fix

src/diffusers/models/transformers/transformer_chroma.py

@@ -21,7 +21,7 @@ import torch.nn as nn
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FluxTransformer2DLoadersMixin, FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import deprecate, logging
+from ...utils import apply_lora_scale, deprecate, logging
 from ...utils.import_utils import is_torch_npu_available
 from ...utils.torch_utils import maybe_allow_in_graph
 from ..attention import AttentionMixin, FeedForward
@@ -473,6 +473,7 @@ class ChromaTransformer2DModel(
 
         self.gradient_checkpointing = False
 
+    @apply_lora_scale("joint_attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,

src/diffusers/models/transformers/transformer_chronoedit.py

@@ -21,7 +21,7 @@ import torch.nn.functional as F
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import deprecate, logging
+from ...utils import apply_lora_scale, deprecate, logging
 from ...utils.torch_utils import maybe_allow_in_graph
 from .._modeling_parallel import ContextParallelInput, ContextParallelOutput
 from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
@@ -638,6 +638,7 @@ class ChronoEditTransformer3DModel(
 
         self.gradient_checkpointing = False
 
+    @apply_lora_scale("attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,

src/diffusers/models/transformers/transformer_glm_image.py

@@ -20,7 +20,7 @@ import torch.nn.functional as F
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin
-from ...utils import apply_lora_scale, logging
+from ...utils import logging
 from ...utils.torch_utils import maybe_allow_in_graph
 from ..attention import FeedForward
 from ..attention_dispatch import dispatch_attention_fn
@@ -595,7 +595,6 @@ class GlmImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, Cach
 
         self.gradient_checkpointing = False
 
-    @apply_lora_scale("attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,

src/diffusers/models/transformers/transformer_hunyuan_video.py

@@ -22,7 +22,7 @@ from diffusers.loaders import FromOriginalModelMixin
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin
-from ...utils import logging
+from ...utils import apply_lora_scale, logging
 from ..attention import AttentionMixin, FeedForward
 from ..attention_dispatch import dispatch_attention_fn
 from ..attention_processor import Attention
@@ -989,6 +989,7 @@ class HunyuanVideoTransformer3DModel(
 
         self.gradient_checkpointing = False
 
+    @apply_lora_scale("attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,

src/diffusers/models/transformers/transformer_hunyuan_video15.py

@@ -22,7 +22,7 @@ from diffusers.loaders import FromOriginalModelMixin
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin
-from ...utils import logging
+from ...utils import apply_lora_scale, logging
 from ..attention import AttentionMixin, FeedForward
 from ..attention_dispatch import dispatch_attention_fn
 from ..attention_processor import Attention
@@ -620,6 +620,7 @@ class HunyuanVideo15Transformer3DModel(
 
         self.gradient_checkpointing = False
 
+    @apply_lora_scale("attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,

src/diffusers/models/transformers/transformer_hunyuan_video_framepack.py

@@ -20,7 +20,7 @@ import torch.nn.functional as F
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import get_logger
+from ...utils import apply_lora_scale, get_logger
 from ..cache_utils import CacheMixin
 from ..embeddings import get_1d_rotary_pos_embed
 from ..modeling_outputs import Transformer2DModelOutput
@@ -198,6 +198,7 @@ class HunyuanVideoFramepackTransformer3DModel(
 
         self.gradient_checkpointing = False
 
+    @apply_lora_scale("attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,

src/diffusers/models/transformers/transformer_hunyuanimage.py

@@ -23,7 +23,7 @@ from diffusers.loaders import FromOriginalModelMixin
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import PeftAdapterMixin
-from ...utils import logging
+from ...utils import apply_lora_scale, logging
 from ...utils.torch_utils import maybe_allow_in_graph
 from ..attention import AttentionMixin, FeedForward
 from ..attention_dispatch import dispatch_attention_fn
@@ -742,6 +742,7 @@ class HunyuanImageTransformer2DModel(
 
         self.gradient_checkpointing = False
 
+    @apply_lora_scale("attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,

src/diffusers/models/transformers/transformer_ltx.py

@@ -22,7 +22,7 @@ import torch.nn as nn
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import deprecate, is_torch_version, logging
+from ...utils import apply_lora_scale, deprecate, is_torch_version, logging
 from ...utils.torch_utils import maybe_allow_in_graph
 from .._modeling_parallel import ContextParallelInput, ContextParallelOutput
 from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
@@ -491,6 +491,7 @@ class LTXVideoTransformer3DModel(
 
         self.gradient_checkpointing = False
 
+    @apply_lora_scale("attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,

src/diffusers/models/transformers/transformer_ltx2.py

@@ -22,11 +22,7 @@ import torch.nn as nn
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import (
-    BaseOutput,
-    is_torch_version,
-    logging,
-)
+from ...utils import BaseOutput, apply_lora_scale, is_torch_version, logging
 from .._modeling_parallel import ContextParallelInput, ContextParallelOutput
 from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
 from ..attention_dispatch import dispatch_attention_fn
@@ -1098,6 +1094,7 @@ class LTX2VideoTransformer3DModel(
 
         self.gradient_checkpointing = False
 
+    @apply_lora_scale("attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,

src/diffusers/models/transformers/transformer_qwenimage.py

@@ -24,7 +24,7 @@ import torch.nn.functional as F
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers
+from ...utils import apply_lora_scale, deprecate, logging
 from ...utils.torch_utils import maybe_allow_in_graph
 from .._modeling_parallel import ContextParallelInput, ContextParallelOutput
 from ..attention import AttentionMixin, FeedForward
@@ -829,6 +829,7 @@ class QwenImageTransformer2DModel(
         self.gradient_checkpointing = False
         self.zero_cond_t = zero_cond_t
 
+    @apply_lora_scale("attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -887,20 +888,6 @@ class QwenImageTransformer2DModel(
                 "The mask-based approach is more flexible and supports variable-length sequences.",
                 standard_warn=False,
             )
-        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 `joint_attention_kwargs` when not using the PEFT backend is ineffective."
-                )
 
         hidden_states = self.img_in(hidden_states)
 

src/diffusers/models/transformers/transformer_skyreels_v2.py

@@ -21,7 +21,7 @@ import torch.nn.functional as F
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import deprecate, logging
+from ...utils import apply_lora_scale, deprecate, logging
 from ...utils.torch_utils import maybe_allow_in_graph
 from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
 from ..attention_dispatch import dispatch_attention_fn
@@ -630,6 +630,7 @@ class SkyReelsV2Transformer3DModel(
 
         self.gradient_checkpointing = False
 
+    @apply_lora_scale("attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,

src/diffusers/models/transformers/transformer_wan.py

@@ -21,7 +21,7 @@ import torch.nn.functional as F
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import deprecate, logging
+from ...utils import apply_lora_scale, deprecate, logging
 from ...utils.torch_utils import maybe_allow_in_graph
 from .._modeling_parallel import ContextParallelInput, ContextParallelOutput
 from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
@@ -622,6 +622,7 @@ class WanTransformer3DModel(
 
         self.gradient_checkpointing = False
 
+    @apply_lora_scale("attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,

src/diffusers/models/transformers/transformer_wan_animate.py

@@ -21,7 +21,7 @@ import torch.nn.functional as F
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import logging
+from ...utils import apply_lora_scale, logging
 from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
 from ..attention_dispatch import dispatch_attention_fn
 from ..cache_utils import CacheMixin
@@ -1140,6 +1140,7 @@ class WanAnimateTransformer3DModel(
 
         self.gradient_checkpointing = False
 
+    @apply_lora_scale("attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,

src/diffusers/models/transformers/transformer_wan_vace.py

@@ -20,7 +20,7 @@ import torch.nn as nn
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import logging
+from ...utils import apply_lora_scale, logging
 from ..attention import AttentionMixin, FeedForward
 from ..cache_utils import CacheMixin
 from ..modeling_outputs import Transformer2DModelOutput
@@ -261,6 +261,7 @@ class WanVACETransformer3DModel(
 
         self.gradient_checkpointing = False
 
+    @apply_lora_scale("attention_kwargs")
     def forward(
         self,
         hidden_states: torch.Tensor,