ZipAR: Accelerating Autoregressive Image Generation through Spatial Locality

Yefei He, Feng Chen, Yuanyu He, Shaoxuan He, Hong Zhou, kaipeng, Bohan Zhuang

ZipAR: Accelerating Autoregressive Image Generation through Spatial Locality: 8 upvotes on Hugging Face Daily Papers, #24 of 31 papers on 2024-12-06. Day-by-day upvote history.

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 page on Hugging Face · arXiv

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