  Image diffusion has recently shown remarkable performance in image synthesis
and implicitly as an image prior. Such a prior has been used with conditioning
to solve the inpainting problem, but only supporting binary user-based
conditioning. We derive a fuzzy-conditioned diffusion, where implicit diffusion
priors can be exploited with controllable strength. Our fuzzy conditioning can
be applied pixel-wise, enabling the modification of different image components
to varying degrees. Additionally, we propose an application to facial image
correction, where we combine our fuzzy-conditioned diffusion with
diffusion-derived attention maps. Our map estimates the degree of anomaly, and
we obtain it by projecting on the diffusion space. We show how our approach
also leads to interpretable and autonomous facial image correction.
