提出分组推测解码，让自回归图像生成快3.7倍且不降质。
Grouped Speculative Decoding for Autoregressive Image Generation
- 用分组方式评估多个可能的图像标记，而非只选最可能的一个。
- 在不训练的前提下实现平均3.7倍加速，图像质量保持不变。
- 适合追求快速生成又不想重训练的图像生成研究者或应用开发者。
近期自回归（AR）图像模型展现出强大的生成能力，成为扩散模型的有力替代。但其序列生成特性导致推理时间长，制约实际应用。本文提出无需训练的分组推测解码（GSD）方法，显著加速AR图像生成。现有推测解码（SD）方法多依赖单一最可能标记，但图像标记具有内在冗余与多样性，仅选单一标记易造成误拒。我们发现静态聚类基于嵌入距离无效，因此提出动态分组策略，评估视觉有效的标记簇。大量实验表明，GSD在不需额外训练的情况下，平均提升生成速度3.7倍，同时保持图像质量。代码已开源。
原文摘要 · Abstract (English)
Recently, autoregressive (AR) image models have demonstrated remarkable generative capabilities, positioning themselves as a compelling alternative to diffusion models. However, their sequential nature leads to long inference times, limiting their practical scalability. In this work, we introduce Grouped Speculative Decoding (GSD), a novel, training-free acceleration method for AR image models. While recent studies have explored Speculative Decoding (SD) as a means to speed up AR image generation, existing approaches either provide only modest acceleration or require additional training. Our in-depth analysis reveals a fundamental difference between language and image tokens: image tokens exhibit inherent redundancy and diversity, meaning multiple tokens can convey valid semantics. However, traditional SD methods are designed to accept only a single most-likely token, which fails to leverage this difference, leading to excessive false-negative rejections. To address this, we propose a new SD strategy that evaluates clusters of visually valid tokens rather than relying on a single target token. Additionally, we observe that static clustering based on embedding distance is ineffective, which motivates our dynamic GSD approach. Extensive experiments show that GSD accelerates AR image models by an average of 3.7x while preserving image quality-all without requiring any additional training. The source code is available at https://github.com/junhyukso/GSD
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