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-rw-r--r--train_lora.py10
-rw-r--r--training/strategy/lora.py4
-rw-r--r--training/strategy/ti.py1
3 files changed, 7 insertions, 8 deletions
diff --git a/train_lora.py b/train_lora.py
index 8fc2d69..cf73645 100644
--- a/train_lora.py
+++ b/train_lora.py
@@ -662,9 +662,13 @@ def main():
662 sample_frequency = math.ceil(num_train_epochs * (sample_frequency / args.num_train_steps)) 662 sample_frequency = math.ceil(num_train_epochs * (sample_frequency / args.num_train_steps))
663 663
664 optimizer = create_optimizer( 664 optimizer = create_optimizer(
665 itertools.chain( 665 (
666 unet.parameters(), 666 param
667 text_encoder.parameters(), 667 for param in itertools.chain(
668 unet.parameters(),
669 text_encoder.parameters(),
670 )
671 if param.requires_grad
668 ), 672 ),
669 lr=args.learning_rate, 673 lr=args.learning_rate,
670 ) 674 )
diff --git a/training/strategy/lora.py b/training/strategy/lora.py
index 8905171..209785a 100644
--- a/training/strategy/lora.py
+++ b/training/strategy/lora.py
@@ -139,10 +139,6 @@ def lora_prepare(
139 train_dataloader: DataLoader, 139 train_dataloader: DataLoader,
140 val_dataloader: Optional[DataLoader], 140 val_dataloader: Optional[DataLoader],
141 lr_scheduler: torch.optim.lr_scheduler._LRScheduler, 141 lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
142 lora_rank: int = 4,
143 lora_alpha: int = 32,
144 lora_dropout: float = 0,
145 lora_bias: str = "none",
146 **kwargs 142 **kwargs
147): 143):
148 return accelerator.prepare(text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler) + ({},) 144 return accelerator.prepare(text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler) + ({},)
diff --git a/training/strategy/ti.py b/training/strategy/ti.py
index 677f5a3..c7520ed 100644
--- a/training/strategy/ti.py
+++ b/training/strategy/ti.py
@@ -209,7 +209,6 @@ def textual_inversion_prepare(
209 text_encoder.text_model.final_layer_norm.requires_grad_(False) 209 text_encoder.text_model.final_layer_norm.requires_grad_(False)
210 text_encoder.text_model.embeddings.position_embedding.requires_grad_(False) 210 text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
211 text_encoder.text_model.embeddings.token_embedding.requires_grad_(False) 211 text_encoder.text_model.embeddings.token_embedding.requires_grad_(False)
212 text_encoder.eval()
213 212
214 return text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, {} 213 return text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler, {}
215 214