Latent-Identity Tuning in Text-to-Image Personalization Models
This work explores the latent space of a pre-trained, frozen encoder for text-to-image personalization, and shows that meaningful directions can be identified within this space and within subspaces defined by selected tokens, enabling localized, fine-grained, and semantically coherent edits.