English(EN) Parallel Jacobi Decoding for Fast Autoregressive Image Generation

新的并行Jacobi解码加速图像生成模型

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-04 04:50

研究人员开发了一种名为并行Jacobi解码（PJD）的新方法，以加速自回归图像生成模型。该技术在二维空间域中扩展草稿令牌，允许并行细化并减轻错误累积。PJD可以在保持高质量的同时，将各种模型的图像生成速度提高4.8倍至6.4倍。 AI

影响加速自回归图像生成，可能实现AI图像工具的更快迭代和部署。

排序理由该集群包含一篇详细介绍图像生成新方法的论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.CV TIER_1 English(EN) · Boya Liao, Ying Li, Siyong Jian, Huan Wang · 2026-06-05 04:00

面向快速自回归图像生成的并行Jacobi解码

arXiv:2606.05703v1 Announce Type: new Abstract: Autoregressive (AR) models have demonstrated remarkable performance in generating high-fidelity images. However, their inherently sequential next-token prediction leads to significantly slower inference. Recent studies have introduc…
arXiv cs.CV TIER_1 English(EN) · Huan Wang · 2026-06-04 04:50

面向快速自回归图像生成的并行Jacobi解码

Autoregressive (AR) models have demonstrated remarkable performance in generating high-fidelity images. However, their inherently sequential next-token prediction leads to significantly slower inference. Recent studies have introduced Jacobi-style decoding to accelerate autoregre…