Update

1 files changed, 26 insertions, 28 deletions

diff --git a/training/strategy/ti.py b/training/strategy/ti.py
index 6f8384f..753dce0 100644
--- a/training/strategy/ti.py
+++ b/training/strategy/ti.py
@@ -27,7 +27,6 @@ def textual_inversion_strategy(
     sample_scheduler: DPMSolverMultistepScheduler,
     train_dataloader: DataLoader,
     val_dataloader: DataLoader,
-    dtype: torch.dtype,
     output_dir: Path,
     seed: int,
     placeholder_tokens: list[str],
@@ -48,6 +47,12 @@ def textual_inversion_strategy(
     sample_guidance_scale: float = 7.5,
     sample_image_size: Optional[int] = None,
 ):
+    weight_dtype = torch.float32
+    if accelerator.state.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.state.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+
     save_samples_ = partial(
         save_samples,
         accelerator=accelerator,
@@ -58,7 +63,7 @@ def textual_inversion_strategy(
         sample_scheduler=sample_scheduler,
         train_dataloader=train_dataloader,
         val_dataloader=val_dataloader,
-        dtype=dtype,
+        dtype=weight_dtype,
         output_dir=output_dir,
         seed=seed,
         batch_size=sample_batch_size,
@@ -78,6 +83,17 @@ def textual_inversion_strategy(
     else:
         ema_embeddings = None
 
+    def ema_context():
+        if use_ema:
+            return ema_embeddings.apply_temporary(
+                text_encoder.text_model.embeddings.temp_token_embedding.parameters()
+            )
+        else:
+            return nullcontext()
+
+    def on_model():
+        return text_encoder
+
     def on_prepare():
         text_encoder.text_model.embeddings.temp_token_embedding.requires_grad_(True)
 
@@ -89,24 +105,15 @@ def textual_inversion_strategy(
 
     @contextmanager
     def on_train(epoch: int):
-        try:
-            tokenizer.train()
-            yield
-        finally:
-            pass
+        tokenizer.train()
+        yield
 
     @contextmanager
     def on_eval():
-        try:
-            tokenizer.eval()
+        tokenizer.eval()
 
-            ema_context = ema_embeddings.apply_temporary(
-                text_encoder.text_model.embeddings.temp_token_embedding.parameters()) if use_ema else nullcontext()
-
-            with ema_context:
-                yield
-        finally:
-            pass
+        with ema_context():
+            yield
 
     @torch.no_grad()
     def on_after_optimize(lr: float):
@@ -131,13 +138,7 @@ def textual_inversion_strategy(
         checkpoints_path = output_dir.joinpath("checkpoints")
         checkpoints_path.mkdir(parents=True, exist_ok=True)
 
-        text_encoder = accelerator.unwrap_model(text_encoder)
-
-        ema_context = ema_embeddings.apply_temporary(
-            text_encoder.text_model.embeddings.temp_token_embedding.parameters()
-        ) if ema_embeddings is not None else nullcontext()
-
-        with ema_context:
+        with ema_context():
             for (token, ids) in zip(placeholder_tokens, placeholder_token_ids):
                 text_encoder.text_model.embeddings.save_embed(
                     ids,
@@ -146,15 +147,12 @@ def textual_inversion_strategy(
 
     @torch.no_grad()
     def on_sample(step):
-        ema_context = ema_embeddings.apply_temporary(
-            text_encoder.text_model.embeddings.temp_token_embedding.parameters()
-        ) if ema_embeddings is not None else nullcontext()
-
-        with ema_context:
+        with ema_context():
             save_samples_(step=step)
 
     return TrainingCallbacks(
         on_prepare=on_prepare,
+        on_model=on_model,
         on_train=on_train,
         on_eval=on_eval,
         on_after_optimize=on_after_optimize,