Awesome
<div align="center"> <h1>CV-VAE: A Compatible Video VAE for Latent Generative Video Models</h1>Sijie Zhao · Yong Zhang* · Xiaodong Cun · Shaoshu Yang · Muyao Niu
Xiaoyu Li · Wenbo Hu · Ying Shan
<sup>*</sup>Corresponding Authors
<a href='https://ailab-cvc.github.io/cvvae/index.html'><img src='https://img.shields.io/badge/Project-Page-green'></a> <a href='https://arxiv.org/abs/2405.20279'><img src='https://img.shields.io/badge/Technique-Report-red'></a>
</div><p align="center"> <img src="assets/i2v_and_t2v_results.gif"> </p>TL; DR: A video VAE for latent generative video models, which is compatible with pretrained image and video models, e.g., SD 2.1 and SVD
News
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2024-10-14 :hugs: We have updated the training code of CV-VAE.
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2024-10-14 We have released the inference code and model weights of CV-VAE-SD3 which is compatible with SD3 and SD3.5.
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2024-10-14 We have updated the CV-VAE with better performance, please check cv-vae-v1-1.
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2024-09-25 CV-VAE is accepted by NeurIPS 2024.
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2024-06-03 We have released the inference code and model weights of CV-VAE.
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2024-05-30 We have updated the arXiv preprint.
Usage
Dependencies
- Python >= 3.8 (Recommend to use Anaconda)
- PyTorch >= 1.13.0
- NVIDIA GPU + CUDA
Video reconstruction
Download the model weight from Hugging Face
python3 cvvae_inference_video.py \
--vae_path MODEL_PATH \
--video_path INPUT_VIDEO_PATH \
--save_path VIDEO_SAVE_PATH \
--height HEIGHT \
--width WIDTH
😉 Citation
@article{zhao2024cvvae,
title={CV-VAE: A Compatible Video VAE for Latent Generative Video Models},
author={Zhao, Sijie and Zhang, Yong and Cun, Xiaodong and Yang, Shaoshu and Niu, Muyao and Li, Xiaoyu and Hu, Wenbo and Shan, Ying},
journal={https://arxiv.org/abs/2405.20279},
year={2024}
}