NextStep-1: Toward Autoregressive Image Generation with Continuous
  Tokens at Scale
Authors: name: NextStep Team, name: Chunrui Han, name: Guopeng Li, name: Jingwei Wu, name: Quan Sun, name: Yan Cai, name: Yuang Peng, name: Zheng Ge, name: Deyu Zhou, name: Haomiao Tang, name: Hongyu Zhou, name: Kenkun Liu, name: Ailin Huang, name: Bin Wang, name: Changxin Miao, name: Deshan Sun, name: En Yu, name: Fukun Yin, name: Gang Yu, name: Hao Nie, name: Haoran Lv, name: Hanpeng Hu, name: Jia Wang, name: Jian Zhou, name: Jianjian Sun, name: Kaijun Tan, name: Kang An, name: Kangheng Lin, name: Liang Zhao, name: Mei Chen, name: Peng Xing, name: Rui Wang, name: Shiyu Liu, name: Shutao Xia, name: Tianhao You, name: Wei Ji, name: Xianfang Zeng, name: Xin Han, name: Xuelin Zhang, name: Yana Wei, name: Yanming Xu, name: Yimin Jiang, name: Yingming Wang, name: Yu Zhou, name: Yucheng Han, name: Ziyang Meng, name: Binxing Jiao, name: Daxin Jiang, name: Xiangyu Zhang, name: Yibo Zhu

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.