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以及作为隐式图像先验方面表现出显著性能。此类先验已结合条件用于解决修复问题，但仅支持基于用户二值条件。我们推导出一种模糊条件扩散方法，可利用具有可控强度的隐式扩散先验。我们的模糊条件可逐像素应用，从而实现不同图像成分不同程度的修改。此外，我们提出一种人脸图像校正应用，将模糊条件扩散与扩散衍生注意力图相结合。我们的注意力图通过投影到扩散空间获得，用于估计异常程度。我们展示了该方法如何实现可解释且自主的人脸图像校正。