Discrete diffusion models have seen a surge of attention with applications on
naturally discrete data such as language and graphs. Although discrete-time
discrete diffusion has been established for a while, only recently Campbell et
al. (2022) introduced the first framework for continuous-time discrete
diffusion. However, their training and sampling processes differ significantly
from the discrete-time version, necessitating nontrivial approximations for
tractability. In this paper, we first present a series of mathematical
simplifications of the variational lower bound that enable more accurate and
easy-to-optimize training for discrete diffusion. In addition, we derive a
simple formulation for backward denoising that enables exact and accelerated
sampling, and importantly, an elegant unification of discrete-time and
continuous-time discrete diffusion. Thanks to simpler analytical formulations,
both forward and now also backward probabilities can flexibly accommodate any
noise distribution, including different noise distributions for multi-element
objects. Experiments show that our proposed USD3 (for Unified Simplified
Discrete Denoising Diffusion) outperform all SOTA baselines on established
datasets. We open-source our unified code at
this https URL

提出了一种用于简化离散扩散的数学简化方案，同时还提出了一种能够精确和加速采样的简单公式，并通过创建一个统一的模型，简化离散扩散的前向和后向概率计算，取得了在现有数据集上优于其他方法的效果。