SJD-PV: 基于短语验证的推测雅可比解码用于自回归图像生成
SJD-PV: Speculative Jacobi Decoding with Phrase Verification for Autoregressive Image Generation
AI总结：
SJD-PV通过短语级推测验证提升自回归图像生成的解码效率，减少函数评估次数并提升生成质量。
摘要：
自回归（AR）图像模型最近展示了显著的生成能力，但其序列性质导致显著的推理延迟。现有的无训练加速方法通常独立验证标记，忽略了相邻视觉标记之间的强共现模式。这种独立假设往往导致上下文不一致，并限制了解码效率。在本文中，我们引入了一种新的无训练加速框架，该框架在每个解码窗口内对多个相关标记进行短语级推测验证，使模型能够联合验证多个相关标记。为了构建这样的短语单元，我们分析了训练语料中的标记共现统计信息，并将频繁共现的标记分组为语义连贯的视觉短语。在推理过程中，所提出的短语级验证评估每个短语的汇总似然比，允许同时接受多个标记，同时保持生成质量。在自回归文本到图像生成的大量实验中，我们的方法显著减少了函数评估次数（NFE），并实现了高达30%的解码速度提升，而不会影响视觉保真度。我们的发现表明，建模短程标记共现提供了一种有效且通用的原则，用于加速自回归推理。
英文摘要：
Autoregressive (AR) image models have recently demonstrated remarkable generative capability, but their sequential nature results in significant inference latency. Existing training-free acceleration methods typically verify tokens independently, overlooking the strong co-occurrence patterns between adjacent visual tokens. This independence assumption often leads to contextual inconsistency and limits decoding efficiency. In this work, we introduce a novel training-free acceleration framework that performs phrase-level speculative verification, enabling the model to jointly validate multiple correlated tokens within each decoding window. To construct such phrase units, we analyze token co-occurrence statistics from the training corpus and group frequently co-occurring tokens into semantically coherent visual phrases. During inference, the proposed phrase-level verification evaluates aggregated likelihood ratios over each phrase, allowing simultaneous acceptance of multiple tokens while preserving generation quality. Extensive experiments on autoregressive text-to-image generation show that our method significantly reduces the number of function evaluations (NFE) and achieves up to 30% faster decoding without compromising visual fidelity. Our findings reveal that modeling short-range token co-occurrence provides an effective and general principle for accelerating autoregressive inference.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
