Latent-Identity Tuning in Text-to-Image Personalization Models
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 latent representation of a specific identity, enabling the generation of diverse images that consiste
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- PossiblePossibly related (embedding) · 49%deepseek-ai/DeepSeek-V3-0324 →
- PossiblePossibly related (embedding) · 48%deepseek-ai/DeepSeek-R1 →
- PossiblePossibly related (embedding) · 46%deepseek-ai/Janus-Pro-7B →
- PossiblePossibly related (embedding) · 46%deepseek-ai/DeepSeek-V3 →
- PossiblePossibly related (embedding) · 45%BAAI/bge-m3 →
- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “Latent-Identity Tuning in Text-to-Image Personalization Mode” ≈ “Tongyi-MAI/Z-Image-Turbo””
- LinkedLinked via arxiv author · 85%Daniel Garibi →
“Latent-Identity Tuning in Text-to-Image Personalization Models”
- LinkedLinked via arxiv author · 85%Ronen Kamenetsky →
“Latent-Identity Tuning in Text-to-Image Personalization Models”