DTLS-Inpaint: Yet Another Efficient Image Inpainting with Domain Transfer

Image inpainting, a process of reconstructing missing or corrupted regions of an image, has evolved significantly with models such as LaMa, AOT GAN, and RePaint with Diffusion models (DMs). These are successful models. However, there is room for improvement, especially on high computation requirement, stability of training and mode collapse. In this paper, we introduce the Domain Transfer in Latent Space (DTLS-Inpaint) model for Inpainting tasks. DTLS-Inpaint adopts a progressive domain transfer strategy, where masked areas are gradually restored by transitioning through increasingly realistic inpainting domains, bearing similarity of DMs but operating without noise-adding and denoising processes. Instead, it utilizes latent space transformations at each timestep which will determine an appropriate amount of inpainting in the new domain, and produce progressively improved intermediate results. Experimental results show that this new approach is extremely fast and able to produce inpainted images comparable or even better than the state-of-the art approaches.