#VideoCrafter2

An open-source video creation toolbox using advanced diffusion models to improve motion and concept integration. Supports text-to-video and image-to-video generation, offering better quality even with limited data. Engage with a creative community on Discord.

VADER improves video-text alignment and video creation efficiency using discriminative-based, pre-trained reward models, overcoming the need for large datasets. This method not only extends video generation to three times the sequence length but also surpasses non-gradient methods. It optimizes video models such as VideoCrafter2, Open-Sora V1.2, and ModelScope for streamlined production.

Delve into innovative methods that transform text into infinite video outputs without training requirements, utilizing minimal VRAM for greater accessibility. The project supports VideoCrafter2 for single GPU use and Open-Sora Plan for distributed inference, catering to varied creative applications with reduced computational load. Uncover advancements in video generation demonstrated at NeurIPS 2024.