title: DiSA: Diffusion Step Annealing in Autoregressive Image Generation
summary: An increasing number of autoregressive models, such as MAR, FlowAR, xAR, and
  Harmon adopt diffusion sampling to improve the quality of image generation.
  However, this strategy leads to low inference efficiency, because it usually
  takes 50 to 100 steps for diffusion to sample a token. This paper explores how
  to effectively address this issue. Our key motivation is that as more tokens
  are generated during the autoregressive process, subsequent tokens follow more
  constrained distributions and are easier to sample. To intuitively explain, if
  a model has generated part of a dog, the remaining tokens must complete the dog
  and thus are more constrained. Empirical evidence supports our motivation: at
  later generation stages, the next tokens can be well predicted by a multilayer
  perceptron, exhibit low variance, and follow closer-to-straight-line denoising
  paths from noise to tokens. Based on our finding, we introduce diffusion step
  annealing (DiSA), a training-free method which gradually uses fewer diffusion
  steps as more tokens are generated, e.g., using 50 steps at the beginning and
  gradually decreasing to 5 steps at later stages. Because DiSA is derived from
  our finding specific to diffusion in autoregressive models, it is complementary
  to existing acceleration methods designed for diffusion alone. DiSA can be
  implemented in only a few lines of code on existing models, and albeit simple,
  achieves 5-10times faster inference for MAR and Harmon and 1.4-2.5times
  for FlowAR and xAR, while maintaining the generation quality.
paper_authors._id: 68364bbf8bb68ffc201e7d83
paper_authors.name: Qinyu Zhao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:58:34.882Z
paper_authors._id: 6503e36206e7b98952fa9052
paper_authors.avatarUrl: /avatars/a8a57683e9e3915d9833d3b9512c1ba0.svg
paper_authors.fullname: Qinyu Zhao
paper_authors.type: user
paper_authors.user: QinyuZhao1116
paper_authors._id: 68364bbf8bb68ffc201e7d84
paper_authors.name: Jaskirat Singh
paper_authors._id: 68364bbf8bb68ffc201e7d85
paper_authors.name: Ming Xu
paper_authors._id: 68364bbf8bb68ffc201e7d86
paper_authors.name: Akshay Asthana
paper_authors._id: 68364bbf8bb68ffc201e7d87
paper_authors.name: Stephen Gould
paper_authors._id: 68364bbf8bb68ffc201e7d88
paper_authors.name: Liang Zheng
paper_title: DiSA: Diffusion Step Annealing in Autoregressive Image Generation
paper_summary: An increasing number of autoregressive models, such as MAR, FlowAR, xAR, and
  Harmon adopt diffusion sampling to improve the quality of image generation.
  However, this strategy leads to low inference efficiency, because it usually
  takes 50 to 100 steps for diffusion to sample a token. This paper explores how
  to effectively address this issue. Our key motivation is that as more tokens
  are generated during the autoregressive process, subsequent tokens follow more
  constrained distributions and are easier to sample. To intuitively explain, if
  a model has generated part of a dog, the remaining tokens must complete the dog
  and thus are more constrained. Empirical evidence supports our motivation: at
  later generation stages, the next tokens can be well predicted by a multilayer
  perceptron, exhibit low variance, and follow closer-to-straight-line denoising
  paths from noise to tokens. Based on our finding, we introduce diffusion step
  annealing (DiSA), a training-free method which gradually uses fewer diffusion
  steps as more tokens are generated, e.g., using 50 steps at the beginning and
  gradually decreasing to 5 steps at later stages. Because DiSA is derived from
  our finding specific to diffusion in autoregressive models, it is complementary
  to existing acceleration methods designed for diffusion alone. DiSA can be
  implemented in only a few lines of code on existing models, and albeit simple,
  achieves 5-10times faster inference for MAR and Harmon and 1.4-2.5times
  for FlowAR and xAR, while maintaining the generation quality.
paper_ai_summary: Diffusion step annealing enhances inference efficiency in autoregressive models by reducing the number of diffusion steps as more tokens are generated, preserving quality.
paper_ai_keywords: autoregressive models MAR FlowAR xAR Harmon diffusion sampling diffusion step annealing DiSA multilayer perceptron denoising paths