Autoregressive Image Generation with Randomized Parallel Decoding

• URL: http://arxiv.org/abs/2503.10568v1
• Date: Thu, 13 Mar 2025 17:19:51 GMT
• Title: Autoregressive Image Generation with Randomized Parallel Decoding
• Authors: Haopeng Li, Jinyue Yang, Guoqi Li, Huan Wang,
• Abstract summary: ARPG is a novel visual autoregressive model that enables randomized parallel generation.<n>Our approach attains an FID of 1.94 with only 64 sampling steps, achieving over a 20-fold increase in throughput.
• Score: 23.714192351237628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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