Mind-the-Glitch: Visual Correspondence for Detecting Inconsistencies in Subject-Driven Generation

We propose a novel approach for disentangling visual and semantic features from the backbones of pre-trained diffusion models, enabling visual correspondence in a manner analogous to the well-established semantic correspondence. While diffusion model backbones are known to encode semantically rich f