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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.23951 |
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| _version_ | 1866910008125423616 |
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| author | Cao, Siyu Chen, Hangting Chen, Peng Cheng, Yiji Cui, Yutao Deng, Xinchi Dong, Ying Gong, Kipper Gu, Tianpeng Gu, Xiusen Hang, Tiankai Huang, Duojun Jiang, Jie Jiang, Zhengkai Kong, Weijie Li, Changlin Li, Donghao Li, Junzhe Li, Xin Li, Yang Li, Zhenxi Li, Zhimin Lin, Jiaxin Linus Liu, Lucaz Liu, Shu Liu, Songtao Liu, Yu Liu, Yuhong Long, Yanxin Lu, Fanbin Lu, Qinglin Peng, Yuyang Peng, Yuanbo Shen, Xiangwei Shi, Yixuan Tao, Jiale Tao, Yangyu Tian, Qi Wan, Pengfei Wang, Chunyu Wang, Kai Wang, Lei Wang, Linqing Wang, Lucas Wang, Qixun Wang, Weiyan Wen, Hao Wu, Bing Wu, Jianbing Wu, Yue Xie, Senhao Yang, Fang Yang, Miles Yang, Xiaofeng Yang, Xuan Yang, Zhantao Yu, Jingmiao Yuan, Zheng Zhang, Chao Zhang, Jian-Wei Zhang, Peizhen Zhang, Shi-Xue Zhang, Tao Zhang, Weigang Zhang, Yepeng Zhang, Yingfang Zhang, Zihao Zhang, Zijian Zhao, Penghao Zhao, Zhiyuan Zhe, Xuefei Zhu, Jianchen Zhong, Zhao |
| author_facet | Cao, Siyu Chen, Hangting Chen, Peng Cheng, Yiji Cui, Yutao Deng, Xinchi Dong, Ying Gong, Kipper Gu, Tianpeng Gu, Xiusen Hang, Tiankai Huang, Duojun Jiang, Jie Jiang, Zhengkai Kong, Weijie Li, Changlin Li, Donghao Li, Junzhe Li, Xin Li, Yang Li, Zhenxi Li, Zhimin Lin, Jiaxin Linus Liu, Lucaz Liu, Shu Liu, Songtao Liu, Yu Liu, Yuhong Long, Yanxin Lu, Fanbin Lu, Qinglin Peng, Yuyang Peng, Yuanbo Shen, Xiangwei Shi, Yixuan Tao, Jiale Tao, Yangyu Tian, Qi Wan, Pengfei Wang, Chunyu Wang, Kai Wang, Lei Wang, Linqing Wang, Lucas Wang, Qixun Wang, Weiyan Wen, Hao Wu, Bing Wu, Jianbing Wu, Yue Xie, Senhao Yang, Fang Yang, Miles Yang, Xiaofeng Yang, Xuan Yang, Zhantao Yu, Jingmiao Yuan, Zheng Zhang, Chao Zhang, Jian-Wei Zhang, Peizhen Zhang, Shi-Xue Zhang, Tao Zhang, Weigang Zhang, Yepeng Zhang, Yingfang Zhang, Zihao Zhang, Zijian Zhao, Penghao Zhao, Zhiyuan Zhe, Xuefei Zhu, Jianchen Zhong, Zhao |
| contents | We present HunyuanImage 3.0, a native multimodal model that unifies multimodal understanding and generation within an autoregressive framework, with its image generation module publicly available. The achievement of HunyuanImage 3.0 relies on several key components, including meticulous data curation, advanced architecture design, a native Chain-of-Thoughts schema, progressive model pre-training, aggressive model post-training, and an efficient infrastructure that enables large-scale training and inference. With these advancements, we successfully trained a Mixture-of-Experts (MoE) model comprising over 80 billion parameters in total, with 13 billion parameters activated per token during inference, making it the largest and most powerful open-source image generative model to date. We conducted extensive experiments and the results of automatic and human evaluation of text-image alignment and visual quality demonstrate that HunyuanImage 3.0 rivals previous state-of-the-art models. By releasing the code and weights of HunyuanImage 3.0, we aim to enable the community to explore new ideas with a state-of-the-art foundation model, fostering a dynamic and vibrant multimodal ecosystem. All open source assets are publicly available at https://github.com/Tencent-Hunyuan/HunyuanImage-3.0 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23951 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HunyuanImage 3.0 Technical Report Cao, Siyu Chen, Hangting Chen, Peng Cheng, Yiji Cui, Yutao Deng, Xinchi Dong, Ying Gong, Kipper Gu, Tianpeng Gu, Xiusen Hang, Tiankai Huang, Duojun Jiang, Jie Jiang, Zhengkai Kong, Weijie Li, Changlin Li, Donghao Li, Junzhe Li, Xin Li, Yang Li, Zhenxi Li, Zhimin Lin, Jiaxin Linus Liu, Lucaz Liu, Shu Liu, Songtao Liu, Yu Liu, Yuhong Long, Yanxin Lu, Fanbin Lu, Qinglin Peng, Yuyang Peng, Yuanbo Shen, Xiangwei Shi, Yixuan Tao, Jiale Tao, Yangyu Tian, Qi Wan, Pengfei Wang, Chunyu Wang, Kai Wang, Lei Wang, Linqing Wang, Lucas Wang, Qixun Wang, Weiyan Wen, Hao Wu, Bing Wu, Jianbing Wu, Yue Xie, Senhao Yang, Fang Yang, Miles Yang, Xiaofeng Yang, Xuan Yang, Zhantao Yu, Jingmiao Yuan, Zheng Zhang, Chao Zhang, Jian-Wei Zhang, Peizhen Zhang, Shi-Xue Zhang, Tao Zhang, Weigang Zhang, Yepeng Zhang, Yingfang Zhang, Zihao Zhang, Zijian Zhao, Penghao Zhao, Zhiyuan Zhe, Xuefei Zhu, Jianchen Zhong, Zhao Computer Vision and Pattern Recognition We present HunyuanImage 3.0, a native multimodal model that unifies multimodal understanding and generation within an autoregressive framework, with its image generation module publicly available. The achievement of HunyuanImage 3.0 relies on several key components, including meticulous data curation, advanced architecture design, a native Chain-of-Thoughts schema, progressive model pre-training, aggressive model post-training, and an efficient infrastructure that enables large-scale training and inference. With these advancements, we successfully trained a Mixture-of-Experts (MoE) model comprising over 80 billion parameters in total, with 13 billion parameters activated per token during inference, making it the largest and most powerful open-source image generative model to date. We conducted extensive experiments and the results of automatic and human evaluation of text-image alignment and visual quality demonstrate that HunyuanImage 3.0 rivals previous state-of-the-art models. By releasing the code and weights of HunyuanImage 3.0, we aim to enable the community to explore new ideas with a state-of-the-art foundation model, fostering a dynamic and vibrant multimodal ecosystem. All open source assets are publicly available at https://github.com/Tencent-Hunyuan/HunyuanImage-3.0 |
| title | HunyuanImage 3.0 Technical Report |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.23951 |