feat: implement apply_lora_scale to remove boilerplate.

src/diffusers/models/controlnets/controlnet.py

@@ -21,7 +21,7 @@
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin
 from ...loaders.single_file_model import FromOriginalModelMixin
-from ...utils import BaseOutput, logging
+from ...utils import BaseOutput, apply_lora_scale, logging
 from ..attention import AttentionMixin
 from ..attention_processor import (
     ADDED_KV_ATTENTION_PROCESSORS,
@@ -598,6 +598,7 @@ def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: list[i
         for module in self.children():
             fn_recursive_set_attention_slice(module, reversed_slice_size)
 
+    @apply_lora_scale("cross_attention_kwargs")
     def forward(
         self,
         sample: torch.Tensor,

src/diffusers/models/controlnets/controlnet_flux.py

@@ -20,7 +20,11 @@
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin
-from ...utils import USE_PEFT_BACKEND, BaseOutput, logging, scale_lora_layers, unscale_lora_layers
+from ...utils import (
+    BaseOutput,
+    apply_lora_scale,
+    logging,
+)
 from ..attention import AttentionMixin
 from ..controlnets.controlnet import ControlNetConditioningEmbedding, zero_module
 from ..embeddings import CombinedTimestepGuidanceTextProjEmbeddings, CombinedTimestepTextProjEmbeddings, FluxPosEmbed
@@ -150,6 +154,7 @@ def from_transformer(
 
         return controlnet
 
+    @apply_lora_scale("joint_attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -197,20 +202,6 @@ def forward(
             If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
             `tuple` where the first element is the sample tensor.
         """
-        if joint_attention_kwargs is not None:
-            joint_attention_kwargs = joint_attention_kwargs.copy()
-            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
-        else:
-            lora_scale = 1.0
-
-        if USE_PEFT_BACKEND:
-            # weight the lora layers by setting `lora_scale` for each PEFT layer
-            scale_lora_layers(self, lora_scale)
-        else:
-            if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
-                logger.warning(
-                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
-                )
         hidden_states = self.x_embedder(hidden_states)
 
         if self.input_hint_block is not None:
@@ -323,10 +314,6 @@ def forward(
             None if len(controlnet_single_block_samples) == 0 else controlnet_single_block_samples
         )
 
-        if USE_PEFT_BACKEND:
-            # remove `lora_scale` from each PEFT layer
-            unscale_lora_layers(self, lora_scale)
-
         if not return_dict:
             return (controlnet_block_samples, controlnet_single_block_samples)
 

src/diffusers/models/controlnets/controlnet_qwenimage.py

@@ -20,7 +20,12 @@
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers
+from ...utils import (
+    BaseOutput,
+    apply_lora_scale,
+    deprecate,
+    logging,
+)
 from ..attention import AttentionMixin
 from ..cache_utils import CacheMixin
 from ..controlnets.controlnet import zero_module
@@ -123,6 +128,7 @@ def from_transformer(
 
         return controlnet
 
+    @apply_lora_scale("joint_attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -181,20 +187,6 @@ def forward(
                 standard_warn=False,
             )
 
-        if joint_attention_kwargs is not None:
-            joint_attention_kwargs = joint_attention_kwargs.copy()
-            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
-        else:
-            lora_scale = 1.0
-
-        if USE_PEFT_BACKEND:
-            # weight the lora layers by setting `lora_scale` for each PEFT layer
-            scale_lora_layers(self, lora_scale)
-        else:
-            if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
-                logger.warning(
-                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
-                )
         hidden_states = self.img_in(hidden_states)
 
         # add
@@ -256,10 +248,6 @@ def forward(
         controlnet_block_samples = [sample * conditioning_scale for sample in controlnet_block_samples]
         controlnet_block_samples = None if len(controlnet_block_samples) == 0 else controlnet_block_samples
 
-        if USE_PEFT_BACKEND:
-            # remove `lora_scale` from each PEFT layer
-            unscale_lora_layers(self, lora_scale)
-
         if not return_dict:
             return controlnet_block_samples
 

src/diffusers/models/controlnets/controlnet_sana.py

@@ -20,7 +20,7 @@
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin
-from ...utils import USE_PEFT_BACKEND, BaseOutput, logging, scale_lora_layers, unscale_lora_layers
+from ...utils import BaseOutput, apply_lora_scale, logging
 from ..attention import AttentionMixin
 from ..embeddings import PatchEmbed, PixArtAlphaTextProjection
 from ..modeling_outputs import Transformer2DModelOutput
@@ -117,6 +117,7 @@ def __init__(
 
         self.gradient_checkpointing = False
 
+    @apply_lora_scale("attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -129,21 +130,6 @@ def forward(
         attention_kwargs: dict[str, Any] | None = None,
         return_dict: bool = True,
     ) -> tuple[torch.Tensor, ...] | Transformer2DModelOutput:
-        if attention_kwargs is not None:
-            attention_kwargs = attention_kwargs.copy()
-            lora_scale = attention_kwargs.pop("scale", 1.0)
-        else:
-            lora_scale = 1.0
-
-        if USE_PEFT_BACKEND:
-            # weight the lora layers by setting `lora_scale` for each PEFT layer
-            scale_lora_layers(self, lora_scale)
-        else:
-            if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
-                logger.warning(
-                    "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
-                )
-
         # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
         #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
         #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
@@ -218,10 +204,6 @@ def forward(
             block_res_sample = controlnet_block(block_res_sample)
             controlnet_block_res_samples = controlnet_block_res_samples + (block_res_sample,)
 
-        if USE_PEFT_BACKEND:
-            # remove `lora_scale` from each PEFT layer
-            unscale_lora_layers(self, lora_scale)
-
         controlnet_block_res_samples = [sample * conditioning_scale for sample in controlnet_block_res_samples]
 
         if not return_dict:

src/diffusers/models/controlnets/controlnet_sd3.py

@@ -21,7 +21,7 @@
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
+from ...utils import apply_lora_scale, logging
 from ..attention import AttentionMixin, JointTransformerBlock
 from ..attention_processor import Attention, FusedJointAttnProcessor2_0
 from ..embeddings import CombinedTimestepTextProjEmbeddings, PatchEmbed
@@ -269,6 +269,7 @@ def from_transformer(
 
         return controlnet
 
+    @apply_lora_scale("joint_attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -308,21 +309,6 @@ def forward(
             If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
             `tuple` where the first element is the sample tensor.
         """
-        if joint_attention_kwargs is not None:
-            joint_attention_kwargs = joint_attention_kwargs.copy()
-            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
-        else:
-            lora_scale = 1.0
-
-        if USE_PEFT_BACKEND:
-            # weight the lora layers by setting `lora_scale` for each PEFT layer
-            scale_lora_layers(self, lora_scale)
-        else:
-            if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
-                logger.warning(
-                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
-                )
-
         if self.pos_embed is not None and hidden_states.ndim != 4:
             raise ValueError("hidden_states must be 4D when pos_embed is used")
 
@@ -382,10 +368,6 @@ def forward(
         # 6. scaling
         controlnet_block_res_samples = [sample * conditioning_scale for sample in controlnet_block_res_samples]
 
-        if USE_PEFT_BACKEND:
-            # remove `lora_scale` from each PEFT layer
-            unscale_lora_layers(self, lora_scale)
-
         if not return_dict:
             return (controlnet_block_res_samples,)
 

src/diffusers/models/transformers/auraflow_transformer_2d.py

@@ -21,7 +21,7 @@
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
+from ...utils import apply_lora_scale, logging
 from ...utils.torch_utils import maybe_allow_in_graph
 from ..attention import AttentionMixin
 from ..attention_processor import (
@@ -397,6 +397,7 @@ def unfuse_qkv_projections(self):
         if self.original_attn_processors is not None:
             self.set_attn_processor(self.original_attn_processors)
 
+    @apply_lora_scale("attention_kwargs")
     def forward(
         self,
         hidden_states: torch.FloatTensor,
@@ -405,21 +406,6 @@ def forward(
         attention_kwargs: dict[str, Any] | None = None,
         return_dict: bool = True,
     ) -> tuple[torch.Tensor] | Transformer2DModelOutput:
-        if attention_kwargs is not None:
-            attention_kwargs = attention_kwargs.copy()
-            lora_scale = attention_kwargs.pop("scale", 1.0)
-        else:
-            lora_scale = 1.0
-
-        if USE_PEFT_BACKEND:
-            # weight the lora layers by setting `lora_scale` for each PEFT layer
-            scale_lora_layers(self, lora_scale)
-        else:
-            if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
-                logger.warning(
-                    "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
-                )
-
         height, width = hidden_states.shape[-2:]
 
         # Apply patch embedding, timestep embedding, and project the caption embeddings.
@@ -486,10 +472,6 @@ def forward(
             shape=(hidden_states.shape[0], out_channels, height * patch_size, width * patch_size)
         )
 
-        if USE_PEFT_BACKEND:
-            # remove `lora_scale` from each PEFT layer
-            unscale_lora_layers(self, lora_scale)
-
         if not return_dict:
             return (output,)
 

src/diffusers/models/transformers/cogvideox_transformer_3d.py

@@ -20,7 +20,7 @@
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin
-from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
+from ...utils import apply_lora_scale, logging
 from ...utils.torch_utils import maybe_allow_in_graph
 from ..attention import Attention, AttentionMixin, FeedForward
 from ..attention_processor import CogVideoXAttnProcessor2_0, FusedCogVideoXAttnProcessor2_0
@@ -363,6 +363,7 @@ def unfuse_qkv_projections(self):
         if self.original_attn_processors is not None:
             self.set_attn_processor(self.original_attn_processors)
 
+    @apply_lora_scale("attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -374,21 +375,6 @@ def forward(
         attention_kwargs: dict[str, Any] | None = None,
         return_dict: bool = True,
     ) -> tuple[torch.Tensor] | Transformer2DModelOutput:
-        if attention_kwargs is not None:
-            attention_kwargs = attention_kwargs.copy()
-            lora_scale = attention_kwargs.pop("scale", 1.0)
-        else:
-            lora_scale = 1.0
-
-        if USE_PEFT_BACKEND:
-            # weight the lora layers by setting `lora_scale` for each PEFT layer
-            scale_lora_layers(self, lora_scale)
-        else:
-            if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
-                logger.warning(
-                    "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
-                )
-
         batch_size, num_frames, channels, height, width = hidden_states.shape
 
         # 1. Time embedding
@@ -454,10 +440,6 @@ def forward(
             )
             output = output.permute(0, 1, 5, 4, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(1, 2)
 
-        if USE_PEFT_BACKEND:
-            # remove `lora_scale` from each PEFT layer
-            unscale_lora_layers(self, lora_scale)
-
         if not return_dict:
             return (output,)
         return Transformer2DModelOutput(sample=output)

src/diffusers/models/transformers/consisid_transformer_3d.py

@@ -20,7 +20,7 @@
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin
-from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
+from ...utils import apply_lora_scale, logging
 from ...utils.torch_utils import maybe_allow_in_graph
 from ..attention import Attention, AttentionMixin, FeedForward
 from ..attention_processor import CogVideoXAttnProcessor2_0
@@ -620,6 +620,7 @@ def _init_face_inputs(self):
             ]
         )
 
+    @apply_lora_scale("attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -632,21 +633,6 @@ def forward(
         id_vit_hidden: torch.Tensor | None = None,
         return_dict: bool = True,
     ) -> tuple[torch.Tensor] | Transformer2DModelOutput:
-        if attention_kwargs is not None:
-            attention_kwargs = attention_kwargs.copy()
-            lora_scale = attention_kwargs.pop("scale", 1.0)
-        else:
-            lora_scale = 1.0
-
-        if USE_PEFT_BACKEND:
-            # weight the lora layers by setting `lora_scale` for each PEFT layer
-            scale_lora_layers(self, lora_scale)
-        else:
-            if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
-                logger.warning(
-                    "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
-                )
-
         # fuse clip and insightface
         valid_face_emb = None
         if self.is_train_face:
@@ -720,10 +706,6 @@ def forward(
         output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, -1, p, p)
         output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
 
-        if USE_PEFT_BACKEND:
-            # remove `lora_scale` from each PEFT layer
-            unscale_lora_layers(self, lora_scale)
-
         if not return_dict:
             return (output,)
         return Transformer2DModelOutput(sample=output)