Nested AutoRegressive Models

• URL: http://arxiv.org/abs/2510.23028v1
• Date: Mon, 27 Oct 2025 05:49:02 GMT
• Title: Nested AutoRegressive Models
• Authors: Hongyu Wu, Xuhui Fan, Zhangkai Wu, Longbing Cao,
• Abstract summary: We propose a Nested AutoRegressive(NestAR) model, which proposes nested AutoRegressive architectures in generating images.<n>NestAR achieves competitive image generation performance while significantly lowering computational cost.
• Score: 31.60548236936739
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AutoRegressive (AR) models have demonstrated competitive performance in image generation, achieving results comparable to those of diffusion models. However, their token-by-token image generation mechanism remains computationally intensive and existing solutions such as VAR often lead to limited sample diversity. In this work, we propose a Nested AutoRegressive~(NestAR) model, which proposes nested AutoRegressive architectures in generating images. NestAR designs multi-scale modules in a hierarchical order. These different scaled modules are constructed in an AR architecture, where one larger-scale module is conditioned on outputs from its previous smaller-scale module. Within each module, NestAR uses another AR structure to generate ``patches'' of tokens. The proposed nested AR architecture reduces the overall complexity from $\mathcal{O}(n)$ to $\mathcal{O}(\log n)$ in generating $n$ image tokens, as well as increases image diversities. NestAR further incorporates flow matching loss to use continuous tokens, and develops objectives to coordinate these multi-scale modules in model training. NestAR achieves competitive image generation performance while significantly lowering computational cost.

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