Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models [CVPR 2024]
| Date | Stars |
|---|---|
| 2026-07-24 | 314 |
| 2026-07-25 | 314 |
| 2026-07-28 | 314 |
| 2026-07-30 | 314 |
| 2026-08-06 | 314 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
### RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models - Official Repo ### CVPR 2024 (Highlight) [Ozgur Kara](https://karaozgur.com/), [Bariscan Kurtkaya](https://bariscankurtkaya.github.io/), [Hidir Yesiltepe](https://sites.google.com/view/hidir-yesiltepe), [James M. Rehg](https://scholar.google.com/citations?hl=en&user=8kA3eDwAAAAJ), [Pinar Yanardag](https://scholar.google.com/citations?user=qzczdd8AAAAJ&hl=en) <a href="https://huggingface.co/spaces/ozgurkara/RAVE"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm-dark.svg" alt="Web Demo"> <a href='https://arxiv.org/abs/2312.04524'><img src='https://img.shields.io/badge/ArXiv-2312.04524-red'></a> <a href='https://rave-video.github.io/'><img src='https://img.shields.io/badge/Project-Page-green'></a> <a href='https://youtu.be/2hQho5AC9T0?si=3R_jYDbcL2olODCV'><img src='https://img.shields.io/badge/YouTube-red?style=for-the-badge&logo=youtube&logoColor=white'></a> <a href='https://rave-video.github.io/supp/supp.html'><img src='https://img.shields.io/badge/Supplementary-Page-yellow'></a> [](https://github.com/rehg-lab/RAVE)   (Note that the videos on GitHub are heavily compressed. The full videos are available on the project webpage.) ## Abstract <b>TL; DR:</b> RAVE is a zero-shot, lightweight, and fast framework for text-guided video editing, supporting videos of any length utilizing text-to-image pretrained diffusion models. <details><summary>Click for the full abstract</summary> > 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 method that leverages pre-trained text-to-image diffusion models without additional training. RAVE takes an input video and a text prompt to produce high-quality videos while preserving the original motion and semantic structure. It employs a novel noise shuffling strategy, leveraging spatio-temporal interactions between frames, to produce temporally consistent videos faster than existing methods. It is also efficient in terms of memory requirements, allowing it to handle longer videos. RAVE is capable of a wide range of edits, from local attribute modifications to shape transformations. In order to demonstrate the versatility of RAVE, we create a comprehensive video evaluation dataset ranging from object-focused scenes to complex human activities like dancing and typing, and dynamic scenes featuring swimming fish and boats. Our qualitative and quantitative experiments highlight the effectiveness of RAVE in diverse video editing scenarios compared to existing methods. </details> <br> **Features**: - *Zero-shot framework* - *Working fast* - *No restriction on video length* - *Standardized dataset for evaluating text-guided video-editing methods* - *Compatible with off-the-shelf pre-trained approaches (e.g. [CivitAI](https://civitai.com/))* ## Updates - [12/2023] Gradio demo is released, HuggingFace Space demo will be released soon - [12/2023] Paper is available on ArXiv, project webpage is ready and code is released. ### TODO - [ ] Share the dataset - [X] Add more examples - [X] Optimize preprocessing - [X] Add CivitAI models to Grad.io - [X] ~~Prepare a grad.io based GUI~~ - [X] ~~Integrate MultiControlNet~~ - [X] ~~Adapt CIVIT AI models~~ ## Installation and Inference ### Setup Environment Please install our environment using 'requirements.txt' file as: ```shell conda create -n rave python=3.8 conda activate rave conda install pip pip cache purge pip install -r requirements.txt ``
Excerpt of 8,349 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:20063ea94f4ed983, topic:video-editing, desc:video editing, readme:video editing
matched fp:20063ea94f4ed983, topic:stable-diffusion, readme:text-to-image