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Wan: Open and Advanced Large-Scale Video Generative Models Wan: Open and Advanced Large-Scale Video Generative Models In this repository, we present Wan2 1, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation Wan2 1 offers these key features:
Video-R1: Reinforcing Video Reasoning in MLLMs - GitHub Video-R1 significantly outperforms previous models across most benchmarks Notably, on VSI-Bench, which focuses on spatial reasoning in videos, Video-R1-7B achieves a new state-of-the-art accuracy of 35 8%, surpassing GPT-4o, a proprietary model, while using only 32 frames and 7B parameters This highlights the necessity of explicit reasoning capability in solving video tasks, and confirms the
GitHub - Lightricks LTX-Video: Official repository for LTX-Video LTX-Video is the first DiT-based video generation model that can generate high-quality videos in real-time It can generate 30 FPS videos at 1216×704 resolution, faster than it takes to watch them The model is trained on a large-scale dataset of diverse videos and can generate high-resolution videos with realistic and diverse content The model supports image-to-video, keyframe-based
WEIFENG2333 VideoCaptioner: 卡卡字幕助手 - GitHub About 🎬 卡卡字幕助手 | VideoCaptioner - 基于 LLM 的智能字幕助手 - 视频字幕生成、断句、校正、字幕翻译全流程处理！ - A powered tool for easy and efficient video subtitling
GitHub - visomaster VisoMaster: Powerful Easy-to-Use Video Face . . . VisoMaster is a powerful yet easy-to-use tool for face swapping and editing in images and videos It utilizes AI to produce natural-looking results with minimal effort, making it ideal for both casual users and professionals
Troubleshoot YouTube video errors - Google Help Check the YouTube video’s resolution and the recommended speed needed to play the video The table below shows the approximate speeds recommended to play each video resolution