  Generative adversarial networks (GANs) have made great success in image
inpainting yet still have difficulties tackling large missing regions. In
contrast, iterative probabilistic algorithms, such as autoregressive and
denoising diffusion models, have to be deployed with massive computing
resources for decent effect. To achieve high-quality results with low
computational cost, we present a novel pixel spread model (PSM) that
iteratively employs decoupled probabilistic modeling, combining the
optimization efficiency of GANs with the prediction tractability of
probabilistic models. As a result, our model selectively spreads informative
pixels throughout the image in a few iterations, largely enhancing the
completion quality and efficiency. On multiple benchmarks, we achieve new
state-of-the-art performance. Code is released at
https://github.com/fenglinglwb/PSM.
