[lora]feat: use exclude modules to loraconfig.

src/diffusers/loaders/peft.py

@@ -244,13 +244,20 @@ def load_lora_adapter(
                     k.removeprefix(f"{prefix}."): v for k, v in network_alphas.items() if k in alpha_keys
                 }
 
-            # create LoraConfig
-            lora_config = _create_lora_config(state_dict, network_alphas, metadata, rank)
-
             # adapter_name
             if adapter_name is None:
                 adapter_name = get_adapter_name(self)
 
+            # create LoraConfig
+            lora_config = _create_lora_config(
+                state_dict,
+                network_alphas,
+                metadata,
+                rank,
+                model_state_dict=self.state_dict(),
+                adapter_name=adapter_name,
+            )
+
             # <Unsafe code
             # We can be sure that the following works as it just sets attention processors, lora layers and puts all in the same dtype
             # Now we remove any existing hooks to `_pipeline`.

src/diffusers/utils/peft_utils.py

@@ -150,7 +150,9 @@ def unscale_lora_layers(model, weight: Optional[float] = None):
                     module.set_scale(adapter_name, 1.0)
 
 
-def get_peft_kwargs(rank_dict, network_alpha_dict, peft_state_dict, is_unet=True):
+def get_peft_kwargs(
+    rank_dict, network_alpha_dict, peft_state_dict, is_unet=True, model_state_dict=None, adapter_name=None
+):
     rank_pattern = {}
     alpha_pattern = {}
     r = lora_alpha = list(rank_dict.values())[0]
@@ -180,7 +182,6 @@ def get_peft_kwargs(rank_dict, network_alpha_dict, peft_state_dict, is_unet=True
         else:
             lora_alpha = set(network_alpha_dict.values()).pop()
 
-    # layer names without the Diffusers specific
     target_modules = list({name.split(".lora")[0] for name in peft_state_dict.keys()})
     use_dora = any("lora_magnitude_vector" in k for k in peft_state_dict)
     # for now we know that the "bias" keys are only associated with `lora_B`.
@@ -195,6 +196,21 @@ def get_peft_kwargs(rank_dict, network_alpha_dict, peft_state_dict, is_unet=True
         "use_dora": use_dora,
         "lora_bias": lora_bias,
     }
+
+    # Example: try load FusionX LoRA into Wan VACE
+    exclude_modules = _derive_exclude_modules(model_state_dict, peft_state_dict, adapter_name)
+    if exclude_modules:
+        if not is_peft_version(">=", "0.14.0"):
+            msg = """
+It seems like there are certain modules that need to be excluded when initializing `LoraConfig`. Your current `peft`
+version doesn't support passing an `exclude_modules` to `LoraConfig`. Please update it by running `pip install -U
+peft`. For most cases, this can be completely ignored. But if it seems unexpected, please file an issue -
+https://github.com/huggingface/diffusers/issues/new
+            """
+            logger.debug(msg)
+        else:
+            lora_config_kwargs.update({"exclude_modules": exclude_modules})
+
     return lora_config_kwargs
 
 
@@ -294,19 +310,20 @@ def check_peft_version(min_version: str) -> None:
 
 
 def _create_lora_config(
-    state_dict,
-    network_alphas,
-    metadata,
-    rank_pattern_dict,
-    is_unet: bool = True,
+    state_dict, network_alphas, metadata, rank_pattern_dict, is_unet=True, model_state_dict=None, adapter_name=None
 ):
     from peft import LoraConfig
 
     if metadata is not None:
         lora_config_kwargs = metadata
     else:
         lora_config_kwargs = get_peft_kwargs(
-            rank_pattern_dict, network_alpha_dict=network_alphas, peft_state_dict=state_dict, is_unet=is_unet
+            rank_pattern_dict,
+            network_alpha_dict=network_alphas,
+            peft_state_dict=state_dict,
+            is_unet=is_unet,
+            model_state_dict=model_state_dict,
+            adapter_name=adapter_name,
         )
 
     _maybe_raise_error_for_ambiguous_keys(lora_config_kwargs)
@@ -371,3 +388,27 @@ def _maybe_warn_for_unhandled_keys(incompatible_keys, adapter_name):
 
     if warn_msg:
         logger.warning(warn_msg)
+
+
+def _derive_exclude_modules(model_state_dict, peft_state_dict, adapter_name=None):
+    """
+    Derives the modules to exclude while initializing `LoraConfig` through `exclude_modules`. It works by comparing the
+    `model_state_dict` and `peft_state_dict` and adds a module from `model_state_dict` to the exclusion set if it
+    doesn't exist in `peft_state_dict`.
+    """
+    if model_state_dict is None:
+        return
+    all_modules = set()
+    string_to_replace = f"{adapter_name}." if adapter_name else ""
+
+    for name in model_state_dict.keys():
+        if string_to_replace:
+            name = name.replace(string_to_replace, "")
+        if "." in name:
+            module_name = name.rsplit(".", 1)[0]
+            all_modules.add(module_name)
+
+    target_modules_set = {name.split(".lora")[0] for name in peft_state_dict.keys()}
+    exclude_modules = list(all_modules - target_modules_set)
+
+    return exclude_modules

tests/lora/test_lora_layers_wan.py

@@ -24,7 +24,11 @@
     WanPipeline,
     WanTransformer3DModel,
 )
-from diffusers.utils.testing_utils import floats_tensor, require_peft_backend, skip_mps
+from diffusers.utils.testing_utils import (
+    floats_tensor,
+    require_peft_backend,
+    skip_mps,
+)
 
 
 sys.path.append(".")

tests/lora/utils.py

@@ -12,6 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import copy
 import inspect
 import os
 import re
@@ -291,6 +292,20 @@ def _get_modules_to_save(self, pipe, has_denoiser=False):
 
         return modules_to_save
 
+    def _get_exclude_modules(self, pipe):
+        from diffusers.utils.peft_utils import _derive_exclude_modules
+
+        modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
+        denoiser = "unet" if self.unet_kwargs is not None else "transformer"
+        modules_to_save = {k: v for k, v in modules_to_save.items() if k == denoiser}
+        denoiser_lora_state_dict = self._get_lora_state_dicts(modules_to_save)[f"{denoiser}_lora_layers"]
+        pipe.unload_lora_weights()
+        denoiser_state_dict = pipe.unet.state_dict() if self.unet_kwargs is not None else pipe.transformer.state_dict()
+        exclude_modules = _derive_exclude_modules(
+            denoiser_state_dict, denoiser_lora_state_dict, adapter_name="default"
+        )
+        return exclude_modules
+
     def add_adapters_to_pipeline(self, pipe, text_lora_config=None, denoiser_lora_config=None, adapter_name="default"):
         if text_lora_config is not None:
             if "text_encoder" in self.pipeline_class._lora_loadable_modules:
@@ -2326,6 +2341,58 @@ def test_lora_unload_add_adapter(self):
         )
         _ = pipe(**inputs, generator=torch.manual_seed(0))[0]
 
+    @require_peft_version_greater("0.13.2")
+    def test_lora_exclude_modules(self):
+        """
+        Test to check if `exclude_modules` works or not. It works in the following way:
+        we first create a pipeline and insert LoRA config into it. We then derive a `set`
+        of modules to exclude by investigating its denoiser state dict and denoiser LoRA
+        state dict.
+
+        We then create a new LoRA config to include the `exclude_modules` and perform tests.
+        """
+        scheduler_cls = self.scheduler_classes[0]
+        components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+        pipe = self.pipeline_class(**components).to(torch_device)
+        _, _, inputs = self.get_dummy_inputs(with_generator=False)
+
+        output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
+        self.assertTrue(output_no_lora.shape == self.output_shape)
+
+        # only supported for `denoiser` now
+        pipe_cp = copy.deepcopy(pipe)
+        pipe_cp, _ = self.add_adapters_to_pipeline(
+            pipe_cp, text_lora_config=text_lora_config, denoiser_lora_config=denoiser_lora_config
+        )
+        denoiser_exclude_modules = self._get_exclude_modules(pipe_cp)
+        pipe_cp.to("cpu")
+        del pipe_cp
+
+        denoiser_lora_config.exclude_modules = denoiser_exclude_modules
+        pipe, _ = self.add_adapters_to_pipeline(
+            pipe, text_lora_config=text_lora_config, denoiser_lora_config=denoiser_lora_config
+        )
+        output_lora_exclude_modules = pipe(**inputs, generator=torch.manual_seed(0))[0]
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
+            lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
+            lora_metadatas = self._get_lora_adapter_metadata(modules_to_save)
+            self.pipeline_class.save_lora_weights(save_directory=tmpdir, **lora_state_dicts, **lora_metadatas)
+            pipe.unload_lora_weights()
+            pipe.load_lora_weights(tmpdir)
+
+            output_lora_pretrained = pipe(**inputs, generator=torch.manual_seed(0))[0]
+
+            self.assertTrue(
+                not np.allclose(output_no_lora, output_lora_exclude_modules, atol=1e-3, rtol=1e-3),
+                "LoRA should change outputs.",
+            )
+            self.assertTrue(
+                np.allclose(output_lora_exclude_modules, output_lora_pretrained, atol=1e-3, rtol=1e-3),
+                "Lora outputs should match.",
+            )
+
     def test_inference_load_delete_load_adapters(self):
         "Tests if `load_lora_weights()` -> `delete_adapters()` -> `load_lora_weights()` works."
         for scheduler_cls in self.scheduler_classes: