[lora] adapt new LoRA config injection method

setup.py

@@ -116,7 +116,7 @@
     "librosa",
     "numpy",
     "parameterized",
-    "peft>=0.15.0",
+    "peft>=0.17.0",
     "protobuf>=3.20.3,<4",
     "pytest",
     "pytest-timeout",

src/diffusers/dependency_versions_table.py

@@ -23,7 +23,7 @@
     "librosa": "librosa",
     "numpy": "numpy",
     "parameterized": "parameterized",
-    "peft": "peft>=0.15.0",
+    "peft": "peft>=0.17.0",
     "protobuf": "protobuf>=3.20.3,<4",
     "pytest": "pytest",
     "pytest-timeout": "pytest-timeout",

src/diffusers/loaders/peft.py

@@ -320,7 +320,9 @@ def map_state_dict_for_hotswap(sd):
                     # it to None
                     incompatible_keys = None
                 else:
-                    inject_adapter_in_model(lora_config, self, adapter_name=adapter_name, **peft_kwargs)
+                    inject_adapter_in_model(
+                        lora_config, self, adapter_name=adapter_name, state_dict=state_dict, **peft_kwargs
+                    )
                     incompatible_keys = set_peft_model_state_dict(self, state_dict, adapter_name, **peft_kwargs)
 
                     if self._prepare_lora_hotswap_kwargs is not None:

src/diffusers/utils/peft_utils.py

@@ -197,20 +197,6 @@ def get_peft_kwargs(
         "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
 
 
@@ -388,27 +374,3 @@ 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/utils.py

@@ -12,7 +12,6 @@
 # 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
@@ -292,20 +291,6 @@ 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:
@@ -2342,58 +2327,6 @@ 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:

tests/models/transformers/test_models_transformer_flux.py

@@ -20,7 +20,7 @@
 from diffusers import FluxTransformer2DModel
 from diffusers.models.attention_processor import FluxIPAdapterJointAttnProcessor2_0
 from diffusers.models.embeddings import ImageProjection
-from diffusers.utils.testing_utils import enable_full_determinism, torch_device
+from diffusers.utils.testing_utils import enable_full_determinism, is_peft_available, torch_device
 
 from ..test_modeling_common import LoraHotSwappingForModelTesterMixin, ModelTesterMixin, TorchCompileTesterMixin
 
@@ -172,6 +172,35 @@ def test_gradient_checkpointing_is_applied(self):
         expected_set = {"FluxTransformer2DModel"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
 
+    # The test exists for cases like
+    # https://github.com/huggingface/diffusers/issues/11874
+    @unittest.skipIf(not is_peft_available(), "Only with PEFT")
+    def test_lora_exclude_modules(self):
+        from peft import LoraConfig, get_peft_model_state_dict, inject_adapter_in_model, set_peft_model_state_dict
+
+        lora_rank = 4
+        target_module = "single_transformer_blocks.0.proj_out"
+        adapter_name = "foo"
+        init_dict, _ = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict).to(torch_device)
+
+        state_dict = model.state_dict()
+        target_mod_shape = state_dict[f"{target_module}.weight"].shape
+        lora_state_dict = {
+            f"{target_module}.lora_A.weight": torch.ones(lora_rank, target_mod_shape[1]) * 22,
+            f"{target_module}.lora_B.weight": torch.ones(target_mod_shape[0], lora_rank) * 33,
+        }
+        # Passing exclude_modules should no longer be necessary (or even passing target_modules, for that matter).
+        config = LoraConfig(
+            r=lora_rank, target_modules=["single_transformer_blocks.0.proj_out"], exclude_modules=["proj_out"]
+        )
+        inject_adapter_in_model(config, model, adapter_name=adapter_name, state_dict=lora_state_dict)
+        set_peft_model_state_dict(model, lora_state_dict, adapter_name)
+        retrieved_lora_state_dict = get_peft_model_state_dict(model, adapter_name=adapter_name)
+        assert len(retrieved_lora_state_dict) == len(lora_state_dict)
+        assert (retrieved_lora_state_dict["single_transformer_blocks.0.proj_out.lora_A.weight"] == 22).all()
+        assert (retrieved_lora_state_dict["single_transformer_blocks.0.proj_out.lora_B.weight"] == 33).all()
+
 
 class FluxTransformerCompileTests(TorchCompileTesterMixin, unittest.TestCase):
     model_class = FluxTransformer2DModel