#
AutoencoderKLHunyuanImageRefiner

The 3D variational autoencoder (VAE) model with KL loss used in HunyuanImage2.1 for its refiner pipeline.

The model can be loaded with the following code snippet.

    from diffusers import AutoencoderKLHunyuanImageRefiner

    vae = AutoencoderKLHunyuanImageRefiner.from_pretrained("hunyuanvideo-community/HunyuanImage-2.1-Refiner-Diffusers", subfolder="vae", torch_dtype=torch.bfloat16)

##
AutoencoderKLHunyuanImageRefiner

###
class diffusers.AutoencoderKLHunyuanImageRefiner

< source >

( in_channels: int = 3 out_channels: int = 3 latent_channels: int = 32 block_out_channels: typing.Tuple[int, ...] = (128, 256, 512, 1024, 1024) layers_per_block: int = 2 spatial_compression_ratio: int = 16 temporal_compression_ratio: int = 4 downsample_match_channel: bool = True upsample_match_channel: bool = True scaling_factor: float = 1.03682 )

A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos. Used for HunyuanImage-2.1 Refiner.

This model inherits from ModelMixin. Check the superclass documentation for it’s generic methods implemented for all models (such as downloading or saving).

####
wrapper

< source >

( *args **kwargs )

####
disable_slicing

< source >

( )

Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing decoding in one step.

####
disable_tiling

< source >

( )

Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing decoding in one step.

####
enable_slicing

< source >

( )

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.

####
enable_tiling

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( tile_sample_min_height: typing.Optional[int] = None tile_sample_min_width: typing.Optional[int] = None tile_sample_stride_height: typing.Optional[float] = None tile_sample_stride_width: typing.Optional[float] = None tile_overlap_factor: typing.Optional[float] = None )

Parameters

- tile_sample_min_height (`int`, optional) — The minimum height required for a sample to be separated into tiles across the height dimension.
- tile_sample_min_width (`int`, optional) — The minimum width required for a sample to be separated into tiles across the width dimension.
- tile_sample_stride_height (`int`, optional) — The minimum amount of overlap between two consecutive vertical tiles. This is to ensure that there are no tiling artifacts produced across the height dimension.
- tile_sample_stride_width (`int`, optional) — The stride between two consecutive horizontal tiles. This is to ensure that there are no tiling artifacts produced across the width dimension.

Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images.

####
forward

< source >

( sample: Tensor sample_posterior: bool = False return_dict: bool = True generator: typing.Optional[torch._C.Generator] = None )

Parameters

- sample (`torch.Tensor`) — Input sample.
- sample_posterior (`bool`, optional, defaults to `False`) — Whether to sample from the posterior.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a `DecoderOutput` instead of a plain tuple.

####
tiled_decode

< source >

( z: Tensor ) →
`~models.vae.DecoderOutput` or `tuple`

Parameters

- z (`torch.Tensor`) — Input batch of latent vectors.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a `~models.vae.DecoderOutput` instead of a plain tuple.

Returns

`~models.vae.DecoderOutput` or `tuple`

If return_dict is True, a `~models.vae.DecoderOutput` is returned, otherwise a plain `tuple` is returned.

Decode a batch of images using a tiled decoder.

####
tiled_encode

< source >

( x: Tensor ) →
`torch.Tensor`

Parameters

- x (`torch.Tensor`) — Input batch of videos.

Returns

`torch.Tensor`

The latent representation of the encoded videos.

Encode a batch of images using a tiled encoder.

##
DecoderOutput

###
class diffusers.models.autoencoders.vae.DecoderOutput

< source >

( sample: Tensor commit_loss: typing.Optional[torch.FloatTensor] = None )

Parameters

- sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`) — The decoded output sample from the last layer of the model.

Output of decoding method.

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