DiffGANPaint: Fast Inpainting Using Denoising Diffusion GANs
Moein Heidari Alireza Morsali Tohid Abedini Samin Heydarian
Free-form image inpainting is the task of reconstructing parts of an image
specified by an arbitrary binary mask. In this task, it is typically desired to
generalize model capabilities to unseen mask types, rather than learning
certain mask distributions. Capitalizing on the advances in diffusion models,
in this paper, we propose a Denoising Diffusion Probabilistic Model (DDPM)
based model capable of filling missing pixels fast as it models the backward
diffusion process using the generator of a generative adversarial network (GAN)
network to reduce sampling cost in diffusion models. Experiments on
general-purpose image inpainting datasets verify that our approach performs
superior or on par with most contemporary works.