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arXiv · 2510.25739

Hawk: Leveraging Spatial Context for Faster Autoregressive Text-to-Image Generation

Zhi-Kai Chen·Jun-Peng Jiang·Han-Jia Ye·De-Chuan Zhan
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Abstract

Autoregressive (AR) image generation models are capable of producing high-fidelity images but often suffer from slow inference due to their inherently sequential, token-by-token decoding process. Speculative decoding, which employs a lightweight draft model to approximate the output of a larger AR model, has shown promise in accelerating text generation without compromising quality. However, its application to image generation remains largely underexplored. The challenges stem from a significantly larger sampling space, which complicates the alignment between the draft and target model outputs, coupled with the inadequate use of the two-dimensional spatial structure inherent in images, thereby limiting the modeling of local dependencies. To overcome these challenges, we introduce Hawk, a new approach that harnesses the spatial structure of images to guide the speculative model toward more accurate and efficient predictions. Experimental results on multiple text-to-image benchmarks demonstrate a 1.71x speedup over standard AR models, while preserving both image fidelity and diversity.

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Zhi-Kai Chen, Jun-Peng Jiang, Han-Jia Ye, De-Chuan Zhan. 2025-10-29. Hawk: Leveraging Spatial Context for Faster Autoregressive Text-to-Image Generation. https://arxiv.org/abs/2510.25739

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