diffusers/pr_11739/en/api/models/autoencoderkl_cogvideox.md

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[[diffusers.AutoencoderKLCogVideoX]]

diffusers.AutoencoderKLCogVideoX[[diffusers.AutoencoderKLCogVideoX]]

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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).

wrapperdiffusers.AutoencoderKLCogVideoX.decodehttps://github.com/huggingface/diffusers/blob/vr_11739/src/diffusers/utils/accelerate_utils.py#L43[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]

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 1.15258426) : 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 losing too much precision in which case force_upcast can be set to False - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix

wrapper[[diffusers.AutoencoderKLCogVideoX.encode]]

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enable_tiling[[diffusers.AutoencoderKLCogVideoX.enable_tiling]]

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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.

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_overlap_factor_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. Must be between 0 and 1. Setting a higher value might cause more tiles to be processed leading to slow down of the decoding process.

tile_overlap_factor_width (int, optional) : The minimum amount of overlap between two consecutive horizontal tiles. This is to ensure that there are no tiling artifacts produced across the width dimension. Must be between 0 and 1. Setting a higher value might cause more tiles to be processed leading to slow down of the decoding process.

tiled_decode[[diffusers.AutoencoderKLCogVideoX.tiled_decode]]

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Decode a batch of images using a tiled decoder.

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.

tiled_encode[[diffusers.AutoencoderKLCogVideoX.tiled_encode]]

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Encode a batch of images using a tiled encoder.

When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several steps. This is useful to keep memory use constant regardless of image size. The end result of tiled encoding is different from non-tiled encoding because each tile uses a different encoder. To avoid tiling artifacts, the tiles overlap and are blended together to form a smooth output. You may still see tile-sized changes in the output, but they should be much less noticeable.

Parameters:

x (torch.Tensor) : Input batch of videos.

Returns:

torch.Tensor

The latent representation of the encoded videos.

AutoencoderKLOutput[[diffusers.models.modeling_outputs.AutoencoderKLOutput]]

diffusers.models.modeling_outputs.AutoencoderKLOutput[[diffusers.models.modeling_outputs.AutoencoderKLOutput]]

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Output of AutoencoderKL encoding method.

Parameters:

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

DecoderOutput[[diffusers.models.autoencoders.vae.DecoderOutput]]

diffusers.models.autoencoders.vae.DecoderOutput[[diffusers.models.autoencoders.vae.DecoderOutput]]

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Output of decoding method.

Parameters:

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