Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms

Understand

Discrete diffusion models have emerged as a powerful generative modeling framework for discrete data with successful applications spanning from text generation to image synthesis.

• However, their deployment faces challenges due to the high dimensionality of the state space, necessitating the development of efficient inference algorithms.
• Current inference approaches mainly fall into two categories: exact simulation and approximate methods such as $\tau$-leaping.
• While exact methods suffer from unpredictable inference time and redundant function evaluations, $\tau$-leaping is limited by its first-order accuracy.