title: ZipAR: Accelerating Autoregressive Image Generation through Spatial
    Locality
summary: In this paper, we propose ZipAR, a training-free, plug-and-play parallel
  decoding framework for accelerating auto-regressive (AR) visual generation. The
  motivation stems from the observation that images exhibit local structures, and
  spatially distant regions tend to have minimal interdependence. Given a
  partially decoded set of visual tokens, in addition to the original next-token
  prediction scheme in the row dimension, the tokens corresponding to spatially
  adjacent regions in the column dimension can be decoded in parallel, enabling
  the ``next-set prediction'' paradigm. By decoding multiple tokens
  simultaneously in a single forward pass, the number of forward passes required
  to generate an image is significantly reduced, resulting in a substantial
  improvement in generation efficiency. Experiments demonstrate that ZipAR can
  reduce the number of model forward passes by up to 91% on the Emu3-Gen model
  without requiring any additional retraining.
paper_authors._id: 67527072dc10e013a0cf4761
paper_authors.name: Yefei He
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-10T10:25:43.174Z
paper_authors._id: 65a88c3d26598b995531fff1
paper_authors.avatarUrl: /avatars/b1a524857d8572d0405476661b434160.svg
paper_authors.fullname: Yefei He
paper_authors.type: user
paper_authors.user: yefly
paper_authors._id: 67527072dc10e013a0cf4762
paper_authors.name: Feng Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-10T10:25:44.967Z
paper_authors._id: 64574380f3ef144c0e69d484
paper_authors.avatarUrl: /avatars/a0a84757cb0bf09c24291803e1389b49.svg
paper_authors.fullname: Feng Chen
paper_authors.type: user
paper_authors.user: chenfeng1271
paper_authors._id: 67527072dc10e013a0cf4763
paper_authors.name: Yuanyu He
paper_authors._id: 67527072dc10e013a0cf4764
paper_authors.name: Shaoxuan He
paper_authors._id: 67527072dc10e013a0cf4765
paper_authors.name: Hong Zhou
paper_authors._id: 67527072dc10e013a0cf4766
paper_authors.name: Kaipeng Zhang
paper_authors._id: 67527072dc10e013a0cf4767
paper_authors.name: Bohan Zhuang
paper_title: ZipAR: Accelerating Autoregressive Image Generation through Spatial
    Locality
paper_summary: In this paper, we propose ZipAR, a training-free, plug-and-play parallel
  decoding framework for accelerating auto-regressive (AR) visual generation. The
  motivation stems from the observation that images exhibit local structures, and
  spatially distant regions tend to have minimal interdependence. Given a
  partially decoded set of visual tokens, in addition to the original next-token
  prediction scheme in the row dimension, the tokens corresponding to spatially
  adjacent regions in the column dimension can be decoded in parallel, enabling
  the ``next-set prediction'' paradigm. By decoding multiple tokens
  simultaneously in a single forward pass, the number of forward passes required
  to generate an image is significantly reduced, resulting in a substantial
  improvement in generation efficiency. Experiments demonstrate that ZipAR can
  reduce the number of model forward passes by up to 91% on the Emu3-Gen model
  without requiring any additional retraining.
paper_ai_keywords: ZipAR autoressive (AR) visual generation decoded set of visual tokens row dimension spatially adjacent regions column dimension next-set prediction forward pass generation efficiency