Merge branch 'main' into custom-code-updates

Author: DN6

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/__init__.py

@@ -139,6 +139,7 @@
             "AutoGuidance",
             "ClassifierFreeGuidance",
             "ClassifierFreeZeroStarGuidance",
+            "FrequencyDecoupledGuidance",
             "PerturbedAttentionGuidance",
             "SkipLayerGuidance",
             "SmoothedEnergyGuidance",
@@ -804,6 +805,7 @@
             AutoGuidance,
             ClassifierFreeGuidance,
             ClassifierFreeZeroStarGuidance,
+            FrequencyDecoupledGuidance,
             PerturbedAttentionGuidance,
             SkipLayerGuidance,
             SmoothedEnergyGuidance,

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/guiders/__init__.py

@@ -22,6 +22,7 @@
     from .auto_guidance import AutoGuidance
     from .classifier_free_guidance import ClassifierFreeGuidance
     from .classifier_free_zero_star_guidance import ClassifierFreeZeroStarGuidance
+    from .frequency_decoupled_guidance import FrequencyDecoupledGuidance
     from .perturbed_attention_guidance import PerturbedAttentionGuidance
     from .skip_layer_guidance import SkipLayerGuidance
     from .smoothed_energy_guidance import SmoothedEnergyGuidance
@@ -32,6 +33,7 @@
         AutoGuidance,
         ClassifierFreeGuidance,
         ClassifierFreeZeroStarGuidance,
+        FrequencyDecoupledGuidance,
         PerturbedAttentionGuidance,
         SkipLayerGuidance,
         SmoothedEnergyGuidance,

src/diffusers/guiders/frequency_decoupled_guidance.py

@@ -133,6 +133,7 @@ def _register_attention_processors_metadata():
             skip_processor_output_fn=_skip_proc_output_fn_Attention_WanAttnProcessor2_0,
         ),
     )
+
     # FluxAttnProcessor
     AttentionProcessorRegistry.register(
         model_class=FluxAttnProcessor,

src/diffusers/hooks/_helpers.py

@@ -0,0 +1,43 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 torch
+
+from ._common import _ALL_TRANSFORMER_BLOCK_IDENTIFIERS, _ATTENTION_CLASSES, _FEEDFORWARD_CLASSES
+
+
+def _get_identifiable_transformer_blocks_in_module(module: torch.nn.Module):
+    module_list_with_transformer_blocks = []
+    for name, submodule in module.named_modules():
+        name_endswith_identifier = any(name.endswith(identifier) for identifier in _ALL_TRANSFORMER_BLOCK_IDENTIFIERS)
+        is_modulelist = isinstance(submodule, torch.nn.ModuleList)
+        if name_endswith_identifier and is_modulelist:
+            module_list_with_transformer_blocks.append((name, submodule))
+    return module_list_with_transformer_blocks
+
+
+def _get_identifiable_attention_layers_in_module(module: torch.nn.Module):
+    attention_layers = []
+    for name, submodule in module.named_modules():
+        if isinstance(submodule, _ATTENTION_CLASSES):
+            attention_layers.append((name, submodule))
+    return attention_layers
+
+
+def _get_identifiable_feedforward_layers_in_module(module: torch.nn.Module):
+    feedforward_layers = []
+    for name, submodule in module.named_modules():
+        if isinstance(submodule, _FEEDFORWARD_CLASSES):
+            feedforward_layers.append((name, submodule))
+    return feedforward_layers

src/diffusers/hooks/utils.py

@@ -817,7 +817,11 @@ def _convert(original_key, diffusers_key, state_dict, new_state_dict):
     # has both `peft` and non-peft state dict.
     has_peft_state_dict = any(k.startswith("transformer.") for k in state_dict)
     if has_peft_state_dict:
-        state_dict = {k: v for k, v in state_dict.items() if k.startswith("transformer.")}
+        state_dict = {
+            k.replace("lora_down.weight", "lora_A.weight").replace("lora_up.weight", "lora_B.weight"): v
+            for k, v in state_dict.items()
+            if k.startswith("transformer.")
+        }
         return state_dict
 
     # Another weird one.

src/diffusers/loaders/lora_conversion_utils.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/loaders/peft.py

@@ -384,7 +384,7 @@ def forward(
         temb: torch.Tensor,
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-    ) -> torch.Tensor:
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         text_seq_len = encoder_hidden_states.shape[1]
         hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)