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b9bc22bd57
* docs: annotate hardcoded cuda device with alternatives in English docs * docs: annotate hardcoded device_map="cuda" with alternatives * update Signed-off-by: kaixuanliu <kaixuan.liu@intel.com> --------- Signed-off-by: kaixuanliu <kaixuan.liu@intel.com>
1.4 KiB
1.4 KiB
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", dtype=torch.float16).to("cuda") # or "mps", "xpu", "cpu"
AutoencoderKLCogVideoX
autodoc AutoencoderKLCogVideoX - decode - encode - all
AutoencoderKLOutput
autodoc models.autoencoders.autoencoder_kl.AutoencoderKLOutput
DecoderOutput
autodoc models.autoencoders.vae.DecoderOutput