#
AutoencoderKLCogVideoX

The 3D variational autoencoder (VAE) model with KL loss used in CogVideoX was introduced in CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer by Tsinghua University & ZhipuAI.

The model can be loaded with the following code snippet.

    from diffusers import AutoencoderKLCogVideoX

    vae = AutoencoderKLCogVideoX.from_pretrained("THUDM/CogVideoX-2b", subfolder="vae", torch_dtype=torch.float16).to("cuda")

##
AutoencoderKLCogVideoX

###
class diffusers.AutoencoderKLCogVideoX

< source >

( in_channels: int = 3 out_channels: int = 3 down_block_types: Tuple = ('CogVideoXDownBlock3D', 'CogVideoXDownBlock3D', 'CogVideoXDownBlock3D', 'CogVideoXDownBlock3D') up_block_types: Tuple = ('CogVideoXUpBlock3D', 'CogVideoXUpBlock3D', 'CogVideoXUpBlock3D', 'CogVideoXUpBlock3D') block_out_channels: Tuple = (128, 256, 256, 512) latent_channels: int = 16 layers_per_block: int = 3 act_fn: str = 'silu' norm_eps: float = 1e-06 norm_num_groups: int = 32 temporal_compression_ratio: float = 4 sample_size: int = 256 scaling_factor: float = 1.15258426 shift_factor: Optional = None latents_mean: Optional = None latents_std: Optional = None force_upcast: float = True use_quant_conv: bool = False use_post_quant_conv: bool = False )

Parameters

- in_channels (int, optional, defaults to 3) — Number of channels in the input image.
- out_channels (int, optional, defaults to 3) — Number of channels in the output.
- down_block_types (`Tuple[str]`, optional, defaults to `("DownEncoderBlock2D",)`) — Tuple of downsample block types.
- up_block_types (`Tuple[str]`, optional, defaults to `("UpDecoderBlock2D",)`) — Tuple of upsample block types.
- block_out_channels (`Tuple[int]`, optional, defaults to `(64,)`) — Tuple of block output channels.
- act_fn (`str`, optional, defaults to `"silu"`) — The activation function to use.
- sample_size (`int`, optional, defaults to `32`) — Sample input size.
- scaling_factor (`float`, optional, defaults to 0.18215) — The component-wise standard deviation of the trained latent space computed using the first batch of the training set. This is used to scale the latent space to have unit variance when training the diffusion model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1 / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the High-Resolution Image Synthesis with Latent Diffusion Models paper.
- force_upcast (`bool`, optional, default to `True`) — If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE can be fine-tuned / trained to a lower range without loosing too much precision in which case `force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix

A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in CogVideoX.

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 )

##
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: Optional = 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