Training update

3 files changed, 8 insertions, 24 deletions

diff --git a/train_dreambooth.py b/train_dreambooth.py
index c7899a0..51e881a 100644
--- a/train_dreambooth.py
+++ b/train_dreambooth.py
@@ -24,6 +24,7 @@ from common import load_text_embeddings
 from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion
 from data.csv import CSVDataModule
 from training.optimization import get_one_cycle_schedule
+from training.ti import patch_trainable_embeddings
 from training.util import AverageMeter, CheckpointerBase, freeze_params, save_args
 from models.clip.prompt import PromptProcessor
 
@@ -567,15 +568,8 @@ def main():
         print(f"Training entire text encoder.")
     else:
         print(f"Training added text embeddings")
-
-        freeze_params(itertools.chain(
-            text_encoder.text_model.encoder.parameters(),
-            text_encoder.text_model.final_layer_norm.parameters(),
-            text_encoder.text_model.embeddings.position_embedding.parameters(),
-        ))
-
-        index_fixed_tokens = torch.arange(len(tokenizer))
-        index_fixed_tokens = index_fixed_tokens[~torch.isin(index_fixed_tokens, torch.tensor(placeholder_token_id))]
+        text_encoder.requires_grad_(False)
+        patch_trainable_embeddings(text_encoder, placeholder_token_id)
 
     prompt_processor = PromptProcessor(tokenizer, text_encoder)
 
@@ -603,7 +597,7 @@ def main():
     if args.train_text_encoder:
         text_encoder_params_to_optimize = text_encoder.parameters()
     else:
-        text_encoder_params_to_optimize = text_encoder.get_input_embeddings().parameters()
+        text_encoder_params_to_optimize = text_encoder.text_model.embeddings.trainable_embedding.parameters()
 
     # Initialize the optimizer
     optimizer = optimizer_class(
@@ -914,12 +908,6 @@ def main():
                         ema_unet.step(unet)
                     optimizer.zero_grad(set_to_none=True)
 
-                    if not args.train_text_encoder:
-                        # Let's make sure we don't update any embedding weights besides the newly added token
-                        with torch.no_grad():
-                            text_encoder.get_input_embeddings(
-                            ).weight[index_fixed_tokens] = original_token_embeds[index_fixed_tokens]
-
                     avg_loss.update(loss.detach_(), bsz)
                     avg_acc.update(acc.detach_(), bsz)
 
diff --git a/train_ti.py b/train_ti.py
index 52bd675..a12b889 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -368,7 +368,6 @@ class Checkpointer(CheckpointerBase):
         tokenizer,
         text_encoder,
         scheduler,
-        text_embeddings,
         placeholder_token,
         placeholder_token_id,
         output_dir: Path,
@@ -394,7 +393,6 @@ class Checkpointer(CheckpointerBase):
         self.tokenizer = tokenizer
         self.text_encoder = text_encoder
         self.scheduler = scheduler
-        self.text_embeddings = text_embeddings
 
     @torch.no_grad()
     def checkpoint(self, step, postfix):
@@ -407,7 +405,7 @@ class Checkpointer(CheckpointerBase):
 
         for (placeholder_token, placeholder_token_id) in zip(self.placeholder_token, self.placeholder_token_id):
             # Save a checkpoint
-            learned_embeds = self.text_embeddings.trainable_embedding.weight[placeholder_token_id]
+            learned_embeds = text_encoder.text_model.embeddings.trainable_embedding.weight[placeholder_token_id]
             learned_embeds_dict = {placeholder_token: learned_embeds.detach().cpu()}
 
             filename = f"%s_%d_%s.bin" % (slugify(placeholder_token), step, postfix)
@@ -517,8 +515,9 @@ def main():
 
     vae.requires_grad_(False)
     unet.requires_grad_(False)
+    text_encoder.requires_grad_(False)
 
-    text_embeddings = patch_trainable_embeddings(text_encoder, placeholder_token_id)
+    patch_trainable_embeddings(text_encoder, placeholder_token_id)
 
     prompt_processor = PromptProcessor(tokenizer, text_encoder)
 
@@ -541,7 +540,7 @@ def main():
 
     # Initialize the optimizer
     optimizer = optimizer_class(
-        text_embeddings.trainable_embedding.parameters(),  # only optimize the embeddings
+        text_encoder.text_model.embeddings.trainable_embedding.parameters(),  # only optimize the embeddings
         lr=args.learning_rate,
         betas=(args.adam_beta1, args.adam_beta2),
         weight_decay=args.adam_weight_decay,
@@ -741,7 +740,6 @@ def main():
         tokenizer=tokenizer,
         text_encoder=text_encoder,
         scheduler=checkpoint_scheduler,
-        text_embeddings=text_embeddings,
         placeholder_token=args.placeholder_token,
         placeholder_token_id=placeholder_token_id,
         output_dir=basepath,
diff --git a/training/ti.py b/training/ti.py
index a5e407b..8b2fdd6 100644
--- a/training/ti.py
+++ b/training/ti.py
@@ -18,8 +18,6 @@ def patch_trainable_embeddings(text_encoder: CLIPTextModel, new_ids: list[int]):
 
     text_encoder.text_model.embeddings = text_embeddings
 
-    return text_embeddings
-
 
 class TrainableEmbeddings(CLIPTextEmbeddings):
     def __init__(self, config: CLIPTextConfig, new_ids: list[int]):