3 files changed, 7 insertions, 7 deletions

diff --git a/training/strategy/dreambooth.py b/training/strategy/dreambooth.py
index 8aaed3a..d697554 100644
--- a/training/strategy/dreambooth.py
+++ b/training/strategy/dreambooth.py
@@ -144,8 +144,8 @@ def dreambooth_strategy_callbacks(
 
         print("Saving model...")
 
-        unet_ = accelerator.unwrap_model(unet)
-        text_encoder_ = accelerator.unwrap_model(text_encoder)
+        unet_ = accelerator.unwrap_model(unet, False)
+        text_encoder_ = accelerator.unwrap_model(text_encoder, False)
 
         with ema_context():
             pipeline = VlpnStableDiffusion(
@@ -167,8 +167,8 @@ def dreambooth_strategy_callbacks(
     @torch.no_grad()
     def on_sample(step):
         with ema_context():
-            unet_ = accelerator.unwrap_model(unet)
-            text_encoder_ = accelerator.unwrap_model(text_encoder)
+            unet_ = accelerator.unwrap_model(unet, False)
+            text_encoder_ = accelerator.unwrap_model(text_encoder, False)
 
             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 4dd1100..ccec215 100644
--- a/training/strategy/lora.py
+++ b/training/strategy/lora.py
@@ -90,7 +90,7 @@ def lora_strategy_callbacks(
     def on_checkpoint(step, postfix):
         print(f"Saving checkpoint for step {step}...")
 
-        unet_ = accelerator.unwrap_model(unet)
+        unet_ = accelerator.unwrap_model(unet, False)
         unet_.save_attn_procs(checkpoint_output_dir / f"{step}_{postfix}")
         del unet_
 
diff --git a/training/strategy/ti.py b/training/strategy/ti.py
index 0de3cb0..66d3129 100644
--- a/training/strategy/ti.py
+++ b/training/strategy/ti.py
@@ -144,8 +144,8 @@ def textual_inversion_strategy_callbacks(
     @torch.no_grad()
     def on_sample(step):
         with ema_context():
-            unet_ = accelerator.unwrap_model(unet)
-            text_encoder_ = accelerator.unwrap_model(text_encoder)
+            unet_ = accelerator.unwrap_model(unet, False)
+            text_encoder_ = accelerator.unwrap_model(text_encoder, False)
 
             orig_unet_dtype = unet_.dtype
             orig_text_encoder_dtype = text_encoder_.dtype