[R] Beyond U: Making Diffusion Models Faster & Lighter

**Paper**: [https://arxiv.org/abs/2310.20092](https://arxiv.org/abs/2310.20092)

**Abstract**:

>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