#
AutoencoderKLMagvit

The 3D variational autoencoder (VAE) model with KL loss used in EasyAnimate was introduced by Alibaba PAI.

The model can be loaded with the following code snippet.

    from diffusers import AutoencoderKLMagvit

    vae = AutoencoderKLMagvit.from_pretrained("alibaba-pai/EasyAnimateV5.1-12b-zh", subfolder="vae", torch_dtype=torch.float16).to("cuda")

##
AutoencoderKLMagvit

###
class diffusers.AutoencoderKLMagvit

< source >

( in_channels: int = 3 latent_channels: int = 16 out_channels: int = 3 block_out_channels: typing.Tuple[int, ...] = [128, 256, 512, 512] down_block_types: typing.Tuple[str, ...] = ['SpatialDownBlock3D', 'SpatialTemporalDownBlock3D', 'SpatialTemporalDownBlock3D', 'SpatialTemporalDownBlock3D'] up_block_types: typing.Tuple[str, ...] = ['SpatialUpBlock3D', 'SpatialTemporalUpBlock3D', 'SpatialTemporalUpBlock3D', 'SpatialTemporalUpBlock3D'] layers_per_block: int = 2 act_fn: str = 'silu' norm_num_groups: int = 32 scaling_factor: float = 0.7125 spatial_group_norm: bool = True )

A VAE model with KL loss for encoding images into latents and decoding latent representations into images. This model is used in EasyAnimate.

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 )

####
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

< source >

( tile_sample_min_height: typing.Optional[int] = None tile_sample_min_width: typing.Optional[int] = None tile_sample_min_num_frames: typing.Optional[int] = None tile_sample_stride_height: typing.Optional[float] = None tile_sample_stride_width: typing.Optional[float] = None tile_sample_stride_num_frames: 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.

##
AutoencoderKLOutput

###
class diffusers.models.modeling_outputs.AutoencoderKLOutput

< source >

( latent_dist: DiagonalGaussianDistribution )

Parameters

- latent_dist (`DiagonalGaussianDistribution`) — Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`. `DiagonalGaussianDistribution` allows for sampling latents from the distribution.

Output of AutoencoderKL encoding method.

##
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.

Update on GitHub