HunyuanVideo: A Systematic Framework For Large Video Generative Models
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916649129476096 |
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| author | Kong, Weijie Tian, Qi Zhang, Zijian Min, Rox Dai, Zuozhuo Zhou, Jin Xiong, Jiangfeng Li, Xin Wu, Bo Zhang, Jianwei Wu, Kathrina Lin, Qin Yuan, Junkun Long, Yanxin Wang, Aladdin Wang, Andong Li, Changlin Huang, Duojun Yang, Fang Tan, Hao Wang, Hongmei Song, Jacob Bai, Jiawang Wu, Jianbing Xue, Jinbao Wang, Joey Wang, Kai Liu, Mengyang Li, Pengyu Li, Shuai Wang, Weiyan Yu, Wenqing Deng, Xinchi Li, Yang Chen, Yi Cui, Yutao Peng, Yuanbo Yu, Zhentao He, Zhiyu Xu, Zhiyong Zhou, Zixiang Xu, Zunnan Tao, Yangyu Lu, Qinglin Liu, Songtao Zhou, Dax Wang, Hongfa Yang, Yong Wang, Di Liu, Yuhong Jiang, Jie Zhong, Caesar |
| author_facet | Kong, Weijie Tian, Qi Zhang, Zijian Min, Rox Dai, Zuozhuo Zhou, Jin Xiong, Jiangfeng Li, Xin Wu, Bo Zhang, Jianwei Wu, Kathrina Lin, Qin Yuan, Junkun Long, Yanxin Wang, Aladdin Wang, Andong Li, Changlin Huang, Duojun Yang, Fang Tan, Hao Wang, Hongmei Song, Jacob Bai, Jiawang Wu, Jianbing Xue, Jinbao Wang, Joey Wang, Kai Liu, Mengyang Li, Pengyu Li, Shuai Wang, Weiyan Yu, Wenqing Deng, Xinchi Li, Yang Chen, Yi Cui, Yutao Peng, Yuanbo Yu, Zhentao He, Zhiyu Xu, Zhiyong Zhou, Zixiang Xu, Zunnan Tao, Yangyu Lu, Qinglin Liu, Songtao Zhou, Dax Wang, Hongfa Yang, Yong Wang, Di Liu, Yuhong Jiang, Jie Zhong, Caesar |
| contents | Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-source, resulting in a notable performance gap between industry capabilities and those available to the public. In this report, we introduce HunyuanVideo, an innovative open-source video foundation model that demonstrates performance in video generation comparable to, or even surpassing, that of leading closed-source models. HunyuanVideo encompasses a comprehensive framework that integrates several key elements, including data curation, advanced architectural design, progressive model scaling and training, and an efficient infrastructure tailored for large-scale model training and inference. As a result, we successfully trained a video generative model with over 13 billion parameters, making it the largest among all open-source models. We conducted extensive experiments and implemented a series of targeted designs to ensure high visual quality, motion dynamics, text-video alignment, and advanced filming techniques. According to evaluations by professionals, HunyuanVideo outperforms previous state-of-the-art models, including Runway Gen-3, Luma 1.6, and three top-performing Chinese video generative models. By releasing the code for the foundation model and its applications, we aim to bridge the gap between closed-source and open-source communities. This initiative will empower individuals within the community to experiment with their ideas, fostering a more dynamic and vibrant video generation ecosystem. The code is publicly available at https://github.com/Tencent/HunyuanVideo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03603 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HunyuanVideo: A Systematic Framework For Large Video Generative Models Kong, Weijie Tian, Qi Zhang, Zijian Min, Rox Dai, Zuozhuo Zhou, Jin Xiong, Jiangfeng Li, Xin Wu, Bo Zhang, Jianwei Wu, Kathrina Lin, Qin Yuan, Junkun Long, Yanxin Wang, Aladdin Wang, Andong Li, Changlin Huang, Duojun Yang, Fang Tan, Hao Wang, Hongmei Song, Jacob Bai, Jiawang Wu, Jianbing Xue, Jinbao Wang, Joey Wang, Kai Liu, Mengyang Li, Pengyu Li, Shuai Wang, Weiyan Yu, Wenqing Deng, Xinchi Li, Yang Chen, Yi Cui, Yutao Peng, Yuanbo Yu, Zhentao He, Zhiyu Xu, Zhiyong Zhou, Zixiang Xu, Zunnan Tao, Yangyu Lu, Qinglin Liu, Songtao Zhou, Dax Wang, Hongfa Yang, Yong Wang, Di Liu, Yuhong Jiang, Jie Zhong, Caesar Computer Vision and Pattern Recognition Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-source, resulting in a notable performance gap between industry capabilities and those available to the public. In this report, we introduce HunyuanVideo, an innovative open-source video foundation model that demonstrates performance in video generation comparable to, or even surpassing, that of leading closed-source models. HunyuanVideo encompasses a comprehensive framework that integrates several key elements, including data curation, advanced architectural design, progressive model scaling and training, and an efficient infrastructure tailored for large-scale model training and inference. As a result, we successfully trained a video generative model with over 13 billion parameters, making it the largest among all open-source models. We conducted extensive experiments and implemented a series of targeted designs to ensure high visual quality, motion dynamics, text-video alignment, and advanced filming techniques. According to evaluations by professionals, HunyuanVideo outperforms previous state-of-the-art models, including Runway Gen-3, Luma 1.6, and three top-performing Chinese video generative models. By releasing the code for the foundation model and its applications, we aim to bridge the gap between closed-source and open-source communities. This initiative will empower individuals within the community to experiment with their ideas, fostering a more dynamic and vibrant video generation ecosystem. The code is publicly available at https://github.com/Tencent/HunyuanVideo. |
| title | HunyuanVideo: A Systematic Framework For Large Video Generative Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.03603 |