Despite significant progress in autoregressive image generation, inference remains slow due to the sequential nature of AR models and the ambiguity of image tokens, even when using speculative decoding. Recent works attempt to address this with relaxed speculative decoding but lack theoretical grounding. In this paper, we establish the theoretical basis of relaxed SD and propose COOL-SD, an annealed relaxation of speculative decoding built on two key insights. The first analyzes the total variation (TV) distance between the target model and relaxed speculative decoding and yields an optimal resampling distribution that minimizes an upper bound of the distance. The second uses perturbation analysis to reveal an annealing behaviour in relaxed speculative decoding, motivating our annealed design. Together, these insights enable COOL-SD to generate images faster with comparable quality, or achieve better quality at similar latency. Experiments validate the effectiveness of COOL-SD, showing consistent improvements over prior methods in speed-quality trade-offs.

翻译：尽管自回归图像生成取得了显著进展，但由于AR模型的序列本质和图像标记的模糊性，即使采用推测解码，推理过程仍然缓慢。近期研究尝试通过松弛推测解码来解决此问题，但缺乏理论依据。本文建立了松弛推测解码的理论基础，并提出了COOL-SD——一种基于两个关键见解构建的退火松弛推测解码方法。第一个见解分析了目标模型与松弛推测解码之间的总变差距离，推导出能最小化该距离上界的最优重采样分布。第二个见解通过扰动分析揭示了松弛推测解码中的退火行为，启发了我们的退火设计。这些见解共同使COOL-SD能够以可比较的质量更快生成图像，或在相似延迟下获得更优质量。实验验证了COOL-SD的有效性，在速度-质量权衡方面较现有方法实现了持续改进。