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feat(autoencoders): add patchify support for AutoencoderKLWan #12180

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feat(autoencoders): add patchify support for AutoencoderKLWan #12180

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8 changes: 8 additions & 0 deletions src/diffusers/models/autoencoders/autoencoder_kl_wan.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1289,6 +1289,10 @@ def tiled_encode(self, x: torch.Tensor) -> AutoencoderKLOutput:
blend_height = tile_latent_min_height - tile_latent_stride_height
blend_width = tile_latent_min_width - tile_latent_stride_width

# Apply patchify if patch_size is specified
if self.config.patch_size is not None:
x = patchify(x, patch_size=self.config.patch_size)

# Split x into overlapping tiles and encode them separately.
# The tiles have an overlap to avoid seams between tiles.
rows = []
Expand Down Expand Up @@ -1392,6 +1396,10 @@ def tiled_decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[Decod

dec = torch.cat(result_rows, dim=3)[:, :, :, :sample_height, :sample_width]

# Apply unpatchify if patch_size is specified
if self.config.patch_size is not None:
dec = unpatchify(dec, patch_size=self.config.patch_size)

if not return_dict:
return (dec,)
return DecoderOutput(sample=dec)
Expand Down