We provide the first complete continuous time framework for denoising
diffusion models of discrete data. This is achieved by formulating the forward
noising process and corresponding reverse time generative process as Continuous
Time Markov Chains (CTMCs). The model can be efficiently trained using a
continuous time version of the ELBO. We simulate the high dimensional CTMC
using techniques developed in chemical physics and exploit our continuous time
framework to derive high performance samplers that we show can outperform
discrete time methods for discrete data. The continuous time treatment also
enables us to derive a novel theoretical result bounding the error between the
generated sample distribution and the true data distribution.

该研究提出了第一个完整的离散数据扩散模型去噪声的连续时间框架，使用连续时间马尔可夫链模型可以有效地训练模型，利用高维 CTMC 模拟技术和连续时间框架可导出高性能抽样器，超越了离散时间方法。此外，还得到了关于生成样本分布与真实数据分布之间误差的新理论结果。