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Running
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Zero
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from typing import Optional, Tuple
import torch
from diffusers.models.normalization import AdaLayerNorm, AdaLayerNormContinuous, AdaLayerNormZero, SD35AdaLayerNormZeroX
class TruncAdaLayerNorm(AdaLayerNorm):
def forward(
self, x: torch.Tensor, timestep: Optional[torch.Tensor] = None, temb: Optional[torch.Tensor] = None
) -> torch.Tensor:
batch_size = x.shape[0]
return self.forward_old(
x,
temb[:batch_size] if temb is not None else None,
)
class TruncAdaLayerNormContinuous(AdaLayerNormContinuous):
def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:
batch_size = x.shape[0]
return self.forward_old(x, conditioning_embedding[:batch_size])
class TruncAdaLayerNormZero(AdaLayerNormZero):
def forward(
self,
x: torch.Tensor,
timestep: Optional[torch.Tensor] = None,
class_labels: Optional[torch.LongTensor] = None,
hidden_dtype: Optional[torch.dtype] = None,
emb: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
batch_size = x.shape[0]
return self.forward_old(
x,
timestep[:batch_size] if timestep is not None else None,
class_labels[:batch_size] if class_labels is not None else None,
hidden_dtype,
emb[:batch_size] if emb is not None else None,
)
class TruncSD35AdaLayerNormZeroX(SD35AdaLayerNormZeroX):
def forward(
self,
hidden_states: torch.Tensor,
emb: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, ...]:
batch_size = hidden_states.shape[0]
return self.forward_old(
hidden_states,
emb[:batch_size] if emb is not None else None,
)
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