{
  "id": 1506,
  "url": "https://arxiv.org/abs/2607.11885",
  "title": "Latent-Identity Tuning in Text-to-Image Personalization Models",
  "summary": "Generating and editing a person's face demands high precision, as even minor modifications can significantly alter a subject's perceived identity. Current personalization and editing methods built on general-purpose text-to-image models, however, often lack the precision required for fine-grained facial edits. We present a method for fine-grained identity tuning in text-to-image personalization models. Unlike standard image editing, which operates on a given image, identity tuning modifies the l",
  "authors": "Daniel Garibi, Ronen Kamenetsky, Hadar Averbuch-Elor, Daniel Cohen-Or, Or Patashnik",
  "category": "research",
  "topics": "children-education",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-13T13:59:49.000Z",
  "fetched_at": "2026-07-14T16:04:12.223Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/1506",
  "original_url": "https://arxiv.org/abs/2607.11885",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}