lucataco/rave

RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models

  • Public
  • 154 runs

Run time and cost

This model runs on Nvidia A40 (Large) GPU hardware. Predictions typically complete within 144 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Ozgur Kara, Bariscan Kurtkaya, Hidir Yesiltepe, James M. Rehg, Pinar Yanardag

GitHub

Web Demo

Abstract

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.

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.


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)