title: D-AR: Diffusion via Autoregressive Models
summary: This paper presents Diffusion via Autoregressive models (D-AR), a new
  paradigm recasting the image diffusion process as a vanilla autoregressive
  procedure in the standard next-token-prediction fashion. We start by designing
  the tokenizer that converts images into sequences of discrete tokens, where
  tokens in different positions can be decoded into different diffusion denoising
  steps in the pixel space. Thanks to the diffusion properties, these tokens
  naturally follow a coarse-to-fine order, which directly lends itself to
  autoregressive modeling. Therefore, we apply standard next-token prediction on
  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 in the
  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 FID using a 775M 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. Code and models will be available at
  https://github.com/showlab/D-AR
paper_authors._id: 683906adf85de1fc563957d8
paper_authors.name: Ziteng Gao
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-30T01:15:28.731Z
paper_authors._id: 648ac3d53470b17ccc90deaf
paper_authors.avatarUrl: /avatars/35b678baa7a91959f40f704c5de2a3e1.svg
paper_authors.fullname: Ziteng Gao
paper_authors.type: user
paper_authors.user: sebgao
paper_authors._id: 683906adf85de1fc563957d9
paper_authors.name: Mike Zheng Shou
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-30T01:15:28.731Z
paper_authors._id: 63a55320ce5763e06f78519c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1671779060549-noauth.jpeg
paper_authors.fullname: Mike Shou
paper_authors.type: user
paper_authors.user: mikeshou
paper_title: D-AR: Diffusion via Autoregressive Models
paper_summary: This paper presents Diffusion via Autoregressive models (D-AR), a new
  paradigm recasting the image diffusion process as a vanilla autoregressive
  procedure in the standard next-token-prediction fashion. We start by designing
  the tokenizer that converts images into sequences of discrete tokens, where
  tokens in different positions can be decoded into different diffusion denoising
  steps in the pixel space. Thanks to the diffusion properties, these tokens
  naturally follow a coarse-to-fine order, which directly lends itself to
  autoregressive modeling. Therefore, we apply standard next-token prediction on
  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 in the
  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 FID using a 775M 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. Code and models will be available at
  https://github.com/showlab/D-AR
paper_ai_summary: Diffusion via Autoregressive models (D-AR) recasts the image diffusion process as a standard autoregressive task, achieving high-quality image generation with consistent previews and layout control using a large language model backbone.
paper_ai_keywords: Diffusion via Autoregressive models autoregressive procedure next-token-prediction tokenizer discrete tokens diffusion denoising coarse-to-fine order autoregressive modeling autoregressive token generation FID Llama backbone