Update

2 files changed, 8 insertions, 8 deletions

diff --git a/training/strategy/dreambooth.py b/training/strategy/dreambooth.py
index bc26ee6..d813b49 100644
--- a/training/strategy/dreambooth.py
+++ b/training/strategy/dreambooth.py
@@ -88,7 +88,7 @@ def dreambooth_strategy_callbacks(
         ema_unet = None
 
     def ema_context():
-        if use_ema:
+        if ema_unet is not None:
             return ema_unet.apply_temporary(unet.parameters())
         else:
             return nullcontext()
@@ -102,7 +102,7 @@ def dreambooth_strategy_callbacks(
         text_encoder.text_model.embeddings.persist()
         text_encoder.text_model.embeddings.temp_token_embedding.requires_grad_(False)
 
-        if use_ema:
+        if ema_unet is not None:
             ema_unet.to(accelerator.device)
 
     @contextmanager
@@ -134,11 +134,11 @@ def dreambooth_strategy_callbacks(
 
     @torch.no_grad()
     def on_after_optimize(lr: float):
-        if use_ema:
+        if ema_unet is not None:
             ema_unet.step(unet.parameters())
 
     def on_log():
-        if use_ema:
+        if ema_unet is not None:
             return {"ema_decay": ema_unet.decay}
         return {}
 
diff --git a/training/strategy/ti.py b/training/strategy/ti.py
index 597abd0..081180f 100644
--- a/training/strategy/ti.py
+++ b/training/strategy/ti.py
@@ -88,7 +88,7 @@ def textual_inversion_strategy_callbacks(
         ema_embeddings = None
 
     def ema_context():
-        if use_ema:
+        if ema_embeddings is not None:
             return ema_embeddings.apply_temporary(
                 text_encoder.text_model.embeddings.temp_token_embedding.parameters()
             )
@@ -101,7 +101,7 @@ def textual_inversion_strategy_callbacks(
     def on_prepare():
         text_encoder.text_model.embeddings.temp_token_embedding.requires_grad_(True)
 
-        if use_ema:
+        if ema_embeddings is not None:
             ema_embeddings.to(accelerator.device)
 
         if gradient_checkpointing:
@@ -120,7 +120,7 @@ def textual_inversion_strategy_callbacks(
             yield
 
     def on_after_optimize(lr: float):
-        if use_ema:
+        if ema_embeddings is not None:
             ema_embeddings.step(text_encoder.text_model.embeddings.temp_token_embedding.parameters())
 
     @torch.no_grad()
@@ -132,7 +132,7 @@ def textual_inversion_strategy_callbacks(
             )
 
     def on_log():
-        if use_ema:
+        if ema_embeddings is not None:
             return {"ema_decay": ema_embeddings.decay}
         return {}