StyleQoRA: Quality-Aware Low-Rank Adaptation for Few-Shot Multi-Style Editing

ArXi:2511.11236v4 Announce Type: replace 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.