从海量LoRA模型中高效选出多样且相关的适配器，提升扩散模型个性化生成效果。
LoRAverse: A Submodular Framework to Retrieve Diverse Adapters for Diffusion Models
- 用子模优化框架建模适配器选择，兼顾相关性与多样性。
- 在多个领域生成多样输出，显著优于随机和贪心选择方法。
- 适合需要快速定制图像风格或角色的AI艺术家与开发者。
低秩适配（LoRA）模型通过针对注意力层设计的低秩分解权重矩阵，革新了预训练扩散模型的个性化能力，可无需大规模重训练即可生成特定对象、人物及艺术风格的内容。尽管Civit.ai等平台已有超过10万款LoRA适配器，用户仍面临筛选困难，因数量庞大、类型多样且缺乏结构化组织。本文将适配器选择问题建模为组合优化任务，提出一种新颖的子模框架，实现高相关性与高多样性并重的适配器检索。定量与定性实验表明，该方法在多个领域均能生成丰富多样的输出结果。
原文摘要 · Abstract (English)
Low-rank Adaptation (LoRA) models have revolutionized the personalization of pre-trained diffusion models by enabling fine-tuning through low-rank, factorized weight matrices specifically optimized for attention layers. These models facilitate the generation of highly customized content across a variety of objects, individuals, and artistic styles without the need for extensive retraining. Despite the availability of over 100K LoRA adapters on platforms like Civit.ai, users often face challenges in navigating, selecting, and effectively utilizing the most suitable adapters due to their sheer volume, diversity, and lack of structured organization. This paper addresses the problem of selecting the most relevant and diverse LoRA models from this vast database by framing the task as a combinatorial optimization problem and proposing a novel submodular framework. Our quantitative and qualitative experiments demonstrate that our method generates diverse outputs across a wide range of domains.
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