RAVE has been released!

New preprint alert! Introducing RAVE - a zero-shot, lightweight, and fast framework for text-guided video editing, supporting videos of any length utilizing text-to-image pretrained diffusion models.
**Project Webpage:** [https://rave-video.github.io](https://rave-video.github.io/)
**ArXiv**: [https://arxiv.org/abs/2312.04524](https://arxiv.org/abs/2312.04524)
**More Examples**: [https://rave-video.github.io/supp/supp.html](https://rave-video.github.io/supp/supp.html)
**Code**: [https://github.com/rehg-lab/RAVE](https://github.com/rehg-lab/RAVE)
**Demo**: [https://github.com/rehg-lab/RAVE/blob/main/demo\_notebook.ipynb](https://github.com/rehg-lab/RAVE/blob/main/demo_notebook.ipynb)
**Abstract:**


>Recent advancements in diffusion-based models have demonstrated significant success in generating images from text. However, video editing models have not yet reached the same level of visual quality and user control. To address this, we introduce RAVE, a zero-shot video editing …

abstract alert computervision diffusion diffusion models editing framework image images quality success text text-to-image video videos visual