Locality-aware Parallel Decoding for Efficient Autoregressive Image Generation
Zhuoyang Zhang, Luke J. Huang, Chengyue Wu, Shang Yang, Kelly Peng, Yao Lu, Song Han
2025-07-03
Summary
This paper talks about Locality-aware Parallel Decoding, a new technique to speed up how AI generates images step-by-step. It changes the order in which the image is created so different parts can be made at the same time, instead of one after another.
What's the problem?
The problem is that autoregressive image generation, where images are made pixel by pixel or part by part in a fixed order, is very slow because each step depends on the last. This causes delays when creating high-quality images.
What's the solution?
The researchers developed a method that understands how close or related parts of the image are and uses this knowledge to generate many parts in parallel without losing quality. This flexible ordering reduces the time it takes to create the image significantly while keeping the result just as good.
Why it matters?
This matters because faster image generation helps make AI tools more practical for real-time applications like games, design, and video editing where waiting for images to appear can be frustrating.
Abstract
Locality-aware Parallel Decoding accelerates autoregressive image generation by enabling flexible parallelization and optimized generation ordering, significantly reducing latency without compromising quality.