fix argument name.

Co-authored-by: a-r-r-o-w <[email protected]>

Author: sayakpaul

src/diffusers/loaders/lora_pipeline.py

@@ -2278,15 +2278,15 @@ def unfuse_lora(self, components: List[str] = ["transformer", "text_encoder"], *
         super().unfuse_lora(components=components)
 
     # We override this here account for `_transformer_norm_layers`.
-    def unload_lora_weights(self, reset_to_overwrriten_params=False):
+    def unload_lora_weights(self, reset_to_overwritten_params=False):
         super().unload_lora_weights()
 
         transformer = getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer
         if hasattr(transformer, "_transformer_norm_layers") and transformer._transformer_norm_layers:
             transformer.load_state_dict(transformer._transformer_norm_layers, strict=False)
             transformer._transformer_norm_layers = None
 
-        if reset_to_overwrriten_params and getattr(transformer, "_overwritten_params", None) is not None:
+        if reset_to_overwritten_params and getattr(transformer, "_overwritten_params", None) is not None:
             overwritten_params = transformer._overwritten_params
             module_names = set()
 

tests/lora/test_lora_layers_flux.py

@@ -606,7 +606,7 @@ def test_lora_unload_with_parameter_expanded_shapes(self):
         self.assertTrue(pipe.transformer.config.in_channels == 2 * in_features)
         self.assertTrue(cap_logger.out.startswith("Expanding the nn.Linear input/output features for module"))
 
-        control_pipe.unload_lora_weights(reset_to_overwrriten_params=True)
+        control_pipe.unload_lora_weights(reset_to_overwritten_params=True)
         self.assertTrue(
             control_pipe.transformer.config.in_channels == num_channels_without_control,
             f"Expected {num_channels_without_control} channels in the modified transformer but has {control_pipe.transformer.config.in_channels=}",
@@ -672,7 +672,7 @@ def test_lora_unload_with_parameter_expanded_shapes_and_no_reset(self):
         self.assertTrue(pipe.transformer.config.in_channels == 2 * in_features)
         self.assertTrue(cap_logger.out.startswith("Expanding the nn.Linear input/output features for module"))
 
-        control_pipe.unload_lora_weights(reset_to_overwrriten_params=False)
+        control_pipe.unload_lora_weights(reset_to_overwritten_params=False)
         self.assertTrue(
             control_pipe.transformer.config.in_channels == 2 * num_channels_without_control,
             f"Expected {num_channels_without_control} channels in the modified transformer but has {control_pipe.transformer.config.in_channels=}",