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