FlexIP：动态控制保存与个性以实现定制化图像生成

FlexIP: Dynamic Control of Preservation and Personality for Customized Image Generation

April 10, 2025

作者: Linyan Huang, Haonan Lin, Yanning Zhou, Kaiwen Xiao

cs.AI

摘要

随着二维生成模型的快速发展，如何在保持主体身份的同时实现多样化编辑已成为一个关键研究焦点。现有方法通常在身份保持与个性化操控之间面临固有的权衡。我们提出了FlexIP这一创新框架，通过两个专用组件——用于风格操控的个性化适配器和用于身份保持的保持适配器——将这两个目标解耦。通过将这两种控制机制显式注入生成模型，我们的框架在推理过程中通过动态调整权重适配器实现了灵活的参数化控制。实验结果表明，我们的方法突破了传统方法的性能局限，在支持更丰富的个性化生成能力的同时，实现了更优的身份保持效果（项目页面：https://flexip-tech.github.io/flexip/）。

English

With the rapid advancement of 2D generative models, preserving subject identity while enabling diverse editing has emerged as a critical research focus. Existing methods typically face inherent trade-offs between identity preservation and personalized manipulation. We introduce FlexIP, a novel framework that decouples these objectives through two dedicated components: a Personalization Adapter for stylistic manipulation and a Preservation Adapter for identity maintenance. By explicitly injecting both control mechanisms into the generative model, our framework enables flexible parameterized control during inference through dynamic tuning of the weight adapter. Experimental results demonstrate that our approach breaks through the performance limitations of conventional methods, achieving superior identity preservation while supporting more diverse personalized generation capabilities (Project Page: https://flexip-tech.github.io/flexip/).