端到端自回归图像生成：基于一维语义标记器的实现

End-to-End Autoregressive Image Generation with 1D Semantic Tokenizer

May 1, 2026

作者: Wenda Chu, Bingliang Zhang, Jiaqi Han, Yizhuo Li, Linjie Yang, Yisong Yue, Qiushan Guo

cs.AI

摘要

自回归图像建模依赖视觉分词器将图像压缩为紧凑的潜在表征。我们设计了端到端的训练流程，通过联合优化重建与生成任务，使生成结果能直接对分词器产生监督信号。这与先前分阶段训练分词器与生成模型的方法形成鲜明对比。我们进一步探索利用视觉基础模型来优化适用于自回归建模的一维分词器。实验表明，我们的自回归生成模型取得了显著成效，在ImageNet 256×256生成任务上无需引导即达到了1.48的最新FID指标。

English

Autoregressive image modeling relies on visual tokenizers to compress images into compact latent representations. We design an end-to-end training pipeline that jointly optimizes reconstruction and generation, enabling direct supervision from generation results to the tokenizer. This contrasts with prior two-stage approaches that train tokenizers and generative models separately. We further investigate leveraging vision foundation models to improve 1D tokenizers for autoregressive modeling. Our autoregressive generative model achieves strong empirical results, including a state-of-the-art FID score of 1.48 without guidance on ImageNet 256x256 generation.