Nov. 14, 2023, 11:01 p.m. | /u/APaperADay

Machine Learning

**Paper**: [](


>Diffusion models are a family of generative models that yield record-breaking performance in tasks such as image synthesis, video generation, and molecule design. Despite their capabilities, their efficiency, especially in the reverse denoising process, remains a challenge due to slow convergence rates and high computational costs. In this work, we introduce an approach that leverages continuous dynamical systems to design a novel denoising network for diffusion models that is more parameter-efficient, exhibits faster convergence, and demonstrates increased …

abstract breaking capabilities challenge computational continuous convergence costs denoising design diffusion diffusion models efficiency family generative generative models image machinelearning performance process synthesis systems tasks video video generation work

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