1 files changed, 12 insertions, 128 deletions

diff --git a/train_lora.py b/train_lora.py
index 8dbe45b..6e21634 100644
--- a/train_lora.py
+++ b/train_lora.py
@@ -159,12 +159,6 @@ def parse_args():
         help="Tag dropout probability.",
     )
     parser.add_argument(
-        "--pti_tag_dropout",
-        type=float,
-        default=0,
-        help="Tag dropout probability.",
-    )
-    parser.add_argument(
         "--no_tag_shuffle",
         action="store_true",
         help="Shuffle tags.",
@@ -236,28 +230,12 @@ def parse_args():
         default=2000
     )
     parser.add_argument(
-        "--num_pti_epochs",
-        type=int,
-        default=None
-    )
-    parser.add_argument(
-        "--num_pti_steps",
-        type=int,
-        default=500
-    )
-    parser.add_argument(
         "--gradient_accumulation_steps",
         type=int,
         default=1,
         help="Number of updates steps to accumulate before performing a backward/update pass.",
     )
     parser.add_argument(
-        "--pti_gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument(
         "--lora_r",
         type=int,
         default=8,
@@ -323,12 +301,6 @@ def parse_args():
         help="Initial learning rate (after the potential warmup period) to use.",
     )
     parser.add_argument(
-        "--learning_rate_pti",
-        type=float,
-        default=1e-4,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
         "--learning_rate_emb",
         type=float,
         default=1e-5,
@@ -467,12 +439,6 @@ def parse_args():
         help="How often to save a checkpoint and sample image",
     )
     parser.add_argument(
-        "--pti_sample_frequency",
-        type=int,
-        default=1,
-        help="How often to save a checkpoint and sample image",
-    )
-    parser.add_argument(
         "--sample_image_size",
         type=int,
         default=768,
@@ -509,12 +475,6 @@ def parse_args():
         help="Batch size (per device) for the training dataloader."
     )
     parser.add_argument(
-        "--pti_batch_size",
-        type=int,
-        default=1,
-        help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument(
         "--sample_steps",
         type=int,
         default=10,
@@ -527,6 +487,12 @@ def parse_args():
         help="The weight of prior preservation loss."
     )
     parser.add_argument(
+        "--emb_dropout",
+        type=float,
+        default=0,
+        help="Embedding dropout probability.",
+    )
+    parser.add_argument(
         "--use_emb_decay",
         action="store_true",
         help="Whether to use embedding decay."
@@ -674,7 +640,7 @@ def main():
     save_args(output_dir, args)
 
     tokenizer, text_encoder, vae, unet, noise_scheduler, sample_scheduler, embeddings = get_models(
-        args.pretrained_model_name_or_path)
+        args.pretrained_model_name_or_path, args.emb_dropout)
 
     unet_config = LoraConfig(
         r=args.lora_r,
@@ -720,6 +686,7 @@ def main():
             raise ValueError("--embeddings_dir must point to an existing directory")
 
         added_tokens, added_ids = load_embeddings_from_dir(tokenizer, embeddings, embeddings_dir)
+        embeddings.persist()
         print(f"Added {len(added_tokens)} tokens from embeddings dir: {list(zip(added_tokens, added_ids))}")
 
     placeholder_token_ids, initializer_token_ids = add_placeholder_tokens(
@@ -744,19 +711,14 @@ def main():
             args.learning_rate_text * args.gradient_accumulation_steps *
             args.train_batch_size * accelerator.num_processes
         )
-        args.learning_rate_pti = (
-            args.learning_rate_pti * args.pti_gradient_accumulation_steps *
-            args.pti_batch_size * accelerator.num_processes
-        )
         args.learning_rate_emb = (
-            args.learning_rate_emb * args.pti_gradient_accumulation_steps *
-            args.pti_batch_size * accelerator.num_processes
+            args.learning_rate_emb * args.gradient_accumulation_steps *
+            args.train_batch_size * accelerator.num_processes
         )
 
     if args.find_lr:
         args.learning_rate_unet = 1e-6
         args.learning_rate_text = 1e-6
-        args.learning_rate_pti = 1e-6
         args.learning_rate_emb = 1e-6
         args.lr_scheduler = "exponential_growth"
 
@@ -817,7 +779,6 @@ def main():
         args.lr_min_lr = args.learning_rate_unet
         args.learning_rate_unet = None
         args.learning_rate_text = None
-        args.learning_rate_pti = None
         args.learning_rate_emb = None
     elif args.optimizer == 'dadam':
         try:
@@ -836,7 +797,6 @@ def main():
 
         args.learning_rate_unet = 1.0
         args.learning_rate_text = 1.0
-        args.learning_rate_pti = 1.0
         args.learning_rate_emb = 1.0
     elif args.optimizer == 'dadan':
         try:
@@ -853,7 +813,6 @@ def main():
 
         args.learning_rate_unet = 1.0
         args.learning_rate_text = 1.0
-        args.learning_rate_pti = 1.0
         args.learning_rate_emb = 1.0
     else:
         raise ValueError(f"Unknown --optimizer \"{args.optimizer}\"")
@@ -920,80 +879,6 @@ def main():
         mid_point=args.lr_mid_point,
     )
 
-    # PTI
-    # --------------------------------------------------------------------------------
-
-    if len(args.placeholder_tokens) != 0:
-        pti_datamodule = create_datamodule(
-            batch_size=args.pti_batch_size,
-            dropout=args.pti_tag_dropout,
-            filter=partial(keyword_filter, args.filter_tokens, args.collection, args.exclude_collections),
-        )
-        pti_datamodule.setup()
-
-        num_pti_epochs = args.num_pti_epochs
-        pti_sample_frequency = args.pti_sample_frequency
-        if num_pti_epochs is None:
-            num_pti_epochs = math.ceil(
-                args.num_pti_steps / len(pti_datamodule.train_dataset)
-            ) * args.pti_gradient_accumulation_steps
-            pti_sample_frequency = math.ceil(num_pti_epochs * (pti_sample_frequency / args.num_pti_steps))
-
-        if num_pti_epochs > 0:
-            pti_optimizer = create_optimizer(
-                [
-                    {
-                        "params": text_encoder.text_model.embeddings.token_override_embedding.parameters(),
-                        "lr": args.learning_rate_pti,
-                        "weight_decay": 0,
-                    },
-                ]
-            )
-
-            pti_lr_scheduler = create_lr_scheduler(
-                gradient_accumulation_steps=args.pti_gradient_accumulation_steps,
-                optimizer=pti_optimizer,
-                num_training_steps_per_epoch=len(pti_datamodule.train_dataloader),
-                train_epochs=num_pti_epochs,
-            )
-
-            continue_training = True
-            training_iter = 1
-
-            while continue_training:
-                print("")
-                print(f"============ PTI cycle {training_iter} ============")
-                print("")
-
-                pti_project = f"pti_{training_iter}"
-                pti_output_dir = output_dir / pti_project
-                pti_checkpoint_output_dir = pti_output_dir / "model"
-                pti_sample_output_dir = pti_output_dir / "samples"
-
-                trainer(
-                    strategy=lora_strategy,
-                    pti_mode=True,
-                    project=pti_project,
-                    train_dataloader=pti_datamodule.train_dataloader,
-                    val_dataloader=pti_datamodule.val_dataloader,
-                    optimizer=pti_optimizer,
-                    lr_scheduler=pti_lr_scheduler,
-                    num_train_epochs=num_pti_epochs,
-                    gradient_accumulation_steps=args.pti_gradient_accumulation_steps,
-                    # --
-                    group_labels=["emb"],
-                    sample_output_dir=pti_sample_output_dir,
-                    checkpoint_output_dir=pti_checkpoint_output_dir,
-                    sample_frequency=pti_sample_frequency,
-                )
-
-                response = input("Run another cycle? [y/n] ")
-                continue_training = response.lower().strip() != "n"
-                training_iter += 1
-
-            if not args.train_emb:
-                embeddings.persist()
-
     # LORA
     # --------------------------------------------------------------------------------
 
@@ -1062,9 +947,8 @@ def main():
         print("")
 
         lora_project = f"lora_{training_iter}"
-        lora_output_dir = output_dir / lora_project
-        lora_checkpoint_output_dir = lora_output_dir / "model"
-        lora_sample_output_dir = lora_output_dir / "samples"
+        lora_checkpoint_output_dir = output_dir / lora_project / "model"
+        lora_sample_output_dir = output_dir / lora_project / "samples"
 
         trainer(
             strategy=lora_strategy,