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.

翻译：生成对抗网络（GANs）在图像修复领域取得了巨大成功，但仍难以处理大面积缺失区域。相比之下，自回归模型和去噪扩散模型等迭代概率算法需要部署大规模计算资源才能获得理想效果。为实现低计算成本下的高质量结果，我们提出了一种新颖的像素扩散模型（PSM），该模型通过迭代采用解耦概率建模，将GANs的优化效率与概率模型的可预测性相结合。通过少量迭代，我们的模型能选择性地将信息像素扩散至整幅图像，显著提升了修复质量与效率。在多个基准测试中，我们取得了新的最优性能。代码已开源至https://github.com/fenglinglwb/PSM。