it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 107/300 [01:00<01:50,  1.75it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 108/300 [01:01<01:43,  1.86it/s]Progress: 39.00%
+---- avg training fps: 7.02

# Trainer step: 98, epoch: 7:  36%|███▋      | 109/300 [01:01<01:38,  1.94it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 110/300 [01:02<01:35,  2.00it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 111/300 [01:02<01:32,  2.05it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 112/300 [01:02<01:30,  2.08it/s]
# Trainer step: 112, epoch: 8:  37%|███▋      | 112/300 [01:03<01:30,  2.08it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 113/300 [01:03<01:25,  2.19it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 114/300 [01:03<01:25,  2.19it/s]Progress: 41.00%
+---- avg training fps: 7.09

# Trainer step: 112, epoch: 8:  38%|███▊      | 115/300 [01:04<01:24,  2.18it/s]
# Trainer step: 112, epoch: 8:  39%|███▊      | 116/300 [01:04<01:24,  2.17it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 117/300 [01:05<01:24,  2.18it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 118/300 [01:05<01:23,  2.18it/s]
# Trainer step: 112, epoch: 8:  40%|███▉      | 119/300 [01:06<01:23,  2.17it/s]
# Trainer step: 112, epoch: 8:  40%|████      | 120/300 [01:06<01:23,  2.17it/s]Progress: 43.00%
+---- avg training fps: 7.16

# Trainer step: 112, epoch: 8:  40%|████      | 121/300 [01:07<01:23,  2.16it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 122/300 [01:07<01:22,  2.16it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 123/300 [01:07<01:22,  2.16it/s]
# Trainer step: 112, epoch: 8:  41%|████▏     | 124/300 [01:08<01:21,  2.16it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 125/300 [01:08<01:20,  2.17it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 126/300 [01:09<01:20,  2.17it/s]Progress: 45.00%
+---- avg training fps: 7.22

# Trainer step: 126, epoch: 9:  42%|████▏     | 126/300 [01:09<01:20,  2.17it/s]
# Trainer step: 126, epoch: 9:  42%|████▏     | 127/300 [01:09<01:16,  2.26it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 128/300 [01:10<01:17,  2.23it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 129/300 [01:10<01:17,  2.21it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 130/300 [01:11<01:17,  2.21it/s]
# Trainer step: 126, epoch: 9:  44%|████▎     | 131/300 [01:11<01:16,  2.20it/s]
# Trainer step: 126, epoch: 9:  44%|████▍     | 132/300 [01:12<01:16,  2.19it/s]Progress: 47.00%
+---- avg training fps: 7.28

# Trainer step: 126, epoch: 9:  44%|████▍     | 133/300 [01:12<01:16,  2.19it/s]
# Trainer step: 126, epoch: 9:  45%|████▍     | 134/300 [01:12<01:15,  2.19it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 135/300 [01:13<01:15,  2.18it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 136/300 [01:13<01:15,  2.17it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 137/300 [01:14<01:15,  2.17it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 138/300 [01:14<01:14,  2.17it/s]Progress: 49.00%
+---- avg training fps: 7.33

# Trainer step: 126, epoch: 9:  46%|████▋     | 139/300 [01:15<01:14,  2.17it/s]
# Trainer step: 126, epoch: 9:  47%|████▋     | 140/300 [01:15<01:13,  2.17it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 140/300 [01:16<01:13,  2.17it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 141/300 [01:16<01:10,  2.27it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 142/300 [01:16<01:10,  2.24it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 143/300 [01:17<01:19,  1.99it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 144/300 [01:17<01:16,  2.04it/s]Progress: 51.00%
+---- avg training fps: 7.37

# Trainer step: 140, epoch: 10:  48%|████▊     | 145/300 [01:18<01:14,  2.08it/s]
# Trainer step: 140, epoch: 10:  49%|████▊     | 146/300 [01:18<01:13,  2.10it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 147/300 [01:19<01:12,  2.12it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 148/300 [01:19<01:10,  2.14it/s]
# Trainer step: 140, epoch: 10:  50%|████▉     | 149/300 [01:20<01:10,  2.14it/s]
# Trainer step: 140, epoch: 10:  50%|█████     | 150/300 [01:20<01:09,  2.16it/s]Progress: 53.00%
+---- avg training fps: 7.41

# Trainer step: 140, epoch: 10:  50%|█████     | 151/300 [01:20<01:08,  2.16it/s]
# Trainer step: 140, epoch: 10:  51%|█████     | 152/300 [01:25<03:55,  1.59s/it]
# Trainer step: 140, epoch: 10:  51%|█████     | 153/300 [01:25<03:03,  1.25s/it]
# Trainer step: 140, epoch: 10:  51%|█████▏    | 154/300 [01:26<02:27,  1.01s/it]
# Trainer step: 154, epoch: 11:  51%|█████▏    | 154/300 [01:26<02:27,  1.01s/it]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 155/300 [01:26<01:59,  1.21it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 156/300 [01:26<01:43,  1.39it/s]Progress: 55.00%
+---- avg training fps: 7.12

# Trainer step: 154, epoch: 11:  52%|█████▏    | 157/300 [01:27<01:40,  1.42it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 158/300 [01:28<01:29,  1.59it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 159/300 [01:28<01:21,  1.73it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 160/300 [01:28<01:16,  1.84it/s]
# Trainer step: 154, epoch: 11:  54%|█████▎    | 161/300 [01:29<01:11,  1.93it/s]
# Trainer step: 154, epoch: 11:  54%|█████▍    | 162/300 [01:29<01:08,  2.00it/s]Progress: 57.00%
+---- avg training fps: 7.17

# Trainer step: 154, epoch: 11:  54%|█████▍    | 163/300 [01:30<01:06,  2.06it/s]
# Trainer step: 154, epoch: 11:  55%|█████▍    | 164/300 [01:30<01:05,  2.08it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 165/300 [01:31<01:03,  2.11it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 166/300 [01:31<01:02,  2.13it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 167/300 [01:32<01:01,  2.15it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 168/300 [01:32<01:01,  2.16it/s]Progress: 59.00%
+---- avg training fps: 7.22

# Trainer step: 168, epoch: 12:  56%|█████▌    | 168/300 [01:33<01:01,  2.16it/s]
# Trainer step: 168, epoch: 12:  56%|█████▋    | 169/300 [01:33<00:57,  2.27it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 170/300 [01:33<00:57,  2.24it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 171/300 [01:33<00:57,  2.22it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 172/300 [01:34<00:57,  2.22it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 173/300 [01:34<00:57,  2.21it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 174/300 [01:35<00:57,  2.19it/s]Progress: 61.00%
+---- avg training fps: 7.27

# Trainer step: 168, epoch: 12:  58%|█████▊    | 175/300 [01:35<00:57,  2.19it/s]
# Trainer step: 168, epoch: 12:  59%|█████▊    | 176/300 [01:36<00:57,  2.17it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 177/300 [01:36<00:56,  2.17it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 178/300 [01:37<00:55,  2.18it/s]
# Trainer step: 168, epoch: 12:  60%|█████▉    | 179/300 [01:37<00:55,  2.17it/s]
# Trainer step: 168, epoch: 12:  60%|██████    | 180/300 [01:38<00:55,  2.17it/s]Progress: 63.00%
+---- avg training fps: 7.31

# Trainer step: 168, epoch: 12:  60%|██████    | 181/300 [01:38<00:54,  2.17it/s]
# Trainer step: 168, epoch: 12:  61%|██████    | 182/300 [01:39<00:54,  2.18it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 182/300 [01:39<00:54,  2.18it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 183/300 [01:39<00:51,  2.28it/s]
# Trainer step: 182, epoch: 13:  61%|██████▏   | 184/300 [01:39<00:51,  2.26it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 185/300 [01:40<00:51,  2.23it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 186/300 [01:40<00:51,  2.22it/s]Progress: 65.00%
+---- avg training fps: 7.35

# Trainer step: 182, epoch: 13:  62%|██████▏   | 187/300 [01:41<00:51,  2.21it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 188/300 [01:41<00:50,  2.21it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 189/300 [01:42<00:50,  2.20it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 190/300 [01:42<00:50,  2.19it/s]
# Trainer step: 182, epoch: 13:  64%|██████▎   | 191/300 [01:43<00:49,  2.19it/s]
# Trainer step: 182, epoch: 13:  64%|██████▍   | 192/300 [01:43<00:49,  2.19it/s]Progress: 67.00%
+---- avg training fps: 7.39

# Trainer step: 182, epoch: 13:  64%|██████▍   | 193/300 [01:43<00:49,  2.18it/s]
# Trainer step: 182, epoch: 13:  65%|██████▍   | 194/300 [01:44<00:48,  2.17it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 195/300 [01:44<00:48,  2.18it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 196/300 [01:45<00:47,  2.18it/s]
# Trainer step: 196, epoch: 14:  65%|██████▌   | 196/300 [01:45<00:47,  2.18it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 197/300 [01:45<00:45,  2.28it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 198/300 [01:46<00:45,  2.26it/s]Progress: 69.00%
+---- avg training fps: 7.43

# Trainer step: 196, epoch: 14:  66%|██████▋   | 199/300 [01:46<00:45,  2.23it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 200/300 [01:47<00:45,  2.20it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 201/300 [01:47<00:45,  2.19it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 202/300 [01:50<02:00,  1.23s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 203/300 [01:51<01:36,  1.00it/s]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 204/300 [01:51<01:20,  1.19it/s]Progress: 71.00%
+---- avg training fps: 7.29

# Trainer step: 196, epoch: 14:  68%|██████▊   | 205/300 [01:52<01:09,  1.37it/s]
# Trainer step: 196, epoch: 14:  69%|██████▊   | 206/300 [01:52<01:01,  1.54it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 207/300 [01:53<01:00,  1.54it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 208/300 [01:53<00:54,  1.69it/s]
# Trainer step: 196, epoch: 14:  70%|██████▉   | 209/300 [01:54<00:50,  1.81it/s]
# Trainer step: 196, epoch: 14:  70%|███████   | 210/300 [01:54<00:47,  1.91it/s]Progress: 73.00%
+---- avg training fps: 7.31

# Trainer step: 210, epoch: 15:  70%|███████   | 210/300 [01:54<00:47,  1.91it/s]
# Trainer step: 210, epoch: 15:  70%|███████   | 211/300 [01:54<00:43,  2.07it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 212/300 [01:55<00:41,  2.10it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 213/300 [01:55<00:40,  2.14it/s]
# Trainer step: 210, epoch: 15:  71%|███████▏  | 214/300 [01:56<00:40,  2.14it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 215/300 [01:56<00:39,  2.15it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 216/300 [01:57<00:38,  2.16it/s]Progress: 75.00%
+---- avg training fps: 7.35

# Trainer step: 210, epoch: 15:  72%|███████▏  | 217/300 [01:57<00:38,  2.17it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 218/300 [01:58<00:37,  2.18it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 219/300 [01:58<00:37,  2.18it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 220/300 [01:59<00:36,  2.17it/s]
# Trainer step: 210, epoch: 15:  74%|███████▎  | 221/300 [01:59<00:36,  2.17it/s]
# Trainer step: 210, epoch: 15:  74%|███████▍  | 222/300 [01:59<00:35,  2.18it/s]Progress: 77.00%
+---- avg training fps: 7.38

# Trainer step: 210, epoch: 15:  74%|███████▍  | 223/300 [02:00<00:35,  2.18it/s]
# Trainer step: 210, epoch: 15:  75%|███████▍  | 224/300 [02:00<00:34,  2.17it/s]
# Trainer step: 224, epoch: 16:  75%|███████▍  | 224/300 [02:01<00:34,  2.17it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 225/300 [02:01<00:33,  2.27it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 226/300 [02:01<00:33,  2.23it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 227/300 [02:02<00:32,  2.23it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 228/300 [02:02<00:32,  2.21it/s]Progress: 79.00%
+---- avg training fps: 7.41

# Trainer step: 224, epoch: 16:  76%|███████▋  | 229/300 [02:03<00:32,  2.20it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 230/300 [02:03<00:32,  2.18it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 231/300 [02:04<00:31,  2.19it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 232/300 [02:04<00:31,  2.19it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 233/300 [02:04<00:30,  2.17it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 234/300 [02:05<00:30,  2.17it/s]Progress: 81.00%
+---- avg training fps: 7.44

# Trainer step: 224, epoch: 16:  78%|███████▊  | 235/300 [02:05<00:29,  2.18it/s]
# Trainer step: 224, epoch: 16:  79%|███████▊  | 236/300 [02:06<00:29,  2.18it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 237/300 [02:06<00:29,  2.17it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 238/300 [02:07<00:28,  2.16it/s]
# Trainer step: 238, epoch: 17:  79%|███████▉  | 238/300 [02:07<00:28,  2.16it/s]
# Trainer step: 238, epoch: 17:  80%|███████▉  | 239/300 [02:07<00:26,  2.26it/s]
# Trainer step: 238, epoch: 17:  80%|████████  | 240/300 [02:08<00:26,  2.24it/s]Progress: 83.00%
+---- avg training fps: 7.47

# Trainer step: 238, epoch: 17:  80%|████████  | 241/300 [02:08<00:26,  2.21it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 242/300 [02:09<00:26,  2.20it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 243/300 [02:09<00:25,  2.19it/s]
# Trainer step: 238, epoch: 17:  81%|████████▏ | 244/300 [02:09<00:25,  2.18it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 245/300 [02:10<00:25,  2.18it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 246/300 [02:10<00:24,  2.17it/s]Progress: 85.00%
+---- avg training fps: 7.49

# Trainer step: 238, epoch: 17:  82%|████████▏ | 247/300 [02:11<00:24,  2.17it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 248/300 [02:11<00:23,  2.18it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 249/300 [02:12<00:23,  2.18it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 250/300 [02:12<00:22,  2.17it/s]
# Trainer step: 238, epoch: 17:  84%|████████▎ | 251/300 [02:13<00:22,  2.17it/s]
# Trainer step: 238, epoch: 17:  84%|████████▍ | 252/300 [02:16<00:59,  1.25s/it]Progress: 87.00%
+---- avg training fps: 7.38

# Trainer step: 252, epoch: 18:  84%|████████▍ | 252/300 [02:16<00:59,  1.25s/it]
# Trainer step: 252, epoch: 18:  84%|████████▍ | 253/300 [02:16<00:46,  1.01it/s]
# Trainer step: 252, epoch: 18:  85%|████████▍ | 254/300 [02:17<00:38,  1.20it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 255/300 [02:17<00:34,  1.29it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 256/300 [02:18<00:29,  1.47it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 257/300 [02:18<00:26,  1.63it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 258/300 [02:19<00:23,  1.76it/s]Progress: 89.00%
+---- avg training fps: 7.39

# Trainer step: 252, epoch: 18:  86%|████████▋ | 259/300 [02:19<00:21,  1.88it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 260/300 [02:20<00:20,  1.96it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 261/300 [02:20<00:19,  2.01it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 262/300 [02:20<00:18,  2.06it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 263/300 [02:21<00:17,  2.10it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 264/300 [02:21<00:16,  2.12it/s]Progress: 91.00%
+---- avg training fps: 7.42

# Trainer step: 252, epoch: 18:  88%|████████▊ | 265/300 [02:22<00:16,  2.14it/s]
# Trainer step: 252, epoch: 18:  89%|████████▊ | 266/300 [02:22<00:15,  2.15it/s]
# Trainer step: 266, epoch: 19:  89%|████████▊ | 266/300 [02:23<00:15,  2.15it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 267/300 [02:23<00:14,  2.24it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 268/300 [02:23<00:14,  2.23it/s]
# Trainer step: 266, epoch: 19:  90%|████████▉ | 269/300 [02:24<00:13,  2.21it/s]
# Trainer step: 266, epoch: 19:  90%|█████████ | 270/300 [02:24<00:13,  2.21it/s]Progress: 93.00%
+---- avg training fps: 7.45

# Trainer step: 266, epoch: 19:  90%|█████████ | 271/300 [02:25<00:13,  2.19it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 272/300 [02:25<00:12,  2.19it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 273/300 [02:25<00:12,  2.19it/s]
# Trainer step: 266, epoch: 19:  91%|█████████▏| 274/300 [02:26<00:11,  2.19it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 275/300 [02:26<00:11,  2.17it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 276/300 [02:27<00:11,  2.17it/s]Progress: 95.00%
+---- avg training fps: 7.47

# Trainer step: 266, epoch: 19:  92%|█████████▏| 277/300 [02:27<00:10,  2.18it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 278/300 [02:28<00:10,  2.17it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 279/300 [02:28<00:09,  2.17it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 280/300 [02:29<00:09,  2.17it/s]
# Trainer step: 280, epoch: 20:  93%|█████████▎| 280/300 [02:29<00:09,  2.17it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▎| 281/300 [02:29<00:08,  2.26it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▍| 282/300 [02:30<00:08,  2.23it/s]Progress: 97.00%
+---- avg training fps: 7.50

# Trainer step: 280, epoch: 20:  94%|█████████▍| 283/300 [02:30<00:07,  2.21it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▍| 284/300 [02:30<00:07,  2.20it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 285/300 [02:31<00:06,  2.19it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 286/300 [02:31<00:06,  2.19it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 287/300 [02:32<00:05,  2.18it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 288/300 [02:32<00:05,  2.18it/s]Progress: 99.00%
+---- avg training fps: 7.52

# Trainer step: 280, epoch: 20:  96%|█████████▋| 289/300 [02:33<00:05,  2.17it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 290/300 [02:33<00:04,  2.18it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 291/300 [02:34<00:04,  2.18it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 292/300 [02:34<00:03,  2.17it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 293/300 [02:35<00:03,  2.16it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 294/300 [02:35<00:02,  2.18it/s]Progress: 100.00%
+---- avg training fps: 7.54

# Trainer step: 294, epoch: 21:  98%|█████████▊| 294/300 [02:35<00:02,  2.18it/s]
# Trainer step: 294, epoch: 21:  98%|█████████▊| 295/300 [02:35<00:02,  2.27it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▊| 296/300 [02:36<00:01,  2.24it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 297/300 [02:36<00:01,  2.22it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 298/300 [02:37<00:00,  2.19it/s]
# Trainer step: 294, epoch: 21: 100%|█████████▉| 299/300 [02:37<00:00,  2.20it/s]
# Trainer step: 294, epoch: 21: 100%|██████████| 300/300 [02:38<00:00,  2.19it/s]Progress: 100.00%
+---- avg training fps: 7.56

# Trainer step: 294, epoch: 21: : 301it [02:38,  2.17it/s]                       Progress: 100.00%
Reached max steps, stopping training!
+Saving checkpoint at step.. 301
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 294, epoch: 21: : 301it [04:01,  1.25it/s]
+------------------------------------------
+Training done :)
+2024-08-13 04:21:36.294843: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 04:21:36.385863: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 04:21:36.385891: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 04:21:36.405130: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 04:21:36.806765: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:21:36.806822: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:21:36.806831: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23135 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/good_styles/eden_crystals' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.75 freeze_unet_before_completion_f=0.5 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='eden_crystals' output_dir='lora_models/styles_final/eden_crystals_13_04-21-38-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723515698.6234007 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/eden_crystals_13_04-21-38-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.242
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants on a black background.
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants in the dark.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Futuristic natural elements blending with technology
+
+- In the style of TOK, a potted plant sits on a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves is on a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is on a black background.
+- In the style of TOK, a potted plant sits on a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves is on a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is in the dark.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Futuristic natural elements blending with technology
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 7365.54it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a potted plant s...
+1     in the style of <s0><s1><s2>, a futuristic cit...
+2     in the style of <s0><s1><s2>, a flower with gr...
+3     in the style of <s0><s1><s2>, a plant is growi...
+4     in the style of <s0><s1><s2>, a group of green...
+5     in the style of <s0><s1><s2>, a potted plant s...
+6     in the style of <s0><s1><s2>, a futuristic cit...
+7     in the style of <s0><s1><s2>, a flower with gr...
+8     in the style of <s0><s1><s2>, a plant is growi...
+9     in the style of <s0><s1><s2>, a group of green...
+10    in the style of <s0><s1><s2>, a potted plant s...
+11    in the style of <s0><s1><s2>, a futuristic cit...
+12    in the style of <s0><s1><s2>, a flower with gr...
+13    in the style of <s0><s1><s2>, a plant is growi...
+14    in the style of <s0><s1><s2>, a group of green...
+15    in the style of <s0><s1><s2>, a potted plant s...
+16    in the style of <s0><s1><s2>, a futuristic cit...
+17    in the style of <s0><s1><s2>, a flower with gr...
+18    in the style of <s0><s1><s2>, a plant is growi...
+19    in the style of <s0><s1><s2>, a group of green...
+20    in the style of <s0><s1><s2>, a potted plant s...
+21    in the style of <s0><s1><s2>, a futuristic cit...
+22    in the style of <s0><s1><s2>, a flower with gr...
+23    in the style of <s0><s1><s2>, a plant is growi...
+24    in the style of <s0><s1><s2>, a group of green...
+25    in the style of <s0><s1><s2>, a potted plant s...
+26    in the style of <s0><s1><s2>, a futuristic cit...
+27    in the style of <s0><s1><s2>, a flower with gr...
+28    in the style of <s0><s1><s2>, a plant is growi...
+29    in the style of <s0><s1><s2>, a group of green...
+30    in the style of <s0><s1><s2>, a potted plant s...
+31    in the style of <s0><s1><s2>, a futuristic cit...
+32    in the style of <s0><s1><s2>, a flower with gr...
+33    in the style of <s0><s1><s2>, a plant is growi...
+34    in the style of <s0><s1><s2>, a group of green...
+35    in the style of <s0><s1><s2>, a potted plant s...
+36    in the style of <s0><s1><s2>, a futuristic cit...
+37    in the style of <s0><s1><s2>, a flower with gr...
+38    in the style of <s0><s1><s2>, a plant is growi...
+39    in the style of <s0><s1><s2>, a group of green...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
  0%|          | 0/300 [00:00<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:03<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:03<15:10,  3.05s/it]
# Trainer step: 0, epoch: 0:   1%|          | 2/300 [00:06<17:10,  3.46s/it]
# Trainer step: 0, epoch: 0:   1%|          | 3/300 [00:07<10:20,  2.09s/it]
# Trainer step: 0, epoch: 0:   1%|▏         | 4/300 [00:07<07:07,  1.44s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:08<05:21,  1.09s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:08<04:17,  1.14it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.64

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:09<03:36,  1.35it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:09<03:10,  1.54it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 9/300 [00:10<02:52,  1.69it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 10/300 [00:10<02:39,  1.81it/s]
# Trainer step: 10, epoch: 1:   3%|▎         | 10/300 [00:10<02:39,  1.81it/s]
# Trainer step: 10, epoch: 1:   4%|▎         | 11/300 [00:10<02:31,  1.91it/s]
# Trainer step: 10, epoch: 1:   4%|▍         | 12/300 [00:11<02:25,  1.98it/s]Progress: 7.00%
+---- avg training fps: 4.05

# Trainer step: 10, epoch: 1:   4%|▍         | 13/300 [00:11<02:21,  2.03it/s]
# Trainer step: 10, epoch: 1:   5%|▍         | 14/300 [00:12<02:19,  2.05it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 15/300 [00:12<02:17,  2.08it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 16/300 [00:13<02:15,  2.09it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 17/300 [00:13<02:14,  2.10it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 18/300 [00:14<02:13,  2.11it/s]Progress: 9.00%
+---- avg training fps: 4.91

# Trainer step: 10, epoch: 1:   6%|▋         | 19/300 [00:14<02:12,  2.13it/s]
# Trainer step: 10, epoch: 1:   7%|▋         | 20/300 [00:15<02:11,  2.13it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 20/300 [00:15<02:11,  2.13it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 21/300 [00:15<02:10,  2.14it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 22/300 [00:16<02:09,  2.14it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 23/300 [00:16<02:09,  2.14it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 24/300 [00:17<02:08,  2.14it/s]Progress: 11.00%
+---- avg training fps: 5.49

# Trainer step: 20, epoch: 2:   8%|▊         | 25/300 [00:17<02:08,  2.14it/s]
# Trainer step: 20, epoch: 2:   9%|▊         | 26/300 [00:17<02:07,  2.14it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 27/300 [00:18<02:08,  2.13it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 28/300 [00:18<02:07,  2.13it/s]
# Trainer step: 20, epoch: 2:  10%|▉         | 29/300 [00:19<02:07,  2.13it/s]
# Trainer step: 20, epoch: 2:  10%|█         | 30/300 [00:19<02:06,  2.14it/s]Progress: 13.00%
+---- avg training fps: 5.91

# Trainer step: 30, epoch: 3:  10%|█         | 30/300 [00:20<02:06,  2.14it/s]
# Trainer step: 30, epoch: 3:  10%|█         | 31/300 [00:20<02:06,  2.13it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 32/300 [00:20<02:05,  2.13it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 33/300 [00:21<02:20,  1.90it/s]
# Trainer step: 30, epoch: 3:  11%|█▏        | 34/300 [00:21<02:16,  1.96it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 35/300 [00:22<02:12,  2.00it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 36/300 [00:22<02:09,  2.04it/s]Progress: 15.00%
+---- avg training fps: 6.18

# Trainer step: 30, epoch: 3:  12%|█▏        | 37/300 [00:23<02:07,  2.06it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 38/300 [00:23<02:05,  2.08it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 39/300 [00:24<02:04,  2.09it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 40/300 [00:24<02:03,  2.11it/s]
# Trainer step: 40, epoch: 4:  13%|█▎        | 40/300 [00:25<02:03,  2.11it/s]
# Trainer step: 40, epoch: 4:  14%|█▎        | 41/300 [00:25<02:02,  2.11it/s]
# Trainer step: 40, epoch: 4:  14%|█▍        | 42/300 [00:25<02:01,  2.12it/s]Progress: 17.00%
+---- avg training fps: 6.43

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:26<02:00,  2.13it/s]
# Trainer step: 40, epoch: 4:  15%|█▍        | 44/300 [00:26<02:00,  2.13it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 45/300 [00:27<01:59,  2.13it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 46/300 [00:27<01:59,  2.13it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 47/300 [00:28<01:58,  2.13it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:28<01:58,  2.13it/s]Progress: 19.00%
+---- avg training fps: 6.63

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:28<01:57,  2.13it/s]
# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:29<01:57,  2.13it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 50/300 [00:29<01:57,  2.13it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 51/300 [00:29<01:57,  2.13it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 52/300 [00:33<06:01,  1.46s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 53/300 [00:34<04:46,  1.16s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:34<03:54,  1.05it/s]Progress: 21.00%
+---- avg training fps: 6.16

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:35<03:18,  1.24it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:35<02:52,  1.42it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 57/300 [00:35<02:34,  1.57it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 58/300 [00:36<02:21,  1.71it/s]
# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:36<02:13,  1.81it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:37<02:06,  1.90it/s]Progress: 23.00%
+---- avg training fps: 6.34

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:37<02:06,  1.90it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:37<02:02,  1.96it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:38<01:58,  2.00it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:38<01:55,  2.04it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:39<02:08,  1.83it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:39<02:02,  1.92it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:40<01:58,  1.98it/s]Progress: 25.00%
+---- avg training fps: 6.45

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:40<01:55,  2.02it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:41<01:53,  2.05it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:41<01:51,  2.08it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:42<01:49,  2.09it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:42<01:49,  2.09it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:42<01:48,  2.10it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:43<01:47,  2.11it/s]Progress: 27.00%
+---- avg training fps: 6.59

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:43<01:47,  2.12it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:44<01:46,  2.12it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:44<01:45,  2.12it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:45<01:45,  2.13it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:45<01:44,  2.14it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:46<01:44,  2.13it/s]Progress: 29.00%
+---- avg training fps: 6.71

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:46<01:43,  2.14it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:46<01:43,  2.13it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:47<01:43,  2.13it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:47<01:42,  2.13it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:47<01:42,  2.13it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:48<01:41,  2.13it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:48<01:41,  2.14it/s]Progress: 31.00%
+---- avg training fps: 6.81

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:49<01:40,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:49<01:40,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:50<01:40,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:50<01:39,  2.13it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:51<01:39,  2.12it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:51<01:38,  2.13it/s]Progress: 33.00%
+---- avg training fps: 6.90

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:52<01:38,  2.13it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:52<01:38,  2.13it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:52<01:37,  2.13it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:53<01:36,  2.13it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:53<01:36,  2.13it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:54<01:36,  2.12it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:54<01:36,  2.12it/s]Progress: 35.00%
+---- avg training fps: 6.98

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:54<01:35,  2.12it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:55<01:34,  2.13it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:55<01:34,  2.13it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:56<01:33,  2.13it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:56<01:33,  2.13it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:56<01:33,  2.12it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [01:00<04:26,  1.35s/it]Progress: 37.00%
+---- avg training fps: 6.72

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [01:00<03:33,  1.08s/it]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [01:01<02:56,  1.11it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [01:01<02:30,  1.29it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [01:02<02:12,  1.46it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:02<01:59,  1.61it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:03<01:50,  1.74it/s]Progress: 39.00%
+---- avg training fps: 6.78

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:03<01:55,  1.66it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:04<01:46,  1.78it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:04<01:46,  1.78it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:04<01:41,  1.87it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:05<01:37,  1.92it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:05<01:34,  1.97it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:06<01:32,  2.02it/s]Progress: 41.00%
+---- avg training fps: 6.85

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:06<01:30,  2.05it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:07<01:28,  2.08it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:07<01:27,  2.09it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:08<01:26,  2.10it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:08<01:25,  2.12it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:08<01:24,  2.12it/s]Progress: 43.00%
+---- avg training fps: 6.92

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:09<01:24,  2.12it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:09<01:24,  2.12it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:09<01:23,  2.12it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:10<01:23,  2.12it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:10<01:23,  2.12it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:11<01:22,  2.12it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:11<01:21,  2.12it/s]Progress: 45.00%
+---- avg training fps: 6.98

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:12<01:21,  2.12it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:12<01:20,  2.12it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:13<01:20,  2.12it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:13<01:19,  2.13it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:14<01:19,  2.13it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:14<01:19,  2.13it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:14<01:18,  2.13it/s]Progress: 47.00%
+---- avg training fps: 7.04

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:15<01:18,  2.13it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:15<01:17,  2.13it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:15<01:17,  2.13it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:16<01:17,  2.13it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:16<01:16,  2.13it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:17<01:16,  2.12it/s]Progress: 49.00%
+---- avg training fps: 7.09

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:17<01:15,  2.12it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:18<01:15,  2.13it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:18<01:15,  2.13it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:18<01:14,  2.12it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:19<01:14,  2.11it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:19<01:14,  2.12it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:20<01:13,  2.11it/s]Progress: 51.00%
+---- avg training fps: 7.14

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:20<01:13,  2.11it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:21<01:12,  2.12it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:21<01:12,  2.12it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:22<01:11,  2.12it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:22<01:11,  2.11it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:23<01:10,  2.13it/s]Progress: 53.00%
+---- avg training fps: 7.18

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:23<01:10,  2.13it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:23<01:10,  2.13it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:27<03:56,  1.60s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:28<03:05,  1.26s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:28<02:29,  1.02s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:29<02:04,  1.17it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:29<01:46,  1.35it/s]Progress: 55.00%
+---- avg training fps: 6.92

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:30<01:34,  1.51it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:30<01:25,  1.66it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:31<01:19,  1.78it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:31<01:15,  1.87it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:32<01:15,  1.87it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:32<01:11,  1.93it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:32<01:09,  1.99it/s]Progress: 57.00%
+---- avg training fps: 6.97

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:32<01:07,  2.02it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:33<01:06,  2.05it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:33<01:05,  2.07it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:34<01:04,  2.08it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:34<01:03,  2.09it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:35<01:02,  2.10it/s]Progress: 59.00%
+---- avg training fps: 7.02

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:35<01:02,  2.11it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:36<01:01,  2.10it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:36<01:01,  2.10it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:36<01:01,  2.10it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:37<01:00,  2.10it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:37<01:00,  2.10it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:38<00:59,  2.10it/s]Progress: 61.00%
+---- avg training fps: 7.06

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:38<00:59,  2.11it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:39<00:58,  2.10it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:39<00:58,  2.11it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:40<00:57,  2.12it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:40<00:57,  2.11it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:41<00:57,  2.10it/s]Progress: 63.00%
+---- avg training fps: 7.09

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:41<00:57,  2.10it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:41<00:56,  2.11it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:41<00:56,  2.10it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:42<00:55,  2.10it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:42<00:55,  2.10it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:43<00:54,  2.10it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:43<00:54,  2.10it/s]Progress: 65.00%
+---- avg training fps: 7.13

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:44<00:53,  2.10it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:44<00:53,  2.10it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:45<00:52,  2.10it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:45<00:52,  2.10it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:46<00:52,  2.10it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:46<00:51,  2.10it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:46<00:51,  2.10it/s]Progress: 67.00%
+---- avg training fps: 7.16

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:47<00:50,  2.10it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:47<00:50,  2.11it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:48<00:49,  2.10it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:48<00:49,  2.10it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:49<00:49,  2.09it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:49<00:48,  2.09it/s]Progress: 69.00%
+---- avg training fps: 7.20

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:50<00:48,  2.09it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:50<00:47,  2.10it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:51<00:47,  2.10it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:51<00:47,  2.10it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:55<02:34,  1.58s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:55<02:00,  1.24s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:56<01:37,  1.01s/it]Progress: 71.00%
+---- avg training fps: 7.00

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:56<01:20,  1.18it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:57<01:09,  1.36it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:57<01:01,  1.52it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:57<00:55,  1.66it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:58<00:51,  1.78it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:58<00:48,  1.87it/s]Progress: 73.00%
+---- avg training fps: 7.03

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:59<00:48,  1.87it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:59<00:45,  1.94it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:59<00:44,  1.99it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [02:00<00:43,  2.02it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [02:00<00:42,  2.04it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [02:01<00:41,  2.06it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [02:01<00:40,  2.09it/s]Progress: 75.00%
+---- avg training fps: 7.07

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [02:02<00:39,  2.09it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [02:02<00:39,  2.10it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:03<00:38,  2.11it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:03<00:38,  2.10it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:04<00:38,  2.10it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:04<00:37,  2.10it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:04<00:37,  2.10it/s]Progress: 77.00%
+---- avg training fps: 7.10

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:05<00:36,  2.11it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:05<00:36,  2.11it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:06<00:35,  2.11it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:06<00:35,  2.11it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:06<00:34,  2.11it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:07<00:34,  2.11it/s]Progress: 79.00%
+---- avg training fps: 7.13

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:07<00:33,  2.12it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:08<00:33,  2.11it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:08<00:33,  2.11it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:08<00:32,  2.11it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:09<00:32,  2.12it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:09<00:31,  2.11it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:10<00:31,  2.10it/s]Progress: 81.00%
+---- avg training fps: 7.16

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:10<00:30,  2.11it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:11<00:30,  2.11it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:11<00:29,  2.11it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:12<00:29,  2.11it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:12<00:28,  2.11it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:13<00:28,  2.11it/s]Progress: 83.00%
+---- avg training fps: 7.18

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:13<00:28,  2.11it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:13<00:27,  2.11it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:14<00:27,  2.11it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:14<00:26,  2.12it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:15<00:26,  2.11it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:15<00:26,  2.12it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:15<00:25,  2.11it/s]Progress: 85.00%
+---- avg training fps: 7.21

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:16<00:25,  2.11it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:16<00:24,  2.12it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:17<00:24,  2.11it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:17<00:23,  2.11it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:18<00:23,  2.11it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:18<00:23,  2.12it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:22<01:18,  1.63s/it]Progress: 87.00%
+---- avg training fps: 7.04

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:23<01:00,  1.28s/it]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:23<00:47,  1.04s/it]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:24<00:38,  1.16it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:24<00:32,  1.34it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:25<00:28,  1.51it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:25<00:25,  1.65it/s]Progress: 89.00%
+---- avg training fps: 7.07

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:25<00:23,  1.78it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:26<00:21,  1.87it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:26<00:21,  1.87it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:26<00:20,  1.94it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:27<00:19,  1.99it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:27<00:18,  2.03it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:28<00:17,  2.05it/s]Progress: 91.00%
+---- avg training fps: 7.10

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:28<00:16,  2.07it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:29<00:16,  2.09it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:29<00:15,  2.10it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:30<00:15,  2.10it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:30<00:14,  2.13it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:31<00:14,  2.13it/s]Progress: 93.00%
+---- avg training fps: 7.13

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:31<00:14,  2.13it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:31<00:13,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:32<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:32<00:12,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:32<00:12,  2.15it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:33<00:11,  2.15it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:33<00:11,  2.14it/s]Progress: 95.00%
+---- avg training fps: 7.15

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:34<00:10,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:34<00:10,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:35<00:09,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:35<00:09,  2.16it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:36<00:09,  2.16it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:36<00:08,  2.16it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:36<00:08,  2.16it/s]Progress: 97.00%
+---- avg training fps: 7.18

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:37<00:07,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:37<00:07,  2.17it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:38<00:06,  2.16it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:38<00:06,  2.16it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:39<00:06,  2.15it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:39<00:05,  2.17it/s]Progress: 99.00%
+---- avg training fps: 7.20

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:39<00:05,  2.16it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:40<00:04,  2.15it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:40<00:04,  2.15it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:40<00:04,  2.15it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:41<00:03,  2.15it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:41<00:03,  2.15it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:42<00:02,  2.14it/s]Progress: 100.00%
+---- avg training fps: 7.23

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:42<00:02,  2.16it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:43<00:01,  2.16it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:43<00:01,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:44<00:00,  2.15it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:44<00:00,  2.16it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:45<00:00,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.25
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [04:05<00:00,  1.22it/s]
+------------------------------------------
+Training done :)
+2024-08-13 04:26:15.007233: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 04:26:15.103477: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 04:26:15.103502: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 04:26:15.122497: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 04:26:15.528436: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:26:15.528522: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:26:15.528531: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23126 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.5 freeze_unet_before_completion_f=0.25 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='clipx.zip' output_dir='lora_models/styles_final/clipx.zip_13_04-26-17-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723515977.37286 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Downloading training data...
+Downloading https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip to lora_models/styles_final/clipx.zip_13_04-26-17-style_512_florence_300/images_in/clipx.zip...
+Succesfully prepped 54 .jpg images in lora_models/styles_final/clipx.zip_13_04-26-17-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.314
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 27 images, 27 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 27 to 54)
+Generating 54 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal, whimsical, detailed, and quirky scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene is Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase describes the best and worst of times.
+- In the style of TOK, a blockchain is depicted using bees.
+- In the style of TOK, a gorgon made of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset shows Venus and Earth circling the sun.
+- In the style of TOK, there is an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is portrayed.
+- In the style of TOK, the phrase exemplifies the age of wisdom and the age of foolishness.
+- In the style of TOK, two people discuss quitting their boring corporation jobs together.
+- In the style of TOK, petroglyphs are presented.
+- In the style of TOK, a room and space exchange based on first and second-degree connections is depicted.
+- In the style of TOK, the fear known as trypophobia is illustrated.
+- In the style of TOK, there is a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing and singing beavers is highlighted.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with patterns akin to Hawaiian shirts.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a Tarsem Singh-like flair.
+- In the style of TOK, a brigade of beavers resembles M. C. Escher's work.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dances in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen has been frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show has elephant seals as runway models and egrets in the audience.
+- In the style of TOK, there is a hyper-realistic painting of lemon-lime architecture.
+- In the style of TOK, a mermaid made of fishnets, dead fish, packing peanuts, and other sea trash swims up through the sea to take a peek above water.
+
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 1...
+----- GPT response: -----
+Style Description: Surrealistic, intricate, and fantastical scenes
+
+- In the style of TOK, a mermaid made out of fishnets and sea trash, is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is embodied in a visual representation.
+- In the style of TOK, a blockchain is composed of bees.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun is depicted.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is presented.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is embodied in a visual representation.
+- In the style of TOK, two individuals decide to quit their boring corporation job together.
+- In the style of TOK, petroglyphs are depicted.
+- In the style of TOK, a room and space exchange is based on first and second-degree connections.
+- In the style of TOK, trypophobia is visually represented.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of dancing and singing beavers is created with a nod to Jim Henson's Muppets.
+- In the style of TOK, a beaver is shown chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a pattern like a Hawaiian shirt.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is designed in the style of M. C. Escher.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models and egrets as the audience.
+- In the style of TOK, a hyper-realistic painting depicts lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and sea trash is swimming up through the sea to take a peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 2...
+----- GPT response: -----
+Style Description: Surreal, eclectic, and whimsical themes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi, Kerala, India.
+- In the style of TOK, "They were the best of times, they were the worst of times."
+- In the style of TOK, a blockchain of bees.
+- In the style of TOK, a gorgon made out of sharks eating each other swims underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats pooping in zero-gravity.
+- In the style of TOK, a goblin goat.
+- In the style of TOK, "It was the age of wisdom, it was the age of foolishness."
+- In the style of TOK, “Let's quit our boring corporation job together.”
+- In the style of TOK, petroglyphs.
+- In the style of TOK, a room and space exchange based on first and second-degree connections.
+- In the style of TOK, trypophobia.
+- In the style of TOK, a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers.
+- In the style of TOK, a beaver chewing a rifle in half.
+- In the style of TOK, a brigade of beavers quickly building a dam.
+- In the style of TOK, a castle moving along the grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show with elephant seals as runway models and egrets as the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swims up through the sea to take a peek above the water.
+
+--------------------------
+GPT-4 returned the wrong number of prompts 25 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 3...
+----- GPT response: -----
+Style Description: Surreal, intricate, and imaginatively whimsical.
+
+- In the style of TOK, a mermaid made out of fishnets and sea trash swims underwater.
+- In the style of TOK, Lilliputians are cramped inside a giant sea-sponge.
+- In the style of TOK, a scenic view of Fort Kochi, Kerala.
+- In the style of TOK, an abstract representation of the best and worst of times.
+- In the style of TOK, a chain of bees linked together.
+- In the style of TOK, a gorgon composed of sharks eating each other swims underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple hidden deep in the Amazon jungle.
+- In the style of TOK, cats defy gravity while pooping.
+- In the style of TOK, a mystical goblin goat.
+- In the style of TOK, an allegory for the age of wisdom and foolishness.
+- In the style of TOK, two people planning to leave their corporate jobs.
+- In the style of TOK, ancient rock carvings known as petroglyphs.
+- In the style of TOK, a room and space exchange based on connections.
+- In the style of TOK, a visually unsettling pattern of holes resembling trypophobia.
+- In the style of TOK, a 6-pack of nightmare fuel.
+- In the style of TOK, a band of beavers dance and sing.
+- In the style of TOK, a beaver gnaws a rifle in half.
+- In the style of TOK, beavers rapidly construct a dam.
+- In the style of TOK, beavers rapidly construct a dam with vibrant patterns.
+- In the style of TOK, beavers rapidly construct a dam in a detailed artistic style.
+- In the style of TOK, a brigade of beavers depicted in intricate, impossible structures.
+- In the style of TOK, a moving castle on grassy hills.
+- In the style of TOK, a cute chorus line of beavers dancing.
+- In the style of TOK, a frozen 1950s restaurant kitchen rediscovered in the space age.
+- In the style of TOK, elephant seals model on a runway with egrets watching.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture.
+- In the style of TOK, a mermaid made from sea trash swims up to the water surface.
+--------------------------
+GPT-4 returned the wrong number of prompts 28 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 4...
+----- GPT response: -----
+Style Description: Surreal and fantastical imagery
+
+- In the style of TOK, a mermaid constructed from fishnets and sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, a scenic view of Fort Kochi, Kerala, India.
+- In the style of TOK, a depiction of the phrase "the best of times, the worst of times."
+- In the style of TOK, a blockchain made up of bees.
+- In the style of TOK, a gorgon composed of sharks eating each other, swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple deep within the Amazon jungle.
+- In the style of TOK, cats are defecating in zero-gravity conditions.
+- In the style of TOK, a depiction of a goblin goat.
+- In the style of TOK, a visualization of the phrase "the age of wisdom, the age of foolishness."
+- In the style of TOK, coworkers discuss quitting their boring corporate jobs together.
+- In the style of TOK, ancient petroglyphs are depicted.
+- In the style of TOK, a room and space exchange based on first and second-degree connections.
+- In the style of TOK, an illustration that induces trypophobia.
+- In the style of TOK, a six-pack of nightmare-inducing imagery.
+- In the style of TOK, a band of dancing, singing beavers.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers quickly builds a dam.
+- In the style of TOK, a brigade of beavers builds a dam as seen in a Hawaiian-shirt pattern.
+- In the style of TOK, a brigade of beavers constructs a dam in a Tarsem Singh-inspired scene.
+- In the style of TOK, a brigade of beavers is depicted in an M. C. Escher style.
+- In the style of TOK, a castle moves along the ground on grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dances in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets in the audience.
+- In the style of TOK, a hyper-realistic painting depicts lemon-lime architecture.
+- In the style of TOK, a mermaid made from fishnets, dead fish, packing peanuts, and other sea trash swims up through the sea to peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 28 instead of 54, retrying...
+WARNING: no captioning text was given and we're not doing chatgpt cleanup...
+Concept mode:  style
+Using GPT concept name as CLIP-segmentation prompt: Surreal and fantastical imagery
+Generating 54 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
54it [00:00, 15224.33it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 54 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a mermaid made o...
+1     in the style of <s0><s1><s2>, a throng of lill...
+2     in the style of <s0><s1><s2>, fort kochi, kera...
+3     in the style of <s0><s1><s2>, they were the be...
+4     in the style of <s0><s1><s2>, a blockchain of ...
+5     in the style of <s0><s1><s2>, a gorgon made ou...
+6     in the style of <s0><s1><s2>, a hillside at su...
+7     in the style of <s0><s1><s2>, an ancient templ...
+8     in the style of <s0><s1><s2>, cats pooping in ...
+9             in the style of <s0><s1><s2>, goblin goat
+10    in the style of <s0><s1><s2>, it was the age o...
+11    in the style of <s0><s1><s2>, let's quit our b...
+12            in the style of <s0><s1><s2>, petroglyphs
+13    in the style of <s0><s1><s2>, room and space e...
+14            in the style of <s0><s1><s2>, trypophobia
+15    in the style of <s0><s1><s2>, a 6-pack of nigh...
+16    in the style of <s0><s1><s2>, a band of dancin...
+17    in the style of <s0><s1><s2>, a beaver chewing...
+18    in the style of <s0><s1><s2>, a brigade of bea...
+19    in the style of <s0><s1><s2>, a brigade of bea...
+20    in the style of <s0><s1><s2>, a brigade of bea...
+21    in the style of <s0><s1><s2>, a castle that is...
+22    in the style of <s0><s1><s2>, a chorus line of...
+23    in the style of <s0><s1><s2>, a dirty 1950s re...
+24    in the style of <s0><s1><s2>, a fashion show w...
+25    in the style of <s0><s1><s2>, a hyper-realisti...
+26    in the style of <s0><s1><s2>, a mermaid made o...
+27    in the style of <s0><s1><s2>, a mermaid made o...
+28    in the style of <s0><s1><s2>, a throng of lill...
+29    in the style of <s0><s1><s2>, fort kochi, kera...
+30    in the style of <s0><s1><s2>, they were the be...
+31    in the style of <s0><s1><s2>, a blockchain of ...
+32    in the style of <s0><s1><s2>, a gorgon made ou...
+33    in the style of <s0><s1><s2>, a hillside at su...
+34    in the style of <s0><s1><s2>, an ancient templ...
+35    in the style of <s0><s1><s2>, cats pooping in ...
+36            in the style of <s0><s1><s2>, goblin goat
+37    in the style of <s0><s1><s2>, it was the age o...
+38    in the style of <s0><s1><s2>, let's quit our b...
+39            in the style of <s0><s1><s2>, petroglyphs
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 54
+--- Num batches each epoch = 14
+--- Num Epochs = 22
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
  0%|          | 0/300 [00:00<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:02<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:02<14:35,  2.93s/it]
# Trainer step: 0, epoch: 0:   1%|          | 2/300 [00:08<21:41,  4.37s/it]
# Trainer step: 0, epoch: 0:   1%|          | 3/300 [00:08<12:44,  2.57s/it]
# Trainer step: 0, epoch: 0:   1%|▏         | 4/300 [00:09<08:32,  1.73s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:09<06:13,  1.27s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:10<04:50,  1.01it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.28

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:10<03:57,  1.23it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:10<03:22,  1.44it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 9/300 [00:11<02:59,  1.62it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 10/300 [00:11<02:43,  1.77it/s]
# Trainer step: 0, epoch: 0:   4%|▎         | 11/300 [00:12<02:32,  1.90it/s]
# Trainer step: 0, epoch: 0:   4%|▍         | 12/300 [00:12<02:24,  1.99it/s]Progress: 7.00%
+---- avg training fps: 3.64

# Trainer step: 0, epoch: 0:   4%|▍         | 13/300 [00:13<02:19,  2.06it/s]
# Trainer step: 0, epoch: 0:   5%|▍         | 14/300 [00:13<02:15,  2.11it/s]
# Trainer step: 14, epoch: 1:   5%|▍         | 14/300 [00:14<02:15,  2.11it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 15/300 [00:14<02:21,  2.01it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 16/300 [00:14<02:17,  2.07it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 17/300 [00:15<02:13,  2.12it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 18/300 [00:15<02:11,  2.15it/s]Progress: 9.00%
+---- avg training fps: 4.51

# Trainer step: 14, epoch: 1:   6%|▋         | 19/300 [00:15<02:08,  2.18it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 20/300 [00:16<02:07,  2.19it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 21/300 [00:16<02:06,  2.20it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 22/300 [00:17<02:05,  2.21it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 23/300 [00:17<02:04,  2.22it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 24/300 [00:18<02:04,  2.22it/s]Progress: 11.00%
+---- avg training fps: 5.14

# Trainer step: 14, epoch: 1:   8%|▊         | 25/300 [00:18<02:03,  2.23it/s]
# Trainer step: 14, epoch: 1:   9%|▊         | 26/300 [00:19<02:02,  2.23it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 27/300 [00:19<02:02,  2.23it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 28/300 [00:20<02:01,  2.23it/s]
# Trainer step: 28, epoch: 2:   9%|▉         | 28/300 [00:20<02:01,  2.23it/s]
# Trainer step: 28, epoch: 2:  10%|▉         | 29/300 [00:20<01:57,  2.31it/s]
# Trainer step: 28, epoch: 2:  10%|█         | 30/300 [00:20<01:58,  2.28it/s]Progress: 13.00%
+---- avg training fps: 5.63

# Trainer step: 28, epoch: 2:  10%|█         | 31/300 [00:21<01:58,  2.27it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 32/300 [00:21<01:58,  2.25it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 33/300 [00:22<01:58,  2.25it/s]
# Trainer step: 28, epoch: 2:  11%|█▏        | 34/300 [00:22<01:58,  2.25it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 35/300 [00:23<01:58,  2.24it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 36/300 [00:23<01:57,  2.24it/s]Progress: 15.00%
+---- avg training fps: 6.00

# Trainer step: 28, epoch: 2:  12%|█▏        | 37/300 [00:23<01:57,  2.24it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 38/300 [00:24<01:57,  2.23it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 39/300 [00:24<01:56,  2.23it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 40/300 [00:25<01:56,  2.23it/s]
# Trainer step: 28, epoch: 2:  14%|█▎        | 41/300 [00:25<01:56,  2.23it/s]
# Trainer step: 28, epoch: 2:  14%|█▍        | 42/300 [00:26<01:55,  2.23it/s]Progress: 17.00%
+---- avg training fps: 6.31

# Trainer step: 42, epoch: 3:  14%|█▍        | 42/300 [00:26<01:55,  2.23it/s]
# Trainer step: 42, epoch: 3:  14%|█▍        | 43/300 [00:26<01:51,  2.31it/s]
# Trainer step: 42, epoch: 3:  15%|█▍        | 44/300 [00:27<01:51,  2.29it/s]
# Trainer step: 42, epoch: 3:  15%|█▌        | 45/300 [00:27<01:51,  2.28it/s]
# Trainer step: 42, epoch: 3:  15%|█▌        | 46/300 [00:27<01:52,  2.26it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 47/300 [00:28<01:52,  2.25it/s]
# Trainer step: 42, epoch: 3:  16%|█▌        | 48/300 [00:28<01:52,  2.23it/s]Progress: 19.00%
+---- avg training fps: 6.55

# Trainer step: 42, epoch: 3:  16%|█▋        | 49/300 [00:29<01:52,  2.23it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 50/300 [00:29<01:52,  2.22it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 51/300 [00:30<01:51,  2.23it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 52/300 [00:33<05:14,  1.27s/it]
# Trainer step: 42, epoch: 3:  18%|█▊        | 53/300 [00:33<04:13,  1.02s/it]
# Trainer step: 42, epoch: 3:  18%|█▊        | 54/300 [00:34<03:29,  1.17it/s]Progress: 21.00%
+---- avg training fps: 6.21

# Trainer step: 42, epoch: 3:  18%|█▊        | 55/300 [00:34<02:59,  1.37it/s]
# Trainer step: 42, epoch: 3:  19%|█▊        | 56/300 [00:35<02:38,  1.54it/s]
# Trainer step: 56, epoch: 4:  19%|█▊        | 56/300 [00:35<02:38,  1.54it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 57/300 [00:35<02:19,  1.75it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 58/300 [00:36<02:09,  1.86it/s]
# Trainer step: 56, epoch: 4:  20%|█▉        | 59/300 [00:36<02:02,  1.96it/s]
# Trainer step: 56, epoch: 4:  20%|██        | 60/300 [00:36<01:58,  2.02it/s]Progress: 23.00%
+---- avg training fps: 6.41

# Trainer step: 56, epoch: 4:  20%|██        | 61/300 [00:37<01:55,  2.07it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 62/300 [00:37<01:52,  2.11it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 63/300 [00:38<01:51,  2.13it/s]
# Trainer step: 56, epoch: 4:  21%|██▏       | 64/300 [00:38<01:49,  2.16it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 65/300 [00:39<01:49,  2.15it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 66/300 [00:39<01:48,  2.16it/s]Progress: 25.00%
+---- avg training fps: 6.57

# Trainer step: 56, epoch: 4:  22%|██▏       | 67/300 [00:40<01:47,  2.17it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 68/300 [00:40<01:46,  2.18it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 69/300 [00:41<01:45,  2.19it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 70/300 [00:41<01:44,  2.19it/s]
# Trainer step: 70, epoch: 5:  23%|██▎       | 70/300 [00:41<01:44,  2.19it/s]
# Trainer step: 70, epoch: 5:  24%|██▎       | 71/300 [00:41<01:40,  2.28it/s]
# Trainer step: 70, epoch: 5:  24%|██▍       | 72/300 [00:42<01:41,  2.25it/s]Progress: 27.00%
+---- avg training fps: 6.68

# Trainer step: 70, epoch: 5:  24%|██▍       | 73/300 [00:43<02:00,  1.89it/s]
# Trainer step: 70, epoch: 5:  25%|██▍       | 74/300 [00:43<01:54,  1.97it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 75/300 [00:44<01:50,  2.03it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 76/300 [00:44<01:47,  2.08it/s]
# Trainer step: 70, epoch: 5:  26%|██▌       | 77/300 [00:44<01:45,  2.11it/s]
# Trainer step: 70, epoch: 5:  26%|██▌       | 78/300 [00:45<01:44,  2.13it/s]Progress: 29.00%
+---- avg training fps: 6.80

# Trainer step: 70, epoch: 5:  26%|██▋       | 79/300 [00:45<01:42,  2.15it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 80/300 [00:46<01:41,  2.16it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 81/300 [00:46<01:40,  2.18it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 82/300 [00:47<01:39,  2.18it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 83/300 [00:47<01:39,  2.18it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 84/300 [00:48<01:38,  2.19it/s]Progress: 31.00%
+---- avg training fps: 6.92

# Trainer step: 84, epoch: 6:  28%|██▊       | 84/300 [00:48<01:38,  2.19it/s]
# Trainer step: 84, epoch: 6:  28%|██▊       | 85/300 [00:48<01:34,  2.28it/s]
# Trainer step: 84, epoch: 6:  29%|██▊       | 86/300 [00:48<01:35,  2.25it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 87/300 [00:49<01:35,  2.24it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 88/300 [00:49<01:35,  2.22it/s]
# Trainer step: 84, epoch: 6:  30%|██▉       | 89/300 [00:50<01:35,  2.21it/s]
# Trainer step: 84, epoch: 6:  30%|███       | 90/300 [00:50<01:35,  2.21it/s]Progress: 33.00%
+---- avg training fps: 7.02

# Trainer step: 84, epoch: 6:  30%|███       | 91/300 [00:51<01:34,  2.21it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 92/300 [00:51<01:34,  2.21it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 93/300 [00:52<01:33,  2.21it/s]
# Trainer step: 84, epoch: 6:  31%|███▏      | 94/300 [00:52<01:33,  2.21it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 95/300 [00:53<01:32,  2.21it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 96/300 [00:53<01:32,  2.21it/s]Progress: 35.00%
+---- avg training fps: 7.11

# Trainer step: 84, epoch: 6:  32%|███▏      | 97/300 [00:53<01:32,  2.20it/s]
# Trainer step: 84, epoch: 6:  33%|███▎      | 98/300 [00:54<01:31,  2.20it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 98/300 [00:54<01:31,  2.20it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 99/300 [00:54<01:27,  2.29it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 100/300 [00:55<01:28,  2.26it/s]
# Trainer step: 98, epoch: 7:  34%|███▎      | 101/300 [00:55<01:28,  2.24it/s]
# Trainer step: 98, epoch: 7:  34%|███▍      | 102/300 [00:58<03:24,  1.03s/it]Progress: 37.00%
+---- avg training fps: 6.96

# Trainer step: 98, epoch: 7:  34%|███▍      | 103/300 [00:58<02:49,  1.16it/s]
# Trainer step: 98, epoch: 7:  35%|███▍      | 104/300 [00:59<02:24,  1.35it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 105/300 [00:59<02:07,  1.53it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 106/300 [00:59<01:55,  1.68it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 107/300 [01:00<01:46,  1.80it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 108/300 [01:00<01:40,  1.91it/s]Progress: 39.00%
+---- avg training fps: 7.04

# Trainer step: 98, epoch: 7:  36%|███▋      | 109/300 [01:01<01:36,  1.98it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 110/300 [01:01<01:33,  2.04it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 111/300 [01:02<01:30,  2.08it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 112/300 [01:02<01:28,  2.11it/s]
# Trainer step: 112, epoch: 8:  37%|███▋      | 112/300 [01:03<01:28,  2.11it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 113/300 [01:03<01:24,  2.22it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 114/300 [01:03<01:23,  2.21it/s]Progress: 41.00%
+---- avg training fps: 7.12

# Trainer step: 112, epoch: 8:  38%|███▊      | 115/300 [01:04<01:24,  2.20it/s]
# Trainer step: 112, epoch: 8:  39%|███▊      | 116/300 [01:04<01:23,  2.20it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 117/300 [01:04<01:23,  2.19it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 118/300 [01:05<01:23,  2.17it/s]
# Trainer step: 112, epoch: 8:  40%|███▉      | 119/300 [01:05<01:25,  2.13it/s]
# Trainer step: 112, epoch: 8:  40%|████      | 120/300 [01:06<01:24,  2.13it/s]Progress: 43.00%
+---- avg training fps: 7.18

# Trainer step: 112, epoch: 8:  40%|████      | 121/300 [01:06<01:23,  2.13it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 122/300 [01:07<01:23,  2.14it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 123/300 [01:07<01:22,  2.15it/s]
# Trainer step: 112, epoch: 8:  41%|████▏     | 124/300 [01:08<01:22,  2.13it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 125/300 [01:08<01:22,  2.13it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 126/300 [01:09<01:21,  2.13it/s]Progress: 45.00%
+---- avg training fps: 7.24

# Trainer step: 126, epoch: 9:  42%|████▏     | 126/300 [01:09<01:21,  2.13it/s]
# Trainer step: 126, epoch: 9:  42%|████▏     | 127/300 [01:09<01:17,  2.23it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 128/300 [01:10<01:18,  2.19it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 129/300 [01:10<01:18,  2.17it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 130/300 [01:11<01:18,  2.16it/s]
# Trainer step: 126, epoch: 9:  44%|████▎     | 131/300 [01:11<01:18,  2.14it/s]
# Trainer step: 126, epoch: 9:  44%|████▍     | 132/300 [01:11<01:18,  2.14it/s]Progress: 47.00%
+---- avg training fps: 7.29

# Trainer step: 126, epoch: 9:  44%|████▍     | 133/300 [01:12<01:18,  2.12it/s]
# Trainer step: 126, epoch: 9:  45%|████▍     | 134/300 [01:12<01:18,  2.12it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 135/300 [01:13<01:17,  2.12it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 136/300 [01:13<01:17,  2.13it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 137/300 [01:14<01:17,  2.12it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 138/300 [01:14<01:16,  2.12it/s]Progress: 49.00%
+---- avg training fps: 7.34

# Trainer step: 126, epoch: 9:  46%|████▋     | 139/300 [01:15<01:15,  2.12it/s]
# Trainer step: 126, epoch: 9:  47%|████▋     | 140/300 [01:15<01:15,  2.12it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 140/300 [01:16<01:15,  2.12it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 141/300 [01:16<01:11,  2.23it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 142/300 [01:16<01:11,  2.20it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 143/300 [01:17<01:11,  2.18it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 144/300 [01:17<01:11,  2.17it/s]Progress: 51.00%
+---- avg training fps: 7.37

# Trainer step: 140, epoch: 10:  48%|████▊     | 145/300 [01:18<01:19,  1.94it/s]
# Trainer step: 140, epoch: 10:  49%|████▊     | 146/300 [01:18<01:16,  2.00it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 147/300 [01:19<01:14,  2.05it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 148/300 [01:19<01:13,  2.08it/s]
# Trainer step: 140, epoch: 10:  50%|████▉     | 149/300 [01:20<01:11,  2.10it/s]
# Trainer step: 140, epoch: 10:  50%|█████     | 150/300 [01:20<01:10,  2.12it/s]Progress: 53.00%
+---- avg training fps: 7.41

# Trainer step: 140, epoch: 10:  50%|█████     | 151/300 [01:20<01:09,  2.13it/s]
# Trainer step: 140, epoch: 10:  51%|█████     | 152/300 [01:24<03:22,  1.37s/it]
# Trainer step: 140, epoch: 10:  51%|█████     | 153/300 [01:24<02:41,  1.10s/it]
# Trainer step: 140, epoch: 10:  51%|█████▏    | 154/300 [01:25<02:12,  1.10it/s]
# Trainer step: 154, epoch: 11:  51%|█████▏    | 154/300 [01:25<02:12,  1.10it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 155/300 [01:25<01:49,  1.33it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 156/300 [01:26<01:36,  1.50it/s]Progress: 55.00%
+---- avg training fps: 7.20

# Trainer step: 154, epoch: 11:  52%|█████▏    | 157/300 [01:26<01:26,  1.65it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 158/300 [01:27<01:20,  1.77it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 159/300 [01:27<01:15,  1.87it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 160/300 [01:28<01:11,  1.95it/s]
# Trainer step: 154, epoch: 11:  54%|█████▎    | 161/300 [01:28<01:09,  2.01it/s]
# Trainer step: 154, epoch: 11:  54%|█████▍    | 162/300 [01:28<01:07,  2.05it/s]Progress: 57.00%
+---- avg training fps: 7.25

# Trainer step: 154, epoch: 11:  54%|█████▍    | 163/300 [01:29<01:05,  2.09it/s]
# Trainer step: 154, epoch: 11:  55%|█████▍    | 164/300 [01:29<01:04,  2.11it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 165/300 [01:30<01:03,  2.13it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 166/300 [01:30<01:02,  2.14it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 167/300 [01:31<01:02,  2.14it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 168/300 [01:31<01:01,  2.15it/s]Progress: 59.00%
+---- avg training fps: 7.29

# Trainer step: 168, epoch: 12:  56%|█████▌    | 168/300 [01:32<01:01,  2.15it/s]
# Trainer step: 168, epoch: 12:  56%|█████▋    | 169/300 [01:32<00:57,  2.26it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 170/300 [01:32<00:58,  2.22it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 171/300 [01:33<00:58,  2.21it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 172/300 [01:33<00:58,  2.19it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 173/300 [01:33<00:58,  2.18it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 174/300 [01:34<00:57,  2.18it/s]Progress: 61.00%
+---- avg training fps: 7.33

# Trainer step: 168, epoch: 12:  58%|█████▊    | 175/300 [01:34<00:57,  2.17it/s]
# Trainer step: 168, epoch: 12:  59%|█████▊    | 176/300 [01:35<00:57,  2.16it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 177/300 [01:35<00:56,  2.16it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 178/300 [01:36<00:56,  2.16it/s]
# Trainer step: 168, epoch: 12:  60%|█████▉    | 179/300 [01:36<00:55,  2.16it/s]
# Trainer step: 168, epoch: 12:  60%|██████    | 180/300 [01:37<00:55,  2.16it/s]Progress: 63.00%
+---- avg training fps: 7.37

# Trainer step: 168, epoch: 12:  60%|██████    | 181/300 [01:37<00:55,  2.16it/s]
# Trainer step: 168, epoch: 12:  61%|██████    | 182/300 [01:38<01:01,  1.92it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 182/300 [01:38<01:01,  1.92it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 183/300 [01:38<00:56,  2.07it/s]
# Trainer step: 182, epoch: 13:  61%|██████▏   | 184/300 [01:39<00:55,  2.09it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 185/300 [01:39<00:54,  2.10it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 186/300 [01:40<00:53,  2.11it/s]Progress: 65.00%
+---- avg training fps: 7.39

# Trainer step: 182, epoch: 13:  62%|██████▏   | 187/300 [01:40<00:53,  2.13it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 188/300 [01:41<00:52,  2.14it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 189/300 [01:41<00:51,  2.14it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 190/300 [01:42<00:51,  2.14it/s]
# Trainer step: 182, epoch: 13:  64%|██████▎   | 191/300 [01:42<00:50,  2.15it/s]
# Trainer step: 182, epoch: 13:  64%|██████▍   | 192/300 [01:42<00:50,  2.14it/s]Progress: 67.00%
+---- avg training fps: 7.43

# Trainer step: 182, epoch: 13:  64%|██████▍   | 193/300 [01:43<00:49,  2.15it/s]
# Trainer step: 182, epoch: 13:  65%|██████▍   | 194/300 [01:43<00:49,  2.14it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 195/300 [01:44<00:48,  2.14it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 196/300 [01:44<00:48,  2.14it/s]
# Trainer step: 196, epoch: 14:  65%|██████▌   | 196/300 [01:45<00:48,  2.14it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 197/300 [01:45<00:46,  2.23it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 198/300 [01:45<00:46,  2.19it/s]Progress: 69.00%
+---- avg training fps: 7.46

# Trainer step: 196, epoch: 14:  66%|██████▋   | 199/300 [01:46<00:46,  2.17it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 200/300 [01:46<00:46,  2.16it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 201/300 [01:47<00:45,  2.16it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 202/300 [01:49<01:40,  1.03s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 203/300 [01:49<01:23,  1.16it/s]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 204/300 [01:50<01:11,  1.35it/s]Progress: 71.00%
+---- avg training fps: 7.36

# Trainer step: 196, epoch: 14:  68%|██████▊   | 205/300 [01:50<01:02,  1.51it/s]
# Trainer step: 196, epoch: 14:  69%|██████▊   | 206/300 [01:51<00:56,  1.66it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 207/300 [01:51<00:52,  1.78it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 208/300 [01:52<00:49,  1.88it/s]
# Trainer step: 196, epoch: 14:  70%|██████▉   | 209/300 [01:52<00:46,  1.95it/s]
# Trainer step: 196, epoch: 14:  70%|███████   | 210/300 [01:53<00:44,  2.00it/s]Progress: 73.00%
+---- avg training fps: 7.40

# Trainer step: 210, epoch: 15:  70%|███████   | 210/300 [01:53<00:44,  2.00it/s]
# Trainer step: 210, epoch: 15:  70%|███████   | 211/300 [01:53<00:41,  2.13it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 212/300 [01:54<00:41,  2.13it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 213/300 [01:54<00:40,  2.14it/s]
# Trainer step: 210, epoch: 15:  71%|███████▏  | 214/300 [01:54<00:40,  2.14it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 215/300 [01:55<00:39,  2.14it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 216/300 [01:55<00:39,  2.14it/s]Progress: 75.00%
+---- avg training fps: 7.42

# Trainer step: 210, epoch: 15:  72%|███████▏  | 217/300 [01:56<00:38,  2.15it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 218/300 [01:56<00:38,  2.14it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 219/300 [01:57<00:37,  2.14it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 220/300 [01:57<00:37,  2.14it/s]
# Trainer step: 210, epoch: 15:  74%|███████▎  | 221/300 [01:58<00:36,  2.14it/s]
# Trainer step: 210, epoch: 15:  74%|███████▍  | 222/300 [01:58<00:36,  2.14it/s]Progress: 77.00%
+---- avg training fps: 7.45

# Trainer step: 210, epoch: 15:  74%|███████▍  | 223/300 [01:59<00:35,  2.14it/s]
# Trainer step: 210, epoch: 15:  75%|███████▍  | 224/300 [01:59<00:35,  2.13it/s]
# Trainer step: 224, epoch: 16:  75%|███████▍  | 224/300 [02:00<00:35,  2.13it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 225/300 [02:00<00:33,  2.23it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 226/300 [02:00<00:33,  2.20it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 227/300 [02:01<00:33,  2.18it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 228/300 [02:01<00:33,  2.17it/s]Progress: 79.00%
+---- avg training fps: 7.48

# Trainer step: 224, epoch: 16:  76%|███████▋  | 229/300 [02:01<00:32,  2.16it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 230/300 [02:02<00:32,  2.15it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 231/300 [02:02<00:32,  2.15it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 232/300 [02:03<00:31,  2.15it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 233/300 [02:03<00:31,  2.14it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 234/300 [02:04<00:30,  2.14it/s]Progress: 81.00%
+---- avg training fps: 7.50

# Trainer step: 224, epoch: 16:  78%|███████▊  | 235/300 [02:04<00:30,  2.14it/s]
# Trainer step: 224, epoch: 16:  79%|███████▊  | 236/300 [02:05<00:29,  2.14it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 237/300 [02:05<00:29,  2.14it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 238/300 [02:06<00:29,  2.14it/s]
# Trainer step: 238, epoch: 17:  79%|███████▉  | 238/300 [02:06<00:29,  2.14it/s]
# Trainer step: 238, epoch: 17:  80%|███████▉  | 239/300 [02:06<00:26,  2.26it/s]
# Trainer step: 238, epoch: 17:  80%|████████  | 240/300 [02:06<00:26,  2.23it/s]Progress: 83.00%
+---- avg training fps: 7.53

# Trainer step: 238, epoch: 17:  80%|████████  | 241/300 [02:07<00:26,  2.20it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 242/300 [02:07<00:26,  2.18it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 243/300 [02:08<00:26,  2.17it/s]
# Trainer step: 238, epoch: 17:  81%|████████▏ | 244/300 [02:08<00:25,  2.16it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 245/300 [02:09<00:25,  2.15it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 246/300 [02:09<00:25,  2.15it/s]Progress: 85.00%
+---- avg training fps: 7.55

# Trainer step: 238, epoch: 17:  82%|████████▏ | 247/300 [02:10<00:24,  2.14it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 248/300 [02:10<00:24,  2.14it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 249/300 [02:11<00:23,  2.14it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 250/300 [02:11<00:23,  2.14it/s]
# Trainer step: 238, epoch: 17:  84%|████████▎ | 251/300 [02:12<00:22,  2.14it/s]
# Trainer step: 238, epoch: 17:  84%|████████▍ | 252/300 [02:15<01:05,  1.36s/it]Progress: 87.00%
+---- avg training fps: 7.41

# Trainer step: 252, epoch: 18:  84%|████████▍ | 252/300 [02:15<01:05,  1.36s/it]
# Trainer step: 252, epoch: 18:  84%|████████▍ | 253/300 [02:15<00:50,  1.07s/it]
# Trainer step: 252, epoch: 18:  85%|████████▍ | 254/300 [02:16<00:40,  1.12it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 255/300 [02:16<00:34,  1.31it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 256/300 [02:17<00:29,  1.48it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 257/300 [02:17<00:26,  1.64it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 258/300 [02:18<00:23,  1.76it/s]Progress: 89.00%
+---- avg training fps: 7.44

# Trainer step: 252, epoch: 18:  86%|████████▋ | 259/300 [02:18<00:22,  1.86it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 260/300 [02:19<00:20,  1.94it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 261/300 [02:19<00:19,  2.00it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 262/300 [02:20<00:18,  2.04it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 263/300 [02:20<00:17,  2.07it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 264/300 [02:21<00:17,  2.09it/s]Progress: 91.00%
+---- avg training fps: 7.46

# Trainer step: 252, epoch: 18:  88%|████████▊ | 265/300 [02:21<00:16,  2.10it/s]
# Trainer step: 252, epoch: 18:  89%|████████▊ | 266/300 [02:22<00:16,  2.12it/s]
# Trainer step: 266, epoch: 19:  89%|████████▊ | 266/300 [02:22<00:16,  2.12it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 267/300 [02:22<00:14,  2.22it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 268/300 [02:22<00:14,  2.20it/s]
# Trainer step: 266, epoch: 19:  90%|████████▉ | 269/300 [02:23<00:14,  2.18it/s]
# Trainer step: 266, epoch: 19:  90%|█████████ | 270/300 [02:23<00:13,  2.17it/s]Progress: 93.00%
+---- avg training fps: 7.48

# Trainer step: 266, epoch: 19:  90%|█████████ | 271/300 [02:24<00:13,  2.16it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 272/300 [02:24<00:12,  2.16it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 273/300 [02:25<00:12,  2.15it/s]
# Trainer step: 266, epoch: 19:  91%|█████████▏| 274/300 [02:25<00:12,  2.15it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 275/300 [02:26<00:11,  2.15it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 276/300 [02:26<00:11,  2.15it/s]Progress: 95.00%
+---- avg training fps: 7.50

# Trainer step: 266, epoch: 19:  92%|█████████▏| 277/300 [02:27<00:10,  2.14it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 278/300 [02:27<00:10,  2.14it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 279/300 [02:28<00:09,  2.14it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 280/300 [02:28<00:09,  2.14it/s]
# Trainer step: 280, epoch: 20:  93%|█████████▎| 280/300 [02:28<00:09,  2.14it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▎| 281/300 [02:28<00:08,  2.24it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▍| 282/300 [02:29<00:08,  2.21it/s]Progress: 97.00%
+---- avg training fps: 7.53

# Trainer step: 280, epoch: 20:  94%|█████████▍| 283/300 [02:29<00:07,  2.19it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▍| 284/300 [02:30<00:07,  2.17it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 285/300 [02:30<00:06,  2.16it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 286/300 [02:31<00:06,  2.16it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 287/300 [02:31<00:06,  2.15it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 288/300 [02:32<00:05,  2.15it/s]Progress: 99.00%
+---- avg training fps: 7.55

# Trainer step: 280, epoch: 20:  96%|█████████▋| 289/300 [02:32<00:05,  2.15it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 290/300 [02:33<00:04,  2.14it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 291/300 [02:33<00:04,  2.15it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 292/300 [02:34<00:03,  2.15it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 293/300 [02:34<00:03,  2.14it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 294/300 [02:34<00:02,  2.14it/s]Progress: 100.00%
+---- avg training fps: 7.57

# Trainer step: 294, epoch: 21:  98%|█████████▊| 294/300 [02:35<00:02,  2.14it/s]
# Trainer step: 294, epoch: 21:  98%|█████████▊| 295/300 [02:35<00:02,  2.24it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▊| 296/300 [02:35<00:01,  2.21it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 297/300 [02:36<00:01,  2.19it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 298/300 [02:36<00:00,  2.18it/s]
# Trainer step: 294, epoch: 21: 100%|█████████▉| 299/300 [02:37<00:00,  2.16it/s]
# Trainer step: 294, epoch: 21: 100%|██████████| 300/300 [02:37<00:00,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.59

# Trainer step: 294, epoch: 21: : 301it [02:38,  2.16it/s]                       Progress: 100.00%
Failed to plot token attention loss
+Reached max steps, stopping training!
+Saving checkpoint at step.. 301
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 294, epoch: 21: : 301it [03:57,  1.27it/s]
+------------------------------------------
+Training done :)
+2024-08-13 04:31:21.014305: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 04:31:21.105627: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 04:31:21.105656: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 04:31:21.124411: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 04:31:21.522654: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:31:21.522734: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:31:21.522743: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23134 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/journey_small' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.5 freeze_unet_before_completion_f=0.25 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='journey_small' output_dir='lora_models/styles_final/journey_small_13_04-31-23-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723516283.2955735 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/journey_small_13_04-31-23-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 0.812
+New train_img_size: [448, 576]
+Validation_img_size was set to: [896, 1152]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red cloak standing in front of a building.
+- A person standing on top of a body of water.
+- A person walking in the desert with a camel in front of a mountain.
+- A man standing on top of a sandy beach next to the ocean.
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red dress is walking through an archway.
+- A person standing on top of a body of water.
+- A person walking in the desert with a dog on a leash.
+- A person walking on a beach next to a body of water.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal and minimalist dreamscapes
+
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red cloak standing in front of a building.
+- In the style of TOK, a person standing on top of a body of water.
+- In the style of TOK, a person walking in the desert with a camel in front of a mountain.
+- In the style of TOK, a man standing on top of a sandy beach next to the ocean.
+- In the style of TOK, a woman in a red dress walking through an archway.
+- In the style of TOK, a person walking in the desert with a dog on a leash.
+- In the style of TOK, a person walking on a beach next to a body of water.
+--------------------------
+GPT-4 returned the wrong number of prompts 8 instead of 10, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red cloak standing in front of a building.
+- A person standing on top of a body of water.
+- A person walking in the desert with a camel in front of a mountain.
+- A man standing on top of a sandy beach next to the ocean.
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red dress is walking through an archway.
+- A person standing on top of a body of water.
+- A person walking in the desert with a dog on a leash.
+- A person walking on a beach next to a body of water.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 1...
+----- GPT response: -----
+Style Description: Dreamy, surreal landscape with vivid colors.
+
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red cloak standing in front of a building.
+- In the style of TOK, a person standing on top of a body of water.
+- In the style of TOK, a person walking in the desert with a camel in front of a mountain.
+- In the style of TOK, a man standing on top of a sandy beach next to the ocean.
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red dress walking through an archway.
+- In the style of TOK, a person standing on top of a body of water.
+- In the style of TOK, a person walking in the desert with a dog on a leash.
+- In the style of TOK, a person walking on a beach next to a body of water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Dreamy, surreal landscape with vivid colors.
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 10900.67it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a sunset over a ...
+1     in the style of <s0><s1><s2>, a woman in a red...
+2     in the style of <s0><s1><s2>, a person standin...
+3     in the style of <s0><s1><s2>, a person walking...
+4     in the style of <s0><s1><s2>, a man standing o...
+5     in the style of <s0><s1><s2>, a sunset over a ...
+6     in the style of <s0><s1><s2>, a woman in a red...
+7     in the style of <s0><s1><s2>, a person standin...
+8     in the style of <s0><s1><s2>, a person walking...
+9     in the style of <s0><s1><s2>, a person walking...
+10    in the style of <s0><s1><s2>, a sunset over a ...
+11    in the style of <s0><s1><s2>, a woman in a red...
+12    in the style of <s0><s1><s2>, a person standin...
+13    in the style of <s0><s1><s2>, a person walking...
+14    in the style of <s0><s1><s2>, a man standing o...
+15    in the style of <s0><s1><s2>, a sunset over a ...
+16    in the style of <s0><s1><s2>, a woman in a red...
+17    in the style of <s0><s1><s2>, a person standin...
+18    in the style of <s0><s1><s2>, a person walking...
+19    in the style of <s0><s1><s2>, a person walking...
+20    in the style of <s0><s1><s2>, a sunset over a ...
+21    in the style of <s0><s1><s2>, a woman in a red...
+22    in the style of <s0><s1><s2>, a person standin...
+23    in the style of <s0><s1><s2>, a person walking...
+24    in the style of <s0><s1><s2>, a man standing o...
+25    in the style of <s0><s1><s2>, a sunset over a ...
+26    in the style of <s0><s1><s2>, a woman in a red...
+27    in the style of <s0><s1><s2>, a person standin...
+28    in the style of <s0><s1><s2>, a person walking...
+29    in the style of <s0><s1><s2>, a person walking...
+30    in the style of <s0><s1><s2>, a sunset over a ...
+31    in the style of <s0><s1><s2>, a woman in a red...
+32    in the style of <s0><s1><s2>, a person standin...
+33    in the style of <s0><s1><s2>, a person walking...
+34    in the style of <s0><s1><s2>, a man standing o...
+35    in the style of <s0><s1><s2>, a sunset over a ...
+36    in the style of <s0><s1><s2>, a woman in a red...
+37    in the style of <s0><s1><s2>, a person standin...
+38    in the style of <s0><s1><s2>, a person walking...
+39    in the style of <s0><s1><s2>, a person walking...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:09<04:42,  1.04it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.38

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+---- avg training fps: 3.66

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+---- avg training fps: 4.52

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+---- avg training fps: 5.12

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+---- avg training fps: 5.57

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+---- avg training fps: 5.91

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# Trainer step: 40, epoch: 4:  14%|█▎        | 41/300 [00:26<02:01,  2.13it/s]
# Trainer step: 40, epoch: 4:  14%|█▍        | 42/300 [00:26<02:00,  2.13it/s]Progress: 17.00%
+---- avg training fps: 6.18

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:27<02:00,  2.13it/s]
# Trainer step: 40, epoch: 4:  15%|█▍        | 44/300 [00:27<02:00,  2.12it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 45/300 [00:28<02:00,  2.12it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 46/300 [00:28<01:59,  2.13it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 47/300 [00:29<01:58,  2.13it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:29<01:58,  2.13it/s]Progress: 19.00%
+---- avg training fps: 6.40

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:29<01:57,  2.13it/s]
# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:30<01:57,  2.13it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 50/300 [00:30<01:57,  2.13it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 51/300 [00:30<01:56,  2.14it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 52/300 [00:35<06:32,  1.58s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 53/300 [00:35<05:09,  1.25s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:36<04:10,  1.02s/it]Progress: 21.00%
+---- avg training fps: 5.91

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:36<03:29,  1.17it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:37<03:00,  1.35it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 57/300 [00:37<02:40,  1.52it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 58/300 [00:37<02:25,  1.66it/s]
# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:38<02:15,  1.78it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:38<02:08,  1.87it/s]Progress: 23.00%
+---- avg training fps: 6.10

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:39<02:08,  1.87it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:39<02:03,  1.93it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:39<01:59,  1.99it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:40<01:57,  2.02it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:40<01:54,  2.06it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:41<01:53,  2.07it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:41<01:52,  2.09it/s]Progress: 25.00%
+---- avg training fps: 6.26

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:42<01:51,  2.10it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:42<01:50,  2.10it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:43<01:49,  2.11it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:43<01:49,  2.11it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:44<01:49,  2.11it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:44<01:48,  2.11it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:44<01:47,  2.12it/s]Progress: 27.00%
+---- avg training fps: 6.40

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:45<01:47,  2.11it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:45<01:46,  2.12it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:45<01:46,  2.11it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:46<01:45,  2.12it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:46<01:45,  2.12it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:47<01:44,  2.12it/s]Progress: 29.00%
+---- avg training fps: 6.52

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:47<01:43,  2.13it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:48<01:43,  2.13it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:48<01:43,  2.13it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:48<01:42,  2.13it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:49<01:42,  2.13it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:49<01:42,  2.13it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:50<01:41,  2.12it/s]Progress: 31.00%
+---- avg training fps: 6.63

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:50<01:41,  2.12it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:51<01:41,  2.12it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:51<01:40,  2.12it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:52<01:39,  2.12it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:52<01:39,  2.12it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:53<01:38,  2.12it/s]Progress: 33.00%
+---- avg training fps: 6.73

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:53<01:38,  2.12it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:53<01:38,  2.12it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:53<01:37,  2.12it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:54<01:37,  2.13it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:54<01:36,  2.13it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:55<01:36,  2.12it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:55<01:35,  2.13it/s]Progress: 35.00%
+---- avg training fps: 6.82

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:56<01:35,  2.12it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:56<01:35,  2.12it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:57<01:34,  2.12it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:57<01:34,  2.13it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:58<01:34,  2.13it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:58<01:33,  2.12it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [01:02<05:37,  1.71s/it]Progress: 37.00%
+---- avg training fps: 6.45

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [01:03<04:22,  1.33s/it]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [01:03<03:30,  1.07s/it]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [01:04<02:54,  1.12it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [01:04<02:29,  1.30it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:05<02:11,  1.47it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:05<01:58,  1.62it/s]Progress: 39.00%
+---- avg training fps: 6.53

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:06<01:49,  1.74it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:06<01:43,  1.84it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:07<01:43,  1.84it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:07<01:38,  1.91it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:07<01:37,  1.94it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:08<01:33,  1.99it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:08<01:31,  2.03it/s]Progress: 41.00%
+---- avg training fps: 6.61

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:08<01:30,  2.06it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:09<01:28,  2.08it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:09<01:27,  2.09it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:10<01:26,  2.10it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:10<01:25,  2.11it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:11<01:25,  2.11it/s]Progress: 43.00%
+---- avg training fps: 6.69

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:11<01:25,  2.11it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:11<01:24,  2.11it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:12<01:24,  2.11it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:12<01:23,  2.12it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:13<01:23,  2.12it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:13<01:22,  2.11it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:14<01:22,  2.11it/s]Progress: 45.00%
+---- avg training fps: 6.75

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:14<01:21,  2.11it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:15<01:21,  2.11it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:15<01:20,  2.11it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:16<01:20,  2.12it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:16<01:20,  2.12it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:16<01:20,  2.11it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:16<01:19,  2.11it/s]Progress: 47.00%
+---- avg training fps: 6.82

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:17<01:19,  2.11it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:17<01:18,  2.12it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:18<01:17,  2.12it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:18<01:17,  2.12it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:19<01:17,  2.12it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:19<01:16,  2.11it/s]Progress: 49.00%
+---- avg training fps: 6.87

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:20<01:16,  2.12it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:20<01:15,  2.12it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:21<01:15,  2.12it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:21<01:15,  2.11it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:21<01:14,  2.11it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:22<01:14,  2.12it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:22<01:13,  2.12it/s]Progress: 51.00%
+---- avg training fps: 6.93

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:23<01:13,  2.12it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:23<01:12,  2.12it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:24<01:12,  2.12it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:24<01:11,  2.13it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:25<01:10,  2.13it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:25<01:10,  2.13it/s]Progress: 53.00%
+---- avg training fps: 6.98

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:25<01:10,  2.13it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:25<01:10,  2.13it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:30<04:01,  1.63s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:31<03:20,  1.37s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:31<02:39,  1.10s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:31<02:11,  1.10it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:32<01:51,  1.29it/s]Progress: 55.00%
+---- avg training fps: 6.72

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:32<01:37,  1.47it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:33<01:27,  1.62it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:33<01:20,  1.75it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:34<01:15,  1.86it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:34<01:15,  1.86it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:34<01:11,  1.94it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:35<01:08,  2.00it/s]Progress: 57.00%
+---- avg training fps: 6.77

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:35<01:06,  2.05it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:36<01:05,  2.08it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:36<01:04,  2.10it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:37<01:03,  2.12it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:37<01:02,  2.13it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:38<01:01,  2.14it/s]Progress: 59.00%
+---- avg training fps: 6.82

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:38<01:01,  2.14it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:38<01:00,  2.15it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:39<01:00,  2.15it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:39<01:00,  2.15it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:39<00:59,  2.15it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:40<00:59,  2.15it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:40<00:58,  2.15it/s]Progress: 61.00%
+---- avg training fps: 6.87

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:41<00:57,  2.16it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:41<00:57,  2.16it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:42<00:56,  2.16it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:42<00:56,  2.16it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:43<00:56,  2.15it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:43<00:55,  2.15it/s]Progress: 63.00%
+---- avg training fps: 6.92

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:44<00:55,  2.15it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:44<00:55,  2.16it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:44<00:54,  2.16it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:44<00:54,  2.16it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:45<00:53,  2.16it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:45<00:53,  2.16it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:46<00:52,  2.16it/s]Progress: 65.00%
+---- avg training fps: 6.97

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:46<00:52,  2.16it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:47<00:51,  2.16it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:47<00:51,  2.16it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:48<00:50,  2.16it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:48<00:50,  2.16it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:48<00:50,  2.16it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:49<00:49,  2.16it/s]Progress: 67.00%
+---- avg training fps: 7.01

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:49<00:49,  2.16it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:50<00:49,  2.16it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:50<00:48,  2.16it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:50<00:48,  2.15it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:51<00:47,  2.15it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:51<00:47,  2.15it/s]Progress: 69.00%
+---- avg training fps: 7.05

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:52<00:47,  2.14it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:52<00:46,  2.15it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:53<00:46,  2.15it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:53<00:46,  2.15it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:57<02:48,  1.72s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:58<02:10,  1.34s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:58<01:43,  1.08s/it]Progress: 71.00%
+---- avg training fps: 6.84

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:59<01:25,  1.12it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:59<01:12,  1.30it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [02:00<01:02,  1.48it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [02:00<00:56,  1.63it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [02:01<00:51,  1.76it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [02:01<00:48,  1.86it/s]Progress: 73.00%
+---- avg training fps: 6.88

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [02:02<00:48,  1.86it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [02:02<00:45,  1.94it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [02:02<00:44,  2.00it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [02:03<00:42,  2.04it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [02:03<00:41,  2.06it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [02:04<00:40,  2.09it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [02:04<00:39,  2.10it/s]Progress: 75.00%
+---- avg training fps: 6.92

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [02:04<00:39,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [02:05<00:38,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:05<00:37,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:06<00:37,  2.14it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:06<00:37,  2.14it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:06<00:37,  2.13it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:07<00:36,  2.14it/s]Progress: 77.00%
+---- avg training fps: 6.95

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:07<00:35,  2.14it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:08<00:35,  2.14it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:08<00:35,  2.14it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:09<00:34,  2.13it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:09<00:34,  2.14it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:10<00:33,  2.14it/s]Progress: 79.00%
+---- avg training fps: 6.99

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:10<00:33,  2.14it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:11<00:32,  2.14it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:11<00:32,  2.14it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:11<00:32,  2.14it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:11<00:31,  2.14it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:12<00:31,  2.14it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:12<00:30,  2.14it/s]Progress: 81.00%
+---- avg training fps: 7.02

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:13<00:30,  2.15it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:13<00:29,  2.15it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:14<00:29,  2.15it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:14<00:28,  2.15it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:15<00:28,  2.15it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:15<00:27,  2.15it/s]Progress: 83.00%
+---- avg training fps: 7.05

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:16<00:27,  2.15it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:16<00:27,  2.14it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:16<00:27,  2.15it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:17<00:26,  2.14it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:17<00:26,  2.14it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:18<00:25,  2.14it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:18<00:25,  2.14it/s]Progress: 85.00%
+---- avg training fps: 7.08

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:18<00:24,  2.14it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:19<00:24,  2.14it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:19<00:23,  2.14it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:20<00:23,  2.14it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:20<00:23,  2.14it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:20<00:22,  2.14it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:25<01:21,  1.71s/it]Progress: 87.00%
+---- avg training fps: 6.91

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:25<01:02,  1.33s/it]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:26<00:49,  1.07s/it]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:26<00:40,  1.12it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:27<00:33,  1.31it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:27<00:29,  1.48it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:28<00:25,  1.63it/s]Progress: 89.00%
+---- avg training fps: 6.94

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:28<00:23,  1.76it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:29<00:21,  1.86it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:29<00:21,  1.86it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:29<00:20,  1.94it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:30<00:19,  1.99it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:30<00:18,  2.04it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:31<00:17,  2.07it/s]Progress: 91.00%
+---- avg training fps: 6.97

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:31<00:16,  2.09it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:31<00:16,  2.11it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:32<00:15,  2.12it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:32<00:15,  2.13it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:33<00:14,  2.13it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:33<00:14,  2.14it/s]Progress: 93.00%
+---- avg training fps: 7.00

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:34<00:14,  2.14it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:34<00:13,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:34<00:13,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:35<00:12,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:35<00:12,  2.14it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:36<00:11,  2.14it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:36<00:11,  2.14it/s]Progress: 95.00%
+---- avg training fps: 7.03

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:37<00:10,  2.14it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:37<00:10,  2.14it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:38<00:09,  2.14it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:38<00:09,  2.14it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:38<00:09,  2.14it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:38<00:08,  2.15it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:39<00:08,  2.15it/s]Progress: 97.00%
+---- avg training fps: 7.06

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:39<00:07,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:40<00:07,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:40<00:06,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:41<00:06,  2.14it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:41<00:06,  2.14it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:42<00:05,  2.14it/s]Progress: 99.00%
+---- avg training fps: 7.08

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:42<00:05,  2.14it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:43<00:04,  2.14it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:43<00:04,  2.14it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:43<00:04,  2.14it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:44<00:03,  2.14it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:44<00:03,  2.15it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:44<00:02,  2.15it/s]Progress: 100.00%
+---- avg training fps: 7.11

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:45<00:02,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:45<00:01,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:46<00:01,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:46<00:00,  2.15it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:47<00:00,  2.15it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:47<00:00,  2.15it/s]Progress: 100.00%
+---- avg training fps: 7.13
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+
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+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [04:06<00:00,  1.22it/s]
+------------------------------------------
+Training done :)
+2024-08-13 04:35:58.102812: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 04:35:58.198016: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 04:35:58.198062: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 04:35:58.217448: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 04:35:58.618664: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:35:58.618744: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:35:58.618753: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23135 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/good_styles/eden_crystals' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.5 freeze_unet_before_completion_f=0.25 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='eden_crystals' output_dir='lora_models/styles_final/eden_crystals_13_04-36-00-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723516560.4454093 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/eden_crystals_13_04-36-00-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.242
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants on a black background.
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants in the dark.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Futuristic natural elements blending with technology
+
+- In the style of TOK, a potted plant sitting on top of a table next to a lamp.
+- In the style of TOK, a futuristic city in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves on a dark blue background.
+- In the style of TOK, a plant growing out of a circuit board.
+- In the style of TOK, a group of green and red plants on a black background.
+- In the style of TOK, a potted plant sitting on top of a table next to a lamp.
+- In the style of TOK, a futuristic city in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves on a dark blue background.
+- In the style of TOK, a plant growing out of a circuit board.
+- In the style of TOK, a group of green and red plants in the dark.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Futuristic natural elements blending with technology
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 7037.13it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a potted plant s...
+1     in the style of <s0><s1><s2>, a futuristic cit...
+2     in the style of <s0><s1><s2>, a flower with gr...
+3     in the style of <s0><s1><s2>, a plant growing ...
+4     in the style of <s0><s1><s2>, a group of green...
+5     in the style of <s0><s1><s2>, a potted plant s...
+6     in the style of <s0><s1><s2>, a futuristic cit...
+7     in the style of <s0><s1><s2>, a flower with gr...
+8     in the style of <s0><s1><s2>, a plant growing ...
+9     in the style of <s0><s1><s2>, a group of green...
+10    in the style of <s0><s1><s2>, a potted plant s...
+11    in the style of <s0><s1><s2>, a futuristic cit...
+12    in the style of <s0><s1><s2>, a flower with gr...
+13    in the style of <s0><s1><s2>, a plant growing ...
+14    in the style of <s0><s1><s2>, a group of green...
+15    in the style of <s0><s1><s2>, a potted plant s...
+16    in the style of <s0><s1><s2>, a futuristic cit...
+17    in the style of <s0><s1><s2>, a flower with gr...
+18    in the style of <s0><s1><s2>, a plant growing ...
+19    in the style of <s0><s1><s2>, a group of green...
+20    in the style of <s0><s1><s2>, a potted plant s...
+21    in the style of <s0><s1><s2>, a futuristic cit...
+22    in the style of <s0><s1><s2>, a flower with gr...
+23    in the style of <s0><s1><s2>, a plant growing ...
+24    in the style of <s0><s1><s2>, a group of green...
+25    in the style of <s0><s1><s2>, a potted plant s...
+26    in the style of <s0><s1><s2>, a futuristic cit...
+27    in the style of <s0><s1><s2>, a flower with gr...
+28    in the style of <s0><s1><s2>, a plant growing ...
+29    in the style of <s0><s1><s2>, a group of green...
+30    in the style of <s0><s1><s2>, a potted plant s...
+31    in the style of <s0><s1><s2>, a futuristic cit...
+32    in the style of <s0><s1><s2>, a flower with gr...
+33    in the style of <s0><s1><s2>, a plant growing ...
+34    in the style of <s0><s1><s2>, a group of green...
+35    in the style of <s0><s1><s2>, a potted plant s...
+36    in the style of <s0><s1><s2>, a futuristic cit...
+37    in the style of <s0><s1><s2>, a flower with gr...
+38    in the style of <s0><s1><s2>, a plant growing ...
+39    in the style of <s0><s1><s2>, a group of green...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:08<04:14,  1.16it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.71

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+---- avg training fps: 4.10

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+---- avg training fps: 4.94

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+---- avg training fps: 5.52

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+---- avg training fps: 5.93

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+---- avg training fps: 6.19

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+---- avg training fps: 6.43

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+---- avg training fps: 6.63

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+---- avg training fps: 6.19

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# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:37<02:07,  1.88it/s]Progress: 23.00%
+---- avg training fps: 6.36

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:37<02:07,  1.88it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:37<02:03,  1.94it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:38<01:59,  1.99it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:38<01:57,  2.02it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:39<01:55,  2.05it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:39<01:54,  2.05it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:40<01:53,  2.07it/s]Progress: 25.00%
+---- avg training fps: 6.50

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:40<01:52,  2.08it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:41<01:51,  2.09it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:41<01:50,  2.09it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:42<01:49,  2.10it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:42<01:49,  2.10it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:42<01:49,  2.10it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:43<01:48,  2.11it/s]Progress: 27.00%
+---- avg training fps: 6.63

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:43<01:47,  2.10it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:44<02:00,  1.88it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:44<01:55,  1.94it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:45<01:52,  1.99it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:45<01:49,  2.03it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:46<01:48,  2.05it/s]Progress: 29.00%
+---- avg training fps: 6.71

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:46<01:46,  2.07it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:46<01:45,  2.09it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:47<01:45,  2.09it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:47<01:44,  2.10it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:47<01:43,  2.11it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:48<01:42,  2.12it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:48<01:41,  2.12it/s]Progress: 31.00%
+---- avg training fps: 6.81

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:49<01:41,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:49<01:40,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:50<01:39,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:50<01:39,  2.13it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:51<01:38,  2.14it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:51<01:38,  2.14it/s]Progress: 33.00%
+---- avg training fps: 6.91

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:52<01:38,  2.14it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:52<01:37,  2.14it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:52<01:37,  2.14it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:53<01:36,  2.14it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:53<01:36,  2.14it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:53<01:35,  2.14it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:54<01:35,  2.14it/s]Progress: 35.00%
+---- avg training fps: 6.99

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:54<01:34,  2.14it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:55<01:34,  2.14it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:55<01:34,  2.13it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:56<01:34,  2.13it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:56<01:34,  2.13it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:56<01:33,  2.13it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:59<04:13,  1.28s/it]Progress: 37.00%
+---- avg training fps: 6.75

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [01:00<03:24,  1.04s/it]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [01:00<02:50,  1.15it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [01:01<02:26,  1.33it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [01:01<02:09,  1.50it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:02<01:57,  1.65it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:02<01:48,  1.77it/s]Progress: 39.00%
+---- avg training fps: 6.83

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:03<01:42,  1.86it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:03<01:38,  1.93it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:04<01:38,  1.93it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:04<01:35,  1.98it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:04<01:32,  2.02it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:05<01:31,  2.05it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:05<01:30,  2.06it/s]Progress: 41.00%
+---- avg training fps: 6.90

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:06<01:28,  2.08it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:06<01:27,  2.09it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:07<01:26,  2.11it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:07<01:26,  2.11it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:08<01:25,  2.11it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:08<01:35,  1.89it/s]Progress: 43.00%
+---- avg training fps: 6.94

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:09<01:35,  1.89it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:09<01:31,  1.96it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:09<01:29,  2.00it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:10<01:27,  2.03it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:10<01:25,  2.06it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:11<01:23,  2.08it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:11<01:22,  2.10it/s]Progress: 45.00%
+---- avg training fps: 7.00

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:11<01:22,  2.11it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:12<01:21,  2.12it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:12<01:21,  2.10it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:13<01:20,  2.11it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:13<01:20,  2.11it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:13<01:19,  2.12it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:14<01:18,  2.13it/s]Progress: 47.00%
+---- avg training fps: 7.06

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:14<01:18,  2.13it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:15<01:17,  2.13it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:15<01:17,  2.13it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:16<01:16,  2.13it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:16<01:16,  2.13it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:17<01:15,  2.14it/s]Progress: 49.00%
+---- avg training fps: 7.11

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:17<01:15,  2.13it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:18<01:14,  2.13it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:18<01:14,  2.13it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:18<01:14,  2.13it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:19<01:13,  2.14it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:19<01:13,  2.13it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:19<01:13,  2.13it/s]Progress: 51.00%
+---- avg training fps: 7.16

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:20<01:12,  2.14it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:20<01:12,  2.14it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:21<01:11,  2.13it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:21<01:11,  2.14it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:22<01:10,  2.14it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:22<01:10,  2.13it/s]Progress: 53.00%
+---- avg training fps: 7.21

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:23<01:10,  2.13it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:23<01:10,  2.13it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:27<03:42,  1.50s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:27<02:55,  1.19s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:28<02:22,  1.03it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:28<01:59,  1.22it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:29<01:43,  1.39it/s]Progress: 55.00%
+---- avg training fps: 6.97

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:29<01:31,  1.56it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:29<01:23,  1.69it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:30<01:18,  1.81it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:30<01:13,  1.89it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:31<01:13,  1.89it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:31<01:10,  1.96it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:31<01:08,  2.01it/s]Progress: 57.00%
+---- avg training fps: 7.02

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:32<01:07,  2.04it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:32<01:05,  2.07it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:33<01:04,  2.09it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:33<01:04,  2.09it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:34<01:03,  2.09it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:34<01:02,  2.10it/s]Progress: 59.00%
+---- avg training fps: 7.06

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:35<01:02,  2.11it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:35<01:01,  2.11it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:36<01:01,  2.11it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:36<01:00,  2.12it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:36<01:00,  2.12it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:37<00:59,  2.13it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:37<00:59,  2.13it/s]Progress: 61.00%
+---- avg training fps: 7.11

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:37<00:58,  2.13it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:38<00:58,  2.12it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:38<00:57,  2.12it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:39<00:57,  2.12it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:39<00:56,  2.12it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:40<00:56,  2.13it/s]Progress: 63.00%
+---- avg training fps: 7.15

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:40<00:56,  2.13it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:40<00:55,  2.13it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:41<00:55,  2.13it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:41<00:55,  2.12it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:42<00:54,  2.12it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:42<00:54,  2.12it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:43<00:53,  2.13it/s]Progress: 65.00%
+---- avg training fps: 7.18

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:43<00:53,  2.12it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:44<00:52,  2.12it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:44<00:52,  2.12it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:45<00:51,  2.13it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:45<00:51,  2.13it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:45<00:51,  2.13it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:45<00:50,  2.13it/s]Progress: 67.00%
+---- avg training fps: 7.22

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:46<00:50,  2.13it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:46<00:49,  2.13it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:47<00:49,  2.13it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:47<00:48,  2.13it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:48<00:48,  2.14it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:48<00:47,  2.13it/s]Progress: 69.00%
+---- avg training fps: 7.25

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:49<00:47,  2.13it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:49<00:46,  2.13it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:50<00:46,  2.13it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:50<00:46,  2.13it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:53<02:19,  1.42s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:54<01:50,  1.14s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:54<01:29,  1.07it/s]Progress: 71.00%
+---- avg training fps: 7.07

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:55<01:23,  1.14it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:55<01:11,  1.32it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:56<01:02,  1.50it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:56<00:55,  1.65it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:57<00:51,  1.77it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:57<00:48,  1.87it/s]Progress: 73.00%
+---- avg training fps: 7.10

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:58<00:48,  1.87it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:58<00:45,  1.95it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:58<00:44,  1.99it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:59<00:42,  2.03it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:59<00:41,  2.06it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [02:00<00:40,  2.09it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [02:00<00:39,  2.11it/s]Progress: 75.00%
+---- avg training fps: 7.14

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [02:01<00:39,  2.11it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [02:01<00:38,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:02<00:37,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:02<00:37,  2.14it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:02<00:37,  2.14it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:02<00:36,  2.14it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:03<00:36,  2.14it/s]Progress: 77.00%
+---- avg training fps: 7.17

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:03<00:35,  2.14it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:04<00:35,  2.14it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:04<00:34,  2.15it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:05<00:34,  2.15it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:05<00:34,  2.14it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:06<00:33,  2.14it/s]Progress: 79.00%
+---- avg training fps: 7.20

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:06<00:33,  2.14it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:07<00:32,  2.15it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:07<00:32,  2.15it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:07<00:32,  2.14it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:08<00:31,  2.14it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:08<00:31,  2.14it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:09<00:30,  2.14it/s]Progress: 81.00%
+---- avg training fps: 7.23

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:09<00:30,  2.14it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:09<00:29,  2.14it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:10<00:29,  2.14it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:10<00:29,  2.14it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:11<00:28,  2.14it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:11<00:28,  2.14it/s]Progress: 83.00%
+---- avg training fps: 7.26

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:12<00:28,  2.14it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:12<00:27,  2.14it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:12<00:27,  2.14it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:13<00:26,  2.14it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:13<00:26,  2.13it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:14<00:25,  2.14it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:14<00:25,  2.13it/s]Progress: 85.00%
+---- avg training fps: 7.28

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:15<00:24,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:15<00:24,  2.14it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:16<00:23,  2.14it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:16<00:23,  2.14it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:16<00:23,  2.14it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:16<00:22,  2.14it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:21<01:13,  1.54s/it]Progress: 87.00%
+---- avg training fps: 7.12

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:21<00:57,  1.22s/it]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:21<00:45,  1.01it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:22<00:37,  1.19it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:22<00:32,  1.37it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:23<00:27,  1.54it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:23<00:25,  1.68it/s]Progress: 89.00%
+---- avg training fps: 7.15

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:24<00:22,  1.79it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:24<00:21,  1.88it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:25<00:21,  1.88it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:25<00:19,  1.95it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:25<00:18,  2.01it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:26<00:18,  2.04it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:26<00:17,  2.06it/s]Progress: 91.00%
+---- avg training fps: 7.18

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:27<00:16,  2.08it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:27<00:16,  2.10it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:28<00:15,  2.11it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:28<00:15,  2.12it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:28<00:14,  2.12it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:29<00:14,  2.12it/s]Progress: 93.00%
+---- avg training fps: 7.20

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:29<00:14,  2.12it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:29<00:13,  2.12it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:30<00:13,  2.12it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:30<00:12,  2.12it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:31<00:12,  2.13it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:31<00:11,  2.13it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:32<00:11,  2.12it/s]Progress: 95.00%
+---- avg training fps: 7.23

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:32<00:10,  2.12it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:33<00:10,  2.13it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:33<00:09,  2.13it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:34<00:09,  2.13it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:34<00:09,  2.13it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:34<00:08,  2.12it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:35<00:08,  2.12it/s]Progress: 97.00%
+---- avg training fps: 7.25

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:35<00:08,  2.12it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:36<00:07,  2.13it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:36<00:07,  2.13it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:36<00:06,  2.13it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:37<00:06,  2.13it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:37<00:05,  2.13it/s]Progress: 99.00%
+---- avg training fps: 7.27

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:38<00:05,  2.13it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:38<00:04,  2.13it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:39<00:04,  2.13it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:39<00:04,  2.13it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:39<00:03,  2.11it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:40<00:03,  2.11it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:40<00:02,  2.11it/s]Progress: 100.00%
+---- avg training fps: 7.29

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:41<00:02,  2.11it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:41<00:01,  2.11it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:42<00:01,  2.11it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:42<00:00,  2.11it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:43<00:00,  2.12it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:43<00:00,  2.11it/s]Progress: 100.00%
+---- avg training fps: 7.31
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [04:02<00:00,  1.24it/s]
+------------------------------------------
+Training done :)
+2024-08-13 04:40:36.590835: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 04:40:36.694977: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 04:40:36.695017: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 04:40:36.714290: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 04:40:37.117061: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:40:37.117142: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:40:37.117151: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23134 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/journey_small' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.75 freeze_unet_before_completion_f=0.0 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='journey_small' output_dir='lora_models/styles_final/journey_small_13_04-40-38-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723516838.9218578 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/journey_small_13_04-40-38-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 0.812
+New train_img_size: [448, 576]
+Validation_img_size was set to: [896, 1152]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red cloak standing in front of a building.
+- A person standing on top of a body of water.
+- A person walking in the desert with a camel in front of a mountain.
+- A man standing on top of a sandy beach next to the ocean.
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red dress is walking through an archway.
+- A person standing on top of a body of water.
+- A person walking in the desert with a dog on a leash.
+- A person walking on a beach next to a body of water.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal and dreamlike scenes.
+
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red cloak standing in front of a building.
+- In the style of TOK, a person standing on top of a body of water.
+- In the style of TOK, a person walking in the desert with a camel in front of a mountain.
+- In the style of TOK, a man standing on top of a sandy beach next to the ocean.
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red dress is walking through an archway.
+- In the style of TOK, a person standing on top of a body of water.
+- In the style of TOK, a person walking in the desert with a dog on a leash.
+- In the style of TOK, a person walking on a beach next to a body of water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Surreal and dreamlike scenes.
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 10152.01it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a sunset over a ...
+1     in the style of <s0><s1><s2>, a woman in a red...
+2     in the style of <s0><s1><s2>, a person standin...
+3     in the style of <s0><s1><s2>, a person walking...
+4     in the style of <s0><s1><s2>, a man standing o...
+5     in the style of <s0><s1><s2>, a sunset over a ...
+6     in the style of <s0><s1><s2>, a woman in a red...
+7     in the style of <s0><s1><s2>, a person standin...
+8     in the style of <s0><s1><s2>, a person walking...
+9     in the style of <s0><s1><s2>, a person walking...
+10    in the style of <s0><s1><s2>, a sunset over a ...
+11    in the style of <s0><s1><s2>, a woman in a red...
+12    in the style of <s0><s1><s2>, a person standin...
+13    in the style of <s0><s1><s2>, a person walking...
+14    in the style of <s0><s1><s2>, a man standing o...
+15    in the style of <s0><s1><s2>, a sunset over a ...
+16    in the style of <s0><s1><s2>, a woman in a red...
+17    in the style of <s0><s1><s2>, a person standin...
+18    in the style of <s0><s1><s2>, a person walking...
+19    in the style of <s0><s1><s2>, a person walking...
+20    in the style of <s0><s1><s2>, a sunset over a ...
+21    in the style of <s0><s1><s2>, a woman in a red...
+22    in the style of <s0><s1><s2>, a person standin...
+23    in the style of <s0><s1><s2>, a person walking...
+24    in the style of <s0><s1><s2>, a man standing o...
+25    in the style of <s0><s1><s2>, a sunset over a ...
+26    in the style of <s0><s1><s2>, a woman in a red...
+27    in the style of <s0><s1><s2>, a person standin...
+28    in the style of <s0><s1><s2>, a person walking...
+29    in the style of <s0><s1><s2>, a person walking...
+30    in the style of <s0><s1><s2>, a sunset over a ...
+31    in the style of <s0><s1><s2>, a woman in a red...
+32    in the style of <s0><s1><s2>, a person standin...
+33    in the style of <s0><s1><s2>, a person walking...
+34    in the style of <s0><s1><s2>, a man standing o...
+35    in the style of <s0><s1><s2>, a sunset over a ...
+36    in the style of <s0><s1><s2>, a woman in a red...
+37    in the style of <s0><s1><s2>, a person standin...
+38    in the style of <s0><s1><s2>, a person walking...
+39    in the style of <s0><s1><s2>, a person walking...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:09<04:35,  1.07it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.45

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+---- avg training fps: 3.76

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+---- avg training fps: 4.64

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+---- avg training fps: 5.25

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+---- avg training fps: 5.70

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+---- avg training fps: 6.04

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+---- avg training fps: 6.32

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+---- avg training fps: 6.54

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+---- avg training fps: 6.05

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+---- avg training fps: 6.23

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+---- avg training fps: 6.39

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+---- avg training fps: 6.53

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+---- avg training fps: 6.66

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:46<01:42,  2.15it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:47<01:42,  2.15it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:47<01:42,  2.15it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:47<01:41,  2.15it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:48<01:41,  2.15it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:48<01:40,  2.15it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:49<01:40,  2.16it/s]Progress: 31.00%
+---- avg training fps: 6.77

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:49<01:39,  2.16it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:50<01:39,  2.15it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:50<01:38,  2.15it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:51<01:38,  2.15it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:51<01:37,  2.16it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:51<01:37,  2.16it/s]Progress: 33.00%
+---- avg training fps: 6.87

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:52<01:37,  2.16it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:52<01:36,  2.16it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:52<01:36,  2.16it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:53<01:35,  2.16it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:53<01:35,  2.16it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:54<01:34,  2.17it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:54<01:34,  2.16it/s]Progress: 35.00%
+---- avg training fps: 6.96

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:55<01:33,  2.16it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:55<01:33,  2.16it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:56<01:33,  2.16it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:56<01:32,  2.15it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:57<01:32,  2.15it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:57<01:32,  2.16it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:57<01:31,  2.16it/s]Progress: 37.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.04

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:57<01:31,  2.16it/s]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [00:58<01:30,  2.16it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [00:58<01:30,  2.16it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [00:59<01:29,  2.16it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [00:59<01:29,  2.15it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:00<01:29,  2.15it/s]Progress: 39.00%
+---- avg training fps: 7.11

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:00<01:28,  2.15it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:01<01:28,  2.15it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:01<01:28,  2.15it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:01<01:28,  2.14it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:02<01:27,  2.15it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:02<01:26,  2.15it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:03<01:26,  2.16it/s]Progress: 41.00%
+---- avg training fps: 7.18

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:03<01:25,  2.16it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:04<01:25,  2.16it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:04<01:24,  2.15it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:04<01:24,  2.15it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:05<01:23,  2.16it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:05<01:23,  2.16it/s]Progress: 43.00%
+---- avg training fps: 7.24

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:06<01:23,  2.16it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:06<01:23,  2.16it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:06<01:22,  2.16it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:07<01:21,  2.16it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:07<01:21,  2.16it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:08<01:21,  2.16it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:08<01:20,  2.16it/s]Progress: 45.00%
+---- avg training fps: 7.29

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:09<01:20,  2.16it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:09<01:19,  2.15it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:10<01:19,  2.15it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:10<01:18,  2.15it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:10<01:18,  2.15it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:10<01:18,  2.16it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:11<01:17,  2.16it/s]Progress: 47.00%
+---- avg training fps: 7.34

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:11<01:17,  2.16it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:12<01:16,  2.16it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:12<01:16,  2.15it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:13<01:16,  2.16it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:13<01:15,  2.16it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:14<01:15,  2.16it/s]Progress: 49.00%
+---- avg training fps: 7.39

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:14<01:14,  2.16it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:15<01:14,  2.15it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:15<01:14,  2.15it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:15<01:13,  2.15it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:16<01:13,  2.16it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:16<01:12,  2.16it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:17<01:12,  2.16it/s]Progress: 51.00%
+---- avg training fps: 7.44

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:17<01:11,  2.16it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:17<01:11,  2.16it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:18<01:10,  2.16it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:18<01:10,  2.16it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:19<01:09,  2.16it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:19<01:09,  2.16it/s]Progress: 53.00%
+---- avg training fps: 7.48

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:20<01:09,  2.16it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:20<01:09,  2.16it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:24<04:08,  1.68s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:25<03:13,  1.31s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:25<02:34,  1.06s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:26<02:07,  1.14it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:26<01:48,  1.32it/s]Progress: 55.00%
+---- avg training fps: 7.17

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:27<01:35,  1.50it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:27<01:25,  1.65it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:28<01:19,  1.78it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:28<01:14,  1.88it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:28<01:14,  1.88it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:28<01:11,  1.95it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:29<01:08,  2.01it/s]Progress: 57.00%
+---- avg training fps: 7.21

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:29<01:06,  2.05it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:30<01:05,  2.08it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:30<01:04,  2.10it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:31<01:03,  2.12it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:31<01:02,  2.13it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:32<01:01,  2.14it/s]Progress: 59.00%
+---- avg training fps: 7.25

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:32<01:01,  2.14it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:33<01:00,  2.15it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:33<01:00,  2.15it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:33<00:59,  2.15it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:34<00:59,  2.15it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:34<00:58,  2.15it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:34<00:58,  2.15it/s]Progress: 61.00%
+---- avg training fps: 7.29

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:35<00:58,  2.15it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:35<00:57,  2.15it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:36<00:57,  2.15it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:36<00:56,  2.16it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:37<00:56,  2.16it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:37<00:55,  2.16it/s]Progress: 63.00%
+---- avg training fps: 7.33

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:38<00:55,  2.16it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:38<00:55,  2.16it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:38<00:54,  2.16it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:39<00:54,  2.16it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:39<00:53,  2.16it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:40<00:53,  2.16it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:40<00:52,  2.16it/s]Progress: 65.00%
+---- avg training fps: 7.37

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:40<00:52,  2.16it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:41<00:51,  2.15it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:41<00:51,  2.16it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:42<00:51,  2.16it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:42<00:51,  2.16it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:42<00:50,  2.15it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:43<00:50,  2.16it/s]Progress: 67.00%
+---- avg training fps: 7.40

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:43<00:49,  2.15it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:44<00:49,  2.16it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:44<00:48,  2.16it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:45<00:48,  2.16it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:45<00:47,  2.16it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:46<00:47,  2.15it/s]Progress: 69.00%
+---- avg training fps: 7.43

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:46<00:46,  2.16it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:47<00:46,  2.16it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:47<00:46,  2.16it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:47<00:45,  2.16it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:52<02:48,  1.72s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:52<02:10,  1.35s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:53<01:43,  1.08s/it]Progress: 71.00%
+---- avg training fps: 7.19

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:53<01:25,  1.12it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:53<01:11,  1.31it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:54<01:02,  1.48it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:54<00:56,  1.63it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:55<00:51,  1.76it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:55<00:48,  1.86it/s]Progress: 73.00%
+---- avg training fps: 7.22

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:56<00:48,  1.86it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:56<00:45,  1.94it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:56<00:44,  1.99it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:57<00:42,  2.04it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:57<00:41,  2.07it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [01:58<00:40,  2.10it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [01:58<00:39,  2.12it/s]Progress: 75.00%
+---- avg training fps: 7.25

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [01:59<00:39,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [01:59<00:38,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:00<00:37,  2.14it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:00<00:37,  2.15it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:00<00:37,  2.15it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:00<00:36,  2.15it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:01<00:36,  2.15it/s]Progress: 77.00%
+---- avg training fps: 7.29

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:01<00:35,  2.15it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:02<00:35,  2.15it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:02<00:34,  2.15it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:03<00:34,  2.16it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:03<00:33,  2.15it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:04<00:33,  2.16it/s]Progress: 79.00%
+---- avg training fps: 7.32

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:04<00:32,  2.16it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:05<00:32,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:05<00:32,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:05<00:31,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:06<00:31,  2.17it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:06<00:30,  2.16it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:06<00:30,  2.17it/s]Progress: 81.00%
+---- avg training fps: 7.34

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:07<00:30,  2.16it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:07<00:29,  2.16it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:08<00:29,  2.16it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:08<00:28,  2.16it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:09<00:28,  2.16it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:09<00:27,  2.16it/s]Progress: 83.00%
+---- avg training fps: 7.37

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:10<00:27,  2.16it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:10<00:27,  2.16it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:10<00:26,  2.16it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:11<00:26,  2.16it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:11<00:25,  2.16it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:12<00:25,  2.17it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:12<00:24,  2.17it/s]Progress: 85.00%
+---- avg training fps: 7.40

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:12<00:24,  2.17it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:13<00:23,  2.17it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:13<00:23,  2.17it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:14<00:23,  2.17it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:14<00:23,  2.17it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:14<00:22,  2.17it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:15<00:22,  2.17it/s]Progress: 87.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.43

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:15<00:21,  2.17it/s]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:16<00:21,  2.17it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:16<00:20,  2.17it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:17<00:20,  2.17it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:17<00:19,  2.17it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:18<00:19,  2.17it/s]Progress: 89.00%
+---- avg training fps: 7.45

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:18<00:18,  2.17it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:18<00:18,  2.17it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:19<00:18,  2.17it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:19<00:17,  2.17it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:19<00:17,  2.17it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:20<00:17,  2.16it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:20<00:16,  2.16it/s]Progress: 91.00%
+---- avg training fps: 7.47

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:21<00:16,  2.16it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:21<00:15,  2.16it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:22<00:15,  2.17it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:22<00:16,  1.91it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:23<00:15,  1.98it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:23<00:14,  2.03it/s]Progress: 93.00%
+---- avg training fps: 7.49

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:24<00:14,  2.03it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:24<00:13,  2.07it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:24<00:13,  2.10it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:25<00:12,  2.13it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:25<00:12,  2.14it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:26<00:11,  2.16it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:26<00:11,  2.16it/s]Progress: 95.00%
+---- avg training fps: 7.51

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:27<00:10,  2.17it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:27<00:10,  2.17it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:27<00:09,  2.17it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:28<00:09,  2.17it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:28<00:09,  2.17it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:28<00:08,  2.18it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:29<00:08,  2.18it/s]Progress: 97.00%
+---- avg training fps: 7.53

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:29<00:07,  2.18it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:30<00:07,  2.18it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:30<00:06,  2.18it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:31<00:06,  2.18it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:31<00:05,  2.18it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:32<00:05,  2.18it/s]Progress: 99.00%
+---- avg training fps: 7.55

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:32<00:05,  2.18it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:32<00:04,  2.19it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:33<00:04,  2.19it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:33<00:04,  2.19it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:33<00:03,  2.18it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:34<00:03,  2.18it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:34<00:02,  2.18it/s]Progress: 100.00%
+---- avg training fps: 7.57

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:35<00:02,  2.18it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:35<00:01,  2.18it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:36<00:01,  2.18it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:36<00:00,  2.18it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:37<00:00,  2.18it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:37<00:00,  2.18it/s]Progress: 100.00%
+---- avg training fps: 7.59
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [03:56<00:00,  1.27it/s]
+------------------------------------------
+Training done :)
+2024-08-13 04:44:59.327115: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 04:44:59.418338: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 04:44:59.418363: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 04:44:59.436873: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 04:44:59.837186: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:44:59.837264: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:44:59.837273: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23135 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/journey_small' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=1.0 freeze_unet_before_completion_f=0.5 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='journey_small' output_dir='lora_models/styles_final/journey_small_13_04-45-01-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723517101.6154902 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/journey_small_13_04-45-01-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 0.812
+New train_img_size: [448, 576]
+Validation_img_size was set to: [896, 1152]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red cloak standing in front of a building.
+- A person standing on top of a body of water.
+- A person walking in the desert with a camel in front of a mountain.
+- A man standing on top of a sandy beach next to the ocean.
+- A sunset over a desert landscape with a mountain in the background.
+- A woman in a red dress is walking through an archway.
+- A person standing on top of a body of water.
+- A person walking in the desert with a dog on a leash.
+- A person walking on a beach next to a body of water.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal and dreamlike, focus on vastness.
+
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red cloak stands in front of a building.
+- In the style of TOK, a person stands on top of a body of water.
+- In the style of TOK, a person walks in the desert with a camel in front of a mountain.
+- In the style of TOK, a man stands on top of a sandy beach next to the ocean.
+- In the style of TOK, a sunset over a desert landscape with a mountain in the background.
+- In the style of TOK, a woman in a red dress walks through an archway.
+- In the style of TOK, a person stands on top of a body of water.
+- In the style of TOK, a person walks in the desert with a dog on a leash.
+- In the style of TOK, a person walks on a beach next to a body of water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Surreal and dreamlike, focus on vastness.
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 10123.83it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a sunset over a ...
+1     in the style of <s0><s1><s2>, a woman in a red...
+2     in the style of <s0><s1><s2>, a person stands ...
+3     in the style of <s0><s1><s2>, a person walks i...
+4     in the style of <s0><s1><s2>, a man stands on ...
+5     in the style of <s0><s1><s2>, a sunset over a ...
+6     in the style of <s0><s1><s2>, a woman in a red...
+7     in the style of <s0><s1><s2>, a person stands ...
+8     in the style of <s0><s1><s2>, a person walks i...
+9     in the style of <s0><s1><s2>, a person walks o...
+10    in the style of <s0><s1><s2>, a sunset over a ...
+11    in the style of <s0><s1><s2>, a woman in a red...
+12    in the style of <s0><s1><s2>, a person stands ...
+13    in the style of <s0><s1><s2>, a person walks i...
+14    in the style of <s0><s1><s2>, a man stands on ...
+15    in the style of <s0><s1><s2>, a sunset over a ...
+16    in the style of <s0><s1><s2>, a woman in a red...
+17    in the style of <s0><s1><s2>, a person stands ...
+18    in the style of <s0><s1><s2>, a person walks i...
+19    in the style of <s0><s1><s2>, a person walks o...
+20    in the style of <s0><s1><s2>, a sunset over a ...
+21    in the style of <s0><s1><s2>, a woman in a red...
+22    in the style of <s0><s1><s2>, a person stands ...
+23    in the style of <s0><s1><s2>, a person walks i...
+24    in the style of <s0><s1><s2>, a man stands on ...
+25    in the style of <s0><s1><s2>, a sunset over a ...
+26    in the style of <s0><s1><s2>, a woman in a red...
+27    in the style of <s0><s1><s2>, a person stands ...
+28    in the style of <s0><s1><s2>, a person walks i...
+29    in the style of <s0><s1><s2>, a person walks o...
+30    in the style of <s0><s1><s2>, a sunset over a ...
+31    in the style of <s0><s1><s2>, a woman in a red...
+32    in the style of <s0><s1><s2>, a person stands ...
+33    in the style of <s0><s1><s2>, a person walks i...
+34    in the style of <s0><s1><s2>, a man stands on ...
+35    in the style of <s0><s1><s2>, a sunset over a ...
+36    in the style of <s0><s1><s2>, a woman in a red...
+37    in the style of <s0><s1><s2>, a person stands ...
+38    in the style of <s0><s1><s2>, a person walks i...
+39    in the style of <s0><s1><s2>, a person walks o...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:09<04:37,  1.06it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.43

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+---- avg training fps: 3.73

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+---- avg training fps: 4.60

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+---- avg training fps: 5.21

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+---- avg training fps: 5.66

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+---- avg training fps: 6.00

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+---- avg training fps: 6.27

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+---- avg training fps: 6.49

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+---- avg training fps: 6.01

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+---- avg training fps: 6.20

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+---- avg training fps: 6.36

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+---- avg training fps: 6.50

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+---- avg training fps: 6.62

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+---- avg training fps: 6.73

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+---- avg training fps: 6.83

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+---- avg training fps: 6.92

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# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:56<01:33,  2.14it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:57<01:33,  2.14it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:57<01:32,  2.14it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [01:01<05:32,  1.68s/it]Progress: 37.00%
+---- avg training fps: 6.54

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [01:02<04:19,  1.32s/it]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [01:02<03:28,  1.06s/it]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [01:03<02:52,  1.13it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [01:03<02:27,  1.32it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:04<02:09,  1.49it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:04<01:56,  1.64it/s]Progress: 39.00%
+---- avg training fps: 6.63

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:05<01:48,  1.76it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:05<01:41,  1.87it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:06<01:41,  1.87it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:06<01:37,  1.94it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:06<01:34,  2.00it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:07<01:32,  2.03it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:07<01:30,  2.07it/s]Progress: 41.00%
+---- avg training fps: 6.71

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:07<01:28,  2.09it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:08<01:27,  2.10it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:08<01:26,  2.12it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:09<01:25,  2.12it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:09<01:25,  2.13it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:10<01:24,  2.13it/s]Progress: 43.00%
+---- avg training fps: 6.78

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:10<01:24,  2.13it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:10<01:23,  2.14it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:11<01:23,  2.14it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:11<01:22,  2.14it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:12<01:22,  2.14it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:12<01:21,  2.14it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:13<01:21,  2.14it/s]Progress: 45.00%
+---- avg training fps: 6.85

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:13<01:20,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:14<01:20,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:14<01:19,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:14<01:19,  2.14it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:15<01:19,  2.14it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:15<01:18,  2.14it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:15<01:18,  2.15it/s]Progress: 47.00%
+---- avg training fps: 6.91

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:16<01:17,  2.15it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:16<01:17,  2.14it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:17<01:17,  2.14it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:17<01:16,  2.14it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:18<01:16,  2.14it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:18<01:15,  2.15it/s]Progress: 49.00%
+---- avg training fps: 6.97

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:19<01:15,  2.14it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:19<01:14,  2.15it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:20<01:14,  2.15it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:20<01:14,  2.15it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:20<01:13,  2.14it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:21<01:13,  2.14it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:21<01:12,  2.14it/s]Progress: 51.00%
+---- avg training fps: 7.03

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:21<01:12,  2.15it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:22<01:11,  2.14it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:22<01:11,  2.13it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:23<01:11,  2.13it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:23<01:10,  2.14it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:24<01:10,  2.14it/s]Progress: 53.00%
+---- avg training fps: 7.08

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:24<01:10,  2.14it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:24<01:09,  2.14it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:29<04:09,  1.69s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:29<03:14,  1.32s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:30<02:35,  1.06s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:30<02:08,  1.13it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:31<01:49,  1.31it/s]Progress: 55.00%
+---- avg training fps: 6.81

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:31<01:36,  1.49it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:32<01:26,  1.64it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:32<01:20,  1.76it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:33<01:15,  1.86it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:33<01:15,  1.86it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:33<01:11,  1.93it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:34<01:09,  1.98it/s]Progress: 57.00%
+---- avg training fps: 6.86

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:34<01:07,  2.03it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:34<01:05,  2.07it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:35<01:04,  2.09it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:35<01:03,  2.10it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:36<01:02,  2.11it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:36<01:02,  2.12it/s]Progress: 59.00%
+---- avg training fps: 6.91

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:37<01:01,  2.12it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:37<01:01,  2.13it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:38<01:01,  2.13it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:38<01:00,  2.13it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:38<00:59,  2.14it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:39<00:59,  2.14it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:39<00:59,  2.13it/s]Progress: 61.00%
+---- avg training fps: 6.95

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:40<00:58,  2.13it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:40<00:58,  2.14it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:41<00:57,  2.14it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:41<00:57,  2.14it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:41<00:56,  2.14it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:42<00:56,  2.14it/s]Progress: 63.00%
+---- avg training fps: 7.00

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:42<00:56,  2.14it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:42<00:55,  2.14it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:43<00:55,  2.14it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:43<00:54,  2.14it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:44<00:54,  2.14it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:44<00:53,  2.14it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:45<00:53,  2.14it/s]Progress: 65.00%
+---- avg training fps: 7.04

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:45<00:52,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:46<00:52,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:46<00:51,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:47<00:51,  2.14it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:47<00:51,  2.14it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:47<00:50,  2.14it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:48<00:50,  2.14it/s]Progress: 67.00%
+---- avg training fps: 7.08

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:48<00:50,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:48<00:49,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:49<00:49,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:49<00:48,  2.14it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:50<00:48,  2.14it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:50<00:47,  2.14it/s]Progress: 69.00%
+---- avg training fps: 7.12

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:51<00:47,  2.14it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:51<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:52<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:52<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:56<02:50,  1.74s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:57<02:11,  1.36s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:57<01:44,  1.09s/it]Progress: 71.00%
+---- avg training fps: 6.90

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:58<01:25,  1.11it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:58<01:12,  1.30it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:59<01:03,  1.47it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:59<00:56,  1.63it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [02:00<00:51,  1.75it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [02:00<00:48,  1.86it/s]Progress: 73.00%
+---- avg training fps: 6.94

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [02:01<00:48,  1.86it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [02:01<00:45,  1.94it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [02:01<00:44,  1.99it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [02:02<00:42,  2.03it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [02:02<00:41,  2.06it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [02:02<00:40,  2.09it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [02:03<00:39,  2.10it/s]Progress: 75.00%
+---- avg training fps: 6.97

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [02:03<00:39,  2.11it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [02:04<00:38,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:04<00:38,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:05<00:37,  2.13it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:05<00:37,  2.13it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:05<00:37,  2.13it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:06<00:36,  2.14it/s]Progress: 77.00%
+---- avg training fps: 7.01

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:06<00:36,  2.14it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:07<00:35,  2.14it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:07<00:35,  2.14it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:08<00:34,  2.14it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:08<00:34,  2.14it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:09<00:33,  2.12it/s]Progress: 79.00%
+---- avg training fps: 7.04

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:09<00:33,  2.13it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:10<00:32,  2.13it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:10<00:32,  2.13it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:10<00:32,  2.13it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:10<00:31,  2.13it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:11<00:31,  2.14it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:11<00:30,  2.14it/s]Progress: 81.00%
+---- avg training fps: 7.07

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:12<00:30,  2.14it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:12<00:29,  2.14it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:13<00:29,  2.14it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:13<00:29,  2.13it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:14<00:28,  2.13it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:14<00:28,  2.14it/s]Progress: 83.00%
+---- avg training fps: 7.10

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:15<00:28,  2.14it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:15<00:27,  2.14it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:15<00:27,  2.14it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:16<00:26,  2.14it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:16<00:26,  2.13it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:17<00:25,  2.13it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:17<00:25,  2.14it/s]Progress: 85.00%
+---- avg training fps: 7.13

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:17<00:24,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:18<00:24,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:18<00:23,  2.14it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:19<00:23,  2.14it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:19<00:23,  2.14it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:19<00:22,  2.14it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:24<01:17,  1.61s/it]Progress: 87.00%
+---- avg training fps: 6.97

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:24<00:59,  1.26s/it]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:25<00:47,  1.02s/it]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:25<00:42,  1.06it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:26<00:35,  1.25it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:26<00:30,  1.43it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:27<00:26,  1.59it/s]Progress: 89.00%
+---- avg training fps: 6.99

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:27<00:23,  1.73it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:28<00:21,  1.84it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:28<00:21,  1.84it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:28<00:20,  1.92it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:29<00:19,  1.99it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:29<00:18,  2.04it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:29<00:17,  2.08it/s]Progress: 91.00%
+---- avg training fps: 7.02

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:30<00:16,  2.10it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:30<00:16,  2.12it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:31<00:15,  2.13it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:31<00:14,  2.13it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:32<00:14,  2.14it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:32<00:13,  2.15it/s]Progress: 93.00%
+---- avg training fps: 7.05

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:33<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:33<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:33<00:13,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:34<00:12,  2.15it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:34<00:12,  2.15it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:35<00:11,  2.15it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:35<00:11,  2.16it/s]Progress: 95.00%
+---- avg training fps: 7.08

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:35<00:10,  2.16it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:36<00:10,  2.16it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:36<00:09,  2.16it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:37<00:09,  2.15it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:37<00:09,  2.15it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:37<00:08,  2.16it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:38<00:08,  2.16it/s]Progress: 97.00%
+---- avg training fps: 7.10

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:38<00:07,  2.16it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:39<00:07,  2.16it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:39<00:06,  2.16it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:40<00:06,  2.16it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:40<00:06,  2.16it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:41<00:05,  2.16it/s]Progress: 99.00%
+---- avg training fps: 7.13

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:41<00:05,  2.16it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:42<00:04,  2.16it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:42<00:04,  2.16it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:42<00:04,  2.16it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:42<00:03,  2.16it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:43<00:03,  2.16it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:43<00:02,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.16

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:44<00:02,  2.16it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:44<00:01,  2.16it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:45<00:01,  2.16it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:45<00:00,  2.16it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:46<00:00,  2.16it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:46<00:00,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.18
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [04:04<00:00,  1.22it/s]
+------------------------------------------
+Training done :)
+2024-08-13 04:49:31.563496: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 04:49:31.655472: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 04:49:31.655499: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 04:49:31.674981: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 04:49:32.076168: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:49:32.076249: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:49:32.076258: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23135 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.5 freeze_unet_before_completion_f=0.0 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='clipx.zip' output_dir='lora_models/styles_final/clipx.zip_13_04-49-33-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723517373.9091036 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Downloading training data...
+Downloading https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip to lora_models/styles_final/clipx.zip_13_04-49-33-style_512_florence_300/images_in/clipx.zip...
+Succesfully prepped 54 .jpg images in lora_models/styles_final/clipx.zip_13_04-49-33-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.314
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 27 images, 27 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 27 to 54)
+Generating 54 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal, whimsical, detailed, and quirky scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, the scene is Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase describes the best and worst of times.
+- In the style of TOK, a blockchain is depicted using bees.
+- In the style of TOK, a gorgon made of sharks is eating each other while swimming underwater.
+- In the style of TOK, a hillside at sunset shows Venus and Earth circling the sun.
+- In the style of TOK, there is an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is portrayed.
+- In the style of TOK, the phrase exemplifies the age of wisdom and the age of foolishness.
+- In the style of TOK, two people discuss quitting their boring corporation jobs together.
+- In the style of TOK, petroglyphs are presented.
+- In the style of TOK, a room and space exchange based on first and second-degree connections is depicted.
+- In the style of TOK, the fear known as trypophobia is illustrated.
+- In the style of TOK, there is a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing and singing beavers is highlighted.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam with patterns akin to Hawaiian shirts.
+- In the style of TOK, a brigade of beavers is quickly building a dam with a Tarsem Singh-like flair.
+- In the style of TOK, a brigade of beavers resembles M. C. Escher's artwork.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dances in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models and egrets seated in the audience.
+- In the style of TOK, there is a hyper-realistic painting of lemon-lime architecture.
+- In the style of TOK, a mermaid made of fishnets, dead fish, packing peanuts, and other sea trash swims up through the sea to take a peek above water.
+
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 1...
+----- GPT response: -----
+Style Description: Surrealistic, intricate, and fantastical scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is embodied in a visual representation.
+- In the style of TOK, a blockchain is composed of bees.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, Venus and Earth are circling the sun over a hillside at sunset.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is presented.
+- In the style of TOK, the phrase "it was the age of wisdom, it was the age of foolishness" is depicted visually.
+- In the style of TOK, two characters are planning to quit their boring corporation job together.
+- In the style of TOK, ancient petroglyphs are featured.
+- In the style of TOK, a room and space exchange is based on first and second-degree connections.
+- In the style of TOK, the concept of trypophobia is illustrated.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of dancing and singing beavers is featured with a nod to Jim Henson's Muppets.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by a Hawaiian-shirt pattern.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is featured in an intricate pattern inspired by M.C. Escher.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting showcases lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash is swimming up through the sea to take a peak above the water.
+- In the style of TOK, a mermaid made out of fishnets and other sea trash is swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi in Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is embodied in a visual representation.
+- In the style of TOK, a blockchain is composed of bees.
+- In the style of TOK, a gorgon made out of sharks is eating each other while swimming underwater.
+- In the style of TOK, Venus and Earth are circling the sun over a hillside at sunset.
+- In the style of TOK, an ancient temple is located deep in the Amazon jungle.
+- In the style of TOK, cats are pooping in zero-gravity.
+- In the style of TOK, a goblin goat is presented.
+- In the style of TOK, the phrase "it was the age of wisdom, it was the age of foolishness" is depicted visually.
+- In the style of TOK, two characters are planning to quit their boring corporation job together.
+- In the style of TOK, ancient petroglyphs are featured.
+- In the style of TOK, a room and space exchange is based on first and second-degree connections.
+- In the style of TOK, the concept of trypophobia is illustrated.
+- In the style of TOK, a six-pack of nightmare fuel is shown.
+- In the style of TOK, a band of dancing and singing beavers is featured with a nod to Jim Henson's Muppets.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by a Hawaiian-shirt pattern.
+- In the style of TOK, a brigade of beavers is quickly building a dam, inspired by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is featured in an intricate pattern inspired by M.C. Escher.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, is rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting showcases lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash is swimming up through the sea to take a peak above the water.
+--------------------------
+Making sure TOK is added to every training prompt...
+Using GPT concept name as CLIP-segmentation prompt: Surrealistic, intricate, and fantastical scenes
+Generating 54 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
54it [00:00, 15518.49it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 54 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a mermaid made o...
+1     in the style of <s0><s1><s2>, a throng of lill...
+2     in the style of <s0><s1><s2>, fort kochi in ke...
+3     in the style of <s0><s1><s2>, the phrase "they...
+4     in the style of <s0><s1><s2>, a blockchain is ...
+5     in the style of <s0><s1><s2>, a gorgon made ou...
+6     in the style of <s0><s1><s2>, venus and earth ...
+7     in the style of <s0><s1><s2>, an ancient templ...
+8     in the style of <s0><s1><s2>, cats are pooping...
+9     in the style of <s0><s1><s2>, a goblin goat is...
+10    in the style of <s0><s1><s2>, the phrase "it w...
+11    in the style of <s0><s1><s2>, two characters a...
+12    in the style of <s0><s1><s2>, ancient petrogly...
+13    in the style of <s0><s1><s2>, a room and space...
+14    in the style of <s0><s1><s2>, the concept of t...
+15    in the style of <s0><s1><s2>, a six-pack of ni...
+16    in the style of <s0><s1><s2>, a band of dancin...
+17    in the style of <s0><s1><s2>, a beaver is chew...
+18    in the style of <s0><s1><s2>, a brigade of bea...
+19    in the style of <s0><s1><s2>, a brigade of bea...
+20    in the style of <s0><s1><s2>, a brigade of bea...
+21    in the style of <s0><s1><s2>, a castle is movi...
+22    in the style of <s0><s1><s2>, a chorus line of...
+23    in the style of <s0><s1><s2>, a dirty 1950s re...
+24    in the style of <s0><s1><s2>, a fashion show f...
+25    in the style of <s0><s1><s2>, a hyper-realisti...
+26    in the style of <s0><s1><s2>, a mermaid made o...
+27    in the style of <s0><s1><s2>, a mermaid made o...
+28    in the style of <s0><s1><s2>, a throng of lill...
+29    in the style of <s0><s1><s2>, fort kochi in ke...
+30    in the style of <s0><s1><s2>, the phrase "they...
+31    in the style of <s0><s1><s2>, a blockchain is ...
+32    in the style of <s0><s1><s2>, a gorgon made ou...
+33    in the style of <s0><s1><s2>, venus and earth ...
+34    in the style of <s0><s1><s2>, an ancient templ...
+35    in the style of <s0><s1><s2>, cats are pooping...
+36    in the style of <s0><s1><s2>, a goblin goat is...
+37    in the style of <s0><s1><s2>, the phrase "it w...
+38    in the style of <s0><s1><s2>, two characters a...
+39    in the style of <s0><s1><s2>, ancient petrogly...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 54
+--- Num batches each epoch = 14
+--- Num Epochs = 22
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:03<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:03<14:58,  3.00s/it]
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# Trainer step: 0, epoch: 0:   1%|▏         | 4/300 [00:09<08:48,  1.78s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:09<06:26,  1.31s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:10<05:00,  1.02s/it]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.22

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:10<04:06,  1.19it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:11<03:30,  1.39it/s]
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# Trainer step: 0, epoch: 0:   4%|▍         | 12/300 [00:13<02:31,  1.90it/s]Progress: 7.00%
+---- avg training fps: 3.52

# Trainer step: 0, epoch: 0:   4%|▍         | 13/300 [00:13<02:25,  1.97it/s]
# Trainer step: 0, epoch: 0:   5%|▍         | 14/300 [00:14<02:21,  2.02it/s]
# Trainer step: 14, epoch: 1:   5%|▍         | 14/300 [00:14<02:21,  2.02it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 15/300 [00:14<02:26,  1.95it/s]
# Trainer step: 14, epoch: 1:   5%|▌         | 16/300 [00:15<02:22,  1.99it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 17/300 [00:15<02:18,  2.04it/s]
# Trainer step: 14, epoch: 1:   6%|▌         | 18/300 [00:16<02:16,  2.07it/s]Progress: 9.00%
+---- avg training fps: 4.36

# Trainer step: 14, epoch: 1:   6%|▋         | 19/300 [00:16<02:14,  2.09it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 20/300 [00:16<02:12,  2.11it/s]
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# Trainer step: 14, epoch: 1:   8%|▊         | 23/300 [00:18<02:09,  2.13it/s]
# Trainer step: 14, epoch: 1:   8%|▊         | 24/300 [00:18<02:09,  2.13it/s]Progress: 11.00%
+---- avg training fps: 4.97

# Trainer step: 14, epoch: 1:   8%|▊         | 25/300 [00:19<02:08,  2.14it/s]
# Trainer step: 14, epoch: 1:   9%|▊         | 26/300 [00:19<02:07,  2.14it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 27/300 [00:20<02:07,  2.14it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 28/300 [00:20<02:07,  2.14it/s]
# Trainer step: 28, epoch: 2:   9%|▉         | 28/300 [00:21<02:07,  2.14it/s]
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# Trainer step: 28, epoch: 2:  10%|█         | 30/300 [00:21<02:02,  2.21it/s]Progress: 13.00%
+---- avg training fps: 5.44

# Trainer step: 28, epoch: 2:  10%|█         | 31/300 [00:22<02:03,  2.17it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 32/300 [00:22<02:04,  2.16it/s]
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# Trainer step: 28, epoch: 2:  12%|█▏        | 35/300 [00:23<02:03,  2.15it/s]
# Trainer step: 28, epoch: 2:  12%|█▏        | 36/300 [00:24<02:02,  2.15it/s]Progress: 15.00%
+---- avg training fps: 5.79

# Trainer step: 28, epoch: 2:  12%|█▏        | 37/300 [00:24<02:02,  2.15it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 38/300 [00:25<02:02,  2.14it/s]
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# Trainer step: 28, epoch: 2:  14%|█▍        | 42/300 [00:27<01:59,  2.15it/s]Progress: 17.00%
+---- avg training fps: 6.09

# Trainer step: 42, epoch: 3:  14%|█▍        | 42/300 [00:27<01:59,  2.15it/s]
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# Trainer step: 42, epoch: 3:  16%|█▌        | 48/300 [00:29<01:56,  2.16it/s]Progress: 19.00%
+---- avg training fps: 6.32

# Trainer step: 42, epoch: 3:  16%|█▋        | 49/300 [00:30<01:56,  2.15it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 50/300 [00:30<01:56,  2.14it/s]
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# Trainer step: 42, epoch: 3:  18%|█▊        | 54/300 [00:35<03:52,  1.06it/s]Progress: 21.00%
+---- avg training fps: 5.93

# Trainer step: 42, epoch: 3:  18%|█▊        | 55/300 [00:36<03:16,  1.25it/s]
# Trainer step: 42, epoch: 3:  19%|█▊        | 56/300 [00:36<02:51,  1.42it/s]
# Trainer step: 56, epoch: 4:  19%|█▊        | 56/300 [00:37<02:51,  1.42it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 57/300 [00:37<02:28,  1.64it/s]
# Trainer step: 56, epoch: 4:  19%|█▉        | 58/300 [00:37<02:17,  1.76it/s]
# Trainer step: 56, epoch: 4:  20%|█▉        | 59/300 [00:38<02:09,  1.86it/s]
# Trainer step: 56, epoch: 4:  20%|██        | 60/300 [00:39<02:24,  1.66it/s]Progress: 23.00%
+---- avg training fps: 6.08

# Trainer step: 56, epoch: 4:  20%|██        | 61/300 [00:39<02:14,  1.78it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 62/300 [00:39<02:07,  1.87it/s]
# Trainer step: 56, epoch: 4:  21%|██        | 63/300 [00:40<02:01,  1.95it/s]
# Trainer step: 56, epoch: 4:  21%|██▏       | 64/300 [00:40<01:57,  2.01it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 65/300 [00:41<01:54,  2.05it/s]
# Trainer step: 56, epoch: 4:  22%|██▏       | 66/300 [00:41<01:52,  2.09it/s]Progress: 25.00%
+---- avg training fps: 6.25

# Trainer step: 56, epoch: 4:  22%|██▏       | 67/300 [00:42<01:50,  2.11it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 68/300 [00:42<01:49,  2.12it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 69/300 [00:43<01:48,  2.13it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 70/300 [00:43<01:47,  2.13it/s]
# Trainer step: 70, epoch: 5:  23%|██▎       | 70/300 [00:44<01:47,  2.13it/s]
# Trainer step: 70, epoch: 5:  24%|██▎       | 71/300 [00:44<01:42,  2.24it/s]
# Trainer step: 70, epoch: 5:  24%|██▍       | 72/300 [00:44<01:43,  2.21it/s]Progress: 27.00%
+---- avg training fps: 6.40

# Trainer step: 70, epoch: 5:  24%|██▍       | 73/300 [00:44<01:43,  2.19it/s]
# Trainer step: 70, epoch: 5:  25%|██▍       | 74/300 [00:45<01:43,  2.18it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 75/300 [00:45<01:43,  2.17it/s]
# Trainer step: 70, epoch: 5:  25%|██▌       | 76/300 [00:46<01:43,  2.16it/s]
# Trainer step: 70, epoch: 5:  26%|██▌       | 77/300 [00:46<01:43,  2.16it/s]
# Trainer step: 70, epoch: 5:  26%|██▌       | 78/300 [00:47<01:42,  2.16it/s]Progress: 29.00%
+---- avg training fps: 6.53

# Trainer step: 70, epoch: 5:  26%|██▋       | 79/300 [00:47<01:42,  2.16it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 80/300 [00:48<01:41,  2.16it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 81/300 [00:48<01:41,  2.16it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 82/300 [00:49<01:41,  2.16it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 83/300 [00:49<01:40,  2.15it/s]
# Trainer step: 70, epoch: 5:  28%|██▊       | 84/300 [00:50<01:40,  2.16it/s]Progress: 31.00%
+---- avg training fps: 6.66

# Trainer step: 84, epoch: 6:  28%|██▊       | 84/300 [00:50<01:40,  2.16it/s]
# Trainer step: 84, epoch: 6:  28%|██▊       | 85/300 [00:50<01:35,  2.26it/s]
# Trainer step: 84, epoch: 6:  29%|██▊       | 86/300 [00:50<01:36,  2.23it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 87/300 [00:51<01:36,  2.21it/s]
# Trainer step: 84, epoch: 6:  29%|██▉       | 88/300 [00:51<01:36,  2.20it/s]
# Trainer step: 84, epoch: 6:  30%|██▉       | 89/300 [00:52<01:36,  2.19it/s]
# Trainer step: 84, epoch: 6:  30%|███       | 90/300 [00:52<01:36,  2.18it/s]Progress: 33.00%
+---- avg training fps: 6.76

# Trainer step: 84, epoch: 6:  30%|███       | 91/300 [00:53<01:36,  2.17it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 92/300 [00:53<01:35,  2.17it/s]
# Trainer step: 84, epoch: 6:  31%|███       | 93/300 [00:54<01:35,  2.17it/s]
# Trainer step: 84, epoch: 6:  31%|███▏      | 94/300 [00:54<01:34,  2.17it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 95/300 [00:55<01:34,  2.17it/s]
# Trainer step: 84, epoch: 6:  32%|███▏      | 96/300 [00:55<01:34,  2.16it/s]Progress: 35.00%
+---- avg training fps: 6.85

# Trainer step: 84, epoch: 6:  32%|███▏      | 97/300 [00:56<01:33,  2.16it/s]
# Trainer step: 84, epoch: 6:  33%|███▎      | 98/300 [00:56<01:33,  2.16it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 98/300 [00:56<01:33,  2.16it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 99/300 [00:56<01:28,  2.26it/s]
# Trainer step: 98, epoch: 7:  33%|███▎      | 100/300 [00:57<01:29,  2.24it/s]
# Trainer step: 98, epoch: 7:  34%|███▎      | 101/300 [00:57<01:30,  2.21it/s]
# Trainer step: 98, epoch: 7:  34%|███▍      | 102/300 [01:00<03:58,  1.21s/it]Progress: 37.00%
+---- avg training fps: 6.66

# Trainer step: 98, epoch: 7:  34%|███▍      | 103/300 [01:01<03:14,  1.02it/s]
# Trainer step: 98, epoch: 7:  35%|███▍      | 104/300 [01:01<02:42,  1.21it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 105/300 [01:02<02:20,  1.39it/s]
# Trainer step: 98, epoch: 7:  35%|███▌      | 106/300 [01:02<02:04,  1.56it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 107/300 [01:03<01:53,  1.70it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 108/300 [01:03<01:46,  1.81it/s]Progress: 39.00%
+---- avg training fps: 6.75

# Trainer step: 98, epoch: 7:  36%|███▋      | 109/300 [01:04<01:40,  1.90it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 110/300 [01:04<01:36,  1.97it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 111/300 [01:04<01:33,  2.02it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 112/300 [01:05<01:31,  2.05it/s]
# Trainer step: 112, epoch: 8:  37%|███▋      | 112/300 [01:05<01:31,  2.05it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 113/300 [01:05<01:25,  2.17it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 114/300 [01:06<01:36,  1.93it/s]Progress: 41.00%
+---- avg training fps: 6.81

# Trainer step: 112, epoch: 8:  38%|███▊      | 115/300 [01:06<01:33,  1.99it/s]
# Trainer step: 112, epoch: 8:  39%|███▊      | 116/300 [01:07<01:30,  2.03it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 117/300 [01:07<01:28,  2.07it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 118/300 [01:08<01:27,  2.09it/s]
# Trainer step: 112, epoch: 8:  40%|███▉      | 119/300 [01:08<01:25,  2.11it/s]
# Trainer step: 112, epoch: 8:  40%|████      | 120/300 [01:09<01:24,  2.13it/s]Progress: 43.00%
+---- avg training fps: 6.88

# Trainer step: 112, epoch: 8:  40%|████      | 121/300 [01:09<01:23,  2.13it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 122/300 [01:10<01:23,  2.14it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 123/300 [01:10<01:22,  2.15it/s]
# Trainer step: 112, epoch: 8:  41%|████▏     | 124/300 [01:11<01:22,  2.14it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 125/300 [01:11<01:21,  2.15it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 126/300 [01:12<01:20,  2.15it/s]Progress: 45.00%
+---- avg training fps: 6.96

# Trainer step: 126, epoch: 9:  42%|████▏     | 126/300 [01:12<01:20,  2.15it/s]
# Trainer step: 126, epoch: 9:  42%|████▏     | 127/300 [01:12<01:16,  2.25it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 128/300 [01:12<01:17,  2.21it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 129/300 [01:13<01:17,  2.20it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 130/300 [01:13<01:17,  2.19it/s]
# Trainer step: 126, epoch: 9:  44%|████▎     | 131/300 [01:14<01:17,  2.17it/s]
# Trainer step: 126, epoch: 9:  44%|████▍     | 132/300 [01:14<01:17,  2.17it/s]Progress: 47.00%
+---- avg training fps: 7.02

# Trainer step: 126, epoch: 9:  44%|████▍     | 133/300 [01:15<01:17,  2.16it/s]
# Trainer step: 126, epoch: 9:  45%|████▍     | 134/300 [01:15<01:16,  2.16it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 135/300 [01:16<01:16,  2.16it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 136/300 [01:16<01:16,  2.15it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 137/300 [01:17<01:15,  2.16it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 138/300 [01:17<01:14,  2.16it/s]Progress: 49.00%
+---- avg training fps: 7.07

# Trainer step: 126, epoch: 9:  46%|████▋     | 139/300 [01:18<01:14,  2.16it/s]
# Trainer step: 126, epoch: 9:  47%|████▋     | 140/300 [01:18<01:14,  2.15it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 140/300 [01:18<01:14,  2.15it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 141/300 [01:18<01:10,  2.25it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 142/300 [01:19<01:11,  2.22it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 143/300 [01:19<01:11,  2.20it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 144/300 [01:20<01:11,  2.18it/s]Progress: 51.00%
+---- avg training fps: 7.13

# Trainer step: 140, epoch: 10:  48%|████▊     | 145/300 [01:20<01:11,  2.17it/s]
# Trainer step: 140, epoch: 10:  49%|████▊     | 146/300 [01:21<01:11,  2.16it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 147/300 [01:21<01:10,  2.16it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 148/300 [01:22<01:10,  2.17it/s]
# Trainer step: 140, epoch: 10:  50%|████▉     | 149/300 [01:22<01:09,  2.16it/s]
# Trainer step: 140, epoch: 10:  50%|█████     | 150/300 [01:23<01:09,  2.16it/s]Progress: 53.00%
+---- avg training fps: 7.18

# Trainer step: 140, epoch: 10:  50%|█████     | 151/300 [01:23<01:09,  2.15it/s]
# Trainer step: 140, epoch: 10:  51%|█████     | 152/300 [01:28<04:27,  1.81s/it]
# Trainer step: 140, epoch: 10:  51%|█████     | 153/300 [01:28<03:26,  1.41s/it]
# Trainer step: 140, epoch: 10:  51%|█████▏    | 154/300 [01:29<02:43,  1.12s/it]
# Trainer step: 154, epoch: 11:  51%|█████▏    | 154/300 [01:29<02:43,  1.12s/it]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 155/300 [01:29<02:11,  1.11it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 156/300 [01:30<01:51,  1.29it/s]Progress: 55.00%
+---- avg training fps: 6.88

# Trainer step: 154, epoch: 11:  52%|█████▏    | 157/300 [01:30<01:37,  1.46it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 158/300 [01:31<01:27,  1.62it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 159/300 [01:31<01:20,  1.75it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 160/300 [01:32<01:15,  1.86it/s]
# Trainer step: 154, epoch: 11:  54%|█████▎    | 161/300 [01:32<01:11,  1.94it/s]
# Trainer step: 154, epoch: 11:  54%|█████▍    | 162/300 [01:33<01:09,  1.99it/s]Progress: 57.00%
+---- avg training fps: 6.93

# Trainer step: 154, epoch: 11:  54%|█████▍    | 163/300 [01:33<01:06,  2.05it/s]
# Trainer step: 154, epoch: 11:  55%|█████▍    | 164/300 [01:34<01:05,  2.07it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 165/300 [01:34<01:04,  2.09it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 166/300 [01:34<01:03,  2.11it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 167/300 [01:35<01:02,  2.12it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 168/300 [01:35<01:01,  2.14it/s]Progress: 59.00%
+---- avg training fps: 6.98

# Trainer step: 168, epoch: 12:  56%|█████▌    | 168/300 [01:36<01:01,  2.14it/s]
# Trainer step: 168, epoch: 12:  56%|█████▋    | 169/300 [01:36<00:58,  2.25it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 170/300 [01:36<00:58,  2.22it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 171/300 [01:37<00:58,  2.20it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 172/300 [01:37<00:58,  2.19it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 173/300 [01:38<00:58,  2.18it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 174/300 [01:38<00:57,  2.18it/s]Progress: 61.00%
+---- avg training fps: 7.03

# Trainer step: 168, epoch: 12:  58%|█████▊    | 175/300 [01:39<00:57,  2.17it/s]
# Trainer step: 168, epoch: 12:  59%|█████▊    | 176/300 [01:39<00:57,  2.16it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 177/300 [01:39<00:57,  2.16it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 178/300 [01:40<00:56,  2.16it/s]
# Trainer step: 168, epoch: 12:  60%|█████▉    | 179/300 [01:40<00:56,  2.16it/s]
# Trainer step: 168, epoch: 12:  60%|██████    | 180/300 [01:41<00:55,  2.16it/s]Progress: 63.00%
+---- avg training fps: 7.07

# Trainer step: 168, epoch: 12:  60%|██████    | 181/300 [01:41<00:55,  2.16it/s]
# Trainer step: 168, epoch: 12:  61%|██████    | 182/300 [01:42<00:54,  2.16it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 182/300 [01:42<00:54,  2.16it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 183/300 [01:42<00:51,  2.27it/s]
# Trainer step: 182, epoch: 13:  61%|██████▏   | 184/300 [01:43<00:51,  2.23it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 185/300 [01:43<00:51,  2.21it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 186/300 [01:44<00:51,  2.19it/s]Progress: 65.00%
+---- avg training fps: 7.12

# Trainer step: 182, epoch: 13:  62%|██████▏   | 187/300 [01:44<00:51,  2.19it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 188/300 [01:44<00:51,  2.18it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 189/300 [01:45<00:51,  2.17it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 190/300 [01:45<00:50,  2.16it/s]
# Trainer step: 182, epoch: 13:  64%|██████▎   | 191/300 [01:46<00:50,  2.16it/s]
# Trainer step: 182, epoch: 13:  64%|██████▍   | 192/300 [01:46<00:50,  2.16it/s]Progress: 67.00%
+---- avg training fps: 7.16

# Trainer step: 182, epoch: 13:  64%|██████▍   | 193/300 [01:47<00:49,  2.16it/s]
# Trainer step: 182, epoch: 13:  65%|██████▍   | 194/300 [01:47<00:49,  2.16it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 195/300 [01:48<00:48,  2.15it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 196/300 [01:48<00:48,  2.16it/s]
# Trainer step: 196, epoch: 14:  65%|██████▌   | 196/300 [01:49<00:48,  2.16it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 197/300 [01:49<00:45,  2.26it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 198/300 [01:49<00:46,  2.22it/s]Progress: 69.00%
+---- avg training fps: 7.20

# Trainer step: 196, epoch: 14:  66%|██████▋   | 199/300 [01:50<00:46,  2.19it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 200/300 [01:50<00:45,  2.18it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 201/300 [01:50<00:45,  2.17it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 202/300 [01:54<02:13,  1.36s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 203/300 [01:54<01:46,  1.09s/it]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 204/300 [01:55<01:26,  1.11it/s]Progress: 71.00%
+---- avg training fps: 7.05

# Trainer step: 196, epoch: 14:  68%|██████▊   | 205/300 [01:55<01:13,  1.29it/s]
# Trainer step: 196, epoch: 14:  69%|██████▊   | 206/300 [01:56<01:04,  1.46it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 207/300 [01:56<00:57,  1.63it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 208/300 [01:57<00:52,  1.76it/s]
# Trainer step: 196, epoch: 14:  70%|██████▉   | 209/300 [01:57<00:48,  1.86it/s]
# Trainer step: 196, epoch: 14:  70%|███████   | 210/300 [01:58<00:46,  1.94it/s]Progress: 73.00%
+---- avg training fps: 7.09

# Trainer step: 210, epoch: 15:  70%|███████   | 210/300 [01:58<00:46,  1.94it/s]
# Trainer step: 210, epoch: 15:  70%|███████   | 211/300 [01:58<00:42,  2.09it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 212/300 [01:59<00:41,  2.10it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 213/300 [01:59<00:40,  2.13it/s]
# Trainer step: 210, epoch: 15:  71%|███████▏  | 214/300 [01:59<00:40,  2.14it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 215/300 [02:00<00:39,  2.14it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 216/300 [02:00<00:39,  2.15it/s]Progress: 75.00%
+---- avg training fps: 7.12

# Trainer step: 210, epoch: 15:  72%|███████▏  | 217/300 [02:01<00:38,  2.15it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 218/300 [02:01<00:38,  2.15it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 219/300 [02:02<00:37,  2.15it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 220/300 [02:02<00:37,  2.15it/s]
# Trainer step: 210, epoch: 15:  74%|███████▎  | 221/300 [02:03<00:36,  2.15it/s]
# Trainer step: 210, epoch: 15:  74%|███████▍  | 222/300 [02:03<00:36,  2.16it/s]Progress: 77.00%
+---- avg training fps: 7.16

# Trainer step: 210, epoch: 15:  74%|███████▍  | 223/300 [02:04<00:35,  2.15it/s]
# Trainer step: 210, epoch: 15:  75%|███████▍  | 224/300 [02:04<00:35,  2.14it/s]
# Trainer step: 224, epoch: 16:  75%|███████▍  | 224/300 [02:04<00:35,  2.14it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 225/300 [02:04<00:33,  2.25it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 226/300 [02:05<00:33,  2.21it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 227/300 [02:05<00:33,  2.19it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 228/300 [02:06<00:32,  2.19it/s]Progress: 79.00%
+---- avg training fps: 7.19

# Trainer step: 224, epoch: 16:  76%|███████▋  | 229/300 [02:06<00:32,  2.17it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 230/300 [02:07<00:32,  2.16it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 231/300 [02:07<00:31,  2.16it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 232/300 [02:08<00:31,  2.16it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 233/300 [02:08<00:31,  2.16it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 234/300 [02:09<00:30,  2.15it/s]Progress: 81.00%
+---- avg training fps: 7.22

# Trainer step: 224, epoch: 16:  78%|███████▊  | 235/300 [02:09<00:30,  2.16it/s]
# Trainer step: 224, epoch: 16:  79%|███████▊  | 236/300 [02:10<00:29,  2.15it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 237/300 [02:10<00:29,  2.15it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 238/300 [02:11<00:28,  2.15it/s]
# Trainer step: 238, epoch: 17:  79%|███████▉  | 238/300 [02:11<00:28,  2.15it/s]
# Trainer step: 238, epoch: 17:  80%|███████▉  | 239/300 [02:11<00:26,  2.29it/s]
# Trainer step: 238, epoch: 17:  80%|████████  | 240/300 [02:11<00:26,  2.24it/s]Progress: 83.00%
+---- avg training fps: 7.26

# Trainer step: 238, epoch: 17:  80%|████████  | 241/300 [02:12<00:26,  2.21it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 242/300 [02:12<00:26,  2.19it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 243/300 [02:13<00:26,  2.18it/s]
# Trainer step: 238, epoch: 17:  81%|████████▏ | 244/300 [02:13<00:25,  2.18it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 245/300 [02:14<00:25,  2.17it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 246/300 [02:14<00:24,  2.16it/s]Progress: 85.00%
+---- avg training fps: 7.28

# Trainer step: 238, epoch: 17:  82%|████████▏ | 247/300 [02:15<00:24,  2.15it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 248/300 [02:15<00:24,  2.15it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 249/300 [02:16<00:23,  2.15it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 250/300 [02:16<00:23,  2.16it/s]
# Trainer step: 238, epoch: 17:  84%|████████▎ | 251/300 [02:16<00:22,  2.15it/s]
# Trainer step: 238, epoch: 17:  84%|████████▍ | 252/300 [02:20<01:11,  1.49s/it]Progress: 87.00%
+---- avg training fps: 7.14

# Trainer step: 252, epoch: 18:  84%|████████▍ | 252/300 [02:21<01:11,  1.49s/it]
# Trainer step: 252, epoch: 18:  84%|████████▍ | 253/300 [02:21<00:54,  1.16s/it]
# Trainer step: 252, epoch: 18:  85%|████████▍ | 254/300 [02:21<00:43,  1.05it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 255/300 [02:22<00:36,  1.24it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 256/300 [02:22<00:30,  1.42it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 257/300 [02:23<00:27,  1.58it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 258/300 [02:23<00:24,  1.73it/s]Progress: 89.00%
+---- avg training fps: 7.17

# Trainer step: 252, epoch: 18:  86%|████████▋ | 259/300 [02:24<00:22,  1.83it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 260/300 [02:24<00:20,  1.91it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 261/300 [02:24<00:19,  1.98it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 262/300 [02:25<00:18,  2.03it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 263/300 [02:25<00:17,  2.07it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 264/300 [02:26<00:17,  2.08it/s]Progress: 91.00%
+---- avg training fps: 7.19

# Trainer step: 252, epoch: 18:  88%|████████▊ | 265/300 [02:26<00:16,  2.11it/s]
# Trainer step: 252, epoch: 18:  89%|████████▊ | 266/300 [02:27<00:16,  2.12it/s]
# Trainer step: 266, epoch: 19:  89%|████████▊ | 266/300 [02:27<00:16,  2.12it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 267/300 [02:27<00:14,  2.23it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 268/300 [02:28<00:14,  2.19it/s]
# Trainer step: 266, epoch: 19:  90%|████████▉ | 269/300 [02:28<00:14,  2.18it/s]
# Trainer step: 266, epoch: 19:  90%|█████████ | 270/300 [02:29<00:13,  2.16it/s]Progress: 93.00%
+---- avg training fps: 7.22

# Trainer step: 266, epoch: 19:  90%|█████████ | 271/300 [02:29<00:13,  2.16it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 272/300 [02:30<00:12,  2.16it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 273/300 [02:30<00:12,  2.16it/s]
# Trainer step: 266, epoch: 19:  91%|█████████▏| 274/300 [02:30<00:12,  2.16it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 275/300 [02:31<00:11,  2.16it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 276/300 [02:31<00:11,  2.15it/s]Progress: 95.00%
+---- avg training fps: 7.25

# Trainer step: 266, epoch: 19:  92%|█████████▏| 277/300 [02:32<00:10,  2.16it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 278/300 [02:32<00:10,  2.16it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 279/300 [02:33<00:09,  2.15it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 280/300 [02:33<00:09,  2.15it/s]
# Trainer step: 280, epoch: 20:  93%|█████████▎| 280/300 [02:34<00:09,  2.15it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▎| 281/300 [02:34<00:08,  2.24it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▍| 282/300 [02:34<00:08,  2.20it/s]Progress: 97.00%
+---- avg training fps: 7.27

# Trainer step: 280, epoch: 20:  94%|█████████▍| 283/300 [02:35<00:07,  2.19it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▍| 284/300 [02:35<00:07,  2.18it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 285/300 [02:36<00:06,  2.17it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 286/300 [02:36<00:06,  2.17it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 287/300 [02:36<00:06,  2.16it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 288/300 [02:37<00:05,  2.16it/s]Progress: 99.00%
+---- avg training fps: 7.30

# Trainer step: 280, epoch: 20:  96%|█████████▋| 289/300 [02:37<00:05,  2.16it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 290/300 [02:38<00:04,  2.15it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 291/300 [02:38<00:04,  2.15it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 292/300 [02:39<00:03,  2.15it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 293/300 [02:39<00:03,  2.15it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 294/300 [02:40<00:02,  2.14it/s]Progress: 100.00%
+---- avg training fps: 7.32

# Trainer step: 294, epoch: 21:  98%|█████████▊| 294/300 [02:40<00:02,  2.14it/s]
# Trainer step: 294, epoch: 21:  98%|█████████▊| 295/300 [02:40<00:02,  2.25it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▊| 296/300 [02:41<00:01,  2.21it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 297/300 [02:41<00:01,  2.20it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 298/300 [02:41<00:00,  2.19it/s]
# Trainer step: 294, epoch: 21: 100%|█████████▉| 299/300 [02:42<00:00,  2.18it/s]
# Trainer step: 294, epoch: 21: 100%|██████████| 300/300 [02:42<00:00,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.34

# Trainer step: 294, epoch: 21: : 301it [02:43,  2.16it/s]                       Progress: 100.00%
Failed to plot token attention loss
+Reached max steps, stopping training!
+Saving checkpoint at step.. 301
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 294, epoch: 21: : 301it [04:01,  1.24it/s]
+------------------------------------------
+Training done :)
+2024-08-13 04:54:19.652070: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 04:54:19.744774: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 04:54:19.744800: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 04:54:19.763730: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 04:54:20.167274: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:54:20.167330: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:54:20.174189: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23118 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/good_styles/eden_crystals' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=1.0 freeze_unet_before_completion_f=0.0 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='eden_crystals' output_dir='lora_models/styles_final/eden_crystals_13_04-54-21-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723517661.9522135 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/eden_crystals_13_04-54-21-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.242
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants on a black background.
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants in the dark.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal blending of nature and technology
+
+- In the style of TOK, a potted plant sits on a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves is set against a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is situated on a black background.
+- In the style of TOK, a potted plant sits on a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves is set against a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is shown in the dark.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Surreal blending of nature and technology
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 6925.58it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a potted plant s...
+1     in the style of <s0><s1><s2>, a futuristic cit...
+2     in the style of <s0><s1><s2>, a flower with gr...
+3     in the style of <s0><s1><s2>, a plant is growi...
+4     in the style of <s0><s1><s2>, a group of green...
+5     in the style of <s0><s1><s2>, a potted plant s...
+6     in the style of <s0><s1><s2>, a futuristic cit...
+7     in the style of <s0><s1><s2>, a flower with gr...
+8     in the style of <s0><s1><s2>, a plant is growi...
+9     in the style of <s0><s1><s2>, a group of green...
+10    in the style of <s0><s1><s2>, a potted plant s...
+11    in the style of <s0><s1><s2>, a futuristic cit...
+12    in the style of <s0><s1><s2>, a flower with gr...
+13    in the style of <s0><s1><s2>, a plant is growi...
+14    in the style of <s0><s1><s2>, a group of green...
+15    in the style of <s0><s1><s2>, a potted plant s...
+16    in the style of <s0><s1><s2>, a futuristic cit...
+17    in the style of <s0><s1><s2>, a flower with gr...
+18    in the style of <s0><s1><s2>, a plant is growi...
+19    in the style of <s0><s1><s2>, a group of green...
+20    in the style of <s0><s1><s2>, a potted plant s...
+21    in the style of <s0><s1><s2>, a futuristic cit...
+22    in the style of <s0><s1><s2>, a flower with gr...
+23    in the style of <s0><s1><s2>, a plant is growi...
+24    in the style of <s0><s1><s2>, a group of green...
+25    in the style of <s0><s1><s2>, a potted plant s...
+26    in the style of <s0><s1><s2>, a futuristic cit...
+27    in the style of <s0><s1><s2>, a flower with gr...
+28    in the style of <s0><s1><s2>, a plant is growi...
+29    in the style of <s0><s1><s2>, a group of green...
+30    in the style of <s0><s1><s2>, a potted plant s...
+31    in the style of <s0><s1><s2>, a futuristic cit...
+32    in the style of <s0><s1><s2>, a flower with gr...
+33    in the style of <s0><s1><s2>, a plant is growi...
+34    in the style of <s0><s1><s2>, a group of green...
+35    in the style of <s0><s1><s2>, a potted plant s...
+36    in the style of <s0><s1><s2>, a futuristic cit...
+37    in the style of <s0><s1><s2>, a flower with gr...
+38    in the style of <s0><s1><s2>, a plant is growi...
+39    in the style of <s0><s1><s2>, a group of green...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:08<04:19,  1.13it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.64

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+---- avg training fps: 4.03

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+---- avg training fps: 4.88

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+---- avg training fps: 5.46

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+---- avg training fps: 5.88

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+---- avg training fps: 6.15

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+---- avg training fps: 6.40

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+---- avg training fps: 6.60

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+---- avg training fps: 6.12

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+---- avg training fps: 6.26

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+---- avg training fps: 6.42

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+---- avg training fps: 6.55

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# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:46<01:44,  2.12it/s]Progress: 29.00%
+---- avg training fps: 6.67

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:46<01:44,  2.12it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:47<01:43,  2.12it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:47<01:43,  2.12it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:47<01:43,  2.12it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:48<01:42,  2.12it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:48<01:42,  2.12it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:49<01:41,  2.13it/s]Progress: 31.00%
+---- avg training fps: 6.77

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:49<01:40,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:50<01:40,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:50<01:40,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:51<01:39,  2.13it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:51<01:38,  2.13it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:51<01:38,  2.14it/s]Progress: 33.00%
+---- avg training fps: 6.87

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:52<01:38,  2.14it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:52<01:37,  2.14it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:52<01:37,  2.14it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:53<01:36,  2.14it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:53<01:36,  2.13it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:54<01:35,  2.14it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:54<01:35,  2.13it/s]Progress: 35.00%
+---- avg training fps: 6.95

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:55<01:34,  2.14it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:55<01:34,  2.13it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:56<01:34,  2.13it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:56<01:34,  2.13it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:57<01:34,  2.13it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:57<01:33,  2.13it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:57<01:32,  2.13it/s]Progress: 37.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.03

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:58<01:32,  2.13it/s]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [00:58<01:32,  2.13it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [00:58<01:31,  2.13it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [00:59<01:31,  2.12it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [00:59<01:30,  2.12it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:00<01:30,  2.12it/s]Progress: 39.00%
+---- avg training fps: 7.10

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:00<01:29,  2.12it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:01<01:29,  2.12it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:01<01:29,  2.12it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:01<01:29,  2.12it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:02<01:28,  2.12it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:02<01:28,  2.12it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:03<01:27,  2.12it/s]Progress: 41.00%
+---- avg training fps: 7.16

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:03<01:26,  2.13it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:04<01:26,  2.13it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:04<01:26,  2.12it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:05<01:25,  2.13it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:05<01:25,  2.12it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:06<01:24,  2.12it/s]Progress: 43.00%
+---- avg training fps: 7.22

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:06<01:24,  2.12it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:06<01:24,  2.12it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:07<01:24,  2.11it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:07<01:23,  2.12it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:07<01:22,  2.12it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:08<01:22,  2.12it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:08<01:21,  2.13it/s]Progress: 45.00%
+---- avg training fps: 7.27

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:09<01:21,  2.13it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:09<01:20,  2.13it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:10<01:20,  2.12it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:10<01:20,  2.12it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:11<01:20,  2.12it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:11<01:19,  2.13it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:11<01:18,  2.13it/s]Progress: 47.00%
+---- avg training fps: 7.32

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:12<01:18,  2.12it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:12<01:18,  2.12it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:13<01:17,  2.12it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:13<01:17,  2.11it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:14<01:16,  2.12it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:14<01:16,  2.12it/s]Progress: 49.00%
+---- avg training fps: 7.36

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:15<01:16,  2.12it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:15<01:15,  2.12it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:15<01:15,  2.12it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:15<01:15,  2.12it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:16<01:14,  2.13it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:16<01:13,  2.13it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:17<01:13,  2.13it/s]Progress: 51.00%
+---- avg training fps: 7.40

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:17<01:12,  2.12it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:18<01:12,  2.13it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:18<01:12,  2.12it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:19<01:11,  2.12it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:19<01:10,  2.13it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:20<01:10,  2.13it/s]Progress: 53.00%
+---- avg training fps: 7.44

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:20<01:10,  2.13it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:20<01:10,  2.13it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:24<03:52,  1.57s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:25<03:02,  1.24s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:25<02:27,  1.01s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:26<02:03,  1.18it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:26<01:45,  1.36it/s]Progress: 55.00%
+---- avg training fps: 7.16

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:27<01:33,  1.52it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:27<01:25,  1.67it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:28<01:19,  1.78it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:28<01:14,  1.87it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:29<01:14,  1.87it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:29<01:11,  1.94it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:29<01:09,  1.99it/s]Progress: 57.00%
+---- avg training fps: 7.20

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:29<01:07,  2.03it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:30<01:06,  2.05it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:30<01:05,  2.07it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:31<01:04,  2.09it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:31<01:03,  2.10it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:32<01:02,  2.10it/s]Progress: 59.00%
+---- avg training fps: 7.24

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:32<01:02,  2.10it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:33<01:01,  2.11it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:33<01:01,  2.11it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:33<01:01,  2.11it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:34<01:00,  2.11it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:34<01:00,  2.12it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:35<00:59,  2.12it/s]Progress: 61.00%
+---- avg training fps: 7.28

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:35<00:59,  2.11it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:36<00:58,  2.12it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:36<00:58,  2.12it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:37<00:57,  2.12it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:37<00:57,  2.12it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:38<00:56,  2.13it/s]Progress: 63.00%
+---- avg training fps: 7.31

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:38<00:56,  2.13it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:38<00:56,  2.12it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:38<00:55,  2.12it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:39<00:55,  2.12it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:39<00:54,  2.12it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:40<00:54,  2.12it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:40<00:53,  2.12it/s]Progress: 65.00%
+---- avg training fps: 7.34

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:41<00:53,  2.12it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:41<00:53,  2.11it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:42<00:52,  2.12it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:42<00:52,  2.11it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:43<00:52,  2.11it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:43<00:51,  2.11it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:43<00:50,  2.12it/s]Progress: 67.00%
+---- avg training fps: 7.37

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:44<00:50,  2.12it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:44<00:49,  2.12it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:45<00:49,  2.12it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:45<00:49,  2.12it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:46<00:48,  2.12it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:46<00:48,  2.12it/s]Progress: 69.00%
+---- avg training fps: 7.40

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:46<00:47,  2.12it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:47<00:47,  2.12it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:47<00:47,  2.12it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:47<00:46,  2.12it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:52<02:36,  1.59s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:52<02:02,  1.26s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:53<01:37,  1.02s/it]Progress: 71.00%
+---- avg training fps: 7.19

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:53<01:21,  1.17it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:54<01:09,  1.35it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:54<01:01,  1.51it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:54<00:55,  1.65it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:55<00:51,  1.78it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:55<00:48,  1.86it/s]Progress: 73.00%
+---- avg training fps: 7.22

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:56<00:48,  1.86it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:56<00:46,  1.93it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:56<00:44,  1.98it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:57<00:43,  2.01it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:57<00:42,  2.04it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [01:58<00:41,  2.06it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [01:58<00:40,  2.07it/s]Progress: 75.00%
+---- avg training fps: 7.25

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [01:59<00:39,  2.08it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [01:59<00:39,  2.09it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:00<00:38,  2.10it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:00<00:37,  2.11it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:01<00:37,  2.11it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:01<00:37,  2.11it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:01<00:36,  2.11it/s]Progress: 77.00%
+---- avg training fps: 7.27

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:02<00:36,  2.12it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:02<00:35,  2.12it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:03<00:35,  2.12it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:03<00:34,  2.12it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:03<00:34,  2.12it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:04<00:34,  2.11it/s]Progress: 79.00%
+---- avg training fps: 7.30

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:04<00:33,  2.11it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:05<00:33,  2.11it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:05<00:33,  2.11it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:05<00:32,  2.12it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:06<00:32,  2.11it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:06<00:31,  2.11it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:07<00:31,  2.11it/s]Progress: 81.00%
+---- avg training fps: 7.33

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:07<00:30,  2.11it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:08<00:30,  2.11it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:08<00:29,  2.11it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:09<00:29,  2.11it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:09<00:28,  2.12it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:10<00:28,  2.13it/s]Progress: 83.00%
+---- avg training fps: 7.35

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:10<00:28,  2.13it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:10<00:27,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:11<00:27,  2.14it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:11<00:26,  2.14it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:11<00:26,  2.14it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:12<00:25,  2.14it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:12<00:25,  2.14it/s]Progress: 85.00%
+---- avg training fps: 7.38

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:13<00:24,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:13<00:24,  2.14it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:14<00:23,  2.14it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:14<00:23,  2.14it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:15<00:23,  2.14it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:15<00:22,  2.14it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:15<00:22,  2.15it/s]Progress: 87.00%
Failed to plot token attention loss
+
+---- avg training fps: 7.40

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:16<00:21,  2.15it/s]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:16<00:21,  2.15it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:17<00:21,  2.14it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:17<00:20,  2.14it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:18<00:20,  2.14it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:18<00:19,  2.14it/s]Progress: 89.00%
+---- avg training fps: 7.43

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:18<00:19,  2.14it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:19<00:18,  2.14it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:19<00:18,  2.14it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:19<00:18,  2.14it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:20<00:17,  2.14it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:21<00:19,  1.89it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:21<00:18,  1.96it/s]Progress: 91.00%
+---- avg training fps: 7.44

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:21<00:17,  2.01it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:22<00:16,  2.05it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:22<00:15,  2.08it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:23<00:15,  2.10it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:23<00:14,  2.12it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:24<00:14,  2.13it/s]Progress: 93.00%
+---- avg training fps: 7.46

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:24<00:14,  2.13it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:24<00:13,  2.13it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:25<00:13,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:25<00:12,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:26<00:12,  2.14it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:26<00:11,  2.14it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:27<00:11,  2.15it/s]Progress: 95.00%
+---- avg training fps: 7.48

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:27<00:10,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:28<00:10,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:28<00:09,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:28<00:09,  2.15it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:29<00:09,  2.15it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:29<00:08,  2.14it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:29<00:08,  2.15it/s]Progress: 97.00%
+---- avg training fps: 7.50

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:30<00:07,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:30<00:07,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:31<00:06,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:31<00:06,  2.15it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:32<00:06,  2.15it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:32<00:05,  2.15it/s]Progress: 99.00%
+---- avg training fps: 7.52

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:33<00:05,  2.15it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:33<00:04,  2.16it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:34<00:04,  2.16it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:34<00:04,  2.16it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:34<00:03,  2.15it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:35<00:03,  2.15it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:35<00:02,  2.15it/s]Progress: 100.00%
+---- avg training fps: 7.54

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:35<00:02,  2.14it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:36<00:01,  2.14it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:36<00:01,  2.14it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:37<00:00,  2.15it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:37<00:00,  2.15it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:38<00:00,  2.15it/s]Progress: 100.00%
+---- avg training fps: 7.56
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [03:56<00:00,  1.27it/s]
+------------------------------------------
+Training done :)
+2024-08-13 04:58:48.216437: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 04:58:48.316150: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 04:58:48.316179: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 04:58:48.334878: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 04:58:48.734572: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:58:48.734625: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 04:58:48.734634: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23123 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.2 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=1.0 freeze_unet_before_completion_f=0.25 token_attention_loss_w=2e-07 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='clipx.zip' output_dir='lora_models/styles_final/clipx.zip_13_04-58-50-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723517930.531857 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
Loading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]Some weights of the model checkpoint were not used when initializing CLIPTextModel: 
+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Downloading training data...
+Downloading https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip to lora_models/styles_final/clipx.zip_13_04-58-50-style_512_florence_300/images_in/clipx.zip...
+Succesfully prepped 54 .jpg images in lora_models/styles_final/clipx.zip_13_04-58-50-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.314
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 27 images, 27 of which have captions.
+upscaling imgs..
+
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100%|██████████| 27/27 [00:00<00:00, 355003.79it/s]
+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 27 to 54)
+Generating 54 captions using mode: style...
+
  0%|          | 0/54 [00:00<?, ?it/s]
100%|██████████| 54/54 [00:00<00:00, 1617802.97it/s]
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Surreal, whimsical, detailed, and quirky scenes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, an image of Fort Kochi, Kerala, India.
+- In the style of TOK, a scene that suggests both the best and worst of times.
+- In the style of TOK, bees form a blockchain.
+- In the style of TOK, a gorgon made of sharks swims underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple is deep in the Amazon jungle.
+- In the style of TOK, cats poop in zero-gravity.
+- In the style of TOK, a creature described as a goblin goat.
+- In the style of TOK, a scene comparing wisdom and foolishness.
+- In the style of TOK, colleagues decide to quit their boring corporation jobs together.
+- In the style of TOK, ancient petroglyphs are depicted.
+- In the style of TOK, a room and space exchange based on first and second-degree connections.
+- In the style of TOK, a depiction relating to trypophobia.
+- In the style of TOK, a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers.
+- In the style of TOK, a beaver chews a rifle in half.
+- In the style of TOK, a brigade of beavers quickly builds a dam.
+- In the style of TOK, a castle moves along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dances in unison.
+- In the style of TOK, a 1950s restaurant kitchen is frozen solid and rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models and egrets as the audience.
+- In the style of TOK, a hyper-realistic painting depicts lemon-lime architecture.
+- In the style of TOK, a mermaid made of fishnets, dead fish, packing peanuts, and other sea trash swims up through the sea to peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 25 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 1...
+----- GPT response: -----
+Style Description: Surrealistic, intricate, and fantastical scenes
+
+- In the style of TOK, a mermaid swims underwater in the ocean, crafted from fishnets and sea trash.
+- In the style of TOK, a throng of Lilliputians are cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi, Kerala, India is depicted.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times" is illustrated.
+- In the style of TOK, a blockchain of bees is depicted.
+- In the style of TOK, a gorgon made out of sharks is depicted swimming underwater, the sharks eating each other.
+- In the style of TOK, a hillside at sunset is shown with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple deep in the Amazon jungle is depicted.
+- In the style of TOK, cats are shown pooping in zero-gravity.
+- In the style of TOK, a goblin goat is depicted.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness" is illustrated.
+- In the style of TOK, the phrase "Let's quit our boring corporation job together" is illustrated.
+- In the style of TOK, petroglyphs are depicted.
+- In the style of TOK, room and space exchange based on first and second-degree connection is depicted.
+- In the style of TOK, trypophobia is illustrated.
+- In the style of TOK, a six-pack of nightmare fuel is depicted.
+- In the style of TOK, a band of dancing, singing beavers is shown.
+- In the style of TOK, a beaver is chewing a rifle in half.
+- In the style of TOK, a brigade of beavers is quickly building a dam in a Hawaiian-shirt pattern.
+- In the style of TOK, a brigade of beavers is quickly building a dam as depicted by Tarsem Singh.
+- In the style of TOK, a brigade of beavers is depicted in the manner of M. C. Escher.
+- In the style of TOK, a castle is moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers is dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen is shown, frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show features elephant seals as runway models with egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture is depicted.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, and packing peanuts is swimming up through the sea to peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 2...
+----- GPT response: -----
+Style Description: Surreal, eclectic, and whimsical themes
+
+- In the style of TOK, a mermaid made out of fishnets and other sea trash swims underwater.
+- In the style of TOK, a throng of Lilliputians is cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi, Kerala, India.
+- In the style of TOK, "They were the best of times, they were the worst of times."
+- In the style of TOK, a blockchain of bees.
+- In the style of TOK, a gorgon made out of sharks eating each other swims underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple deep in the Amazon jungle.
+- In the style of TOK, cats pooping in zero-gravity.
+- In the style of TOK, a goblin goat.
+- In the style of TOK, "It was the age of wisdom, it was the age of foolishness."
+- In the style of TOK, “Let's quit our boring corporation job together.”
+- In the style of TOK, petroglyphs.
+- In the style of TOK, a room and space exchange based on first and second-degree connections.
+- In the style of TOK, trypophobia.
+- In the style of TOK, a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers.
+- In the style of TOK, a beaver chewing a rifle in half.
+- In the style of TOK, a brigade of beavers quickly building a dam.
+- In the style of TOK, a castle moving along the grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show with elephant seals as runway models and egrets as the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swims up through the sea to take a peek above the water.
+
+--------------------------
+GPT-4 returned the wrong number of prompts 25 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 3...
+----- GPT response: -----
+Style Description: Surreal and intricate marine or forest scenes
+
+- In the style of TOK, a mermaid crafted from fishnets and sea trash swims underwater in the ocean.
+- In the style of TOK, Lilliputians are cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, Fort Kochi, Kerala, India.
+- In the style of TOK, narrating the best and worst of times.
+- In the style of TOK, a blockchain composed of bees.
+- In the style of TOK, a gorgon formed from sharks cannibalizing each other, swimming underwater.
+- In the style of TOK, a sunset over a hillside with Venus and Earth orbiting the sun.
+- In the style of TOK, an ancient temple nestled in the Amazon jungle.
+- In the style of TOK, cats defecating in zero-gravity.
+- In the style of TOK, a goblin goat.
+- In the style of TOK, it was the age of wisdom and foolishness.
+- In the style of TOK, two people planning to leave their corporate jobs together.
+- In the style of TOK, ancient rock carvings.
+- In the style of TOK, room and space exchange based on connections.
+- In the style of TOK, a pattern inducing trypophobia.
+- In the style of TOK, a six-pack of nightmare fuel.
+- In the style of TOK, dancing and singing beavers perform.
+- In the style of TOK, a beaver chews a rifle in half.
+- In the style of TOK, a brigade of beavers quickly constructs a dam in a Hawaiian shirt pattern.
+- In the style of TOK, a brigade of beavers quickly constructs a dam in a Tarsem Singh-like manner.
+- In the style of TOK, beavers designed in M. C. Escher's style.
+- In the style of TOK, a castle moves along grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dances in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen frozen and preserved in the space age.
+- In the style of TOK, elephant seals parade as runway models with egrets in the audience.
+- In the style of TOK, a hyper-realistic painting featuring lemon-lime architecture.
+- In the style of TOK, a mermaid constructed out of fishnets, dead fish, packing peanuts, and sea trash swims to the surface for a peek.
+
+Note: A few descriptions have small repetitions in the list since asked in the question description.
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+- A mermaid made out of fishnets and other sea trash, swimming underwater in the ocean
+- A throng of Lilliputians cramped inside the pores of a giant sea-sponge | C. G. Jung - "Liber Novus"
+- Fort Kochi, Kerala, India
+- They were the best of times, they were the worst of times
+- a blockchain of bees
+- a gorgon made out of sharks eating each other swimming underwater
+- a hillside at sunset, Venus and earth circling the sun
+- an ancient temple deep in the amazon jungle
+- cats pooping in zero-gravity
+- goblin goat
+- it was the age of wisdom, it was the age of foolishness
+- let's quit our boring corporation job together.
+- petroglyphs
+- room and space exchange based on first and second degree connection
+- trypophobia
+- A 6-pack of nightmare fuel
+- A band of dancing, singing beavers | Jim Henson's Muppets
+- A beaver chewing a rifle in half
+- A brigade of beavers quickly building a dam | Hawaiian-shirt pattern
+- A brigade of beavers quickly building a dam | Tarsem Singh
+- A brigade of beavers | M. C. Escher
+- A castle that is moving along the ground in grassy hills
+- A chorus line of adorable beavers dancing in unison
+- A dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age
+- A fashion show with elephant seals as the runway models and egrets seated in the audience
+- A hyper-realistic painting of lemon-lime architecture
+- A mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peak above the water
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 4...
+----- GPT response: -----
+Style Description: Surreal and fantastical imagery
+
+- In the style of TOK, a mermaid made out of fishnets, swimming underwater in the ocean.
+- In the style of TOK, a throng of Lilliputians cramped inside the pores of a giant sea-sponge.
+- In the style of TOK, a scene of Fort Kochi, Kerala, India.
+- In the style of TOK, the phrase "They were the best of times, they were the worst of times."
+- In the style of TOK, a blockchain composed of bees.
+- In the style of TOK, a gorgon made out of sharks eating each other, swimming underwater.
+- In the style of TOK, a hillside at sunset with Venus and Earth circling the sun.
+- In the style of TOK, an ancient temple located deep in the Amazon jungle.
+- In the style of TOK, cats pooping in zero-gravity.
+- In the style of TOK, a goblin goat.
+- In the style of TOK, the phrase "It was the age of wisdom, it was the age of foolishness."
+- In the style of TOK, the phrase "Let's quit our boring corporation job together."
+- In the style of TOK, various petroglyphs.
+- In the style of TOK, a room and space exchange based on first and second-degree connections.
+- In the style of TOK, an image invoking trypophobia.
+- In the style of TOK, a 6-pack of nightmare fuel.
+- In the style of TOK, a band of dancing, singing beavers.
+- In the style of TOK, a beaver chewing a rifle in half.
+- In the style of TOK, a brigade of beavers quickly building a dam (Hawaiian-shirt pattern).
+- In the style of TOK, a brigade of beavers quickly building a dam (Tarsem Singh).
+- In the style of TOK, a brigade of beavers (M. C. Escher).
+- In the style of TOK, a castle moving along the ground in grassy hills.
+- In the style of TOK, a chorus line of adorable beavers dancing in unison.
+- In the style of TOK, a dirty 1950s restaurant kitchen, frozen solid and preserved, rediscovered in the space age.
+- In the style of TOK, a fashion show with elephant seals as runway models and egrets seated in the audience.
+- In the style of TOK, a hyper-realistic painting of lemon-lime architecture.
+- In the style of TOK, a mermaid made out of fishnets, dead fish, packing peanuts, and other sea trash, swimming up through the sea to take a peek above the water.
+--------------------------
+GPT-4 returned the wrong number of prompts 27 instead of 54, retrying...
+WARNING: no captioning text was given and we're not doing chatgpt cleanup...
+Concept mode:  style
+Using GPT concept name as CLIP-segmentation prompt: Surreal and fantastical imagery
+Generating 54 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
54it [00:00, 15809.89it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 54 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a mermaid made o...
+1     in the style of <s0><s1><s2>, a throng of lill...
+2     in the style of <s0><s1><s2>, fort kochi, kera...
+3     in the style of <s0><s1><s2>, they were the be...
+4     in the style of <s0><s1><s2>, a blockchain of ...
+5     in the style of <s0><s1><s2>, a gorgon made ou...
+6     in the style of <s0><s1><s2>, a hillside at su...
+7     in the style of <s0><s1><s2>, an ancient templ...
+8     in the style of <s0><s1><s2>, cats pooping in ...
+9             in the style of <s0><s1><s2>, goblin goat
+10    in the style of <s0><s1><s2>, it was the age o...
+11    in the style of <s0><s1><s2>, let's quit our b...
+12            in the style of <s0><s1><s2>, petroglyphs
+13    in the style of <s0><s1><s2>, room and space e...
+14            in the style of <s0><s1><s2>, trypophobia
+15    in the style of <s0><s1><s2>, a 6-pack of nigh...
+16    in the style of <s0><s1><s2>, a band of dancin...
+17    in the style of <s0><s1><s2>, a beaver chewing...
+18    in the style of <s0><s1><s2>, a brigade of bea...
+19    in the style of <s0><s1><s2>, a brigade of bea...
+20    in the style of <s0><s1><s2>, a brigade of bea...
+21    in the style of <s0><s1><s2>, a castle that is...
+22    in the style of <s0><s1><s2>, a chorus line of...
+23    in the style of <s0><s1><s2>, a dirty 1950s re...
+24    in the style of <s0><s1><s2>, a fashion show w...
+25    in the style of <s0><s1><s2>, a hyper-realisti...
+26    in the style of <s0><s1><s2>, a mermaid made o...
+27    in the style of <s0><s1><s2>, a mermaid made o...
+28    in the style of <s0><s1><s2>, a throng of lill...
+29    in the style of <s0><s1><s2>, fort kochi, kera...
+30    in the style of <s0><s1><s2>, they were the be...
+31    in the style of <s0><s1><s2>, a blockchain of ...
+32    in the style of <s0><s1><s2>, a gorgon made ou...
+33    in the style of <s0><s1><s2>, a hillside at su...
+34    in the style of <s0><s1><s2>, an ancient templ...
+35    in the style of <s0><s1><s2>, cats pooping in ...
+36            in the style of <s0><s1><s2>, goblin goat
+37    in the style of <s0><s1><s2>, it was the age o...
+38    in the style of <s0><s1><s2>, let's quit our b...
+39            in the style of <s0><s1><s2>, petroglyphs
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 54
+--- Num batches each epoch = 14
+--- Num Epochs = 22
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
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# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:02<?, ?it/s]
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# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:10<04:56,  1.01s/it]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.26

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:10<04:03,  1.20it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:11<03:28,  1.40it/s]
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# Trainer step: 0, epoch: 0:   4%|▍         | 12/300 [00:12<02:30,  1.92it/s]Progress: 7.00%
+---- avg training fps: 3.58

# Trainer step: 0, epoch: 0:   4%|▍         | 13/300 [00:13<02:24,  1.99it/s]
# Trainer step: 0, epoch: 0:   5%|▍         | 14/300 [00:13<02:20,  2.03it/s]
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# Trainer step: 14, epoch: 1:   6%|▌         | 18/300 [00:15<02:14,  2.09it/s]Progress: 9.00%
+---- avg training fps: 4.42

# Trainer step: 14, epoch: 1:   6%|▋         | 19/300 [00:16<02:12,  2.11it/s]
# Trainer step: 14, epoch: 1:   7%|▋         | 20/300 [00:16<02:11,  2.12it/s]
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# Trainer step: 14, epoch: 1:   8%|▊         | 24/300 [00:18<02:08,  2.14it/s]Progress: 11.00%
+---- avg training fps: 5.04

# Trainer step: 14, epoch: 1:   8%|▊         | 25/300 [00:19<02:07,  2.15it/s]
# Trainer step: 14, epoch: 1:   9%|▊         | 26/300 [00:19<02:07,  2.15it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 27/300 [00:19<02:06,  2.15it/s]
# Trainer step: 14, epoch: 1:   9%|▉         | 28/300 [00:20<02:06,  2.15it/s]
# Trainer step: 28, epoch: 2:   9%|▉         | 28/300 [00:20<02:06,  2.15it/s]
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# Trainer step: 28, epoch: 2:  10%|█         | 30/300 [00:21<02:01,  2.22it/s]Progress: 13.00%
+---- avg training fps: 5.51

# Trainer step: 28, epoch: 2:  10%|█         | 31/300 [00:21<02:02,  2.19it/s]
# Trainer step: 28, epoch: 2:  11%|█         | 32/300 [00:22<02:02,  2.18it/s]
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# Trainer step: 28, epoch: 2:  12%|█▏        | 36/300 [00:24<02:01,  2.17it/s]Progress: 15.00%
+---- avg training fps: 5.86

# Trainer step: 28, epoch: 2:  12%|█▏        | 37/300 [00:24<02:01,  2.16it/s]
# Trainer step: 28, epoch: 2:  13%|█▎        | 38/300 [00:25<02:01,  2.16it/s]
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# Trainer step: 28, epoch: 2:  14%|█▍        | 42/300 [00:26<01:59,  2.17it/s]Progress: 17.00%
+---- avg training fps: 6.16

# Trainer step: 42, epoch: 3:  14%|█▍        | 42/300 [00:27<01:59,  2.17it/s]
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# Trainer step: 42, epoch: 3:  16%|█▌        | 48/300 [00:29<01:56,  2.17it/s]Progress: 19.00%
+---- avg training fps: 6.39

# Trainer step: 42, epoch: 3:  16%|█▋        | 49/300 [00:30<01:56,  2.16it/s]
# Trainer step: 42, epoch: 3:  17%|█▋        | 50/300 [00:30<01:55,  2.16it/s]
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# Trainer step: 42, epoch: 3:  18%|█▊        | 54/300 [00:35<03:31,  1.16it/s]Progress: 21.00%
+---- avg training fps: 6.08

# Trainer step: 42, epoch: 3:  18%|█▊        | 55/300 [00:35<03:01,  1.35it/s]
# Trainer step: 42, epoch: 3:  19%|█▊        | 56/300 [00:36<02:40,  1.52it/s]
# Trainer step: 56, epoch: 4:  19%|█▊        | 56/300 [00:36<02:40,  1.52it/s]
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# Trainer step: 56, epoch: 4:  20%|██        | 60/300 [00:37<02:01,  1.98it/s]Progress: 23.00%
+---- avg training fps: 6.27

# Trainer step: 56, epoch: 4:  20%|██        | 61/300 [00:38<01:57,  2.03it/s]
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# Trainer step: 56, epoch: 4:  22%|██▏       | 66/300 [00:40<01:49,  2.14it/s]Progress: 25.00%
+---- avg training fps: 6.43

# Trainer step: 56, epoch: 4:  22%|██▏       | 67/300 [00:41<01:48,  2.14it/s]
# Trainer step: 56, epoch: 4:  23%|██▎       | 68/300 [00:41<01:47,  2.15it/s]
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# Trainer step: 70, epoch: 5:  23%|██▎       | 70/300 [00:42<01:47,  2.14it/s]
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# Trainer step: 70, epoch: 5:  24%|██▍       | 72/300 [00:43<01:43,  2.21it/s]Progress: 27.00%
+---- avg training fps: 6.58

# Trainer step: 70, epoch: 5:  24%|██▍       | 73/300 [00:43<01:43,  2.19it/s]
# Trainer step: 70, epoch: 5:  25%|██▍       | 74/300 [00:44<01:43,  2.18it/s]
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# Trainer step: 70, epoch: 5:  26%|██▌       | 78/300 [00:46<01:51,  1.98it/s]Progress: 29.00%
+---- avg training fps: 6.66

# Trainer step: 70, epoch: 5:  26%|██▋       | 79/300 [00:46<01:48,  2.03it/s]
# Trainer step: 70, epoch: 5:  27%|██▋       | 80/300 [00:47<01:46,  2.07it/s]
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# Trainer step: 70, epoch: 5:  28%|██▊       | 84/300 [00:49<01:41,  2.13it/s]Progress: 31.00%
+---- avg training fps: 6.78

# Trainer step: 84, epoch: 6:  28%|██▊       | 84/300 [00:49<01:41,  2.13it/s]
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# Trainer step: 84, epoch: 6:  30%|███       | 90/300 [00:51<01:36,  2.18it/s]Progress: 33.00%
+---- avg training fps: 6.88

# Trainer step: 84, epoch: 6:  30%|███       | 91/300 [00:52<01:36,  2.17it/s]
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# Trainer step: 84, epoch: 6:  32%|███▏      | 96/300 [00:54<01:34,  2.17it/s]Progress: 35.00%
+---- avg training fps: 6.97

# Trainer step: 84, epoch: 6:  32%|███▏      | 97/300 [00:55<01:33,  2.16it/s]
# Trainer step: 84, epoch: 6:  33%|███▎      | 98/300 [00:55<01:33,  2.15it/s]
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# Trainer step: 98, epoch: 7:  34%|███▍      | 102/300 [00:59<03:27,  1.05s/it]Progress: 37.00%
+---- avg training fps: 6.82

# Trainer step: 98, epoch: 7:  34%|███▍      | 103/300 [00:59<02:51,  1.15it/s]
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# Trainer step: 98, epoch: 7:  35%|███▌      | 106/300 [01:01<01:57,  1.65it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 107/300 [01:01<01:48,  1.77it/s]
# Trainer step: 98, epoch: 7:  36%|███▌      | 108/300 [01:02<01:42,  1.87it/s]Progress: 39.00%
+---- avg training fps: 6.90

# Trainer step: 98, epoch: 7:  36%|███▋      | 109/300 [01:02<01:38,  1.94it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 110/300 [01:03<01:34,  2.00it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 111/300 [01:03<01:32,  2.04it/s]
# Trainer step: 98, epoch: 7:  37%|███▋      | 112/300 [01:03<01:30,  2.07it/s]
# Trainer step: 112, epoch: 8:  37%|███▋      | 112/300 [01:04<01:30,  2.07it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 113/300 [01:04<01:25,  2.19it/s]
# Trainer step: 112, epoch: 8:  38%|███▊      | 114/300 [01:04<01:25,  2.17it/s]Progress: 41.00%
+---- avg training fps: 6.98

# Trainer step: 112, epoch: 8:  38%|███▊      | 115/300 [01:05<01:25,  2.16it/s]
# Trainer step: 112, epoch: 8:  39%|███▊      | 116/300 [01:05<01:25,  2.15it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 117/300 [01:06<01:24,  2.15it/s]
# Trainer step: 112, epoch: 8:  39%|███▉      | 118/300 [01:06<01:24,  2.15it/s]
# Trainer step: 112, epoch: 8:  40%|███▉      | 119/300 [01:07<01:23,  2.15it/s]
# Trainer step: 112, epoch: 8:  40%|████      | 120/300 [01:07<01:23,  2.15it/s]Progress: 43.00%
+---- avg training fps: 7.05

# Trainer step: 112, epoch: 8:  40%|████      | 121/300 [01:08<01:23,  2.15it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 122/300 [01:08<01:23,  2.14it/s]
# Trainer step: 112, epoch: 8:  41%|████      | 123/300 [01:09<01:22,  2.14it/s]
# Trainer step: 112, epoch: 8:  41%|████▏     | 124/300 [01:09<01:22,  2.14it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 125/300 [01:09<01:21,  2.14it/s]
# Trainer step: 112, epoch: 8:  42%|████▏     | 126/300 [01:10<01:20,  2.15it/s]Progress: 45.00%
+---- avg training fps: 7.12

# Trainer step: 126, epoch: 9:  42%|████▏     | 126/300 [01:10<01:20,  2.15it/s]
# Trainer step: 126, epoch: 9:  42%|████▏     | 127/300 [01:10<01:16,  2.25it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 128/300 [01:11<01:17,  2.22it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 129/300 [01:11<01:17,  2.20it/s]
# Trainer step: 126, epoch: 9:  43%|████▎     | 130/300 [01:12<01:17,  2.18it/s]
# Trainer step: 126, epoch: 9:  44%|████▎     | 131/300 [01:12<01:17,  2.18it/s]
# Trainer step: 126, epoch: 9:  44%|████▍     | 132/300 [01:13<01:17,  2.17it/s]Progress: 47.00%
+---- avg training fps: 7.17

# Trainer step: 126, epoch: 9:  44%|████▍     | 133/300 [01:13<01:17,  2.16it/s]
# Trainer step: 126, epoch: 9:  45%|████▍     | 134/300 [01:14<01:17,  2.15it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 135/300 [01:14<01:16,  2.15it/s]
# Trainer step: 126, epoch: 9:  45%|████▌     | 136/300 [01:15<01:16,  2.15it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 137/300 [01:15<01:15,  2.16it/s]
# Trainer step: 126, epoch: 9:  46%|████▌     | 138/300 [01:15<01:15,  2.16it/s]Progress: 49.00%
+---- avg training fps: 7.22

# Trainer step: 126, epoch: 9:  46%|████▋     | 139/300 [01:16<01:14,  2.15it/s]
# Trainer step: 126, epoch: 9:  47%|████▋     | 140/300 [01:16<01:14,  2.15it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 140/300 [01:17<01:14,  2.15it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 141/300 [01:17<01:10,  2.26it/s]
# Trainer step: 140, epoch: 10:  47%|████▋     | 142/300 [01:17<01:10,  2.23it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 143/300 [01:18<01:11,  2.20it/s]
# Trainer step: 140, epoch: 10:  48%|████▊     | 144/300 [01:18<01:11,  2.18it/s]Progress: 51.00%
+---- avg training fps: 7.28

# Trainer step: 140, epoch: 10:  48%|████▊     | 145/300 [01:19<01:11,  2.17it/s]
# Trainer step: 140, epoch: 10:  49%|████▊     | 146/300 [01:19<01:11,  2.16it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 147/300 [01:20<01:10,  2.16it/s]
# Trainer step: 140, epoch: 10:  49%|████▉     | 148/300 [01:20<01:10,  2.16it/s]
# Trainer step: 140, epoch: 10:  50%|████▉     | 149/300 [01:21<01:10,  2.15it/s]
# Trainer step: 140, epoch: 10:  50%|█████     | 150/300 [01:21<01:09,  2.15it/s]Progress: 53.00%
+---- avg training fps: 7.31

# Trainer step: 140, epoch: 10:  50%|█████     | 151/300 [01:22<01:17,  1.93it/s]
# Trainer step: 140, epoch: 10:  51%|█████     | 152/300 [01:25<03:26,  1.40s/it]
# Trainer step: 140, epoch: 10:  51%|█████     | 153/300 [01:26<02:44,  1.12s/it]
# Trainer step: 140, epoch: 10:  51%|█████▏    | 154/300 [01:26<02:14,  1.09it/s]
# Trainer step: 154, epoch: 11:  51%|█████▏    | 154/300 [01:26<02:14,  1.09it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 155/300 [01:26<01:50,  1.31it/s]
# Trainer step: 154, epoch: 11:  52%|█████▏    | 156/300 [01:27<01:36,  1.49it/s]Progress: 55.00%
+---- avg training fps: 7.11

# Trainer step: 154, epoch: 11:  52%|█████▏    | 157/300 [01:27<01:27,  1.63it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 158/300 [01:28<01:20,  1.76it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 159/300 [01:28<01:16,  1.85it/s]
# Trainer step: 154, epoch: 11:  53%|█████▎    | 160/300 [01:29<01:12,  1.93it/s]
# Trainer step: 154, epoch: 11:  54%|█████▎    | 161/300 [01:29<01:09,  1.99it/s]
# Trainer step: 154, epoch: 11:  54%|█████▍    | 162/300 [01:30<01:07,  2.03it/s]Progress: 57.00%
+---- avg training fps: 7.15

# Trainer step: 154, epoch: 11:  54%|█████▍    | 163/300 [01:30<01:06,  2.07it/s]
# Trainer step: 154, epoch: 11:  55%|█████▍    | 164/300 [01:31<01:04,  2.09it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 165/300 [01:31<01:04,  2.10it/s]
# Trainer step: 154, epoch: 11:  55%|█████▌    | 166/300 [01:32<01:03,  2.12it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 167/300 [01:32<01:02,  2.13it/s]
# Trainer step: 154, epoch: 11:  56%|█████▌    | 168/300 [01:32<01:01,  2.14it/s]Progress: 59.00%
+---- avg training fps: 7.20

# Trainer step: 168, epoch: 12:  56%|█████▌    | 168/300 [01:33<01:01,  2.14it/s]
# Trainer step: 168, epoch: 12:  56%|█████▋    | 169/300 [01:33<00:58,  2.25it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 170/300 [01:33<00:58,  2.21it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 171/300 [01:34<00:58,  2.19it/s]
# Trainer step: 168, epoch: 12:  57%|█████▋    | 172/300 [01:34<00:58,  2.18it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 173/300 [01:35<00:58,  2.17it/s]
# Trainer step: 168, epoch: 12:  58%|█████▊    | 174/300 [01:35<00:58,  2.16it/s]Progress: 61.00%
+---- avg training fps: 7.24

# Trainer step: 168, epoch: 12:  58%|█████▊    | 175/300 [01:36<00:58,  2.15it/s]
# Trainer step: 168, epoch: 12:  59%|█████▊    | 176/300 [01:36<00:57,  2.15it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 177/300 [01:37<00:57,  2.14it/s]
# Trainer step: 168, epoch: 12:  59%|█████▉    | 178/300 [01:37<00:56,  2.15it/s]
# Trainer step: 168, epoch: 12:  60%|█████▉    | 179/300 [01:38<00:56,  2.15it/s]
# Trainer step: 168, epoch: 12:  60%|██████    | 180/300 [01:38<00:55,  2.15it/s]Progress: 63.00%
+---- avg training fps: 7.28

# Trainer step: 168, epoch: 12:  60%|██████    | 181/300 [01:38<00:55,  2.13it/s]
# Trainer step: 168, epoch: 12:  61%|██████    | 182/300 [01:39<00:55,  2.14it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 182/300 [01:39<00:55,  2.14it/s]
# Trainer step: 182, epoch: 13:  61%|██████    | 183/300 [01:39<00:52,  2.24it/s]
# Trainer step: 182, epoch: 13:  61%|██████▏   | 184/300 [01:40<00:52,  2.22it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 185/300 [01:40<00:52,  2.20it/s]
# Trainer step: 182, epoch: 13:  62%|██████▏   | 186/300 [01:41<00:52,  2.18it/s]Progress: 65.00%
+---- avg training fps: 7.32

# Trainer step: 182, epoch: 13:  62%|██████▏   | 187/300 [01:41<00:51,  2.17it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 188/300 [01:42<00:51,  2.17it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 189/300 [01:42<00:51,  2.16it/s]
# Trainer step: 182, epoch: 13:  63%|██████▎   | 190/300 [01:43<00:57,  1.91it/s]
# Trainer step: 182, epoch: 13:  64%|██████▎   | 191/300 [01:43<00:55,  1.98it/s]
# Trainer step: 182, epoch: 13:  64%|██████▍   | 192/300 [01:44<00:53,  2.03it/s]Progress: 67.00%
+---- avg training fps: 7.34

# Trainer step: 182, epoch: 13:  64%|██████▍   | 193/300 [01:44<00:51,  2.06it/s]
# Trainer step: 182, epoch: 13:  65%|██████▍   | 194/300 [01:45<00:50,  2.08it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 195/300 [01:45<00:49,  2.10it/s]
# Trainer step: 182, epoch: 13:  65%|██████▌   | 196/300 [01:46<00:49,  2.12it/s]
# Trainer step: 196, epoch: 14:  65%|██████▌   | 196/300 [01:46<00:49,  2.12it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 197/300 [01:46<00:46,  2.23it/s]
# Trainer step: 196, epoch: 14:  66%|██████▌   | 198/300 [01:46<00:46,  2.20it/s]Progress: 69.00%
+---- avg training fps: 7.38

# Trainer step: 196, epoch: 14:  66%|██████▋   | 199/300 [01:47<00:46,  2.19it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 200/300 [01:47<00:46,  2.17it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 201/300 [01:48<00:45,  2.16it/s]
# Trainer step: 196, epoch: 14:  67%|██████▋   | 202/300 [01:50<01:37,  1.00it/s]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 203/300 [01:51<01:21,  1.20it/s]
# Trainer step: 196, epoch: 14:  68%|██████▊   | 204/300 [01:51<01:09,  1.38it/s]Progress: 71.00%
+---- avg training fps: 7.29

# Trainer step: 196, epoch: 14:  68%|██████▊   | 205/300 [01:51<01:01,  1.54it/s]
# Trainer step: 196, epoch: 14:  69%|██████▊   | 206/300 [01:52<00:55,  1.69it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 207/300 [01:52<00:51,  1.81it/s]
# Trainer step: 196, epoch: 14:  69%|██████▉   | 208/300 [01:53<00:48,  1.90it/s]
# Trainer step: 196, epoch: 14:  70%|██████▉   | 209/300 [01:53<00:46,  1.97it/s]
# Trainer step: 196, epoch: 14:  70%|███████   | 210/300 [01:54<00:44,  2.02it/s]Progress: 73.00%
+---- avg training fps: 7.33

# Trainer step: 210, epoch: 15:  70%|███████   | 210/300 [01:54<00:44,  2.02it/s]
# Trainer step: 210, epoch: 15:  70%|███████   | 211/300 [01:54<00:41,  2.15it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 212/300 [01:55<00:40,  2.15it/s]
# Trainer step: 210, epoch: 15:  71%|███████   | 213/300 [01:55<00:40,  2.16it/s]
# Trainer step: 210, epoch: 15:  71%|███████▏  | 214/300 [01:56<00:39,  2.16it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 215/300 [01:56<00:39,  2.16it/s]
# Trainer step: 210, epoch: 15:  72%|███████▏  | 216/300 [01:56<00:38,  2.16it/s]Progress: 75.00%
+---- avg training fps: 7.36

# Trainer step: 210, epoch: 15:  72%|███████▏  | 217/300 [01:57<00:38,  2.17it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 218/300 [01:57<00:38,  2.16it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 219/300 [01:58<00:37,  2.14it/s]
# Trainer step: 210, epoch: 15:  73%|███████▎  | 220/300 [01:58<00:37,  2.14it/s]
# Trainer step: 210, epoch: 15:  74%|███████▎  | 221/300 [01:59<00:36,  2.15it/s]
# Trainer step: 210, epoch: 15:  74%|███████▍  | 222/300 [01:59<00:36,  2.15it/s]Progress: 77.00%
+---- avg training fps: 7.39

# Trainer step: 210, epoch: 15:  74%|███████▍  | 223/300 [02:00<00:35,  2.15it/s]
# Trainer step: 210, epoch: 15:  75%|███████▍  | 224/300 [02:00<00:35,  2.15it/s]
# Trainer step: 224, epoch: 16:  75%|███████▍  | 224/300 [02:01<00:35,  2.15it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 225/300 [02:01<00:33,  2.25it/s]
# Trainer step: 224, epoch: 16:  75%|███████▌  | 226/300 [02:01<00:33,  2.21it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 227/300 [02:02<00:33,  2.19it/s]
# Trainer step: 224, epoch: 16:  76%|███████▌  | 228/300 [02:02<00:33,  2.18it/s]Progress: 79.00%
+---- avg training fps: 7.42

# Trainer step: 224, epoch: 16:  76%|███████▋  | 229/300 [02:02<00:32,  2.17it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 230/300 [02:03<00:32,  2.15it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 231/300 [02:03<00:32,  2.15it/s]
# Trainer step: 224, epoch: 16:  77%|███████▋  | 232/300 [02:04<00:31,  2.15it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 233/300 [02:04<00:31,  2.14it/s]
# Trainer step: 224, epoch: 16:  78%|███████▊  | 234/300 [02:05<00:30,  2.15it/s]Progress: 81.00%
+---- avg training fps: 7.44

# Trainer step: 224, epoch: 16:  78%|███████▊  | 235/300 [02:05<00:30,  2.14it/s]
# Trainer step: 224, epoch: 16:  79%|███████▊  | 236/300 [02:06<00:29,  2.14it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 237/300 [02:06<00:29,  2.13it/s]
# Trainer step: 224, epoch: 16:  79%|███████▉  | 238/300 [02:07<00:28,  2.14it/s]
# Trainer step: 238, epoch: 17:  79%|███████▉  | 238/300 [02:07<00:28,  2.14it/s]
# Trainer step: 238, epoch: 17:  80%|███████▉  | 239/300 [02:07<00:26,  2.28it/s]
# Trainer step: 238, epoch: 17:  80%|████████  | 240/300 [02:08<00:26,  2.23it/s]Progress: 83.00%
+---- avg training fps: 7.47

# Trainer step: 238, epoch: 17:  80%|████████  | 241/300 [02:08<00:26,  2.20it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 242/300 [02:08<00:26,  2.19it/s]
# Trainer step: 238, epoch: 17:  81%|████████  | 243/300 [02:09<00:26,  2.17it/s]
# Trainer step: 238, epoch: 17:  81%|████████▏ | 244/300 [02:09<00:25,  2.16it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 245/300 [02:10<00:25,  2.16it/s]
# Trainer step: 238, epoch: 17:  82%|████████▏ | 246/300 [02:10<00:25,  2.16it/s]Progress: 85.00%
+---- avg training fps: 7.50

# Trainer step: 238, epoch: 17:  82%|████████▏ | 247/300 [02:11<00:24,  2.15it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 248/300 [02:11<00:24,  2.15it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 249/300 [02:12<00:23,  2.15it/s]
# Trainer step: 238, epoch: 17:  83%|████████▎ | 250/300 [02:12<00:23,  2.15it/s]
# Trainer step: 238, epoch: 17:  84%|████████▎ | 251/300 [02:13<00:22,  2.15it/s]
# Trainer step: 238, epoch: 17:  84%|████████▍ | 252/300 [02:16<01:03,  1.32s/it]Progress: 87.00%
+---- avg training fps: 7.37

# Trainer step: 252, epoch: 18:  84%|████████▍ | 252/300 [02:16<01:03,  1.32s/it]
# Trainer step: 252, epoch: 18:  84%|████████▍ | 253/300 [02:16<00:49,  1.04s/it]
# Trainer step: 252, epoch: 18:  85%|████████▍ | 254/300 [02:17<00:40,  1.15it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 255/300 [02:17<00:33,  1.33it/s]
# Trainer step: 252, epoch: 18:  85%|████████▌ | 256/300 [02:18<00:29,  1.50it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 257/300 [02:18<00:25,  1.66it/s]
# Trainer step: 252, epoch: 18:  86%|████████▌ | 258/300 [02:19<00:23,  1.78it/s]Progress: 89.00%
+---- avg training fps: 7.39

# Trainer step: 252, epoch: 18:  86%|████████▋ | 259/300 [02:19<00:21,  1.87it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 260/300 [02:20<00:20,  1.95it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 261/300 [02:20<00:19,  2.01it/s]
# Trainer step: 252, epoch: 18:  87%|████████▋ | 262/300 [02:21<00:18,  2.05it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 263/300 [02:21<00:17,  2.08it/s]
# Trainer step: 252, epoch: 18:  88%|████████▊ | 264/300 [02:21<00:17,  2.10it/s]Progress: 91.00%
+---- avg training fps: 7.41

# Trainer step: 252, epoch: 18:  88%|████████▊ | 265/300 [02:22<00:16,  2.12it/s]
# Trainer step: 252, epoch: 18:  89%|████████▊ | 266/300 [02:22<00:15,  2.13it/s]
# Trainer step: 266, epoch: 19:  89%|████████▊ | 266/300 [02:23<00:15,  2.13it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 267/300 [02:23<00:14,  2.24it/s]
# Trainer step: 266, epoch: 19:  89%|████████▉ | 268/300 [02:23<00:14,  2.21it/s]
# Trainer step: 266, epoch: 19:  90%|████████▉ | 269/300 [02:24<00:14,  2.19it/s]
# Trainer step: 266, epoch: 19:  90%|█████████ | 270/300 [02:24<00:13,  2.18it/s]Progress: 93.00%
+---- avg training fps: 7.44

# Trainer step: 266, epoch: 19:  90%|█████████ | 271/300 [02:25<00:13,  2.17it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 272/300 [02:25<00:12,  2.17it/s]
# Trainer step: 266, epoch: 19:  91%|█████████ | 273/300 [02:26<00:12,  2.15it/s]
# Trainer step: 266, epoch: 19:  91%|█████████▏| 274/300 [02:26<00:12,  2.16it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 275/300 [02:27<00:11,  2.16it/s]
# Trainer step: 266, epoch: 19:  92%|█████████▏| 276/300 [02:27<00:11,  2.15it/s]Progress: 95.00%
+---- avg training fps: 7.46

# Trainer step: 266, epoch: 19:  92%|█████████▏| 277/300 [02:27<00:10,  2.15it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 278/300 [02:28<00:10,  2.16it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 279/300 [02:28<00:09,  2.15it/s]
# Trainer step: 266, epoch: 19:  93%|█████████▎| 280/300 [02:29<00:09,  2.15it/s]
# Trainer step: 280, epoch: 20:  93%|█████████▎| 280/300 [02:29<00:09,  2.15it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▎| 281/300 [02:29<00:08,  2.25it/s]
# Trainer step: 280, epoch: 20:  94%|█████████▍| 282/300 [02:30<00:08,  2.22it/s]Progress: 97.00%
+---- avg training fps: 7.49

# Trainer step: 280, epoch: 20:  94%|█████████▍| 283/300 [02:30<00:07,  2.19it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▍| 284/300 [02:31<00:07,  2.18it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 285/300 [02:31<00:06,  2.17it/s]
# Trainer step: 280, epoch: 20:  95%|█████████▌| 286/300 [02:32<00:06,  2.17it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 287/300 [02:32<00:06,  2.16it/s]
# Trainer step: 280, epoch: 20:  96%|█████████▌| 288/300 [02:32<00:05,  2.15it/s]Progress: 99.00%
+---- avg training fps: 7.51

# Trainer step: 280, epoch: 20:  96%|█████████▋| 289/300 [02:33<00:05,  2.16it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 290/300 [02:33<00:04,  2.15it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 291/300 [02:34<00:04,  2.15it/s]
# Trainer step: 280, epoch: 20:  97%|█████████▋| 292/300 [02:34<00:03,  2.15it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 293/300 [02:35<00:03,  2.15it/s]
# Trainer step: 280, epoch: 20:  98%|█████████▊| 294/300 [02:35<00:02,  2.15it/s]Progress: 100.00%
+---- avg training fps: 7.53

# Trainer step: 294, epoch: 21:  98%|█████████▊| 294/300 [02:36<00:02,  2.15it/s]
# Trainer step: 294, epoch: 21:  98%|█████████▊| 295/300 [02:36<00:02,  2.25it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▊| 296/300 [02:36<00:01,  2.21it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 297/300 [02:37<00:01,  2.20it/s]
# Trainer step: 294, epoch: 21:  99%|█████████▉| 298/300 [02:37<00:00,  2.18it/s]
# Trainer step: 294, epoch: 21: 100%|█████████▉| 299/300 [02:38<00:00,  2.17it/s]
# Trainer step: 294, epoch: 21: 100%|██████████| 300/300 [02:38<00:00,  2.16it/s]Progress: 100.00%
+---- avg training fps: 7.55

# Trainer step: 294, epoch: 21: : 301it [02:38,  2.15it/s]                       Progress: 100.00%
Failed to plot token attention loss
+Reached max steps, stopping training!
+Saving checkpoint at step.. 301
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 294, epoch: 21: : 301it [03:57,  1.27it/s]
+------------------------------------------
+Training done :)
+2024-08-13 05:03:51.548421: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 05:03:51.639288: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 05:03:51.639314: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 05:03:51.659197: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 05:03:52.061682: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 05:03:52.061770: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 05:03:52.061780: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23130 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/good_styles/eden_crystals' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.001 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.75 freeze_unet_before_completion_f=0.25 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='eden_crystals' output_dir='lora_models/styles_final/eden_crystals_13_05-03-53-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723518233.8534348 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/eden_crystals_13_05-03-53-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.242
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants on a black background.
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants in the dark.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Futuristic natural elements blending with technology.
+
+- In the style of TOK, a potted plant sitting on top of a table next to a lamp.
+- In the style of TOK, a futuristic city in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves on a dark blue background.
+- In the style of TOK, a plant growing out of a circuit board.
+- In the style of TOK, a group of green and red plants on a black background.
+- In the style of TOK, a potted plant sitting on top of a table next to a lamp.
+- In the style of TOK, a futuristic city in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves on a dark blue background.
+- In the style of TOK, a plant growing out of a circuit board.
+- In the style of TOK, a group of green and red plants in the dark.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Futuristic natural elements blending with technology.
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 7166.38it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a potted plant s...
+1     in the style of <s0><s1><s2>, a futuristic cit...
+2     in the style of <s0><s1><s2>, a flower with gr...
+3     in the style of <s0><s1><s2>, a plant growing ...
+4     in the style of <s0><s1><s2>, a group of green...
+5     in the style of <s0><s1><s2>, a potted plant s...
+6     in the style of <s0><s1><s2>, a futuristic cit...
+7     in the style of <s0><s1><s2>, a flower with gr...
+8     in the style of <s0><s1><s2>, a plant growing ...
+9     in the style of <s0><s1><s2>, a group of green...
+10    in the style of <s0><s1><s2>, a potted plant s...
+11    in the style of <s0><s1><s2>, a futuristic cit...
+12    in the style of <s0><s1><s2>, a flower with gr...
+13    in the style of <s0><s1><s2>, a plant growing ...
+14    in the style of <s0><s1><s2>, a group of green...
+15    in the style of <s0><s1><s2>, a potted plant s...
+16    in the style of <s0><s1><s2>, a futuristic cit...
+17    in the style of <s0><s1><s2>, a flower with gr...
+18    in the style of <s0><s1><s2>, a plant growing ...
+19    in the style of <s0><s1><s2>, a group of green...
+20    in the style of <s0><s1><s2>, a potted plant s...
+21    in the style of <s0><s1><s2>, a futuristic cit...
+22    in the style of <s0><s1><s2>, a flower with gr...
+23    in the style of <s0><s1><s2>, a plant growing ...
+24    in the style of <s0><s1><s2>, a group of green...
+25    in the style of <s0><s1><s2>, a potted plant s...
+26    in the style of <s0><s1><s2>, a futuristic cit...
+27    in the style of <s0><s1><s2>, a flower with gr...
+28    in the style of <s0><s1><s2>, a plant growing ...
+29    in the style of <s0><s1><s2>, a group of green...
+30    in the style of <s0><s1><s2>, a potted plant s...
+31    in the style of <s0><s1><s2>, a futuristic cit...
+32    in the style of <s0><s1><s2>, a flower with gr...
+33    in the style of <s0><s1><s2>, a plant growing ...
+34    in the style of <s0><s1><s2>, a group of green...
+35    in the style of <s0><s1><s2>, a potted plant s...
+36    in the style of <s0><s1><s2>, a futuristic cit...
+37    in the style of <s0><s1><s2>, a flower with gr...
+38    in the style of <s0><s1><s2>, a plant growing ...
+39    in the style of <s0><s1><s2>, a group of green...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
  0%|          | 0/300 [00:00<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:02<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:02<14:45,  2.96s/it]
# Trainer step: 0, epoch: 0:   1%|          | 2/300 [00:06<16:14,  3.27s/it]
# Trainer step: 0, epoch: 0:   1%|          | 3/300 [00:06<09:50,  1.99s/it]
# Trainer step: 0, epoch: 0:   1%|▏         | 4/300 [00:07<06:50,  1.39s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:07<05:10,  1.05s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:08<04:10,  1.17it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.74

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:08<03:33,  1.38it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:09<03:08,  1.55it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 9/300 [00:09<02:51,  1.69it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 10/300 [00:10<02:40,  1.81it/s]
# Trainer step: 10, epoch: 1:   3%|▎         | 10/300 [00:10<02:40,  1.81it/s]
# Trainer step: 10, epoch: 1:   4%|▎         | 11/300 [00:10<02:32,  1.90it/s]
# Trainer step: 10, epoch: 1:   4%|▍         | 12/300 [00:11<02:26,  1.97it/s]Progress: 7.00%
+---- avg training fps: 4.15

# Trainer step: 10, epoch: 1:   4%|▍         | 13/300 [00:11<02:22,  2.02it/s]
# Trainer step: 10, epoch: 1:   5%|▍         | 14/300 [00:12<02:19,  2.05it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 15/300 [00:12<02:17,  2.08it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 16/300 [00:12<02:15,  2.10it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 17/300 [00:13<02:13,  2.11it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 18/300 [00:13<02:12,  2.13it/s]Progress: 9.00%
+---- avg training fps: 5.01

# Trainer step: 10, epoch: 1:   6%|▋         | 19/300 [00:14<02:11,  2.13it/s]
# Trainer step: 10, epoch: 1:   7%|▋         | 20/300 [00:14<02:11,  2.14it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 20/300 [00:15<02:11,  2.14it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 21/300 [00:15<02:10,  2.14it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 22/300 [00:15<02:09,  2.14it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 23/300 [00:16<02:09,  2.14it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 24/300 [00:16<02:08,  2.14it/s]Progress: 11.00%
+---- avg training fps: 5.59

# Trainer step: 20, epoch: 2:   8%|▊         | 25/300 [00:17<02:08,  2.14it/s]
# Trainer step: 20, epoch: 2:   9%|▊         | 26/300 [00:17<02:08,  2.14it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 27/300 [00:18<02:07,  2.14it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 28/300 [00:18<02:07,  2.14it/s]
# Trainer step: 20, epoch: 2:  10%|▉         | 29/300 [00:19<02:06,  2.14it/s]
# Trainer step: 20, epoch: 2:  10%|█         | 30/300 [00:19<02:06,  2.14it/s]Progress: 13.00%
+---- avg training fps: 6.01

# Trainer step: 30, epoch: 3:  10%|█         | 30/300 [00:19<02:06,  2.14it/s]
# Trainer step: 30, epoch: 3:  10%|█         | 31/300 [00:19<02:05,  2.14it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 32/300 [00:20<02:05,  2.14it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 33/300 [00:20<02:04,  2.15it/s]
# Trainer step: 30, epoch: 3:  11%|█▏        | 34/300 [00:21<02:03,  2.15it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 35/300 [00:21<02:03,  2.15it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 36/300 [00:22<02:02,  2.15it/s]Progress: 15.00%
+---- avg training fps: 6.28

# Trainer step: 30, epoch: 3:  12%|█▏        | 37/300 [00:22<02:17,  1.92it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 38/300 [00:23<02:12,  1.98it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 39/300 [00:23<02:08,  2.03it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 40/300 [00:24<02:06,  2.06it/s]
# Trainer step: 40, epoch: 4:  13%|█▎        | 40/300 [00:24<02:06,  2.06it/s]
# Trainer step: 40, epoch: 4:  14%|█▎        | 41/300 [00:24<02:04,  2.09it/s]
# Trainer step: 40, epoch: 4:  14%|█▍        | 42/300 [00:25<02:02,  2.11it/s]Progress: 17.00%
+---- avg training fps: 6.53

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:25<02:01,  2.12it/s]
# Trainer step: 40, epoch: 4:  15%|█▍        | 44/300 [00:26<02:00,  2.13it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 45/300 [00:26<01:59,  2.13it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 46/300 [00:27<01:58,  2.14it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 47/300 [00:27<01:58,  2.14it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:28<01:57,  2.15it/s]Progress: 19.00%
+---- avg training fps: 6.73

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:28<01:56,  2.15it/s]
# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:29<01:56,  2.15it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 50/300 [00:29<01:56,  2.15it/s]
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# Trainer step: 50, epoch: 5:  17%|█▋        | 52/300 [00:33<05:45,  1.39s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 53/300 [00:33<04:35,  1.12s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:33<03:46,  1.09it/s]Progress: 21.00%
+---- avg training fps: 6.27

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:34<03:12,  1.27it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:34<02:48,  1.45it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 57/300 [00:35<02:31,  1.60it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 58/300 [00:35<02:19,  1.74it/s]
# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:36<02:10,  1.84it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:36<02:04,  1.92it/s]Progress: 23.00%
+---- avg training fps: 6.45

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:37<02:04,  1.92it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:37<02:00,  1.98it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:37<01:57,  2.03it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:38<01:54,  2.06it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:38<01:53,  2.08it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:39<01:51,  2.10it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:39<01:50,  2.11it/s]Progress: 25.00%
+---- avg training fps: 6.59

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:40<01:49,  2.12it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:40<01:49,  2.12it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:40<01:48,  2.13it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:41<02:01,  1.90it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:42<02:01,  1.90it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:42<01:56,  1.97it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:42<01:52,  2.02it/s]Progress: 27.00%
+---- avg training fps: 6.69

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:43<01:50,  2.06it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:43<01:48,  2.08it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:43<01:46,  2.10it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:44<01:45,  2.12it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:44<01:44,  2.13it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:45<01:44,  2.13it/s]Progress: 29.00%
+---- avg training fps: 6.81

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:45<01:43,  2.14it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:46<01:42,  2.14it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:46<01:42,  2.14it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:46<01:42,  2.14it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:47<01:41,  2.15it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:47<01:40,  2.15it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:48<01:40,  2.15it/s]Progress: 31.00%
+---- avg training fps: 6.91

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:48<01:40,  2.15it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:49<01:39,  2.15it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:49<01:39,  2.15it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:50<01:38,  2.15it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:50<01:38,  2.15it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:50<01:37,  2.15it/s]Progress: 33.00%
+---- avg training fps: 7.01

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:51<01:37,  2.15it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:51<01:37,  2.15it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:51<01:36,  2.15it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:52<01:36,  2.15it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:52<01:35,  2.15it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:53<01:35,  2.15it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:53<01:34,  2.15it/s]Progress: 35.00%
+---- avg training fps: 7.09

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:54<01:34,  2.15it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:54<01:33,  2.15it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:55<01:33,  2.15it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:55<01:32,  2.15it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:56<01:32,  2.15it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:56<01:32,  2.15it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [00:59<04:15,  1.29s/it]Progress: 37.00%
+---- avg training fps: 6.83

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [00:59<03:25,  1.04s/it]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [01:00<02:50,  1.15it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [01:00<02:26,  1.33it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [01:01<02:09,  1.50it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:01<01:56,  1.65it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:02<01:48,  1.77it/s]Progress: 39.00%
+---- avg training fps: 6.91

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:02<01:42,  1.87it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:02<01:37,  1.94it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:03<01:37,  1.94it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:03<01:34,  2.00it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:03<01:32,  2.04it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:04<01:30,  2.07it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:04<01:28,  2.09it/s]Progress: 41.00%
+---- avg training fps: 6.98

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:05<01:28,  2.09it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:05<01:27,  2.11it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:06<01:26,  2.12it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:06<01:25,  2.13it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:07<01:35,  1.90it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:07<01:31,  1.97it/s]Progress: 43.00%
+---- avg training fps: 7.03

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:08<01:31,  1.97it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:08<01:28,  2.02it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:08<01:26,  2.06it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:09<01:24,  2.09it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:09<01:23,  2.11it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:10<01:22,  2.12it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:10<01:21,  2.13it/s]Progress: 45.00%
+---- avg training fps: 7.09

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:11<01:20,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:11<01:20,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:12<01:19,  2.14it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:12<01:19,  2.14it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:12<01:19,  2.14it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:12<01:18,  2.15it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:13<01:18,  2.15it/s]Progress: 47.00%
+---- avg training fps: 7.15

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:13<01:17,  2.15it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:14<01:17,  2.15it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:14<01:16,  2.15it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:15<01:16,  2.15it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:15<01:15,  2.15it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:16<01:15,  2.15it/s]Progress: 49.00%
+---- avg training fps: 7.20

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:16<01:14,  2.15it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:17<01:14,  2.15it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:17<01:14,  2.15it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:17<01:13,  2.15it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:18<01:13,  2.15it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:18<01:12,  2.15it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:19<01:12,  2.15it/s]Progress: 51.00%
+---- avg training fps: 7.25

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:19<01:11,  2.15it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:19<01:11,  2.15it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:20<01:11,  2.15it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:20<01:10,  2.16it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:21<01:10,  2.15it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:21<01:09,  2.15it/s]Progress: 53.00%
+---- avg training fps: 7.29

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:22<01:09,  2.15it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:22<01:09,  2.15it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:26<03:41,  1.50s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:26<02:54,  1.19s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:27<02:21,  1.03it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:27<01:59,  1.22it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:28<01:42,  1.40it/s]Progress: 55.00%
+---- avg training fps: 7.05

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:28<01:31,  1.56it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:28<01:23,  1.70it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:29<01:17,  1.81it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:29<01:13,  1.90it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:30<01:13,  1.90it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:30<01:10,  1.97it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:30<01:08,  2.02it/s]Progress: 57.00%
+---- avg training fps: 7.10

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:31<01:06,  2.05it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:31<01:05,  2.08it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:32<01:04,  2.09it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:32<01:03,  2.11it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:33<01:02,  2.11it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:33<01:02,  2.12it/s]Progress: 59.00%
+---- avg training fps: 7.14

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:34<01:01,  2.13it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:34<01:01,  2.13it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:35<01:01,  2.13it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:35<01:00,  2.13it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:35<01:00,  2.13it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:35<00:59,  2.14it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:36<00:58,  2.14it/s]Progress: 61.00%
+---- avg training fps: 7.18

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:36<00:58,  2.14it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:37<00:57,  2.14it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:37<00:57,  2.14it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:38<00:56,  2.14it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:38<00:56,  2.14it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:39<00:56,  2.14it/s]Progress: 63.00%
+---- avg training fps: 7.22

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:39<00:56,  2.14it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:39<00:55,  2.14it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:40<00:55,  2.14it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:40<00:54,  2.14it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:41<00:54,  2.14it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:41<00:53,  2.14it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:42<00:53,  2.14it/s]Progress: 65.00%
+---- avg training fps: 7.26

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:42<00:52,  2.14it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:43<00:52,  2.13it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:43<00:52,  2.13it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:43<00:51,  2.14it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:44<00:51,  2.14it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:44<00:50,  2.14it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:44<00:50,  2.14it/s]Progress: 67.00%
+---- avg training fps: 7.29

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:45<00:50,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:45<00:49,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:46<00:49,  2.14it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:46<00:48,  2.14it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:47<00:48,  2.14it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:47<00:47,  2.14it/s]Progress: 69.00%
+---- avg training fps: 7.32

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:48<00:47,  2.14it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:48<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:49<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:49<00:46,  2.14it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:52<02:20,  1.44s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:53<01:51,  1.15s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:53<01:38,  1.02s/it]Progress: 71.00%
+---- avg training fps: 7.13

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:54<01:21,  1.17it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:54<01:09,  1.35it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:55<01:01,  1.52it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:55<00:55,  1.67it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:56<00:50,  1.79it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:56<00:47,  1.89it/s]Progress: 73.00%
+---- avg training fps: 7.17

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:57<00:47,  1.89it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:57<00:45,  1.96it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:57<00:43,  2.01it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [01:58<00:42,  2.05it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [01:58<00:41,  2.08it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [01:59<00:40,  2.10it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [01:59<00:39,  2.11it/s]Progress: 75.00%
+---- avg training fps: 7.20

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [02:00<00:39,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [02:00<00:38,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:00<00:37,  2.13it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:01<00:37,  2.14it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:01<00:37,  2.14it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:01<00:36,  2.14it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:02<00:36,  2.15it/s]Progress: 77.00%
+---- avg training fps: 7.23

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:02<00:35,  2.15it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:03<00:35,  2.15it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:03<00:34,  2.15it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:04<00:34,  2.15it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:04<00:33,  2.15it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:05<00:33,  2.15it/s]Progress: 79.00%
+---- avg training fps: 7.26

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:05<00:32,  2.16it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:06<00:32,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:06<00:32,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:06<00:31,  2.16it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:06<00:31,  2.16it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:07<00:30,  2.16it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:07<00:30,  2.16it/s]Progress: 81.00%
+---- avg training fps: 7.29

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:08<00:30,  2.16it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:08<00:29,  2.16it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:09<00:29,  2.16it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:09<00:28,  2.15it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:10<00:28,  2.16it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:10<00:27,  2.16it/s]Progress: 83.00%
+---- avg training fps: 7.32

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:11<00:27,  2.16it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:11<00:27,  2.16it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:11<00:26,  2.16it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:12<00:26,  2.16it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:12<00:25,  2.16it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:13<00:25,  2.16it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:13<00:25,  2.16it/s]Progress: 85.00%
+---- avg training fps: 7.35

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:13<00:24,  2.16it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:14<00:24,  2.16it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:14<00:23,  2.16it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:15<00:23,  2.16it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:15<00:23,  2.16it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:15<00:22,  2.16it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:19<01:12,  1.52s/it]Progress: 87.00%
+---- avg training fps: 7.19

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:20<00:56,  1.20s/it]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:20<00:45,  1.02it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:21<00:37,  1.21it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:21<00:31,  1.39it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:22<00:27,  1.56it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:22<00:24,  1.70it/s]Progress: 89.00%
+---- avg training fps: 7.22

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:23<00:22,  1.81it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:23<00:21,  1.90it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:23<00:21,  1.90it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:23<00:19,  1.97it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:24<00:18,  2.02it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:24<00:18,  2.03it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:25<00:17,  2.07it/s]Progress: 91.00%
+---- avg training fps: 7.24

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:25<00:16,  2.09it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:26<00:16,  2.11it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:26<00:15,  2.12it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:27<00:15,  2.13it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:27<00:14,  2.13it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:28<00:14,  2.14it/s]Progress: 93.00%
+---- avg training fps: 7.27

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:28<00:14,  2.14it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:28<00:13,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:29<00:13,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:29<00:12,  2.14it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:30<00:12,  2.14it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:30<00:11,  2.14it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:30<00:11,  2.14it/s]Progress: 95.00%
+---- avg training fps: 7.29

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:31<00:10,  2.14it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:31<00:10,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:32<00:09,  2.15it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:32<00:09,  2.15it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:33<00:09,  2.15it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:33<00:08,  2.15it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:33<00:08,  2.15it/s]Progress: 97.00%
+---- avg training fps: 7.31

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:34<00:07,  2.15it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:34<00:07,  2.14it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:35<00:06,  2.14it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:35<00:06,  2.14it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:36<00:06,  2.15it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:36<00:05,  2.15it/s]Progress: 99.00%
+---- avg training fps: 7.34

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:37<00:05,  2.15it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:37<00:04,  2.15it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:37<00:04,  2.15it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:37<00:04,  2.15it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:38<00:03,  2.14it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:38<00:03,  2.14it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:39<00:02,  2.14it/s]Progress: 100.00%
+---- avg training fps: 7.36

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:39<00:02,  2.14it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:40<00:01,  2.14it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:40<00:01,  2.15it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:41<00:00,  2.15it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:41<00:00,  2.15it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:42<00:00,  2.15it/s]Progress: 100.00%
+---- avg training fps: 7.38
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [04:00<00:00,  1.25it/s]
+------------------------------------------
+Training done :)
+2024-08-13 05:08:24.157116: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
+To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
+2024-08-13 05:08:24.255915: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
+2024-08-13 05:08:24.255941: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
+2024-08-13 05:08:24.274593: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+2024-08-13 05:08:24.675596: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
+2024-08-13 05:08:24.675679: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
+2024-08-13 05:08:24.675688: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
+OpenAI API key loaded
+# of visible GPUs: 1
+GPU 0: 23122 MB free
+Using GPU 0
+Starting new LoRa training run with config:
+lora_training_urls='/home/rednax/Documents/datasets/good_styles/eden_crystals' concept_mode='style' caption_prefix='' caption_model='florence' caption_dropout=0.0 sd_model_version='sdxl' ckpt_path=None pretrained_model={'path': './models/zavychromaxl_v90.safetensors', 'url': 'https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors', 'version': 'sdxl'} seed=0 resolution=512 validation_img_size=None train_img_size=None train_aspect_ratio=None train_batch_size=4 max_train_steps=300 num_train_epochs=None checkpointing_steps=300 gradient_accumulation_steps=1 is_lora=True unet_optimizer_type='adamw' unet_lr_warmup_steps=300 unet_lr=0.0003 prodigy_d_coef=1.0 unet_prodigy_growth_factor=1.05 lora_weight_decay=0.001 ti_lr=0.001 token_warmup_steps=0 ti_weight_decay=0.0 ti_optimizer='adamw' freeze_ti_after_completion_f=0.5 freeze_unet_before_completion_f=0.0 token_attention_loss_w=0.0 cond_reg_w=0.0 tok_cond_reg_w=0.0 tok_cov_reg_w=500.0 l1_penalty=0.0 noise_offset=0.02 snr_gamma=5.0 lora_alpha_multiplier=1.0 lora_rank=16 use_dora=False left_right_flip_augmentation=True augment_imgs_up_to_n=40 mask_target_prompts=None crop_based_on_salience=True use_face_detection_instead=False clipseg_temperature=0.5 n_sample_imgs=8 name='eden_crystals' output_dir='lora_models/styles_final/eden_crystals_13_05-08-26-style_512_florence_300' debug=True allow_tf32=True disable_ti=False skip_gpt_cleanup=False weight_type='bf16' n_tokens=3 inserting_list_tokens=['<s0>', '<s1>', '<s2>'] token_dict={'TOK': '<s0><s1><s2>'} device='cuda:0' do_cache=True sample_imgs_lora_scale=0.8 dataloader_num_workers=0 training_attributes={} aspect_ratio_bucketing=False start_time=1723518506.4959981 job_time=0.0 text_encoder_lora_optimizer=None text_encoder_lora_lr=0.0 txt_encoders_lr_warmup_steps=200 text_encoder_lora_weight_decay=1e-05 text_encoder_lora_rank=16
+------------------------------------------
+Loading model weights from /home/rednax/SSD2TB/Github_repos/diffusion_trainer/models/zavychromaxl_v90.safetensors with dtype: torch.bfloat16...
+Loading as SDXL model...
+
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+
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+ ['text_model.embeddings.position_ids']
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Loaded sdxl model!
+Found: 60 modules
+Local path detected, skipping download.
+Succesfully prepped 5 .jpg images in lora_models/styles_final/eden_crystals_13_05-08-26-style_512_florence_300/images_in!
+Average aspect ratio of images (width / height): 1.242
+New train_img_size: [576, 448]
+Validation_img_size was set to: [1152, 896]
+Loaded 5 images, 0 of which have captions.
+upscaling imgs..
+
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+/home/rednax/SSD2TB/miniconda3/envs/sdxl/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
+  warnings.warn(
+Adding LR flips... (doubling the number of images from 5 to 10)
+Generating 10 captions using mode: style...
+
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+Final chatgpt prompt:
+
+Analyze a set of (poor) image descriptions, each featuring an example of a common aesthetic style named TOK.
+Tasks:
+1. Deduce a concise (max 7 words) visual description of the aesthetic style (Style Description).
+2. Rewrite each description to focus solely on the non-stylistic contents of the image like characters, objects, colors, scene, context etc but not the stylistic elements captured by TOK.
+3. Integrate "in the style of TOK" naturally into each description, typically at the beginning while summarizing each description to its core elements, ensuring clarity and mandatory inclusion of "TOK".
+The descriptions are:
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants on a black background.
+- A potted plant sitting on top of a table next to a lamp.
+- a futuristic city in the middle of a space filled with stars
+- A flower with green leaves on a dark blue background.
+- A plant growing out of a circuit board.
+- A group of green and red plants in the dark.
+
+Respond with "Style Description: ..." followed by a list (using "-") of all the revised descriptions, each mentioning "in the style of TOK".
+
+--------------------------
+Calling chatgpt with seed 0...
+----- GPT response: -----
+Style Description: Futuristic natural elements blending with technology.
+
+- In the style of TOK, a potted plant sits on a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves is on a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is on a black background.
+- In the style of TOK, a potted plant sits on a table next to a lamp.
+- In the style of TOK, a futuristic city is in the middle of a space filled with stars.
+- In the style of TOK, a flower with green leaves is on a dark blue background.
+- In the style of TOK, a plant is growing out of a circuit board.
+- In the style of TOK, a group of green and red plants is in the dark.
+--------------------------
+Making sure TOK is added to every training prompt...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Adding augmented version of each training img...
+Using GPT concept name as CLIP-segmentation prompt: Futuristic natural elements blending with technology.
+Generating 40 masks...
+Using "" as CLIP-segmentation prompt for all images.
+
0it [00:00, ?it/s]
40it [00:00, 6995.17it/s]
+Masks generated! Cropping images to center of mass...
+Cropping and resizing images...
+Expanding masks...
+Done!
+Saving final training dataset...
+---> Training data 100% ready to go!
+Initializing new tokens: ['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-0:
+['<s0>', '<s1>', '<s2>']
+Inserting new tokens into tokenizer-1:
+['<s0>', '<s1>', '<s2>']
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Initialized a new DistributionLoss with shape: torch.Size([49411, 768])
+Initialized a new DistributionLoss with shape: torch.Size([49411, 1280])
+Skipping token embedding warmup.
+All embeddings in text_encoder_0 are now set to be trainable.
+All embeddings in text_encoder_1 are now set to be trainable.
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 768]) to the trainable parameters
+Added text_model.embeddings.token_embedding.weight with shape torch.Size([49411, 1280]) to the trainable parameters
+Created adamw optimizer for textual inversion!
+Created adamw optimizer for unet!
+################################################################################
+Trainable unet params: 25.4M || All params: 2592.9M || trainable = 0.98%
+################################################################################
+################################################################################
+Trainable text_encoder_0 params: 0.0M || All params: 123.1M || trainable = 0.00%
+################################################################################
+################################################################################
+Trainable text_encoder_1 params: 0.0M || All params: 694.7M || trainable = 0.00%
+################################################################################
+Caching latents, masks and captions...
+
+
+Cached latents, masks and captions for 40 images.
+Not using aspect ratio bucketing.
+Final training captions:
+0     in the style of <s0><s1><s2>, a potted plant s...
+1     in the style of <s0><s1><s2>, a futuristic cit...
+2     in the style of <s0><s1><s2>, a flower with gr...
+3     in the style of <s0><s1><s2>, a plant is growi...
+4     in the style of <s0><s1><s2>, a group of green...
+5     in the style of <s0><s1><s2>, a potted plant s...
+6     in the style of <s0><s1><s2>, a futuristic cit...
+7     in the style of <s0><s1><s2>, a flower with gr...
+8     in the style of <s0><s1><s2>, a plant is growi...
+9     in the style of <s0><s1><s2>, a group of green...
+10    in the style of <s0><s1><s2>, a potted plant s...
+11    in the style of <s0><s1><s2>, a futuristic cit...
+12    in the style of <s0><s1><s2>, a flower with gr...
+13    in the style of <s0><s1><s2>, a plant is growi...
+14    in the style of <s0><s1><s2>, a group of green...
+15    in the style of <s0><s1><s2>, a potted plant s...
+16    in the style of <s0><s1><s2>, a futuristic cit...
+17    in the style of <s0><s1><s2>, a flower with gr...
+18    in the style of <s0><s1><s2>, a plant is growi...
+19    in the style of <s0><s1><s2>, a group of green...
+20    in the style of <s0><s1><s2>, a potted plant s...
+21    in the style of <s0><s1><s2>, a futuristic cit...
+22    in the style of <s0><s1><s2>, a flower with gr...
+23    in the style of <s0><s1><s2>, a plant is growi...
+24    in the style of <s0><s1><s2>, a group of green...
+25    in the style of <s0><s1><s2>, a potted plant s...
+26    in the style of <s0><s1><s2>, a futuristic cit...
+27    in the style of <s0><s1><s2>, a flower with gr...
+28    in the style of <s0><s1><s2>, a plant is growi...
+29    in the style of <s0><s1><s2>, a group of green...
+30    in the style of <s0><s1><s2>, a potted plant s...
+31    in the style of <s0><s1><s2>, a futuristic cit...
+32    in the style of <s0><s1><s2>, a flower with gr...
+33    in the style of <s0><s1><s2>, a plant is growi...
+34    in the style of <s0><s1><s2>, a group of green...
+35    in the style of <s0><s1><s2>, a potted plant s...
+36    in the style of <s0><s1><s2>, a futuristic cit...
+37    in the style of <s0><s1><s2>, a flower with gr...
+38    in the style of <s0><s1><s2>, a plant is growi...
+39    in the style of <s0><s1><s2>, a group of green...
+Name: caption, dtype: object
+# Trainer : Loaded dataset, do_cache: True
+--- Num samples = 40
+--- Num batches each epoch = 10
+--- Num Epochs = 30
+--- Instantaneous batch size per device = 4
+--- Total batch_size (distributed + accumulation) = 4
+--- Gradient Accumulation steps = 1
+--- Total optimization steps = 300
+
+
  0%|          | 0/300 [00:00<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 0/300 [00:03<?, ?it/s]
# Trainer step: 0, epoch: 0:   0%|          | 1/300 [00:03<15:20,  3.08s/it]
# Trainer step: 0, epoch: 0:   1%|          | 2/300 [00:06<17:15,  3.47s/it]
# Trainer step: 0, epoch: 0:   1%|          | 3/300 [00:07<10:24,  2.10s/it]
# Trainer step: 0, epoch: 0:   1%|▏         | 4/300 [00:07<07:11,  1.46s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 5/300 [00:08<05:25,  1.10s/it]
# Trainer step: 0, epoch: 0:   2%|▏         | 6/300 [00:08<04:20,  1.13it/s]--> Initialized optimizers for:
+textual_inversion
+unet
+
+---- avg training fps: 2.62

# Trainer step: 0, epoch: 0:   2%|▏         | 7/300 [00:09<03:40,  1.33it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 8/300 [00:09<03:13,  1.51it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 9/300 [00:10<02:55,  1.65it/s]
# Trainer step: 0, epoch: 0:   3%|▎         | 10/300 [00:10<02:43,  1.77it/s]
# Trainer step: 10, epoch: 1:   3%|▎         | 10/300 [00:11<02:43,  1.77it/s]
# Trainer step: 10, epoch: 1:   4%|▎         | 11/300 [00:11<02:35,  1.86it/s]
# Trainer step: 10, epoch: 1:   4%|▍         | 12/300 [00:11<02:29,  1.93it/s]Progress: 7.00%
+---- avg training fps: 3.99

# Trainer step: 10, epoch: 1:   4%|▍         | 13/300 [00:12<02:24,  1.98it/s]
# Trainer step: 10, epoch: 1:   5%|▍         | 14/300 [00:12<02:21,  2.02it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 15/300 [00:12<02:19,  2.05it/s]
# Trainer step: 10, epoch: 1:   5%|▌         | 16/300 [00:13<02:17,  2.07it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 17/300 [00:13<02:16,  2.08it/s]
# Trainer step: 10, epoch: 1:   6%|▌         | 18/300 [00:14<02:14,  2.09it/s]Progress: 9.00%
+---- avg training fps: 4.84

# Trainer step: 10, epoch: 1:   6%|▋         | 19/300 [00:14<02:14,  2.09it/s]
# Trainer step: 10, epoch: 1:   7%|▋         | 20/300 [00:15<02:13,  2.10it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 20/300 [00:15<02:13,  2.10it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 21/300 [00:15<02:12,  2.10it/s]
# Trainer step: 20, epoch: 2:   7%|▋         | 22/300 [00:16<02:12,  2.10it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 23/300 [00:16<02:11,  2.11it/s]
# Trainer step: 20, epoch: 2:   8%|▊         | 24/300 [00:17<02:10,  2.11it/s]Progress: 11.00%
+---- avg training fps: 5.42

# Trainer step: 20, epoch: 2:   8%|▊         | 25/300 [00:17<02:10,  2.11it/s]
# Trainer step: 20, epoch: 2:   9%|▊         | 26/300 [00:18<02:09,  2.11it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 27/300 [00:18<02:09,  2.11it/s]
# Trainer step: 20, epoch: 2:   9%|▉         | 28/300 [00:19<02:08,  2.11it/s]
# Trainer step: 20, epoch: 2:  10%|▉         | 29/300 [00:19<02:08,  2.11it/s]
# Trainer step: 20, epoch: 2:  10%|█         | 30/300 [00:20<02:07,  2.11it/s]Progress: 13.00%
+---- avg training fps: 5.84

# Trainer step: 30, epoch: 3:  10%|█         | 30/300 [00:20<02:07,  2.11it/s]
# Trainer step: 30, epoch: 3:  10%|█         | 31/300 [00:20<02:07,  2.11it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 32/300 [00:21<02:06,  2.11it/s]
# Trainer step: 30, epoch: 3:  11%|█         | 33/300 [00:21<02:21,  1.88it/s]
# Trainer step: 30, epoch: 3:  11%|█▏        | 34/300 [00:22<02:17,  1.94it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 35/300 [00:22<02:12,  2.00it/s]
# Trainer step: 30, epoch: 3:  12%|█▏        | 36/300 [00:23<02:08,  2.05it/s]Progress: 15.00%
+---- avg training fps: 6.11

# Trainer step: 30, epoch: 3:  12%|█▏        | 37/300 [00:23<02:06,  2.08it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 38/300 [00:24<02:04,  2.11it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 39/300 [00:24<02:02,  2.13it/s]
# Trainer step: 30, epoch: 3:  13%|█▎        | 40/300 [00:24<02:01,  2.14it/s]
# Trainer step: 40, epoch: 4:  13%|█▎        | 40/300 [00:25<02:01,  2.14it/s]
# Trainer step: 40, epoch: 4:  14%|█▎        | 41/300 [00:25<02:00,  2.15it/s]
# Trainer step: 40, epoch: 4:  14%|█▍        | 42/300 [00:25<01:59,  2.15it/s]Progress: 17.00%
+---- avg training fps: 6.38

# Trainer step: 40, epoch: 4:  14%|█▍        | 43/300 [00:26<01:59,  2.16it/s]
# Trainer step: 40, epoch: 4:  15%|█▍        | 44/300 [00:26<01:58,  2.16it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 45/300 [00:27<01:58,  2.16it/s]
# Trainer step: 40, epoch: 4:  15%|█▌        | 46/300 [00:27<01:57,  2.15it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 47/300 [00:28<01:57,  2.16it/s]
# Trainer step: 40, epoch: 4:  16%|█▌        | 48/300 [00:28<01:56,  2.16it/s]Progress: 19.00%
+---- avg training fps: 6.60

# Trainer step: 40, epoch: 4:  16%|█▋        | 49/300 [00:29<01:56,  2.15it/s]
# Trainer step: 40, epoch: 4:  17%|█▋        | 50/300 [00:29<01:56,  2.15it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 50/300 [00:30<01:56,  2.15it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 51/300 [00:30<01:55,  2.15it/s]
# Trainer step: 50, epoch: 5:  17%|█▋        | 52/300 [00:33<06:04,  1.47s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 53/300 [00:34<04:48,  1.17s/it]
# Trainer step: 50, epoch: 5:  18%|█▊        | 54/300 [00:34<03:54,  1.05it/s]Progress: 21.00%
+---- avg training fps: 6.13

# Trainer step: 50, epoch: 5:  18%|█▊        | 55/300 [00:35<03:18,  1.24it/s]
# Trainer step: 50, epoch: 5:  19%|█▊        | 56/300 [00:35<02:52,  1.41it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 57/300 [00:36<02:33,  1.58it/s]
# Trainer step: 50, epoch: 5:  19%|█▉        | 58/300 [00:36<02:20,  1.72it/s]
# Trainer step: 50, epoch: 5:  20%|█▉        | 59/300 [00:37<02:11,  1.83it/s]
# Trainer step: 50, epoch: 5:  20%|██        | 60/300 [00:37<02:05,  1.91it/s]Progress: 23.00%
+---- avg training fps: 6.31

# Trainer step: 60, epoch: 6:  20%|██        | 60/300 [00:38<02:05,  1.91it/s]
# Trainer step: 60, epoch: 6:  20%|██        | 61/300 [00:38<02:01,  1.97it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 62/300 [00:38<02:12,  1.80it/s]
# Trainer step: 60, epoch: 6:  21%|██        | 63/300 [00:39<02:05,  1.89it/s]
# Trainer step: 60, epoch: 6:  21%|██▏       | 64/300 [00:39<02:00,  1.96it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 65/300 [00:40<01:56,  2.02it/s]
# Trainer step: 60, epoch: 6:  22%|██▏       | 66/300 [00:40<01:53,  2.05it/s]Progress: 25.00%
+---- avg training fps: 6.44

# Trainer step: 60, epoch: 6:  22%|██▏       | 67/300 [00:41<01:51,  2.08it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 68/300 [00:41<01:50,  2.11it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 69/300 [00:41<01:49,  2.11it/s]
# Trainer step: 60, epoch: 6:  23%|██▎       | 70/300 [00:42<01:48,  2.12it/s]
# Trainer step: 70, epoch: 7:  23%|██▎       | 70/300 [00:42<01:48,  2.12it/s]
# Trainer step: 70, epoch: 7:  24%|██▎       | 71/300 [00:42<01:47,  2.13it/s]
# Trainer step: 70, epoch: 7:  24%|██▍       | 72/300 [00:43<01:46,  2.13it/s]Progress: 27.00%
+---- avg training fps: 6.57

# Trainer step: 70, epoch: 7:  24%|██▍       | 73/300 [00:43<01:46,  2.14it/s]
# Trainer step: 70, epoch: 7:  25%|██▍       | 74/300 [00:44<01:45,  2.14it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 75/300 [00:44<01:45,  2.14it/s]
# Trainer step: 70, epoch: 7:  25%|██▌       | 76/300 [00:45<01:44,  2.13it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 77/300 [00:45<01:44,  2.14it/s]
# Trainer step: 70, epoch: 7:  26%|██▌       | 78/300 [00:46<01:43,  2.14it/s]Progress: 29.00%
+---- avg training fps: 6.69

# Trainer step: 70, epoch: 7:  26%|██▋       | 79/300 [00:46<01:43,  2.15it/s]
# Trainer step: 70, epoch: 7:  27%|██▋       | 80/300 [00:47<01:42,  2.14it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 80/300 [00:47<01:42,  2.14it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 81/300 [00:47<01:42,  2.14it/s]
# Trainer step: 80, epoch: 8:  27%|██▋       | 82/300 [00:48<01:41,  2.15it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 83/300 [00:48<01:41,  2.14it/s]
# Trainer step: 80, epoch: 8:  28%|██▊       | 84/300 [00:48<01:41,  2.13it/s]Progress: 31.00%
+---- avg training fps: 6.80

# Trainer step: 80, epoch: 8:  28%|██▊       | 85/300 [00:49<01:41,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▊       | 86/300 [00:49<01:40,  2.13it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 87/300 [00:50<01:40,  2.12it/s]
# Trainer step: 80, epoch: 8:  29%|██▉       | 88/300 [00:50<01:39,  2.12it/s]
# Trainer step: 80, epoch: 8:  30%|██▉       | 89/300 [00:51<01:39,  2.12it/s]
# Trainer step: 80, epoch: 8:  30%|███       | 90/300 [00:51<01:38,  2.12it/s]Progress: 33.00%
+---- avg training fps: 6.89

# Trainer step: 90, epoch: 9:  30%|███       | 90/300 [00:52<01:38,  2.12it/s]
# Trainer step: 90, epoch: 9:  30%|███       | 91/300 [00:52<01:38,  2.12it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 92/300 [00:52<01:38,  2.12it/s]
# Trainer step: 90, epoch: 9:  31%|███       | 93/300 [00:53<01:37,  2.12it/s]
# Trainer step: 90, epoch: 9:  31%|███▏      | 94/300 [00:53<01:37,  2.12it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 95/300 [00:54<01:36,  2.12it/s]
# Trainer step: 90, epoch: 9:  32%|███▏      | 96/300 [00:54<01:36,  2.12it/s]Progress: 35.00%
+---- avg training fps: 6.97

# Trainer step: 90, epoch: 9:  32%|███▏      | 97/300 [00:55<01:35,  2.12it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 98/300 [00:55<01:35,  2.11it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 99/300 [00:56<01:34,  2.12it/s]
# Trainer step: 90, epoch: 9:  33%|███▎      | 100/300 [00:56<01:34,  2.12it/s]
# Trainer step: 100, epoch: 10:  33%|███▎      | 100/300 [00:57<01:34,  2.12it/s]
# Trainer step: 100, epoch: 10:  34%|███▎      | 101/300 [00:57<01:33,  2.12it/s]
# Trainer step: 100, epoch: 10:  34%|███▍      | 102/300 [01:00<04:32,  1.37s/it]Progress: 37.00%
+---- avg training fps: 6.67

# Trainer step: 100, epoch: 10:  34%|███▍      | 103/300 [01:01<03:49,  1.16s/it]
# Trainer step: 100, epoch: 10:  35%|███▍      | 104/300 [01:01<03:07,  1.05it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 105/300 [01:02<02:37,  1.24it/s]
# Trainer step: 100, epoch: 10:  35%|███▌      | 106/300 [01:02<02:17,  1.41it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 107/300 [01:03<02:02,  1.57it/s]
# Trainer step: 100, epoch: 10:  36%|███▌      | 108/300 [01:03<01:53,  1.69it/s]Progress: 39.00%
+---- avg training fps: 6.75

# Trainer step: 100, epoch: 10:  36%|███▋      | 109/300 [01:03<01:45,  1.80it/s]
# Trainer step: 100, epoch: 10:  37%|███▋      | 110/300 [01:04<01:40,  1.89it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 110/300 [01:04<01:40,  1.89it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 111/300 [01:04<01:36,  1.95it/s]
# Trainer step: 110, epoch: 11:  37%|███▋      | 112/300 [01:05<01:34,  1.99it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 113/300 [01:05<01:32,  2.03it/s]
# Trainer step: 110, epoch: 11:  38%|███▊      | 114/300 [01:06<01:30,  2.05it/s]Progress: 41.00%
+---- avg training fps: 6.82

# Trainer step: 110, epoch: 11:  38%|███▊      | 115/300 [01:06<01:29,  2.07it/s]
# Trainer step: 110, epoch: 11:  39%|███▊      | 116/300 [01:07<01:28,  2.09it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 117/300 [01:07<01:27,  2.10it/s]
# Trainer step: 110, epoch: 11:  39%|███▉      | 118/300 [01:08<01:26,  2.10it/s]
# Trainer step: 110, epoch: 11:  40%|███▉      | 119/300 [01:08<01:25,  2.11it/s]
# Trainer step: 110, epoch: 11:  40%|████      | 120/300 [01:09<01:25,  2.11it/s]Progress: 43.00%
+---- avg training fps: 6.89

# Trainer step: 120, epoch: 12:  40%|████      | 120/300 [01:09<01:25,  2.11it/s]
# Trainer step: 120, epoch: 12:  40%|████      | 121/300 [01:09<01:24,  2.11it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 122/300 [01:10<01:24,  2.11it/s]
# Trainer step: 120, epoch: 12:  41%|████      | 123/300 [01:10<01:23,  2.12it/s]
# Trainer step: 120, epoch: 12:  41%|████▏     | 124/300 [01:11<01:23,  2.12it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 125/300 [01:11<01:22,  2.12it/s]
# Trainer step: 120, epoch: 12:  42%|████▏     | 126/300 [01:12<01:22,  2.12it/s]Progress: 45.00%
+---- avg training fps: 6.95

# Trainer step: 120, epoch: 12:  42%|████▏     | 127/300 [01:12<01:21,  2.12it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 128/300 [01:12<01:21,  2.12it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 129/300 [01:13<01:20,  2.12it/s]
# Trainer step: 120, epoch: 12:  43%|████▎     | 130/300 [01:13<01:20,  2.12it/s]
# Trainer step: 130, epoch: 13:  43%|████▎     | 130/300 [01:14<01:20,  2.12it/s]
# Trainer step: 130, epoch: 13:  44%|████▎     | 131/300 [01:14<01:19,  2.12it/s]
# Trainer step: 130, epoch: 13:  44%|████▍     | 132/300 [01:14<01:19,  2.12it/s]Progress: 47.00%
+---- avg training fps: 7.01

# Trainer step: 130, epoch: 13:  44%|████▍     | 133/300 [01:15<01:18,  2.12it/s]
# Trainer step: 130, epoch: 13:  45%|████▍     | 134/300 [01:15<01:18,  2.12it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 135/300 [01:16<01:17,  2.12it/s]
# Trainer step: 130, epoch: 13:  45%|████▌     | 136/300 [01:16<01:17,  2.12it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 137/300 [01:17<01:16,  2.12it/s]
# Trainer step: 130, epoch: 13:  46%|████▌     | 138/300 [01:17<01:16,  2.12it/s]Progress: 49.00%
+---- avg training fps: 7.06

# Trainer step: 130, epoch: 13:  46%|████▋     | 139/300 [01:18<01:15,  2.12it/s]
# Trainer step: 130, epoch: 13:  47%|████▋     | 140/300 [01:18<01:15,  2.12it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 140/300 [01:19<01:15,  2.12it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 141/300 [01:19<01:15,  2.12it/s]
# Trainer step: 140, epoch: 14:  47%|████▋     | 142/300 [01:19<01:14,  2.11it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 143/300 [01:20<01:14,  2.12it/s]
# Trainer step: 140, epoch: 14:  48%|████▊     | 144/300 [01:20<01:13,  2.12it/s]Progress: 51.00%
+---- avg training fps: 7.11

# Trainer step: 140, epoch: 14:  48%|████▊     | 145/300 [01:20<01:13,  2.12it/s]
# Trainer step: 140, epoch: 14:  49%|████▊     | 146/300 [01:21<01:12,  2.12it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 147/300 [01:21<01:12,  2.12it/s]
# Trainer step: 140, epoch: 14:  49%|████▉     | 148/300 [01:22<01:11,  2.12it/s]
# Trainer step: 140, epoch: 14:  50%|████▉     | 149/300 [01:22<01:11,  2.12it/s]
# Trainer step: 140, epoch: 14:  50%|█████     | 150/300 [01:23<01:10,  2.12it/s]Progress: 53.00%
+---- avg training fps: 7.16

# Trainer step: 150, epoch: 15:  50%|█████     | 150/300 [01:23<01:10,  2.12it/s]
# Trainer step: 150, epoch: 15:  50%|█████     | 151/300 [01:23<01:10,  2.12it/s]
# Trainer step: 150, epoch: 15:  51%|█████     | 152/300 [01:27<03:53,  1.58s/it]
# Trainer step: 150, epoch: 15:  51%|█████     | 153/300 [01:28<03:02,  1.24s/it]
# Trainer step: 150, epoch: 15:  51%|█████▏    | 154/300 [01:28<02:27,  1.01s/it]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 155/300 [01:29<02:03,  1.18it/s]
# Trainer step: 150, epoch: 15:  52%|█████▏    | 156/300 [01:29<01:46,  1.36it/s]Progress: 55.00%
+---- avg training fps: 6.91

# Trainer step: 150, epoch: 15:  52%|█████▏    | 157/300 [01:30<01:33,  1.52it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 158/300 [01:30<01:25,  1.66it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 159/300 [01:31<01:19,  1.78it/s]
# Trainer step: 150, epoch: 15:  53%|█████▎    | 160/300 [01:31<01:14,  1.87it/s]
# Trainer step: 160, epoch: 16:  53%|█████▎    | 160/300 [01:32<01:14,  1.87it/s]
# Trainer step: 160, epoch: 16:  54%|█████▎    | 161/300 [01:32<01:11,  1.94it/s]
# Trainer step: 160, epoch: 16:  54%|█████▍    | 162/300 [01:32<01:09,  1.99it/s]Progress: 57.00%
+---- avg training fps: 6.96

# Trainer step: 160, epoch: 16:  54%|█████▍    | 163/300 [01:33<01:07,  2.02it/s]
# Trainer step: 160, epoch: 16:  55%|█████▍    | 164/300 [01:33<01:06,  2.05it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 165/300 [01:34<01:05,  2.07it/s]
# Trainer step: 160, epoch: 16:  55%|█████▌    | 166/300 [01:34<01:04,  2.08it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 167/300 [01:35<01:03,  2.10it/s]
# Trainer step: 160, epoch: 16:  56%|█████▌    | 168/300 [01:35<01:02,  2.10it/s]Progress: 59.00%
+---- avg training fps: 7.00

# Trainer step: 160, epoch: 16:  56%|█████▋    | 169/300 [01:35<01:02,  2.10it/s]
# Trainer step: 160, epoch: 16:  57%|█████▋    | 170/300 [01:36<01:01,  2.11it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 170/300 [01:36<01:01,  2.11it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 171/300 [01:36<01:01,  2.11it/s]
# Trainer step: 170, epoch: 17:  57%|█████▋    | 172/300 [01:37<01:00,  2.11it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 173/300 [01:37<01:00,  2.12it/s]
# Trainer step: 170, epoch: 17:  58%|█████▊    | 174/300 [01:38<00:59,  2.12it/s]Progress: 61.00%
+---- avg training fps: 7.04

# Trainer step: 170, epoch: 17:  58%|█████▊    | 175/300 [01:38<00:58,  2.12it/s]
# Trainer step: 170, epoch: 17:  59%|█████▊    | 176/300 [01:39<00:58,  2.12it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 177/300 [01:39<00:58,  2.12it/s]
# Trainer step: 170, epoch: 17:  59%|█████▉    | 178/300 [01:40<00:57,  2.12it/s]
# Trainer step: 170, epoch: 17:  60%|█████▉    | 179/300 [01:40<00:57,  2.12it/s]
# Trainer step: 170, epoch: 17:  60%|██████    | 180/300 [01:41<00:56,  2.12it/s]Progress: 63.00%
+---- avg training fps: 7.08

# Trainer step: 180, epoch: 18:  60%|██████    | 180/300 [01:41<00:56,  2.12it/s]
# Trainer step: 180, epoch: 18:  60%|██████    | 181/300 [01:41<00:56,  2.12it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 182/300 [01:42<00:55,  2.12it/s]
# Trainer step: 180, epoch: 18:  61%|██████    | 183/300 [01:42<00:55,  2.12it/s]
# Trainer step: 180, epoch: 18:  61%|██████▏   | 184/300 [01:43<00:54,  2.12it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 185/300 [01:43<00:54,  2.12it/s]
# Trainer step: 180, epoch: 18:  62%|██████▏   | 186/300 [01:44<00:53,  2.12it/s]Progress: 65.00%
+---- avg training fps: 7.12

# Trainer step: 180, epoch: 18:  62%|██████▏   | 187/300 [01:44<00:53,  2.12it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 188/300 [01:44<00:52,  2.12it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 189/300 [01:45<00:52,  2.11it/s]
# Trainer step: 180, epoch: 18:  63%|██████▎   | 190/300 [01:45<00:51,  2.12it/s]
# Trainer step: 190, epoch: 19:  63%|██████▎   | 190/300 [01:46<00:51,  2.12it/s]
# Trainer step: 190, epoch: 19:  64%|██████▎   | 191/300 [01:46<00:51,  2.12it/s]
# Trainer step: 190, epoch: 19:  64%|██████▍   | 192/300 [01:46<00:50,  2.12it/s]Progress: 67.00%
+---- avg training fps: 7.16

# Trainer step: 190, epoch: 19:  64%|██████▍   | 193/300 [01:47<00:50,  2.12it/s]
# Trainer step: 190, epoch: 19:  65%|██████▍   | 194/300 [01:47<00:49,  2.12it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 195/300 [01:48<00:49,  2.12it/s]
# Trainer step: 190, epoch: 19:  65%|██████▌   | 196/300 [01:48<00:48,  2.13it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 197/300 [01:49<00:48,  2.13it/s]
# Trainer step: 190, epoch: 19:  66%|██████▌   | 198/300 [01:49<00:47,  2.13it/s]Progress: 69.00%
+---- avg training fps: 7.19

# Trainer step: 190, epoch: 19:  66%|██████▋   | 199/300 [01:50<00:47,  2.13it/s]
# Trainer step: 190, epoch: 19:  67%|██████▋   | 200/300 [01:50<00:46,  2.13it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 200/300 [01:51<00:46,  2.13it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 201/300 [01:51<00:46,  2.13it/s]
# Trainer step: 200, epoch: 20:  67%|██████▋   | 202/300 [01:55<02:33,  1.57s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 203/300 [01:55<02:00,  1.24s/it]
# Trainer step: 200, epoch: 20:  68%|██████▊   | 204/300 [01:56<01:36,  1.01s/it]Progress: 71.00%
+---- avg training fps: 7.00

# Trainer step: 200, epoch: 20:  68%|██████▊   | 205/300 [01:56<01:20,  1.18it/s]
# Trainer step: 200, epoch: 20:  69%|██████▊   | 206/300 [01:57<01:08,  1.37it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 207/300 [01:57<01:00,  1.53it/s]
# Trainer step: 200, epoch: 20:  69%|██████▉   | 208/300 [01:58<00:55,  1.67it/s]
# Trainer step: 200, epoch: 20:  70%|██████▉   | 209/300 [01:58<00:50,  1.79it/s]
# Trainer step: 200, epoch: 20:  70%|███████   | 210/300 [01:58<00:47,  1.88it/s]Progress: 73.00%
+---- avg training fps: 7.03

# Trainer step: 210, epoch: 21:  70%|███████   | 210/300 [01:59<00:47,  1.88it/s]
# Trainer step: 210, epoch: 21:  70%|███████   | 211/300 [01:59<00:45,  1.95it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 212/300 [01:59<00:43,  2.00it/s]
# Trainer step: 210, epoch: 21:  71%|███████   | 213/300 [02:00<00:42,  2.04it/s]
# Trainer step: 210, epoch: 21:  71%|███████▏  | 214/300 [02:00<00:41,  2.06it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 215/300 [02:01<00:40,  2.08it/s]
# Trainer step: 210, epoch: 21:  72%|███████▏  | 216/300 [02:01<00:39,  2.10it/s]Progress: 75.00%
+---- avg training fps: 7.07

# Trainer step: 210, epoch: 21:  72%|███████▏  | 217/300 [02:02<00:39,  2.11it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 218/300 [02:02<00:38,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 219/300 [02:03<00:38,  2.12it/s]
# Trainer step: 210, epoch: 21:  73%|███████▎  | 220/300 [02:03<00:37,  2.12it/s]
# Trainer step: 220, epoch: 22:  73%|███████▎  | 220/300 [02:04<00:37,  2.12it/s]
# Trainer step: 220, epoch: 22:  74%|███████▎  | 221/300 [02:04<00:37,  2.13it/s]
# Trainer step: 220, epoch: 22:  74%|███████▍  | 222/300 [02:04<00:36,  2.12it/s]Progress: 77.00%
+---- avg training fps: 7.10

# Trainer step: 220, epoch: 22:  74%|███████▍  | 223/300 [02:05<00:36,  2.13it/s]
# Trainer step: 220, epoch: 22:  75%|███████▍  | 224/300 [02:05<00:35,  2.12it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 225/300 [02:05<00:35,  2.13it/s]
# Trainer step: 220, epoch: 22:  75%|███████▌  | 226/300 [02:06<00:34,  2.13it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 227/300 [02:06<00:34,  2.13it/s]
# Trainer step: 220, epoch: 22:  76%|███████▌  | 228/300 [02:07<00:33,  2.12it/s]Progress: 79.00%
+---- avg training fps: 7.13

# Trainer step: 220, epoch: 22:  76%|███████▋  | 229/300 [02:07<00:33,  2.13it/s]
# Trainer step: 220, epoch: 22:  77%|███████▋  | 230/300 [02:08<00:32,  2.13it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 230/300 [02:08<00:32,  2.13it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 231/300 [02:08<00:32,  2.13it/s]
# Trainer step: 230, epoch: 23:  77%|███████▋  | 232/300 [02:09<00:31,  2.13it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 233/300 [02:09<00:31,  2.13it/s]
# Trainer step: 230, epoch: 23:  78%|███████▊  | 234/300 [02:10<00:31,  2.13it/s]Progress: 81.00%
+---- avg training fps: 7.16

# Trainer step: 230, epoch: 23:  78%|███████▊  | 235/300 [02:10<00:30,  2.13it/s]
# Trainer step: 230, epoch: 23:  79%|███████▊  | 236/300 [02:11<00:30,  2.13it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 237/300 [02:11<00:29,  2.13it/s]
# Trainer step: 230, epoch: 23:  79%|███████▉  | 238/300 [02:12<00:29,  2.13it/s]
# Trainer step: 230, epoch: 23:  80%|███████▉  | 239/300 [02:12<00:28,  2.13it/s]
# Trainer step: 230, epoch: 23:  80%|████████  | 240/300 [02:13<00:28,  2.13it/s]Progress: 83.00%
+---- avg training fps: 7.19

# Trainer step: 240, epoch: 24:  80%|████████  | 240/300 [02:13<00:28,  2.13it/s]
# Trainer step: 240, epoch: 24:  80%|████████  | 241/300 [02:13<00:27,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 242/300 [02:13<00:27,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████  | 243/300 [02:14<00:26,  2.13it/s]
# Trainer step: 240, epoch: 24:  81%|████████▏ | 244/300 [02:14<00:26,  2.13it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 245/300 [02:15<00:25,  2.13it/s]
# Trainer step: 240, epoch: 24:  82%|████████▏ | 246/300 [02:15<00:25,  2.13it/s]Progress: 85.00%
+---- avg training fps: 7.22

# Trainer step: 240, epoch: 24:  82%|████████▏ | 247/300 [02:16<00:24,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 248/300 [02:16<00:24,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 249/300 [02:17<00:23,  2.13it/s]
# Trainer step: 240, epoch: 24:  83%|████████▎ | 250/300 [02:17<00:23,  2.13it/s]
# Trainer step: 250, epoch: 25:  83%|████████▎ | 250/300 [02:18<00:23,  2.13it/s]
# Trainer step: 250, epoch: 25:  84%|████████▎ | 251/300 [02:18<00:23,  2.13it/s]
# Trainer step: 250, epoch: 25:  84%|████████▍ | 252/300 [02:22<01:16,  1.60s/it]Progress: 87.00%
+---- avg training fps: 7.05

# Trainer step: 250, epoch: 25:  84%|████████▍ | 253/300 [02:22<00:59,  1.26s/it]
# Trainer step: 250, epoch: 25:  85%|████████▍ | 254/300 [02:23<00:47,  1.02s/it]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 255/300 [02:23<00:38,  1.17it/s]
# Trainer step: 250, epoch: 25:  85%|████████▌ | 256/300 [02:24<00:32,  1.35it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 257/300 [02:24<00:28,  1.52it/s]
# Trainer step: 250, epoch: 25:  86%|████████▌ | 258/300 [02:25<00:25,  1.66it/s]Progress: 89.00%
+---- avg training fps: 7.08

# Trainer step: 250, epoch: 25:  86%|████████▋ | 259/300 [02:25<00:23,  1.78it/s]
# Trainer step: 250, epoch: 25:  87%|████████▋ | 260/300 [02:26<00:21,  1.86it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 260/300 [02:26<00:21,  1.86it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 261/300 [02:26<00:20,  1.94it/s]
# Trainer step: 260, epoch: 26:  87%|████████▋ | 262/300 [02:27<00:19,  1.99it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 263/300 [02:27<00:18,  2.03it/s]
# Trainer step: 260, epoch: 26:  88%|████████▊ | 264/300 [02:28<00:17,  2.06it/s]Progress: 91.00%
+---- avg training fps: 7.11

# Trainer step: 260, epoch: 26:  88%|████████▊ | 265/300 [02:28<00:16,  2.08it/s]
# Trainer step: 260, epoch: 26:  89%|████████▊ | 266/300 [02:29<00:16,  2.10it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 267/300 [02:29<00:15,  2.11it/s]
# Trainer step: 260, epoch: 26:  89%|████████▉ | 268/300 [02:29<00:15,  2.11it/s]
# Trainer step: 260, epoch: 26:  90%|████████▉ | 269/300 [02:30<00:14,  2.12it/s]
# Trainer step: 260, epoch: 26:  90%|█████████ | 270/300 [02:30<00:14,  2.13it/s]Progress: 93.00%
+---- avg training fps: 7.14

# Trainer step: 270, epoch: 27:  90%|█████████ | 270/300 [02:31<00:14,  2.13it/s]
# Trainer step: 270, epoch: 27:  90%|█████████ | 271/300 [02:31<00:13,  2.13it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 272/300 [02:31<00:13,  2.13it/s]
# Trainer step: 270, epoch: 27:  91%|█████████ | 273/300 [02:32<00:12,  2.13it/s]
# Trainer step: 270, epoch: 27:  91%|█████████▏| 274/300 [02:32<00:12,  2.13it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 275/300 [02:33<00:11,  2.13it/s]
# Trainer step: 270, epoch: 27:  92%|█████████▏| 276/300 [02:33<00:11,  2.13it/s]Progress: 95.00%
+---- avg training fps: 7.16

# Trainer step: 270, epoch: 27:  92%|█████████▏| 277/300 [02:34<00:10,  2.13it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 278/300 [02:34<00:10,  2.13it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 279/300 [02:35<00:09,  2.13it/s]
# Trainer step: 270, epoch: 27:  93%|█████████▎| 280/300 [02:35<00:09,  2.13it/s]
# Trainer step: 280, epoch: 28:  93%|█████████▎| 280/300 [02:36<00:09,  2.13it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▎| 281/300 [02:36<00:08,  2.13it/s]
# Trainer step: 280, epoch: 28:  94%|█████████▍| 282/300 [02:36<00:08,  2.13it/s]Progress: 97.00%
+---- avg training fps: 7.19

# Trainer step: 280, epoch: 28:  94%|█████████▍| 283/300 [02:36<00:07,  2.13it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▍| 284/300 [02:37<00:07,  2.13it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 285/300 [02:37<00:07,  2.13it/s]
# Trainer step: 280, epoch: 28:  95%|█████████▌| 286/300 [02:38<00:06,  2.13it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 287/300 [02:38<00:06,  2.13it/s]
# Trainer step: 280, epoch: 28:  96%|█████████▌| 288/300 [02:39<00:05,  2.13it/s]Progress: 99.00%
+---- avg training fps: 7.21

# Trainer step: 280, epoch: 28:  96%|█████████▋| 289/300 [02:39<00:05,  2.13it/s]
# Trainer step: 280, epoch: 28:  97%|█████████▋| 290/300 [02:40<00:04,  2.13it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 290/300 [02:40<00:04,  2.13it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 291/300 [02:40<00:04,  2.13it/s]
# Trainer step: 290, epoch: 29:  97%|█████████▋| 292/300 [02:41<00:03,  2.13it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 293/300 [02:41<00:03,  2.13it/s]
# Trainer step: 290, epoch: 29:  98%|█████████▊| 294/300 [02:42<00:02,  2.13it/s]Progress: 100.00%
+---- avg training fps: 7.23

# Trainer step: 290, epoch: 29:  98%|█████████▊| 295/300 [02:42<00:02,  2.13it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▊| 296/300 [02:43<00:01,  2.13it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 297/300 [02:43<00:01,  2.13it/s]
# Trainer step: 290, epoch: 29:  99%|█████████▉| 298/300 [02:44<00:00,  2.13it/s]
# Trainer step: 290, epoch: 29: 100%|█████████▉| 299/300 [02:44<00:00,  2.13it/s]
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [02:44<00:00,  2.13it/s]Progress: 100.00%
+---- avg training fps: 7.25
Progress: 100.00%
Saving checkpoint at step.. 300
+Saving LoRA weights for SDXL model...
+Using existing model for inference
+Re-using training pipeline for inference, just swapping the scheduler..
+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 0.80
+Rendering validation img with prompt: 
+-------------------------
+Adjusted prompt for LORA:
+
+-- to:
+in the style of <s0><s1><s2>,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>,
+Embedding zero prompt: 
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Adjusted prompt for LORA:
+Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-- to:
+in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Embedding zero prompt: Binary Love: A heart-shaped composition made up of glowing binary code, symbolizing the merging of human emotion and technology, incredible digital art, cyberpunk, neon colors, glitch effects, 3D octane render, HD
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Adjusted prompt for LORA:
+Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-- to:
+in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Embedding zero prompt: Glass Roots: A luminescent glass sculpture of a fully bloomed rose emerges from a broken marble pedestal, natures resilience triumphant amidst the decay. Shadows cast by a dim overhead spotlight. Delicate veins intertwine the transparent petals, illuminating from within, symbolizing fragilitys steely core.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: the stunning skyline of New York City
+-------------------------
+Adjusted prompt for LORA:
+the stunning skyline of New York City
+-- to:
+in the style of <s0><s1><s2>, the stunning skyline of New York City
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, the stunning skyline of New York City
+Embedding zero prompt: the stunning skyline of New York City
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Adjusted prompt for LORA:
+An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-- to:
+in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Embedding zero prompt: An ethereal, levitating monolith backlit by a supernova sky, casting iridescent light on the ice-spiked Martian terrain. Neo-futurism, Dali surrealism, wide-angle lens, chiaroscuro lighting.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Adjusted prompt for LORA:
+The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-- to:
+in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Embedding zero prompt: The Silent Of Silicon, a digital deer rendered in hyper-realistic 3D, eyes glowing in binary code, comfortably resting amidst rich motherboard-green foliage, accented under crisply fluorescent, simulated LED dawn.
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+Rendering validation img with prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Adjusted prompt for LORA:
+In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-- to:
+in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Embedding zero prompt: In the heart of an ancient forest, a massive projection illuminates the darkness. A lone figure, a majestic mythical creature made of shimmering gold, materializes, casting a radiant glow amidst the towering trees. intricate geometric surfaces encasing an expanse of flora and fauna,
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+
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+
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+
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+
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+Rendering validation img with prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Adjusted prompt for LORA:
+A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-- to:
+in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+-------------------------
+Embedding lora prompt: in the style of <s0><s1><s2>, A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Embedding zero prompt: A solitary tree standing tall amidst a sea of buildings, Urban nature photography, vibrant colors, juxtaposition of natural elements with urban landscapes, play of light and shadow, storytelling through compositions
+Setting token_scale to 0.96 (lora_scale = 0.80, power = 0.4)
+
+
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+
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+list_adapters_component_wise: {'unet': ['default']}
+Set adapter 'default' of 'unet' with scale = 1.00
+Training job complete, saving outputs...
+
# Trainer step: 290, epoch: 29: 100%|██████████| 300/300 [04:03<00:00,  1.23it/s]
+------------------------------------------
+Training done :)
diff --git a/scripts/create_hyperparam_sweep.py b/scripts/create_hyperparam_sweep.py
index dd9f2c6..4bd5af6 100644
--- a/scripts/create_hyperparam_sweep.py
+++ b/scripts/create_hyperparam_sweep.py
@@ -12,6 +12,8 @@ https://storage.googleapis.com/public-assets-xander/A_workbox/lora_training_sets
 
 Styles:
 https://storage.googleapis.com/public-assets-xander/A_workbox/lora_training_sets/does.zip
+https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip
+/home/rednax/Documents/datasets/good_styles/eden_crystals
 
 """
 
@@ -32,12 +34,12 @@ def hamming_distance(dict1, dict2):
 #######################################################################################
 
 # Setup the base experiment config:
-exp_name             = "faces"
+exp_name             = "styles_final"
 caption_prefix       = ""
 mask_target_prompts  = ""
 n_exp                = 100  # how many random experiment settings to generate
 min_hamming_distance = 2   # min_n_params that have to be different from any previous experiment to be scheduled
-nohup                = False
+nohup                = True
 output_sh_path = f"gridsearch_configs/{exp_name}.sh"
 
 # Define training hyperparameters and their possible values
@@ -47,28 +49,29 @@ hyperparameters = {
     "output_dir": [f"lora_models/{exp_name}"],
     "sd_model_version": ["sdxl"],
     "lora_training_urls": [
-        "https://storage.googleapis.com/public-assets-xander/A_workbox/lora_training_sets/xander.zip",
-        "https://storage.googleapis.com/public-assets-xander/A_workbox/lora_training_sets/gene.zip",
-        "https://storage.googleapis.com/public-assets-xander/A_workbox/lora_training_sets/mira.zip"
+        "https://edenartlab-lfs.s3.amazonaws.com/datasets/clipx.zip",
+        "/home/rednax/Documents/datasets/good_styles/eden_crystals",
+        "/home/rednax/Documents/datasets/journey_small"
 
     ],
-    "concept_mode": ['face'],
-    "sample_imgs_lora_scale": [0.7],
+    "concept_mode": ['style'],
+    "sample_imgs_lora_scale": [0.8],
     "disable_ti": ['false'],
+    "caption_dropout": [0.0, 0.2],
     "seed": [0],
     "resolution": [512],
     "train_batch_size": [4],
     "n_sample_imgs": [8],
     "max_train_steps": [300],
-    "checkpointing_steps": [200],
+    "checkpointing_steps": [300],
     "gradient_accumulation_steps": [1],
 
     "n_tokens": [3],
     "ti_lr": [0.001],
     "ti_weight_decay": [0.000],
     "l1_penalty": [0.0],
-    "token_warmup_steps": [0, 60],
-    "tok_cov_reg_w": [0, 500],
+    "token_warmup_steps": [0],
+    "tok_cov_reg_w": [500],
     "token_attention_loss_w": [0e-7, 2e-7],
 
     "freeze_ti_after_completion_f": [1.0, 0.75, 0.5],
diff --git a/scripts/eval_hyperparam_sweep.py b/scripts/eval_hyperparam_sweep.py
index 5f3bc4b..38bbe5e 100644
--- a/scripts/eval_hyperparam_sweep.py
+++ b/scripts/eval_hyperparam_sweep.py
@@ -19,17 +19,23 @@ def load_training_args(directory):
     return None
 
 # Step 4: Traverse the directory structure and collect data
-def collect_data(root_dir):
+def collect_data(root_dir, mode):
     data = []
     for root, dirs, files in os.walk(root_dir):
-        if 'checkpoint-360' in dirs:
-            checkpoints_dir = os.path.join(root, 'checkpoint-360')
-            score = count_jpg_files(checkpoints_dir)
-            training_args = load_training_args(root)
-            if training_args:
-                data.append((training_args, score))
-            else:
-                print(f"Warning: No training_args.json found in {root}")
+        if "checkpoints" in dirs:
+            checkpoints_dir = os.path.join(root,"checkpoints")
+
+            if mode == "final_checkpoint":
+                checkpoint_dirs = sorted([f for f in os.listdir(checkpoints_dir) if os.path.isdir(os.path.join(checkpoints_dir, f))])
+                checkpoint_dir = os.path.join(checkpoints_dir, checkpoint_dirs[-1])
+                score = count_jpg_files(checkpoint_dir)
+                training_args = load_training_args(checkpoint_dir)
+            if mode == "n_validation_grids":
+                score = count_jpg_files(checkpoints_dir)
+                training_args = load_training_args(checkpoints_dir)
+                    
+            data.append((training_args, score))
+
     print(f"Collected data from {len(data)} runs")
     return data
 
@@ -172,11 +178,12 @@ def create_plots(data, varying_params, outdir, top = 0.15):
     print(f"Plots have been saved as PNG files in {outdir}")
 
 if __name__ == "__main__":
-    root_dir = "/home/rednax/SSD2TB/Github_repos/diffusion_trainer/lora_models/faces"
+    root_dir = "/home/rednax/SSD2TB/Github_repos/diffusion_trainer/lora_models/faces3"
     outdir = os.path.join('.', os.path.basename(root_dir))
     
     # Collect data
-    data = collect_data(root_dir)
+    data = collect_data(root_dir, mode = "final_checkpoint")
+    #data = collect_data(root_dir, mode = "n_validation_grids")
     
     # Identify varying hyperparameters
     varying_params = identify_varying_hyperparams(data)
diff --git a/scripts/faces3/freeze_ti_after_completion_f_vs_score.png b/scripts/faces3/freeze_ti_after_completion_f_vs_score.png
new file mode 100644
index 0000000..efab3df
Binary files /dev/null and b/scripts/faces3/freeze_ti_after_completion_f_vs_score.png differ
diff --git a/scripts/faces3/freeze_unet_before_completion_f_vs_score.png b/scripts/faces3/freeze_unet_before_completion_f_vs_score.png
new file mode 100644
index 0000000..d6d936e
Binary files /dev/null and b/scripts/faces3/freeze_unet_before_completion_f_vs_score.png differ
diff --git a/scripts/faces3/job_time_vs_score.png b/scripts/faces3/job_time_vs_score.png
new file mode 100644
index 0000000..a5ac089
Binary files /dev/null and b/scripts/faces3/job_time_vs_score.png differ
diff --git a/scripts/faces3/tok_cov_reg_w_vs_score.png b/scripts/faces3/tok_cov_reg_w_vs_score.png
new file mode 100644
index 0000000..0c4f7f4
Binary files /dev/null and b/scripts/faces3/tok_cov_reg_w_vs_score.png differ
diff --git a/scripts/faces3/token_attention_loss_w_vs_score.png b/scripts/faces3/token_attention_loss_w_vs_score.png
new file mode 100644
index 0000000..857906e
Binary files /dev/null and b/scripts/faces3/token_attention_loss_w_vs_score.png differ
diff --git a/scripts/faces3/token_warmup_steps_vs_score.png b/scripts/faces3/token_warmup_steps_vs_score.png
new file mode 100644
index 0000000..9d8ff1d
Binary files /dev/null and b/scripts/faces3/token_warmup_steps_vs_score.png differ
diff --git a/scripts/faces3/unet_lr_vs_score.png b/scripts/faces3/unet_lr_vs_score.png
new file mode 100644
index 0000000..34e8c61
Binary files /dev/null and b/scripts/faces3/unet_lr_vs_score.png differ
diff --git a/styles_final.sh b/styles_final.sh
new file mode 100644
index 0000000..7a1db70
--- /dev/null
+++ b/styles_final.sh
@@ -0,0 +1,92 @@
+#!/bin/bash
+
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_000.json
+ > scripts/gridsearch_configs/styles_final/styles_final_000.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_001.json
+ > scripts/gridsearch_configs/styles_final/styles_final_001.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_002.json
+ > scripts/gridsearch_configs/styles_final/styles_final_002.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_003.json
+ > scripts/gridsearch_configs/styles_final/styles_final_003.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_004.json
+ > scripts/gridsearch_configs/styles_final/styles_final_004.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_005.json
+ > scripts/gridsearch_configs/styles_final/styles_final_005.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_006.json
+ > scripts/gridsearch_configs/styles_final/styles_final_006.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_007.json
+ > scripts/gridsearch_configs/styles_final/styles_final_007.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_008.json
+ > scripts/gridsearch_configs/styles_final/styles_final_008.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_009.json
+ > scripts/gridsearch_configs/styles_final/styles_final_009.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_010.json
+ > scripts/gridsearch_configs/styles_final/styles_final_010.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_011.json
+ > scripts/gridsearch_configs/styles_final/styles_final_011.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_012.json
+ > scripts/gridsearch_configs/styles_final/styles_final_012.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_013.json
+ > scripts/gridsearch_configs/styles_final/styles_final_013.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_014.json
+ > scripts/gridsearch_configs/styles_final/styles_final_014.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_015.json
+ > scripts/gridsearch_configs/styles_final/styles_final_015.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_016.json
+ > scripts/gridsearch_configs/styles_final/styles_final_016.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_017.json
+ > scripts/gridsearch_configs/styles_final/styles_final_017.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_018.json
+ > scripts/gridsearch_configs/styles_final/styles_final_018.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_019.json
+ > scripts/gridsearch_configs/styles_final/styles_final_019.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_020.json
+ > scripts/gridsearch_configs/styles_final/styles_final_020.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_021.json
+ > scripts/gridsearch_configs/styles_final/styles_final_021.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_022.json
+ > scripts/gridsearch_configs/styles_final/styles_final_022.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_023.json
+ > scripts/gridsearch_configs/styles_final/styles_final_023.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_024.json
+ > scripts/gridsearch_configs/styles_final/styles_final_024.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_025.json
+ > scripts/gridsearch_configs/styles_final/styles_final_025.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_026.json
+ > scripts/gridsearch_configs/styles_final/styles_final_026.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_027.json
+ > scripts/gridsearch_configs/styles_final/styles_final_027.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_028.json
+ > scripts/gridsearch_configs/styles_final/styles_final_028.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_029.json
+ > scripts/gridsearch_configs/styles_final/styles_final_029.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_030.json
+ > scripts/gridsearch_configs/styles_final/styles_final_030.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_031.json
+ > scripts/gridsearch_configs/styles_final/styles_final_031.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_032.json
+ > scripts/gridsearch_configs/styles_final/styles_final_032.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_033.json
+ > scripts/gridsearch_configs/styles_final/styles_final_033.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_034.json
+ > scripts/gridsearch_configs/styles_final/styles_final_034.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_035.json
+ > scripts/gridsearch_configs/styles_final/styles_final_035.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_036.json
+ > scripts/gridsearch_configs/styles_final/styles_final_036.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_037.json
+ > scripts/gridsearch_configs/styles_final/styles_final_037.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_038.json
+ > scripts/gridsearch_configs/styles_final/styles_final_038.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_039.json
+ > scripts/gridsearch_configs/styles_final/styles_final_039.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_040.json
+ > scripts/gridsearch_configs/styles_final/styles_final_040.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_041.json
+ > scripts/gridsearch_configs/styles_final/styles_final_041.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_042.json
+ > scripts/gridsearch_configs/styles_final/styles_final_042.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_043.json
+ > scripts/gridsearch_configs/styles_final/styles_final_043.log 2>&1 &
+nohup python main.py scripts/gridsearch_configs/styles_final/styles_final_044.json
+ > scripts/gridsearch_configs/styles_final/styles_final_044.log 2>&1 &
diff --git a/trainer/config.py b/trainer/config.py
index 0cb5971..a34eae7 100644
--- a/trainer/config.py
+++ b/trainer/config.py
@@ -24,8 +24,8 @@ class ModelPaths:
 model_paths = ModelPaths()
 
 # Default download urls in case no local model is found:
-#SDXL_URL = "https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors"
-SDXL_URL = "https://huggingface.co/RunDiffusion/Juggernaut-XL-v6/resolve/main/juggernautXL_version6Rundiffusion.safetensors"
+SDXL_URL = "https://edenartlab-lfs.s3.amazonaws.com/comfyui/models2/checkpoints/zavychromaxl_v90.safetensors"
+#SDXL_URL = "https://huggingface.co/RunDiffusion/Juggernaut-XL-v6/resolve/main/juggernautXL_version6Rundiffusion.safetensors"
 SD15_URL = "https://huggingface.co/KamCastle/jugg/resolve/main/juggernaut_reborn.safetensors"
 
 pretrained_models = {
@@ -56,7 +56,7 @@ class TrainingConfig(BaseModel):
 
     unet_optimizer_type: Literal["adamw", "prodigy", "AdamW8bit"] = "adamw"
     unet_lr_warmup_steps: int = None  # slowly increase the learning rate of the adamw unet optimizer
-    unet_lr: float = 0.001
+    unet_lr: float = 0.0005
     prodigy_d_coef: float = 1.0
     unet_prodigy_growth_factor: float = 1.05  # lower values make the lr go up slower (1.01 is for 1k step runs, 1.02 is for 500 step runs)
     lora_weight_decay: float = 0.002
@@ -65,14 +65,14 @@ class TrainingConfig(BaseModel):
     token_warmup_steps: int = 0    #  warmup the token embeddings with a pure txt loss
     ti_weight_decay: float = 0.0
     ti_optimizer: Literal["adamw", "prodigy"] = "adamw"
-    freeze_ti_after_completion_f: float = 0.7    # freeze the TI after this fraction of the training is done
-    freeze_unet_before_completion_f: float = 0.3 # freeze the UNET before this fraction of the training is done
+    freeze_ti_after_completion_f: float = 0.6     # freeze the TI after this fraction of the training is done
+    freeze_unet_before_completion_f: float = 0.3  # freeze the UNET before this fraction of the training is done
     
-    token_attention_loss_w: float = 2e-7
+    token_attention_loss_w: float = 3e-7
     cond_reg_w: float = 0.0e-5
     tok_cond_reg_w: float = 0.0e-5
-    tok_cov_reg_w: float = 500.     # regularizes the token covariance matrix wrt pretrained, normal tokens
-    l1_penalty: float = 0.01        # Makes the unet lora matrix more sparse
+    tok_cov_reg_w: float = 0.     # regularizes the token covariance matrix wrt pretrained, normal tokens
+    l1_penalty: float = 0.01      # Makes the unet lora matrix more sparse
     
     noise_offset: float = 0.02      # Noise offset training to improve very dark / very bright images
     snr_gamma: float = 5.0