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[core] remove unneeded autoencoder methods when subclassing from AutoencoderMixin#12873

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Merged
yiyixuxu merged 1 commit into from
Jan 6, 2026
Merged

[core] remove unneeded autoencoder methods when subclassing from AutoencoderMixin#12873

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25 changes: 2 additions & 23 deletions src/diffusers/models/autoencoders/autoencoder_kl_hunyuanimage.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -27,7 +27,7 @@
from ..activations import get_activation
from ..modeling_outputs import AutoencoderKLOutput
from ..modeling_utils import ModelMixin
from .vae import DecoderOutput, DiagonalGaussianDistribution
from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution


logger = logging.get_logger(__name__) # pylint: disable=invalid-name
Expand DownExpand Up@@ -410,7 +410,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
return h


class AutoencoderKLHunyuanImage(ModelMixin, ConfigMixin, FromOriginalModelMixin):
class AutoencoderKLHunyuanImage(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModelMixin):
r"""
A VAE model for 2D images with spatial tiling support.

Expand DownExpand Up@@ -486,27 +486,6 @@ def enable_tiling(
self.tile_overlap_factor = tile_overlap_factor or self.tile_overlap_factor
self.tile_latent_min_size = self.tile_sample_min_size // self.config.spatial_compression_ratio

def disable_tiling(self) -> None:
r"""
Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
decoding in one step.
"""
self.use_tiling = False

def enable_slicing(self) -> None:
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.use_slicing = True

def disable_slicing(self) -> None:
r"""
Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
decoding in one step.
"""
self.use_slicing = False

def _encode(self, x: torch.Tensor):

batch_size, num_channels, height, width = x.shape
Expand Down
25 changes: 2 additions & 23 deletions src/diffusers/models/autoencoders/autoencoder_kl_hunyuanimage_refiner.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -26,7 +26,7 @@
from ..activations import get_activation
from ..modeling_outputs import AutoencoderKLOutput
from ..modeling_utils import ModelMixin
from .vae import DecoderOutput, DiagonalGaussianDistribution
from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution


logger = logging.get_logger(__name__) # pylint: disable=invalid-name
Expand DownExpand Up@@ -584,7 +584,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
return hidden_states


class AutoencoderKLHunyuanImageRefiner(ModelMixin, ConfigMixin):
class AutoencoderKLHunyuanImageRefiner(ModelMixin, AutoencoderMixin, ConfigMixin):
r"""
A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos. Used for
HunyuanImage-2.1 Refiner.
Expand DownExpand Up@@ -685,27 +685,6 @@ def enable_tiling(
self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
self.tile_overlap_factor = tile_overlap_factor or self.tile_overlap_factor

def disable_tiling(self) -> None:
r"""
Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
decoding in one step.
"""
self.use_tiling = False

def enable_slicing(self) -> None:
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.use_slicing = True

def disable_slicing(self) -> None:
r"""
Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
decoding in one step.
"""
self.use_slicing = False

def _encode(self, x: torch.Tensor) -> torch.Tensor:
_, _, _, height, width = x.shape

Expand Down
25 changes: 2 additions & 23 deletions src/diffusers/models/autoencoders/autoencoder_kl_hunyuanvideo15.py
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Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -26,7 +26,7 @@
from ..activations import get_activation
from ..modeling_outputs import AutoencoderKLOutput
from ..modeling_utils import ModelMixin
from .vae import DecoderOutput, DiagonalGaussianDistribution
from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution


logger = logging.get_logger(__name__) # pylint: disable=invalid-name
Expand DownExpand Up@@ -625,7 +625,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
return hidden_states


class AutoencoderKLHunyuanVideo15(ModelMixin, ConfigMixin):
class AutoencoderKLHunyuanVideo15(ModelMixin, AutoencoderMixin, ConfigMixin):
r"""
A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos. Used for
HunyuanVideo-1.5.
Expand DownExpand Up@@ -723,27 +723,6 @@ def enable_tiling(
self.tile_latent_min_width = tile_latent_min_width or self.tile_latent_min_width
self.tile_overlap_factor = tile_overlap_factor or self.tile_overlap_factor

def disable_tiling(self) -> None:
r"""
Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
decoding in one step.
"""
self.use_tiling = False

def enable_slicing(self) -> None:
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.use_slicing = True

def disable_slicing(self) -> None:
r"""
Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
decoding in one step.
"""
self.use_slicing = False

def _encode(self, x: torch.Tensor) -> torch.Tensor:
_, _, _, height, width = x.shape

Expand Down
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