DiffGANPaint: Fast Inpainting Using Denoising Diffusion GANs
This addresses the need for efficient image inpainting with arbitrary masks, though it appears incremental as it builds on existing diffusion and GAN methods.
The paper tackles the problem of fast free-form image inpainting by proposing a model that combines denoising diffusion with a GAN generator to reduce sampling costs, achieving performance superior or on par with contemporary works.
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.