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.

翻译：图像扩散最近在图像合成及隐式图像先验方面展现出显著性能。此类先验已结合条件机制用于解决图像修复问题，但仅支持基于用户二值化条件。我们推导出模糊条件扩散，使得隐式扩散先验能够以可控强度被利用。我们的模糊条件可逐像素应用，从而实现不同图像成分的差异化修正。此外，我们提出一种面部图像校正应用，将模糊条件扩散与扩散衍生注意力图相结合。该注意力图通过投影至扩散空间获取，用于评估异常程度。实验表明，我们的方法可实现可解释且自主的面部图像校正。