FlexIP: Dynamic Control of Preservation and Personality for Customized
  Image Generation
Authors: name: Linyan Huang, name: Haonan Lin, name: Yanning Zhou, name: Kaiwen Xiao

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/).