Oral Fri, Apr 24, 2026 • 7:06 AM – 7:16 AM PDT 201 A/B

NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale

chunrui han ⋅ Guopeng Li ⋅ Jingwei Wu ⋅ Quan Sun ⋅ Yan Cai ⋅ Yuang Peng ⋅ Zheng Ge ⋅ Deyu Zhou ⋅ Haomiao Tang ⋅ Hongyu Zhou ⋅ Kenkun Liu ⋅ Shu-Tao Xia ⋅ Binxing Jiao ⋅ Daxin Jiang ⋅ Xiangyu Zhang ⋅ Yibo Zhu

Abstract

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, trained 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 have released our code and models to the community at https://github.com/stepfun-ai/NextStep-1.