3 files changed, 12 insertions, 12 deletions

diff --git a/training/strategy/dreambooth.py b/training/strategy/dreambooth.py
index e5e84c8..28fccff 100644
--- a/training/strategy/dreambooth.py
+++ b/training/strategy/dreambooth.py
@@ -137,8 +137,8 @@ def dreambooth_strategy_callbacks(
 
         print("Saving model...")
 
-        unet_ = accelerator.unwrap_model(unet, False)
-        text_encoder_ = accelerator.unwrap_model(text_encoder, False)
+        unet_ = accelerator.unwrap_model(unet, keep_fp32_wrapper=False)
+        text_encoder_ = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=False)
 
         with ema_context():
             pipeline = VlpnStableDiffusion(
@@ -160,8 +160,8 @@ def dreambooth_strategy_callbacks(
     @torch.no_grad()
     def on_sample(step):
         with ema_context():
-            unet_ = accelerator.unwrap_model(unet, False)
-            text_encoder_ = accelerator.unwrap_model(text_encoder, False)
+            unet_ = accelerator.unwrap_model(unet, keep_fp32_wrapper=True)
+            text_encoder_ = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True)
 
             orig_unet_dtype = unet_.dtype
             orig_text_encoder_dtype = text_encoder_.dtype
diff --git a/training/strategy/lora.py b/training/strategy/lora.py
index aa75bec..1c8fad6 100644
--- a/training/strategy/lora.py
+++ b/training/strategy/lora.py
@@ -47,7 +47,6 @@ def lora_strategy_callbacks(
     save_samples_ = partial(
         save_samples,
         accelerator=accelerator,
-        text_encoder=text_encoder,
         tokenizer=tokenizer,
         vae=vae,
         sample_scheduler=sample_scheduler,
@@ -72,6 +71,7 @@ def lora_strategy_callbacks(
     @contextmanager
     def on_train(epoch: int):
         tokenizer.train()
+        text_encoder.train()
         yield
 
     @contextmanager
@@ -89,8 +89,8 @@ def lora_strategy_callbacks(
     def on_checkpoint(step, postfix):
         print(f"Saving checkpoint for step {step}...")
 
-        unet_ = accelerator.unwrap_model(unet, False)
-        text_encoder_ = accelerator.unwrap_model(text_encoder, False)
+        unet_ = accelerator.unwrap_model(unet, keep_fp32_wrapper=False)
+        text_encoder_ = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=False)
 
         lora_config = {}
         state_dict = get_peft_model_state_dict(unet, state_dict=accelerator.get_state_dict(unet))
@@ -111,10 +111,10 @@ def lora_strategy_callbacks(
 
     @torch.no_grad()
     def on_sample(step):
-        unet_ = accelerator.unwrap_model(unet, False)
-        text_encoder_ = accelerator.unwrap_model(text_encoder, False)
+        unet_ = accelerator.unwrap_model(unet, keep_fp32_wrapper=True)
+        text_encoder_ = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True)
 
-        save_samples_(step=step, unet=unet_)
+        save_samples_(step=step, unet=unet_, text_encoder=text_encoder_)
 
         del unet_
         del text_encoder_
diff --git a/training/strategy/ti.py b/training/strategy/ti.py
index bd0d178..2038e34 100644
--- a/training/strategy/ti.py
+++ b/training/strategy/ti.py
@@ -156,8 +156,8 @@ def textual_inversion_strategy_callbacks(
     @torch.no_grad()
     def on_sample(step):
         with ema_context():
-            unet_ = accelerator.unwrap_model(unet, False)
-            text_encoder_ = accelerator.unwrap_model(text_encoder, False)
+            unet_ = accelerator.unwrap_model(unet, keep_fp32_wrapper=True)
+            text_encoder_ = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True)
 
             orig_unet_dtype = unet_.dtype
             orig_text_encoder_dtype = text_encoder_.dtype