  Image inpainting refers to the task of generating a complete, natural image
based on a partially revealed reference image. Recently, many research
interests have been focused on addressing this problem using fixed diffusion
models. These approaches typically directly replace the revealed region of the
intermediate or final generated images with that of the reference image or its
variants. However, since the unrevealed regions are not directly modified to
match the context, it results in incoherence between revealed and unrevealed
regions. To address the incoherence problem, a small number of methods
introduce a rigorous Bayesian framework, but they tend to introduce mismatches
between the generated and the reference images due to the approximation errors
in computing the posterior distributions. In this paper, we propose COPAINT,
which can coherently inpaint the whole image without introducing mismatches.
COPAINT also uses the Bayesian framework to jointly modify both revealed and
unrevealed regions, but approximates the posterior distribution in a way that
allows the errors to gradually drop to zero throughout the denoising steps,
thus strongly penalizing any mismatches with the reference image. Our
experiments verify that COPAINT can outperform the existing diffusion-based
methods under both objective and subjective metrics. The codes are available at
https://github.com/UCSB-NLP-Chang/CoPaint/.
