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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:
Wan: Open and Advanced Large-Scale Video Generative Models Visit their webpage for more details LightX2V, a lightweight and efficient video generation framework that integrates Wan2 1 and Wan2 2, supporting multiple engineering acceleration techniques for fast inference LightX2V-HuggingFace, offers a variety of Wan-based step-distillation models, quantized models, and lightweight VAE models
YouTube Help - Google Help Learn more about YouTube YouTube help videos Browse our video library for helpful tips, feature overviews, and step-by-step tutorials YouTube Known Issues Get information on reported technical issues or scheduled maintenance
Find videos in Search - Google Help You can find video results for most searches on Google Search To help you find specific info, some videos are tagged with Key Moments Key Moments work like chapters in a book to help you find the info you want Important: Key Moments are added by video creators, or in some cases Google may detect the content and add Key Moments automatically
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
Video-R1: Reinforcing Video Reasoning in MLLMs - GitHub Our Video-R1-7B obtain strong performance on several video reasoning benchmarks For example, Video-R1-7B attains a 35 8% accuracy on video spatial reasoning benchmark VSI-bench, surpassing the commercial proprietary model GPT-4o
GitHub - DepthAnything Video-Depth-Anything: [CVPR 2025 Highlight . . . This work presents Video Depth Anything based on Depth Anything V2, which can be applied to arbitrarily long videos without compromising quality, consistency, or generalization ability Compared with other diffusion-based models, it enjoys faster inference speed, fewer parameters, and higher consistent depth accuracy