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xander
2024-05-02 16:53:49 +02:00
parent 0bd26033cd
commit fc63399317
8 changed files with 266 additions and 34 deletions
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.gitignore
+13 -12
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@@ -1,23 +1,24 @@
models
lora_models*
datasets/
remove
cache
__pycache__
scripts/gridsearch*
models
lora_models*
datasets
*.tar
.env
.cog
xander*.sh
.huggingface
tests/
train.py
debug/*
rendered_images*
!debug/*.py
gridsearch*
xander*
aesthetic_score_best_model.pth
# experiment folders:
conditioning_spaces/
xander*.sh
training_args_x_*.json
eval_images/
test.json
xander*
debug/*
scripts/evaluate_gridsearch.py
+19 -3
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@@ -11,10 +11,14 @@ from sklearn.metrics import r2_score
# Define paths
render_dir = "/home/rednax/SSD2TB/Github_repos/diffusion_trainer/lora_models/unet2"
config_dir = "/home/rednax/SSD2TB/Github_repos/diffusion_trainer/gridsearch_configs/unet_adam_object"
render_dir = "/home/rednax/SSD2TB/Github_repos/diffusion_trainer/lora_models/XANDER_SD15_SWEEP/400"
config_dir = "/home/rednax/SSD2TB/Github_repos/diffusion_trainer/gridsearch_configs/sd15_face_sweep"
ignore_threshold_relative = 0.25 # ignore any datapoint with a score below this threshold
ignore_threshold_relative = 0.0 # ignore any datapoint with a score below this threshold
filters = {
"resolution": 512
}
output_dir = f"gridsearch_configs/results/{os.path.basename(config_dir)}"
output_suffix = f"{os.path.basename(render_dir)}"
@@ -40,6 +44,11 @@ for i, exp_subdir in enumerate(sorted(os.listdir(render_dir))):
if os.path.isfile(json_path):
with open(json_path, 'r') as file:
config = json.load(file)
# Filter out experiments that do not match the filters
if not all(config[key] == value for key, value in filters.items()):
continue
# Step 4: Append all key/value pairs to the total experiment dictionary
for key, value in config.items():
parameters[key]['values'].append(value)
@@ -47,6 +56,13 @@ for i, exp_subdir in enumerate(sorted(os.listdir(render_dir))):
else:
print(f"Could not find JSON file for experiment {exp_subdir}")
# Print the parameters['output_dir'] with the highest scores (there are usually multiple ties):
max_score = max(parameters['output_dir']['scores'])
best_output_dirs = [output_dir for output_dir, score in zip(parameters['output_dir']['values'], parameters['output_dir']['scores']) if score == max_score]
for best_output_dir in best_output_dirs:
print(f"Best output_dir: {best_output_dir} with score {max_score}")
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
scripts/test_inference.py
+6 -5
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@@ -17,11 +17,11 @@ from trainer.checkpoint import load_checkpoint
if __name__ == "__main__":
pretrained_model = pretrained_models['sdxl']
lora_path = 'lora_models/xander_one_img--23_15-25-00-sdxl_face_lora_512_1.0_gpt4-v/checkpoints/checkpoint-360'
model_version = "sd15"
lora_path = 'lora_models/XANDER_SD15_SWEEP/sd15_face_sweep__004--29_20-43-17-sd15_face_dora_640_1.0_blip_800/checkpoints/checkpoint-800'
lora_scales = np.linspace(0.6, 0.9, 4)
token_scale = None # None means it well get automatically set using lora_scale
render_size = (1024, 1024) # H,W
render_size = (576, 704) # H,W
n_imgs = 14
n_loops = 2
@@ -32,6 +32,7 @@ if __name__ == "__main__":
#####################################################################################
pretrained_model = pretrained_models[model_version]
output_dir = f'rendered_images/{lora_path.split("/")[-1]}'
os.makedirs(output_dir, exist_ok=True)
@@ -39,8 +40,8 @@ if __name__ == "__main__":
pick_best_gpu_id()
pipe = load_checkpoint(
pretrained_model_version="sdxl",
pretrained_model_path=pretrained_models["sdxl"]["path"],
pretrained_model_version=model_version,
pretrained_model_path=pretrained_model["path"],
checkpoint_folder=lora_path,
is_lora=True,
device="cuda:0"
sd15_face_sweep.sh
+202
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@@ -0,0 +1,202 @@
#!/bin/bash
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_000.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_001.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_002.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_003.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_004.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_005.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_006.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_007.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_008.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_009.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_010.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_011.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_012.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_013.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_014.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_015.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_016.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_017.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_018.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_019.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_020.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_021.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_022.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_023.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_024.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_025.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_026.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_027.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_028.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_029.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_030.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_031.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_032.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_033.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_034.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_035.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_036.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_037.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_038.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_039.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_040.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_041.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_042.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_043.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_044.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_045.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_046.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_047.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_048.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_049.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_050.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_051.json
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python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_053.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_054.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_055.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_056.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_057.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_058.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_059.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_060.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_061.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_062.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_063.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_064.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_065.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_066.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_067.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_068.json
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python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_070.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_071.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_072.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_073.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_074.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_075.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_076.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_077.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_078.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_079.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_080.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_081.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_082.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_083.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_084.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_085.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_086.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_087.json
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python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_089.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_090.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_091.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_092.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_093.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_094.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_095.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_096.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_097.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_098.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_099.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_100.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_101.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_102.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_103.json
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python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_110.json
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python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_115.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_116.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_117.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_118.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_119.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_120.json
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python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_122.json
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python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_124.json
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python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_127.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_128.json
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python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_132.json
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python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_134.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_135.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_136.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_137.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_138.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_139.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_140.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_141.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_142.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_143.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_144.json
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python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_146.json
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python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_148.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_149.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_150.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_151.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_152.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_153.json
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python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_183.json
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python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_187.json
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python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_190.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_191.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_192.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_193.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_194.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_195.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_196.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_197.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_198.json
python main.py gridsearch_configs/sd15_face_sweep/sd15_face_sweep_199.json
trainer/config.py
+4 -4
View File
@@ -27,7 +27,7 @@ class TrainingConfig(BaseModel):
unet_optimizer_type: Literal["adamw", "prodigy"] = "adamw"
unet_lr_warmup_steps: int = None # slowly increase the learning rate of the adamw unet optimizer
unet_lr: float = 5.0e-4
unet_lr: float = 1.0e-3
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
@@ -39,8 +39,8 @@ class TrainingConfig(BaseModel):
ti_optimizer: Literal["adamw", "prodigy"] = "adamw"
freeze_ti_after_completion_f: float = 1.0 # freeze the TI after this fraction of the training is done
cond_reg_w: float = 2.0e-5
tok_cond_reg_w: float = 1.0e-5
cond_reg_w: float = 0.0e-5
tok_cond_reg_w: float = 0.0e-5
tok_cov_reg_w: float = 2000. # regularizes the token covariance matrix wrt pretrained "healthy" tokens
off_ratio_power: float = 0.02 # Pulls the std of the token distribution towards the target std
l1_penalty: float = 0.01 # Makes the unet lora matrix more sparse
@@ -85,7 +85,7 @@ class TrainingConfig(BaseModel):
if text_encoder_lora_optimizer is not None then everything else is used.
Else the other variables are ignored.
"""
text_encoder_lora_optimizer: Union[None, Literal["adamw"]] = "adamw"
text_encoder_lora_optimizer: Union[None, Literal["adamw"]] = null
text_encoder_lora_lr: float = 1.0e-5
txt_encoders_lr_warmup_steps: int = 200
text_encoder_lora_weight_decay: float = 1.0e-5
trainer/inference.py
+4 -1
View File
@@ -247,8 +247,11 @@ def encode_prompt_advanced(
Helper function to encode the lora_prompt (containing a trained token) and a zero prompt (without the token)
This allows interpolating the strength of the trained token in the final image.
"""
if lora_path:
lora_prompt = prepare_prompt_for_lora(prompt, lora_path, verbose=1)
else:
lora_prompt = prompt
lora_prompt = prepare_prompt_for_lora(prompt, lora_path, verbose=1)
if concept_mode == "face":
replace_str = "person"
elif concept_mode == "object":
trainer/loss.py
+8
View File
@@ -177,8 +177,16 @@ class ConditioningRegularizer:
class DistributionLoss(torch.nn.Module):
"""
Initialized a new DistributionLoss with shape: torch.Size([49410, 768])
Initialized a new DistributionLoss with shape: torch.Size([49410, 1280])
Class to simplify the calculation of the covariance loss between the trained token embeddings and the pretrained embeddings.
"""
def __init__(self, pretrained_embeddings, dtype=torch.float32, outdir = None):
super(DistributionLoss, self).__init__()
print(f"Initialized a new DistributionLoss with shape: {pretrained_embeddings.shape}")
self.dtype = dtype
self.target_cov = self._calculate_covariance(pretrained_embeddings)
self.target_stds = pretrained_embeddings.std(-1)
training_args_face.json
+10 -9
View File
@@ -1,27 +1,28 @@
{
"output_dir": "lora_models/kofi",
"sd_model_version": "sdxl",
"lora_training_urls": "/home/rednax/Documents/datasets/DOV/kofi",
"output_dir": "lora_models/xander_best",
"sd_model_version": "sd15",
"lora_training_urls": "/home/rednax/Documents/datasets/people/xander",
"concept_mode": "face",
"seed": 0,
"resolution": 512,
"validation_img_size": [1024, 1024],
"train_batch_size": 4,
"n_sample_imgs": 4,
"max_train_steps": 360,
"n_sample_imgs": 6,
"max_train_steps": 500,
"token_warmup_steps": 0,
"checkpointing_steps": 120,
"checkpointing_steps": 100,
"gradient_accumulation_steps": 1,
"n_tokens": 2,
"ti_lr": 0.001,
"ti_weight_decay": 0.0005,
"text_encoder_lora_optimizer": "adamw",
"text_encoder_lora_optimizer": null,
"text_encoder_lora_lr": 1.0e-4,
"text_encoder_lora_weight_decay": 1e-5,
"text_encoder_lora_rank": 12,
"unet_lr": 0.001,
"prodigy_d_coef": 1.0,
"unet_prodigy_growth_factor": 1.05,
"lora_rank": 12,
"lora_rank": 16,
"use_dora": false,
"caption_model": "gpt4-v",
"debug": true