[LoRA] fix: lora unloading when using expanded Flux LoRAs.

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):
+    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 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()
+        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=}",
@@ -624,6 +624,65 @@ def test_lora_unload_with_parameter_expanded_shapes(self):
         self.assertTrue(pipe.transformer.x_embedder.weight.data.shape[1] == in_features)
         self.assertTrue(pipe.transformer.config.in_channels == in_features)
 
+    def test_lora_unload_with_parameter_expanded_shapes_and_no_reset(self):
+        components, _, _ = self.get_dummy_components(FlowMatchEulerDiscreteScheduler)
+
+        logger = logging.get_logger("diffusers.loaders.lora_pipeline")
+        logger.setLevel(logging.DEBUG)
+
+        # Change the transformer config to mimic a real use case.
+        num_channels_without_control = 4
+        transformer = FluxTransformer2DModel.from_config(
+            components["transformer"].config, in_channels=num_channels_without_control
+        ).to(torch_device)
+        self.assertTrue(
+            transformer.config.in_channels == num_channels_without_control,
+            f"Expected {num_channels_without_control} channels in the modified transformer but has {transformer.config.in_channels=}",
+        )
+
+        # This should be initialized with a Flux pipeline variant that doesn't accept `control_image`.
+        components["transformer"] = transformer
+        pipe = FluxPipeline(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        _, _, inputs = self.get_dummy_inputs(with_generator=False)
+        control_image = inputs.pop("control_image")
+        original_out = pipe(**inputs, generator=torch.manual_seed(0))[0]
+
+        control_pipe = self.pipeline_class(**components)
+        out_features, in_features = control_pipe.transformer.x_embedder.weight.shape
+        rank = 4
+
+        dummy_lora_A = torch.nn.Linear(2 * in_features, rank, bias=False)
+        dummy_lora_B = torch.nn.Linear(rank, out_features, bias=False)
+        lora_state_dict = {
+            "transformer.x_embedder.lora_A.weight": dummy_lora_A.weight,
+            "transformer.x_embedder.lora_B.weight": dummy_lora_B.weight,
+        }
+        with CaptureLogger(logger) as cap_logger:
+            control_pipe.load_lora_weights(lora_state_dict, "adapter-1")
+            self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in denoiser")
+
+        inputs["control_image"] = control_image
+        lora_out = control_pipe(**inputs, generator=torch.manual_seed(0))[0]
+
+        self.assertFalse(np.allclose(original_out, lora_out, rtol=1e-4, atol=1e-4))
+        self.assertTrue(pipe.transformer.x_embedder.weight.data.shape[1] == 2 * in_features)
+        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_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=}",
+        )
+        no_lora_out = control_pipe(**inputs, generator=torch.manual_seed(0))[0]
+
+        self.assertFalse(np.allclose(no_lora_out, lora_out, rtol=1e-4, atol=1e-4))
+        self.assertTrue(pipe.transformer.x_embedder.weight.data.shape[1] == in_features * 2)
+        self.assertTrue(pipe.transformer.config.in_channels == in_features * 2)
+
     @unittest.skip("Not supported in Flux.")
     def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
         pass