让图像生成同时保身份、可个性化，动态调节更灵活。
FlexIP: Dynamic Control of Preservation and Personality for Customized Image Generation
- 拆分风格与身份控制，用两个适配器分别管理
- 动态调整权重，实现生成时灵活切换保真与创意
- 适合需要稳定身份又想自由改风格的用户
随着2D生成模型的快速发展，如何在保持主体身份的同时支持多样编辑成为关键挑战。现有方法通常在身份保留与个性化操控间存在固有权衡。本文提出FlexIP框架，通过两个专用组件解耦这两个目标：个性化适配器用于风格操控，保真适配器用于身份维护。通过将两种控制机制显式注入生成模型，可在推理阶段通过动态调节权重适配器实现参数化灵活控制。实验表明，该方法突破传统方法性能瓶颈，在保持更优身份一致性的同时，支持更丰富的个性化生成能力。
原文摘要 · Abstract (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/).
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