Title: D-AR: Diffusion via Autoregressive Models
Primary area: generative models
Keywords: visual generation, diffusion models, autoregressive models, flow matching

Abstract: This paper introduces Diffusion via Autoregressive (D-AR) models, a new paradigm recasting the pixel diffusion process as a vanilla autoregressive procedure in the standard next-token-prediction fashion. We start by designing the tokenizer that converts an image into the sequence of discrete tokens, where tokens in different positions can be decoded into different diffusion denoising steps in the pixel space. Thanks to the diffusion property, these tokens naturally follow a coarse-to-fine order, which directly lends itself to autoregressive modeling. Then, we apply standard next-token prediction to these tokens, without modifying any underlying designs (either causal masks or training/inference strategies), and such sequential autoregressive token generation directly mirrors the diffusion procedure in image space. That is, once the autoregressive model generates an increment of tokens, we can directly decode these tokens into the corresponding diffusion denoising step on pixels in a streaming manner. Our pipeline naturally reveals several intriguing properties, for example, it supports consistent previews when generating only a subset of tokens and enables zero-shot layout-controlled synthesis. On the standard ImageNet benchmark, our method achieves 2.09 and 2.00 FID using a 775M and 1.4B Llama backbone with 256 discrete tokens. We hope our work can inspire future research on unified autoregressive architectures of visual synthesis, especially with large language models.