Less-to-More Generalization: Unlocking More Controllability by
  In-Context Generation
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.
67efd1cd40e0a904109cac33
Shaojin Wu
claimed_verified
2025-04-08T06:54:08.610Z
660114b38ae190912a61be5d
/avatars/abc4ab10d6f9769d2b5e697ccbf3fb70.svg
ShaojinWu
user
fenfan
67efd1cd40e0a904109cac34
Mengqi Huang
admin_assigned
2025-04-09T16:38:32.742Z
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/avatars/c3ba695d339eaaf9e810bfa0d9a7689a.svg
Mengqi Huang
user
CoreloneH
67efd1cd40e0a904109cac35
Wenxu Wu
claimed_verified
2025-04-08T06:54:06.171Z
635634171c93c1ef4e9eb1c2
/avatars/66b31b801960612057ecfd1e26410075.svg
wuwenxu
user
wuwx
67efd1cd40e0a904109cac36
Yufeng Cheng
claimed_verified
2025-05-13T08:13:22.381Z
66a0aed210de1ccd3d409721
/avatars/bc6e6c1d60b6601dfb3fe3a697e02ce9.svg
Yufeng Cheng
user
cb1cyf
67efd1cd40e0a904109cac37
Fei Ding
67efd1cd40e0a904109cac38
Qian He
Less-to-More Generalization: Unlocking More Controllability by
  In-Context Generation
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.
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.
diffusion transformers progressive cross-modal alignment universal rotary position embedding subject-to-image model text-to-image model high consistency controllability