MC-SJD：用于自回归视觉生成加速的最大耦合投机雅可比解码
MC-SJD : Maximal Coupling Speculative Jacobi Decoding for Autoregressive Visual Generation Acceleration
AI总结：
针对自回归视觉生成推理速度慢的问题，提出基于耦合的MC-SJD无损并行解码框架，仅需修改一行代码即可显著提升SJD的接受率，实现图像和视频生成的大幅加速且无损输出质量。
摘要：
尽管自回归（AR）建模近来已成为视觉生成领域的新范式，但其实际应用受到逐token生成推理速度缓慢的严重制约——生成单个样本通常需要数千步。为解决这一挑战，我们提出MC-SJD，这是一种无需训练的无损并行解码框架，旨在通过扩展新近提出的Speculative Jacobi Decoding（投机雅可比解码，SJD）来加速AR视觉生成。虽然SJD在加速AR生成方面展现出巨大潜力，但我们发现迭代间的token不稳定性会显著降低接受率，这一限制主要源于草稿token生成过程中采用的独立采样机制。为克服该问题，我们提出MC-SJD——一种基于耦合的信息论方法，它通过最大化连续迭代间采样得到相同草稿token的概率，大幅提升了标准SJD的速度，同时保留了其无损特性。值得注意的是，该方法仅需对现有算法做一行代码修改，却能实现显著的性能提升：与标准AR解码相比，图像生成加速比最高达约4.2倍，视频生成加速比最高达约13.3倍，且输出质量无任何下降。
英文摘要：
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.
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