title: Less-to-More Generalization: Unlocking More Controllability by
    In-Context Generation
summary: Although subject-driven generation has been extensively explored in image
  generation due to its wide applications, it still has challenges in data
  scalability and subject expansibility. For the first challenge, moving from
  curating single-subject datasets to multiple-subject ones and scaling them is
  particularly difficult. For the second, most recent methods center on
  single-subject generation, making it hard to apply when dealing with
  multi-subject scenarios. In this study, we propose a highly-consistent data
  synthesis pipeline to tackle this challenge. This pipeline harnesses the
  intrinsic in-context generation capabilities of diffusion transformers and
  generates high-consistency multi-subject paired data. Additionally, we
  introduce UNO, which consists of progressive cross-modal alignment and
  universal rotary position embedding. It is a multi-image conditioned
  subject-to-image model iteratively trained from a text-to-image model.
  Extensive experiments show that our method can achieve high consistency while
  ensuring controllability in both single-subject and multi-subject driven
  generation.
paper_authors._id: 67efd1cd40e0a904109cac33
paper_authors.name: Shaojin Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-08T06:54:08.610Z
paper_authors._id: 660114b38ae190912a61be5d
paper_authors.avatarUrl: /avatars/abc4ab10d6f9769d2b5e697ccbf3fb70.svg
paper_authors.fullname: ShaojinWu
paper_authors.type: user
paper_authors.user: fenfan
paper_authors._id: 67efd1cd40e0a904109cac34
paper_authors.name: Mengqi Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-09T16:38:32.742Z
paper_authors._id: 630636bcd37ce67e0e4d1d42
paper_authors.avatarUrl: /avatars/c3ba695d339eaaf9e810bfa0d9a7689a.svg
paper_authors.fullname: Mengqi Huang
paper_authors.type: user
paper_authors.user: CoreloneH
paper_authors._id: 67efd1cd40e0a904109cac35
paper_authors.name: Wenxu Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-08T06:54:06.171Z
paper_authors._id: 635634171c93c1ef4e9eb1c2
paper_authors.avatarUrl: /avatars/66b31b801960612057ecfd1e26410075.svg
paper_authors.fullname: wuwenxu
paper_authors.type: user
paper_authors.user: wuwx
paper_authors._id: 67efd1cd40e0a904109cac36
paper_authors.name: Yufeng Cheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-13T08:13:22.381Z
paper_authors._id: 66a0aed210de1ccd3d409721
paper_authors.avatarUrl: /avatars/bc6e6c1d60b6601dfb3fe3a697e02ce9.svg
paper_authors.fullname: Yufeng Cheng
paper_authors.type: user
paper_authors.user: cb1cyf
paper_authors._id: 67efd1cd40e0a904109cac37
paper_authors.name: Fei Ding
paper_authors._id: 67efd1cd40e0a904109cac38
paper_authors.name: Qian He
paper_title: Less-to-More Generalization: Unlocking More Controllability by
    In-Context Generation
paper_summary: Although subject-driven generation has been extensively explored in image
  generation due to its wide applications, it still has challenges in data
  scalability and subject expansibility. For the first challenge, moving from
  curating single-subject datasets to multiple-subject ones and scaling them is
  particularly difficult. For the second, most recent methods center on
  single-subject generation, making it hard to apply when dealing with
  multi-subject scenarios. In this study, we propose a highly-consistent data
  synthesis pipeline to tackle this challenge. This pipeline harnesses the
  intrinsic in-context generation capabilities of diffusion transformers and
  generates high-consistency multi-subject paired data. Additionally, we
  introduce UNO, which consists of progressive cross-modal alignment and
  universal rotary position embedding. It is a multi-image conditioned
  subject-to-image model iteratively trained from a text-to-image model.
  Extensive experiments show that our method can achieve high consistency while
  ensuring controllability in both single-subject and multi-subject driven
  generation.
paper_ai_summary: A new data synthesis pipeline uses diffusion transformers and UNO, featuring progressive cross-modal alignment and universal rotary position embedding, to generate high-consistency multi-subject data while maintaining controllability.
paper_ai_keywords: diffusion transformers progressive cross-modal alignment universal rotary position embedding subject-to-image model text-to-image model high consistency controllability