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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.10711 |
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| _version_ | 1866911109009637376 |
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| author | NextStep Team Han, Chunrui Li, Guopeng Wu, Jingwei Sun, Quan Cai, Yan Peng, Yuang Ge, Zheng Zhou, Deyu Tang, Haomiao Zhou, Hongyu Liu, Kenkun Huang, Ailin Wang, Bin Miao, Changxin Sun, Deshan Yu, En Yin, Fukun Yu, Gang Nie, Hao Lv, Haoran Hu, Hanpeng Wang, Jia Zhou, Jian Sun, Jianjian Tan, Kaijun An, Kang Lin, Kangheng Zhao, Liang Chen, Mei Xing, Peng Wang, Rui Liu, Shiyu Xia, Shutao You, Tianhao Ji, Wei Zeng, Xianfang Han, Xin Zhang, Xuelin Wei, Yana Xu, Yanming Jiang, Yimin Wang, Yingming Zhou, Yu Han, Yucheng Meng, Ziyang Jiao, Binxing Jiang, Daxin Zhang, Xiangyu Zhu, Yibo |
| author_facet | NextStep Team Han, Chunrui Li, Guopeng Wu, Jingwei Sun, Quan Cai, Yan Peng, Yuang Ge, Zheng Zhou, Deyu Tang, Haomiao Zhou, Hongyu Liu, Kenkun Huang, Ailin Wang, Bin Miao, Changxin Sun, Deshan Yu, En Yin, Fukun Yu, Gang Nie, Hao Lv, Haoran Hu, Hanpeng Wang, Jia Zhou, Jian Sun, Jianjian Tan, Kaijun An, Kang Lin, Kangheng Zhao, Liang Chen, Mei Xing, Peng Wang, Rui Liu, Shiyu Xia, Shutao You, Tianhao Ji, Wei Zeng, Xianfang Han, Xin Zhang, Xuelin Wei, Yana Xu, Yanming Jiang, Yimin Wang, Yingming Zhou, Yu Han, Yucheng Meng, Ziyang Jiao, Binxing Jiang, Daxin Zhang, Xiangyu Zhu, Yibo |
| contents | Prevailing autoregressive (AR) models for text-to-image generation either rely on heavy, computationally-intensive diffusion models to process continuous image tokens, or employ vector quantization (VQ) to obtain discrete tokens with quantization loss. In this paper, we push the autoregressive paradigm forward with NextStep-1, a 14B autoregressive model paired with a 157M flow matching head, training on discrete text tokens and continuous image tokens with next-token prediction objectives. NextStep-1 achieves state-of-the-art performance for autoregressive models in text-to-image generation tasks, exhibiting strong capabilities in high-fidelity image synthesis. Furthermore, our method shows strong performance in image editing, highlighting the power and versatility of our unified approach. To facilitate open research, we will release our code and models to the community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10711 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale NextStep Team Han, Chunrui Li, Guopeng Wu, Jingwei Sun, Quan Cai, Yan Peng, Yuang Ge, Zheng Zhou, Deyu Tang, Haomiao Zhou, Hongyu Liu, Kenkun Huang, Ailin Wang, Bin Miao, Changxin Sun, Deshan Yu, En Yin, Fukun Yu, Gang Nie, Hao Lv, Haoran Hu, Hanpeng Wang, Jia Zhou, Jian Sun, Jianjian Tan, Kaijun An, Kang Lin, Kangheng Zhao, Liang Chen, Mei Xing, Peng Wang, Rui Liu, Shiyu Xia, Shutao You, Tianhao Ji, Wei Zeng, Xianfang Han, Xin Zhang, Xuelin Wei, Yana Xu, Yanming Jiang, Yimin Wang, Yingming Zhou, Yu Han, Yucheng Meng, Ziyang Jiao, Binxing Jiang, Daxin Zhang, Xiangyu Zhu, Yibo Computer Vision and Pattern Recognition Prevailing autoregressive (AR) models for text-to-image generation either rely on heavy, computationally-intensive diffusion models to process continuous image tokens, or employ vector quantization (VQ) to obtain discrete tokens with quantization loss. In this paper, we push the autoregressive paradigm forward with NextStep-1, a 14B autoregressive model paired with a 157M flow matching head, training on discrete text tokens and continuous image tokens with next-token prediction objectives. NextStep-1 achieves state-of-the-art performance for autoregressive models in text-to-image generation tasks, exhibiting strong capabilities in high-fidelity image synthesis. Furthermore, our method shows strong performance in image editing, highlighting the power and versatility of our unified approach. To facilitate open research, we will release our code and models to the community. |
| title | NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.10711 |