FlexIP：面向定制化图像生成的保真度与个性化动态控制
FlexIP: Dynamic Control of Preservation and Personality for Customized Image Generation
AI总结：
FlexIP框架通过个性化适配器和保真适配器解耦风格操控与身份维护，实现推理阶段灵活参数化控制，突破身份保真与个性化编辑之间的性能权衡。
摘要：
随着二维生成模型的快速发展，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。现有方法通常面临身份保真与个性化操控之间的固有权衡。我们提出FlexIP，这是一个新颖的框架，通过两个专用组件解耦这些目标：一个用于风格操控的个性化适配器和一个用于身份维护的保真适配器。通过将两种控制机制显式注入生成模型，我们的框架能够在推理过程中通过动态调节权重适配器实现灵活的参数化控制。实验结果表明，我们的方法突破了传统方法的性能限制，在实现更优身份保真的同时支持更多样化的个性化生成能力（项目页面：https://flexip-tech.github.io/flexip/）。
英文摘要：
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/).
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。
