Autoregressive Image Generation with Randomized Parallel Decoding
Authors: name: Haopeng Li, name: Jinyue Yang, name: Guoqi Li, name: Huan Wang

We introduce ARPG, a novel visual autoregressive model that enables
randomized parallel generation, addressing the inherent limitations of
conventional raster-order approaches, which hinder inference efficiency and
zero-shot generalization due to their sequential, predefined token generation
order. Our key insight is that effective random-order modeling necessitates
explicit guidance for determining the position of the next predicted token. To
this end, we propose a novel guided decoding framework that decouples
positional guidance from content representation, encoding them separately as
queries and key-value pairs. By directly incorporating this guidance into the
causal attention mechanism, our approach enables fully random-order training
and generation, eliminating the need for bidirectional attention. Consequently,
ARPG readily generalizes to zero-shot tasks such as image inpainting,
outpainting, and resolution expansion. Furthermore, it supports parallel
inference by concurrently processing multiple queries using a shared KV cache.
On the ImageNet-1K 256 benchmark, our approach attains an FID of 1.94 with only
64 sampling steps, achieving over a 20-fold increase in throughput while
reducing memory consumption by over 75% compared to representative recent
autoregressive models at a similar scale.