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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
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
DepthAnything Video-Depth-Anything - GitHub 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
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ai-video-generator · GitHub Topics · GitHub GitHub is where people build software More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects
GitHub - THUDM CogVideo: text and image to video generation: CogVideoX . . . VideoTuna: VideoTuna is the first repo that integrates multiple AI video generation models for text-to-video, image-to-video, text-to-image generation ConsisID: An identity-preserving text-to-video generation model, bases on CogVideoX-5B, which keep the face consistent in the generated video by frequency decomposition