  Image completion techniques have made significant progress in filling missing
regions (i.e., holes) in images. However, large-hole completion remains
challenging due to limited structural information. In this paper, we address
this problem by integrating explicit structural guidance into diffusion-based
image completion, forming our structure-guided diffusion model (SGDM). It
consists of two cascaded diffusion probabilistic models: structure and texture
generators. The structure generator generates an edge image representing
plausible structures within the holes, which is then used for guiding the
texture generation process. To train both generators jointly, we devise a novel
strategy that leverages optimal Bayesian denoising, which denoises the output
of the structure generator in a single step and thus allows backpropagation.
Our diffusion-based approach enables a diversity of plausible completions,
while the editable edges allow for editing parts of an image. Our experiments
on natural scene (Places) and face (CelebA-HQ) datasets demonstrate that our
method achieves a superior or comparable visual quality compared to
state-of-the-art approaches. The code is available for research purposes at
https://github.com/UdonDa/Structure_Guided_Diffusion_Model.
