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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
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
HunyuanVideo: A Systematic Framework For Large Video . . . - GitHub We present HunyuanVideo, a novel open-source video foundation model that exhibits performance in video generation that is comparable to, if not superior to, leading closed-source models In order to train HunyuanVideo model, we adopt several key technologies for model learning, including data
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