[LoRA] feat: support unload_lora_weights() for Flux Control.

src/diffusers/loaders/lora_pipeline.py

@@ -2286,6 +2286,50 @@ def unload_lora_weights(self):
             transformer.load_state_dict(transformer._transformer_norm_layers, strict=False)
             transformer._transformer_norm_layers = None
 
+        if getattr(transformer, "_overwritten_params", None) is not None:
+            overwritten_params = transformer._overwritten_params
+            module_names = set()
+
+            for param_name in overwritten_params:
+                if param_name.endswith(".weight"):
+                    module_names.add(param_name.replace(".weight", ""))
+
+            for name, module in transformer.named_modules():
+                if isinstance(module, torch.nn.Linear) and name in module_names:
+                    module_weight = module.weight.data
+                    module_bias = module.bias.data if module.bias is not None else None
+                    bias = module_bias is not None
+
+                    parent_module_name, _, current_module_name = name.rpartition(".")
+                    parent_module = transformer.get_submodule(parent_module_name)
+
+                    current_param_weight = overwritten_params[f"{name}.weight"]
+                    in_features, out_features = current_param_weight.shape[1], current_param_weight.shape[0]
+                    with torch.device("meta"):
+                        original_module = torch.nn.Linear(
+                            in_features,
+                            out_features,
+                            bias=bias,
+                            dtype=module_weight.dtype,
+                        )
+
+                    tmp_state_dict = {"weight": current_param_weight}
+                    if module_bias is not None:
+                        tmp_state_dict.update({"bias": overwritten_params[f"{name}.bias"]})
+                    original_module.load_state_dict(tmp_state_dict, assign=True, strict=True)
+                    setattr(parent_module, current_module_name, original_module)
+
+                    del tmp_state_dict
+
+                    if current_module_name in _MODULE_NAME_TO_ATTRIBUTE_MAP_FLUX:
+                        attribute_name = _MODULE_NAME_TO_ATTRIBUTE_MAP_FLUX[current_module_name]
+                        new_value = int(current_param_weight.shape[1])
+                        old_value = getattr(transformer.config, attribute_name)
+                        setattr(transformer.config, attribute_name, new_value)
+                        logger.info(
+                            f"Set the {attribute_name} attribute of the model to {new_value} from {old_value}."
+                        )
+
     @classmethod
     def _maybe_expand_transformer_param_shape_or_error_(
         cls,
@@ -2312,6 +2356,8 @@ def _maybe_expand_transformer_param_shape_or_error_(
 
         # Expand transformer parameter shapes if they don't match lora
         has_param_with_shape_update = False
+        overwritten_params = {}
+
         is_peft_loaded = getattr(transformer, "peft_config", None) is not None
         for name, module in transformer.named_modules():
             if isinstance(module, torch.nn.Linear):
@@ -2386,6 +2432,16 @@ def _maybe_expand_transformer_param_shape_or_error_(
                             f"Set the {attribute_name} attribute of the model to {new_value} from {old_value}."
                         )
 
+                    # For `unload_lora_weights()`.
+                    # TODO: this could lead to more memory overhead if the number of overwritten params
+                    # are large. Should be revisited later and tackled through a `discard_original_layers` arg.
+                    overwritten_params[f"{current_module_name}.weight"] = module_weight
+                    if module_bias is not None:
+                        overwritten_params[f"{current_module_name}.bias"] = module_bias
+
+        if len(overwritten_params) > 0:
+            transformer._overwritten_params = overwritten_params
+
         return has_param_with_shape_update
 
     @classmethod

tests/lora/test_lora_layers_flux.py

@@ -558,6 +558,72 @@ def test_load_regular_lora(self):
         self.assertTrue(pipe.transformer.x_embedder.weight.data.shape[1] == in_features * 2)
         self.assertFalse(np.allclose(original_output, lora_output, atol=1e-3, rtol=1e-3))
 
+    def test_lora_unload_with_parameter_expanded_shapes(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()
+        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=}",
+        )
+        loaded_pipe = FluxPipeline.from_pipe(control_pipe)
+        self.assertTrue(
+            loaded_pipe.transformer.config.in_channels == num_channels_without_control,
+            f"Expected {num_channels_without_control} channels in the modified transformer but has {loaded_pipe.transformer.config.in_channels=}",
+        )
+        inputs.pop("control_image")
+        unloaded_lora_out = loaded_pipe(**inputs, generator=torch.manual_seed(0))[0]
+
+        self.assertFalse(np.allclose(unloaded_lora_out, lora_out, rtol=1e-4, atol=1e-4))
+        self.assertTrue(np.allclose(unloaded_lora_out, original_out, atol=1e-4, rtol=1e-4))
+        self.assertTrue(pipe.transformer.x_embedder.weight.data.shape[1] == in_features)
+        self.assertTrue(pipe.transformer.config.in_channels == in_features)
+
     @unittest.skip("Not supported in Flux.")
     def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
         pass