Author

Or Patashnik

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Jul 2026

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.

Daniel Garibi, Ronen Kamenetsky, Hadar Averbuch-Elor et al. · arXiv.org · 0 citations