StyleQoRA: 为少样本多风格编辑的高质量低秩适应
StyleQoRA: Quality-Aware Low-Rank Adaptation for Few-Shot Multi-Style Editing
AI总结：
本文提出StyleQoRA框架，通过自适应秩确定和混合专家LoRA设计，实现少样本多风格图像编辑的高质量适应，实验表明其参数更少却性能更优。
摘要：
近年来，图像编辑逐渐受到关注，但通用模型在面对新风格时难以产生满意结果。本文提出StyleQoRA框架，构建包含五种风格的基准数据集，通过估计单秩组件的重要性分数来自动确定每层最优秩。设计混合专家LoRA与混合路由，包含风格特定路由和共享路由，探索最佳LoRA插入位置并结合对抗学习和流匹配指导扩散训练。实验表明，所提方法在更少LoRA参数下优于现有最佳方法。
英文摘要：
In recent years, image editing has garnered growing attention. However, general image editing models often fail to produce satisfactory results when confronted with new styles. The challenge lies in how to effectively fine-tune general image editing models to new styles using only a limited amount of paired data and a minimum number of parameters. To address this issue, this paper proposes a novel few-shot multi-style editing framework. For this task, we construct a benchmark dataset that encompasses five distinct styles. Correspondingly, we propose Quality-Aware Low-Rank Adaptation for few-shot multi-style editing (StyleQoRA). Our StyleQoRA can automatically determine the optimal rank for each layer through a novel approach that estimates the importance score of each single-rank component using an image quality metric. To balance specialization and knowledge sharing, we design a Mixture-of-Experts (MoE) LoRA with hybrid routing in our StyleQoRA, consisting of style-specific routing to prevent cross-style confusion and style-shared routing to capture common transformation patterns. Additionally, we explore the optimal location to insert LoRA within the Diffusion in Transformer (DiT) model and integrate adversarial learning and flow matching to guide the diffusion training process. Experimental results demonstrate that our proposed method outperforms existing state-of-the-art approaches with significantly fewer LoRA parameters. Our code and dataset are available at https://github.com/cao-cong/FSMSE.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
