LoFA: Learning to Predict Personalized Priors for Fast Adaptation of Visual Generative Models

• URL: http://arxiv.org/abs/2512.08785v1
• Date: Tue, 09 Dec 2025 16:39:31 GMT
• Title: LoFA: Learning to Predict Personalized Priors for Fast Adaptation of Visual Generative Models
• Authors: Yiming Hao, Mutian Xu, Chongjie Ye, Jie Qin, Shunlin Lu, Yipeng Qin, Xiaoguang Han,
• Abstract summary: Low-Rank Adaptation (LoRA) remains impractical due to their demand for task-specific data and lengthy optimization.<n>We propose LoFA, a general framework that efficiently predicts personalized priors for fast model adaptation.<n>Our method consistently predicts high-quality personalized priors within seconds, across multiple tasks and user prompts, even outperforming conventional LoRA that requires hours of processing.
• Score: 50.46815266062554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalizing visual generative models to meet specific user needs has gained increasing attention, yet current methods like Low-Rank Adaptation (LoRA) remain impractical due to their demand for task-specific data and lengthy optimization. While a few hypernetwork-based approaches attempt to predict adaptation weights directly, they struggle to map fine-grained user prompts to complex LoRA distributions, limiting their practical applicability. To bridge this gap, we propose LoFA, a general framework that efficiently predicts personalized priors for fast model adaptation. We first identify a key property of LoRA: structured distribution patterns emerge in the relative changes between LoRA and base model parameters. Building on this, we design a two-stage hypernetwork: first predicting relative distribution patterns that capture key adaptation regions, then using these to guide final LoRA weight prediction. Extensive experiments demonstrate that our method consistently predicts high-quality personalized priors within seconds, across multiple tasks and user prompts, even outperforming conventional LoRA that requires hours of processing. Project page: https://jaeger416.github.io/lofa/.

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