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 noise robustness. Experimenting with denoising probabilistic diffusion models, our framework operates with approximately a quarter of the parameters and 30% of the Floating Point Operations (FLOPs) compared to standard U-Nets in Denoising Diffusion Probabilistic Models (DDPMs). Furthermore, our model is up to 70% faster in inference than the baseline models when measured in equal conditions while converging to better quality solutions.

利用连续动力系统设计一种新型去噪网络，以提高扩散模型的参数效率、收敛速度和噪声鲁棒性。与基准模型相比，该模型具有约四分之一的参数量和百分之三十的浮点操作数（FLOPs），推理速度提高了70％，并且收敛到了更好的质量解。

超越 U：打造更快速、更轻量级的扩散模型