For image inpainting, the existing Denoising Diffusion Probabilistic Model (DDPM) based method i.e. RePaint can produce high-quality images for any inpainting form. It utilizes a pre-trained DDPM as a prior and generates inpainting results by conditioning on the reverse diffusion process, namely denoising process. However, this process is significantly time-consuming. In this paper, we propose an efficient DDPM-based image inpainting method which includes three speed-up strategies. First, we utilize a pre-trained Light-Weight Diffusion Model (LWDM) to reduce the number of parameters. Second, we introduce a skip-step sampling scheme of Denoising Diffusion Implicit Models (DDIM) for the denoising process. Finally, we propose Coarse-to-Fine Sampling (CFS), which speeds up inference by reducing image resolution in the coarse stage and decreasing denoising timesteps in the refinement stage. We conduct extensive experiments on both faces and general-purpose image inpainting tasks, and our method achieves competitive performance with approximately 60 times speedup.

翻译：对于图像修复任务，现有的基于去噪扩散概率模型（DDPM）的方法（例如RePaint）能够为任意修复形式生成高质量图像。该方法利用预训练的DDPM作为先验，并通过以反向扩散过程（即去噪过程）为条件来生成修复结果。然而，该过程极为耗时。本文提出一种高效的基于DDPM的图像修复方法，包含三种加速策略。首先，我们采用预训练的轻量级扩散模型（LWDM）以减少参数量。其次，我们为去噪过程引入了去噪扩散隐式模型（DDIM）的跳步采样方案。最后，我们提出粗到细采样（CFS）策略，通过在粗粒度阶段降低图像分辨率并在细化阶段减少去噪时间步长来加速推理。我们在人脸和通用图像修复任务上进行了大量实验，所提方法在实现约60倍加速的同时取得了具有竞争力的性能。