We present Locality-aware Parallel Decoding (LPD) to accelerate autoregressive image generation. Traditional autoregressive image generation relies on next-patch prediction, a memory-bound process that leads to high latency. Existing works have tried to parallelize next-patch prediction by shifting to multi-patch prediction to accelerate the process, but only achieved limited parallelization. To achieve high parallelization while maintaining generation quality, we introduce two key techniques: (1) Flexible Parallelized Autoregressive Modeling, a novel architecture that enables arbitrary generation ordering and degrees of parallelization. It uses learnable position query tokens to guide generation at target positions while ensuring mutual visibility among concurrently generated tokens for consistent parallel decoding. (2) Locality-aware Generation Ordering, a novel schedule that forms groups to minimize intra-group dependencies and maximize contextual support, enhancing generation quality. With these designs, we reduce the generation steps from 256 to 20 (256×256 res.) and 1024 to 48 (512×512 res.) without compromising quality on the ImageNet class-conditional generation, and achieving at least 3.4× lower latency than previous parallelized autoregressive models.
@article{zhang2025locality,
title={Locality-aware parallel decoding for efficient autoregressive image generation},
author={Zhang, Zhuoyang and Huang, Luke J and Wu, Chengyue and Yang, Shang and Peng, Kelly and Lu, Yao and Han, Song},
journal={arXiv preprint arXiv:2507.01957},
year={2025}
}
We thank MIT-IBM Watson AI Lab, National Science Foundation, Hyundai, and Amazon for supporting this research.
