Abstract: Consistency models, which were proposed to mitigate the high computational overhead during thesampling phase of diffusion models, facilitate single-step sampling while attaining state-of-the-art empiricalperformance. When integrated into the training phase, consistency models attempt to train a sequenceof consistency functions capable of mapping any point at any time step of the diffusion process to itsstarting point. Despite the empirical success, a comprehensive theoretical understanding of consistencytraining remains elusive. This paper takes a first step towards establishing theoretical underpinnings forconsistency models. We demonstrate that, in order to generate samples within $\varepsilon$ proximity to the targetin distribution (measured by some Wasserstein metric), it suffices for the number of steps in consistencylearning to exceed the order of $d^{5/2}/\varepsilon$, with $d$ the data dimension. Our theory offers rigorous insights intothe validity and efficacy of consistency models, illuminating their utility in downstream inference tasks.