VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference

Diffusion probabilistic models learn to remove noise added during training, generating novel data (e.g., images) from Gaussian noise through sequential denoising. However, conditioning the generative process on corrupted or masked images is challenging. While various methods have been proposed for inpainting masked images with diffusion priors, they often fail to produce samples from the true conditional distribution, especially for large masked regions. Additionally, many can't be applied to latent diffusion models which have been demonstrated to generate high-quality images at a significantly lower computational cost. We propose a hierarchical variational inference algorithm that optimizes a non-Gaussian Markov approximation of the true diffusion posterior. Our VIPaint method outperforms existing approaches to inpainting, producing diverse high-quality imputations, while also being effective for other inverse problems like deblurring and superresolution.