MC-SJD：基于最大耦合推测性雅可比解码的自回归视觉生成加速方法

摘要

尽管自回归建模近年来已成为视觉生成的新范式，但其实际应用受限于逐令牌生成的缓慢推理速度——单样本生成往往需要数千步计算。为解决这一难题，我们提出MC-SJD：一种基于耦合理论的无训练、无损并行解码框架，通过扩展新近提出的推测雅可比解码（SJD）来加速自回归视觉生成。虽然SJD在加速自回归生成方面展现出强大潜力，但我们发现迭代间的令牌不稳定性会显著降低接受率，这一局限主要源于草稿令牌生成过程中采用的独立采样机制。为此，我们引入信息论层面的耦合方法MC-SJD，通过最大化连续迭代间采样相同草稿令牌的概率，在保持无损特性的同时大幅提升标准SJD效率。值得注意的是，该方法仅需对现有算法进行单行修改即可实现显著性能提升：相较于标准自回归解码，图像生成速度最高提升约4.2倍，视频生成速度最高提升约13.3倍，且输出质量无损。

English

While autoregressive (AR) modeling has recently emerged as a new paradigm in visual generation, its practical adoption is severely constrained by the slow inference speed of per-token generation, which often requires thousands of steps to produce a single sample. To address this challenge, we propose MC-SJD, a training-free, lossless parallel decoding framework designed to accelerate AR visual generation by extending the recently introduced Speculative Jacobi Decoding (SJD). Although SJD shows strong potential for accelerating AR generation, we demonstrate that token instability across iterations significantly reduces the acceptance rate, a limitation that primarily arises from the independent sampling process used during draft token generation. To overcome this, we introduce MC-SJD, an information-theoretic approach based on coupling, which substantially accelerates standard SJD by maximizing the probability of sampling identical draft tokens across consecutive iterations, all while preserving its lossless property. Remarkably, this method requires only a single-line modification to the existing algorithm, yet achieves substantial performance gains, delivering up to a ~4.2x acceleration in image generation and ~13.3x acceleration in video generation compared to standard AR decoding, without any degradation in output quality.

MC-SJD：基于最大耦合推测性雅可比解码的自回归视觉生成加速方法

MC-SJD : Maximal Coupling Speculative Jacobi Decoding for Autoregressive Visual Generation Acceleration

摘要

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