Meta-LoRA: Meta-Learning LoRA Components for Domain-Aware ID Personalization

• URL: http://arxiv.org/abs/2503.22352v2
• Date: Wed, 09 Apr 2025 07:33:11 GMT
• Title: Meta-LoRA: Meta-Learning LoRA Components for Domain-Aware ID Personalization
• Authors: Barış Batuhan Topal, Umut Özyurt, Zafer Doğan Budak, Ramazan Gokberk Cinbis,
• Abstract summary: We introduce Meta-LoRA, a framework that separates identity-agnostic knowledge from identity-specific adaptation.<n>Our results demonstrate that Meta-LoRA achieves superior identity retention, computational efficiency, and adaptability across diverse identity conditions.
• Score: 5.874782446136915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in text-to-image generative models, particularly latent diffusion models (LDMs), have demonstrated remarkable capabilities in synthesizing high-quality images from textual prompts. However, achieving identity personalization-ensuring that a model consistently generates subject-specific outputs from limited reference images-remains a fundamental challenge. To address this, we introduce Meta-Low-Rank Adaptation (Meta-LoRA), a novel framework that leverages meta-learning to encode domain-specific priors into LoRA-based identity personalization. Our method introduces a structured three-layer LoRA architecture that separates identity-agnostic knowledge from identity-specific adaptation. In the first stage, the LoRA Meta-Down layers are meta-trained across multiple subjects, learning a shared manifold that captures general identity-related features. In the second stage, only the LoRA-Mid and LoRA-Up layers are optimized to specialize on a given subject, significantly reducing adaptation time while improving identity fidelity. To evaluate our approach, we introduce Meta-PHD, a new benchmark dataset for identity personalization, and compare Meta-LoRA against state-of-the-art methods. Our results demonstrate that Meta-LoRA achieves superior identity retention, computational efficiency, and adaptability across diverse identity conditions. Our code, model weights, and dataset are released on barisbatuhan.github.io/Meta-LoRA.

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