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AutoencoderKLCogVideoX

docs/source/en/api/models/autoencoderkl_cogvideox.md

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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.

python
from diffusers import AutoencoderKLCogVideoX

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

AutoencoderKLCogVideoX

[[autodoc]] AutoencoderKLCogVideoX - decode - encode - all

AutoencoderKLOutput

[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput

DecoderOutput

[[autodoc]] models.autoencoders.vae.DecoderOutput