ZipAR: Accelerating Autoregressive Image Generation through Spatial
  Locality
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.
67527072dc10e013a0cf4761
Yefei He
claimed_verified
2024-12-10T10:25:43.174Z
65a88c3d26598b995531fff1
/avatars/b1a524857d8572d0405476661b434160.svg
Yefei He
user
yefly
67527072dc10e013a0cf4762
Feng Chen
claimed_verified
2024-12-10T10:25:44.967Z
64574380f3ef144c0e69d484
/avatars/a0a84757cb0bf09c24291803e1389b49.svg
Feng Chen
user
chenfeng1271
67527072dc10e013a0cf4763
Yuanyu He
67527072dc10e013a0cf4764
Shaoxuan He
67527072dc10e013a0cf4765
Hong Zhou
67527072dc10e013a0cf4766
Kaipeng Zhang
claimed_verified
2025-06-02T07:55:37.508Z
65f1713552c38a91e0a445e8
/avatars/47ab3ada51c9b9976ac1cd0c4301c373.svg
kaipeng
user
kpzhang996
67527072dc10e013a0cf4767
Bohan Zhuang
ZipAR: Accelerating Autoregressive Image Generation through Spatial
  Locality
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.
ZipAR accelerates auto-regressive visual generation by decoding spatially adjacent tokens in parallel, reducing the number of forward passes by up to 91%.
ZipAR auto-regressive visual generation forward passes visual tokens next-token prediction next-set prediction Emu3-Gen model