QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation

• URL: http://arxiv.org/abs/2507.04599v2
• Date: Thu, 24 Jul 2025 09:12:08 GMT
• Title: QR-LoRA: Efficient and Disentangled Fine-tuning via QR Decomposition for Customized Generation
• Authors: Jiahui Yang, Yongjia Ma, Donglin Di, Hao Li, Wei Chen, Yan Xie, Jianxun Cui, Xun Yang, Wangmeng Zuo,
• Abstract summary: We propose QR-LoRA, a novel fine-tuning framework leveraging QR decomposition for structured parameter updates.<n>Our key insight is that the Q matrix naturally minimizes interference between different visual features.<n>Experiments demonstrate that QR-LoRA achieves superior disentanglement in content-style fusion tasks.
• Score: 52.024845354511555
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing text-to-image models often rely on parameter fine-tuning techniques such as Low-Rank Adaptation (LoRA) to customize visual attributes. However, when combining multiple LoRA models for content-style fusion tasks, unstructured modifications of weight matrices often lead to undesired feature entanglement between content and style attributes. We propose QR-LoRA, a novel fine-tuning framework leveraging QR decomposition for structured parameter updates that effectively separate visual attributes. Our key insight is that the orthogonal Q matrix naturally minimizes interference between different visual features, while the upper triangular R matrix efficiently encodes attribute-specific transformations. Our approach fixes both Q and R matrices while only training an additional task-specific $\Delta R$ matrix. This structured design reduces trainable parameters to half of conventional LoRA methods and supports effective merging of multiple adaptations without cross-contamination due to the strong disentanglement properties between $\Delta R$ matrices. Experiments demonstrate that QR-LoRA achieves superior disentanglement in content-style fusion tasks, establishing a new paradigm for parameter-efficient, disentangled fine-tuning in generative models. The project page is available at: https://luna-ai-lab.github.io/QR-LoRA/.

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