Diffusion models have seen notable success in continuous domains, leading to the development of discrete diffusion models (DDMs) for discrete variables. Despite recent advances, DDMs face the challenge of slow sampling speeds. While parallel sampling methods like $\tau$-leaping accelerate this process, they introduce $\textit{Compounding Decoding Error}$ (CDE), where discrepancies arise between the true distribution and the approximation from parallel token generation, leading to degraded sample quality. In this work, we present $\textit{Jump Your Steps}$ (JYS), a novel approach that optimizes the allocation of discrete sampling timesteps by minimizing CDE without extra computational cost. More precisely, we derive a practical upper bound on CDE and propose an efficient algorithm for searching for the optimal sampling schedule. Extensive experiments across image, music, and text generation show that JYS significantly improves sampling quality, establishing it as a versatile framework for enhancing DDM performance for fast sampling.

本文解决了离散扩散模型（DDMs）在采样速度上较慢的问题，提出了一种名为“跳跃你的一步”（JYS）的新方法，通过优化离散采样时间步的分配，最小化复合解码误差（CDE），且无需额外计算成本。实验结果表明，JYS在图像、音乐和文本生成中的采样质量显著提高，彰显了其在快速采样中的广泛应用潜力。

跳跃你的一步：优化离散扩散模型的采样计划