Less-to-More Generalization: Unlocking More Controllability by
  In-Context Generation
Authors: name: Shaojin Wu, name: Mengqi Huang, name: Wenxu Wu, name: Yufeng Cheng, name: Fei Ding, name: Qian He

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.