Score-based diffusion models have emerged as one of the most promising
frameworks for deep generative modelling. In this work we conduct a systematic
comparison and theoretical analysis of different approaches to learning
conditional probability distributions with score-based diffusion models. In
particular, we prove results which provide a theoretical justification for one
of the most successful estimators of the conditional score. Moreover, we
introduce a multi-speed diffusion framework, which leads to a new estimator for
the conditional score, performing on par with previous state-of-the-art
approaches. Our theoretical and experimental findings are accompanied by an
open source library MSDiff which allows for application and further research of
multi-speed diffusion models.

本文研究使用基于得分的扩散模型进行深层生成建模的方法，系统比较和理论分析不同方法学习条件概率分布的效果，并证明得出条件得分最成功的估计器的理论依据。同时，介绍了多速度扩散框架，提出了一个新的条件分数估计器，与之前的最先进方法相当。伴随着本文的理论和实验研究是一个开放源代码库 MSDiff，可用于应用和进一步研究多速度扩散模型。