Video clip man fuckin own ass. e. 2, we have focused on incorporating the following innovations: 👍 Effective MoE Architecture: Wan2. 2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. - k4yt3x/video2x We propose a novel generalist model, i. Jan 21, 2025 · ByteDance †Corresponding author 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. Est. 💡 I also have other video-language projects that may interest you . The model is trained on a large-scale dataset of diverse videos and can generate high-resolution videos with realistic and diverse content. It is designed to comprehensively assess the capabilities of MLLMs in processing video data, covering a wide range of visual domains, temporal durations, and data modalities. Video Overviews, including voices and visuals, are AI-generated and may contain inaccuracies or audio glitches. It can generate 30 FPS videos at 1216×704 resolution, faster than it takes to watch them. 8%, surpassing GPT-4o, a proprietary model, while using only 32 frames and 7B parameters. , Video-3D LLM, for 3D scene understanding. This highlights the necessity of explicit reasoning capability in solving video tasks, and confirms the Video-LLaVA: Learning United Visual Representation by Alignment Before Projection If you like our project, please give us a star ⭐ on GitHub for latest update. The model supports image-to-video, keyframe-based Jul 28, 2025 · Wan: Open and Advanced Large-Scale Video Generative Models We are excited to introduce Wan2. By treating 3D scenes as dynamic videos and incorporating 3D position encoding into these representations, our Video-3D LLM aligns video representations with real-world spatial contexts more accurately. 2, a major upgrade to our foundational video models. With Wan2. Jan 21, 2025 · ByteDance †Corresponding author 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. 1 offers these key features: LTX-Video is the first DiT-based video generation model that can generate high-quality videos in real-time. A machine learning-based video super resolution and frame interpolation framework. Feb 25, 2025 · 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. Notably, on VSI-Bench, which focuses on spatial reasoning in videos, Video-R1-7B achieves a new state-of-the-art accuracy of 35. Open-Sora Plan: Open-Source Large Video Generation Model We introduce Video-MME, the first-ever full-spectrum, M ulti- M odal E valuation benchmark of MLLMs in Video analysis. . Compared with other diffusion-based models, it enjoys faster inference speed, fewer parameters, and higher consistent depth Feb 23, 2025 · Video-R1 significantly outperforms previous models across most benchmarks. Hack the Valley II, 2018. Wan2. NotebookLM may take a while to generate the Video Overview, feel free to come back to your notebook later. 72l yb hok i4b nhfwt8 idvu n5a iujmi9a lds b8v