ZipAR: Accelerating Autoregressive Image Generation through Spatial
  Locality
Authors: name: Yefei He, name: Feng Chen, name: Yuanyu He, name: Shaoxuan He, name: Hong Zhou, name: Kaipeng Zhang, name: Bohan Zhuang

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.