FlexIP: Dynamic Control of Preservation and Personality for Customized
  Image Generation
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/).
67fa383909d06d0501a5e34e
Linyan Huang
claimed_verified
2025-04-13T19:24:47.680Z
660d844462d63ad0009a9859
/avatars/6822fc1a82c64dbfd40b88080a5fb1ae.svg
Linyan Huang
user
DevLinyan
67fa383909d06d0501a5e34f
Haonan Lin
67fa383909d06d0501a5e350
Yanning Zhou
67fa383909d06d0501a5e351
Kaiwen Xiao
FlexIP: Dynamic Control of Preservation and Personality for Customized
  Image Generation
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/).
FlexIP framework decouples identity preservation and stylistic manipulation in 2D generative models through dedicated adapters, enhancing flexibility and performance.
FlexIP Personalization Adapter Preservation Adapter generative models identity preservation stylistic manipulation parameterized control dynamic tuning weight adapter