超越U：讓擴散模型更快速輕巧

摘要

擴散模型是一類生成模型，能在圖像合成、視頻生成和分子設計等任務中取得突破性表現。儘管具有強大功能，但其效率，特別是在反去噪過程中，由於收斂速度緩慢和計算成本高而面臨挑戰。在這項工作中，我們提出了一種利用連續動態系統來設計新型去噪網絡的方法，以改進擴散模型，使其更節省參數、收斂速度更快，並表現出更高的噪聲韌性。通過對去噪概率擴散模型進行實驗，我們的框架使用的參數量約為標準去噪擴散概率模型（DDPMs）中U-Net的四分之一，浮點運算（FLOPs）的30％。此外，我們的模型在相同條件下的推理速度比基準模型快70％，同時收斂到更優質的解決方案。

English

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.

超越U：讓擴散模型更快速輕巧

Beyond U: Making Diffusion Models Faster & Lighter

摘要

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