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.

翻译：基于分数的传播模型已成为最有希望的深层基因模型框架之一。在这项工作中,我们对学习有条件概率分布的不同方法与基于分数的传播模型进行系统比较和理论分析。特别是,我们证明了为最成功的有条件得分估计者之一提供了理论依据的结果。此外,我们引入了多速传播框架,导致对有条件得分进行新的估计,与以前最先进的方法相同。我们的理论和实验结果由一个开放源库MSDiff伴随,允许应用和进一步研究多速传播模型。