tweak params

main.py

@@ -327,8 +327,7 @@ def train(config: TrainingConfig):
 
             token_attention_loss = compute_token_attention_loss(pipe, embedding_handler, captions, mask, daam_loss)
             losses['token_attention_loss'].append(token_attention_loss.item())
-            #loss = loss + 0.0000002 * token_attention_loss
-            loss = loss + 0.000002 * token_attention_loss
+            loss = loss + 0.0000002 * token_attention_loss
 
             if global_step%40 == 0:
                 img_ratio = config.train_img_size[0] / config.train_img_size[1]

scripts/create_hyperparam_sweep.py

@@ -50,9 +50,7 @@ hyperparameters = {
     "output_dir": [f"lora_models/{exp_name}"],
     "sd_model_version": ["sdxl"],
     "lora_training_urls": [
-        "/home/rednax/SSD2TB/Github_repos/Eden/images/beeple_large",
-        "/home/rednax/SSD2TB/Github_repos/Eden/images/beeple"
-
+        "https://storage.googleapis.com/public-assets-xander/A_workbox/lora_training_sets/clipx.zip",
     ],
     "concept_mode": ['style'],
     "sample_imgs_lora_scale": [0.8],

train_configs/test.json

@@ -1,8 +1,8 @@
 {
     "name": "xander_test",
     "sd_model_version": "sdxl",
-    "lora_training_urls": "https://storage.googleapis.com/public-assets-xander/A_workbox/lora_training_sets/gene.zip",
-    "concept_mode": "face",
+    "lora_training_urls": "https://storage.googleapis.com/public-assets-xander/A_workbox/lora_training_sets/clipx.zip",
+    "concept_mode": "style",
     "seed": 0,
     "resolution": 512,
     "train_batch_size": 4,
@@ -15,14 +15,14 @@
     "ti_weight_decay": 0.0005,
 
     "disable_ti": false,
-    "n_tokens": 3,
+    "n_tokens": 2,
     "text_encoder_lora_optimizer": null,
     "text_encoder_lora_lr": 1.0e-4,
     "text_encoder_lora_weight_decay": 1e-5,
     "text_encoder_lora_rank": 12,
 
-    "sample_imgs_lora_scale": 1.0,
-    "unet_lr": 0.00,
+    "sample_imgs_lora_scale": 0.8,
+    "unet_lr": 0.0002,
     "lora_rank": 16,
     "use_dora": false,
     "caption_model": "florence",

trainer/loss.py

@@ -54,11 +54,11 @@ def compute_token_attention_loss(pipe, embedding_handler,
     ti_masks = torch.stack(ti_masks)
 
     # Penalize large, positive mean token attentions:
-    reg_loss_0 = 5*torch.stack(att_L2_losses).mean()
+    reg_loss_0 = 10*torch.stack(att_L2_losses).mean()
 
     # Where the segmentation mask is one, we want to avoid very large attention scores:
     threshold = 0.0
-    reg_loss_1 = (torch.relu(ti_heatmaps*ti_masks - threshold)**2).mean()
+    reg_loss_1 = 0.5*(torch.relu(ti_heatmaps*ti_masks - threshold)**2).mean()
 
     # Where the segmentation mask is zero, we want to avoid somewhat large attention scores:
     threshold = -15.0

trainer/preprocess.py

@@ -557,7 +557,7 @@ def fixed_get_imports(filename: str | os.PathLike) -> list[str]:
     return imports
 
 from transformers import AutoProcessor, AutoModelForCausalLM 
-
+@torch.no_grad()
 def florence_caption_dataset(images, captions):
 
     with patch("transformers.dynamic_module_utils.get_imports", fixed_get_imports): #workaround for unnecessary flash_attn requirement
@@ -574,7 +574,7 @@ def florence_caption_dataset(images, captions):
                 input_ids=inputs["input_ids"],
                 pixel_values=inputs["pixel_values"],
                 max_new_tokens=1024,
-                num_beams=random.choice(2,3,4)
+                num_beams=random.choice([2,3,4])
                 )
 
             generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
@@ -582,6 +582,11 @@ def florence_caption_dataset(images, captions):
             caption = parsed_answer[prompt]
             captions[i] = caption.replace("The image shows a ", "A ")
 
+    del model
+    del processor
+    gc.collect()
+    torch.cuda.empty_cache()
+
     return captions