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propose a deep compression VAE for videos, achieving 16x16 spatial and 8x temporal compression ratios. Feb 23, 2025 · 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. Jan 13, 2025 · 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 Jan 21, 2025 · 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 May 8, 2025 · Customized video generation aims to produce videos featuring specific subjects under flexible user-defined conditions, yet existing methods often struggle with identity consistency and limited input modalities. In this paper, we propose HunyuanCustom, a multi-modal customized video generation 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. It can generate 30 FPS videos at 1216×704 resolution, faster than it takes to watch them. Sequence Conditioning – Allows motion interpolation from a given frame sequence, enabling video extension from the beginning, end, or middle of the original video. Prompt Enhancer – A new node that helps generate prompts optimized for the best model performance. See the Example Workflows section for more details. Contribute to pandaUrf/video-mms development by

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