Accelerating Discrete Diffusion Models with Parallel-In-Time Sampling
Discrete diffusion models are widely used for learning and generating discrete distributions. As the generation process is inherently sequential, the acceleration of sampling is of significant importance. In this work, we parallelize the mainstream $τ$-leaping algorithm for absorbing discrete diffusion in a Continuous-Time Markov Chain (CTMC) framework. By leveraging the continuous-time stochastic integral form of the $τ$-leaping algorithm and the Picard iteration method, we achieve parallel-in-time sampling acceleration and provide a proof of exponential-factorial convergence for our algorithm. We improve the overall time complexity of $τ$-leaping under absorbing settings from ${\mathcal...
| Authors | Yu Yao, Huanjian Zhou, Andi Han et al. |
|---|---|
| Primary category | cs.LG |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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