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remove.

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sayakpaul
2026-01-28 12:16:39 +05:30
parent 458ac949a0
commit 8c402d3a32
11 changed files with 10 additions and 177 deletions
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17
src/diffusers/models/transformers/transformer_chronoedit.py
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@@ -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 USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers
from ...utils import deprecate, logging
from ...utils.torch_utils import maybe_allow_in_graph
from .._modeling_parallel import ContextParallelInput, ContextParallelOutput
from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
@@ -647,21 +647,6 @@ class ChronoEditTransformer3DModel(
return_dict: bool = True,
attention_kwargs: Optional[Dict[str, Any]] = None,
) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
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_channels, num_frames, height, width = hidden_states.shape
p_t, p_h, p_w = self.config.patch_size
post_patch_num_frames = num_frames // p_t

17
src/diffusers/models/transformers/transformer_hunyuan_video.py
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@@ -22,7 +22,7 @@ from diffusers.loaders import FromOriginalModelMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...loaders import PeftAdapterMixin
from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers
from ...utils import logging
from ..attention import AttentionMixin, FeedForward
from ..attention_dispatch import dispatch_attention_fn
from ..attention_processor import Attention
@@ -1000,21 +1000,6 @@ class HunyuanVideoTransformer3DModel(
attention_kwargs: Optional[Dict[str, Any]] = None,
return_dict: bool = True,
) -> Union[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_channels, num_frames, height, width = hidden_states.shape
p, p_t = self.config.patch_size, self.config.patch_size_t
post_patch_num_frames = num_frames // p_t

17
src/diffusers/models/transformers/transformer_hunyuan_video15.py
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@@ -22,7 +22,7 @@ from diffusers.loaders import FromOriginalModelMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...loaders import PeftAdapterMixin
from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers
from ...utils import logging
from ..attention import AttentionMixin, FeedForward
from ..attention_dispatch import dispatch_attention_fn
from ..attention_processor import Attention
@@ -633,21 +633,6 @@ class HunyuanVideo15Transformer3DModel(
attention_kwargs: Optional[Dict[str, Any]] = None,
return_dict: bool = True,
) -> Union[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_channels, num_frames, height, width = hidden_states.shape
p_t, p_h, p_w = self.config.patch_size_t, self.config.patch_size, self.config.patch_size
post_patch_num_frames = num_frames // p_t

17
src/diffusers/models/transformers/transformer_hunyuan_video_framepack.py
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@@ -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 USE_PEFT_BACKEND, get_logger, scale_lora_layers
from ...utils import get_logger
from ..cache_utils import CacheMixin
from ..embeddings import get_1d_rotary_pos_embed
from ..modeling_outputs import Transformer2DModelOutput
@@ -217,21 +217,6 @@ class HunyuanVideoFramepackTransformer3DModel(
attention_kwargs: Optional[Dict[str, Any]] = None,
return_dict: bool = True,
) -> Union[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_channels, num_frames, height, width = hidden_states.shape
p, p_t = self.config.patch_size, self.config.patch_size_t
post_patch_num_frames = num_frames // p_t

17
src/diffusers/models/transformers/transformer_hunyuanimage.py
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@@ -23,7 +23,7 @@ from diffusers.loaders import FromOriginalModelMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...loaders import PeftAdapterMixin
from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers
from ...utils import logging
from ...utils.torch_utils import maybe_allow_in_graph
from ..attention import AttentionMixin, FeedForward
from ..attention_dispatch import dispatch_attention_fn
@@ -755,21 +755,6 @@ class HunyuanImageTransformer2DModel(
attention_kwargs: Optional[Dict[str, Any]] = None,
return_dict: bool = True,
) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
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."
)
if hidden_states.ndim == 4:
batch_size, channels, height, width = hidden_states.shape
sizes = (height, width)

17
src/diffusers/models/transformers/transformer_ltx.py
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@@ -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 USE_PEFT_BACKEND, deprecate, is_torch_version, logging, scale_lora_layers
from ...utils import 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
@@ -505,21 +505,6 @@ class LTXVideoTransformer3DModel(
attention_kwargs: Optional[Dict[str, Any]] = None,
return_dict: bool = True,
) -> torch.Tensor:
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."
)
image_rotary_emb = self.rope(hidden_states, num_frames, height, width, rope_interpolation_scale, video_coords)
# convert encoder_attention_mask to a bias the same way we do for attention_mask

17
src/diffusers/models/transformers/transformer_ltx2.py
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@@ -23,11 +23,9 @@ import torch.nn as nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
from ...utils import (
USE_PEFT_BACKEND,
BaseOutput,
is_torch_version,
logging,
scale_lora_layers,
)
from .._modeling_parallel import ContextParallelInput, ContextParallelOutput
from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
@@ -1170,21 +1168,6 @@ class LTX2VideoTransformer3DModel(
`tuple` is returned where the first element is the denoised video latent patch sequence and the second
element is the denoised audio latent patch sequence.
"""
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."
)
# Determine timestep for audio.
audio_timestep = audio_timestep if audio_timestep is not None else timestep

17
src/diffusers/models/transformers/transformer_skyreels_v2.py
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@@ -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 USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers
from ...utils import deprecate, logging
from ...utils.torch_utils import maybe_allow_in_graph
from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
from ..attention_dispatch import dispatch_attention_fn
@@ -641,21 +641,6 @@ class SkyReelsV2Transformer3DModel(
return_dict: bool = True,
attention_kwargs: Optional[Dict[str, Any]] = None,
) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
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_channels, num_frames, height, width = hidden_states.shape
p_t, p_h, p_w = self.config.patch_size
post_patch_num_frames = num_frames // p_t

17
src/diffusers/models/transformers/transformer_wan.py
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@@ -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 USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers
from ...utils import deprecate, logging
from ...utils.torch_utils import maybe_allow_in_graph
from .._modeling_parallel import ContextParallelInput, ContextParallelOutput
from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
@@ -631,21 +631,6 @@ class WanTransformer3DModel(
return_dict: bool = True,
attention_kwargs: Optional[Dict[str, Any]] = None,
) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
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_channels, num_frames, height, width = hidden_states.shape
p_t, p_h, p_w = self.config.patch_size
post_patch_num_frames = num_frames // p_t

17
src/diffusers/models/transformers/transformer_wan_animate.py
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@@ -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 USE_PEFT_BACKEND, logging, scale_lora_layers
from ...utils import logging
from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
from ..attention_dispatch import dispatch_attention_fn
from ..cache_utils import CacheMixin
@@ -1178,21 +1178,6 @@ class WanAnimateTransformer3DModel(
Whether to return the output as a dict or tuple.
"""
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."
)
# Check that shapes match up
if pose_hidden_states is not None and pose_hidden_states.shape[2] + 1 != hidden_states.shape[2]:
raise ValueError(

17
src/diffusers/models/transformers/transformer_wan_vace.py
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@@ -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 USE_PEFT_BACKEND, logging, scale_lora_layers
from ...utils import logging
from ..attention import AttentionMixin, FeedForward
from ..cache_utils import CacheMixin
from ..modeling_outputs import Transformer2DModelOutput
@@ -272,21 +272,6 @@ class WanVACETransformer3DModel(
return_dict: bool = True,
attention_kwargs: Optional[Dict[str, Any]] = None,
) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
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_channels, num_frames, height, width = hidden_states.shape
p_t, p_h, p_w = self.config.patch_size
post_patch_num_frames = num_frames // p_t