title: MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset
    via Attention Routing
summary: Current multi-subject customization approaches encounter two critical
  challenges: the difficulty in acquiring diverse multi-subject training data,
  and attribute entanglement across different subjects. To bridge these gaps, we
  propose MUSAR - a simple yet effective framework to achieve robust
  multi-subject customization while requiring only single-subject training data.
  Firstly, to break the data limitation, we introduce debiased diptych learning.
  It constructs diptych training pairs from single-subject images to facilitate
  multi-subject learning, while actively correcting the distribution bias
  introduced by diptych construction via static attention routing and dual-branch
  LoRA. Secondly, to eliminate cross-subject entanglement, we introduce dynamic
  attention routing mechanism, which adaptively establishes bijective mappings
  between generated images and conditional subjects. This design not only
  achieves decoupling of multi-subject representations but also maintains
  scalable generalization performance with increasing reference subjects.
  Comprehensive experiments demonstrate that our MUSAR outperforms existing
  methods - even those trained on multi-subject dataset - in image quality,
  subject consistency, and interaction naturalness, despite requiring only
  single-subject dataset.
paper_authors._id: 6819893117007d963b997a0b
paper_authors.name: Zinan Guo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-06T09:14:34.748Z
paper_authors._id: 66b2e5f5523bf90aa7057467
paper_authors.avatarUrl: /avatars/ccdb58c2e56cf861e9dcec50c85d7778.svg
paper_authors.fullname: Guo
paper_authors.type: user
paper_authors.user: Zinan123212
paper_authors._id: 6819893117007d963b997a0c
paper_authors.name: Pengze Zhang
paper_authors._id: 6819893117007d963b997a0d
paper_authors.name: Yanze Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-06T09:15:02.034Z
paper_authors._id: 639709c2be8a14bb9eeea8f6
paper_authors.avatarUrl: /avatars/c142d71b541dccff91fcfd08a2cc0ce0.svg
paper_authors.fullname: Yanze Wu
paper_authors.type: user
paper_authors.user: yanze
paper_authors._id: 6819893117007d963b997a0e
paper_authors.name: Chong Mou
paper_authors._id: 6819893117007d963b997a0f
paper_authors.name: Songtao Zhao
paper_authors._id: 6819893117007d963b997a10
paper_authors.name: Qian He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-06T09:15:28.996Z
paper_authors._id: 645dcad7a19f3e64bbf35e6c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/rV1uHDSnZv7jAvFq4ftj4.jpeg
paper_authors.fullname: Qian He
paper_authors.type: user
paper_authors.user: heqian
paper_title: MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset
    via Attention Routing
paper_summary: Current multi-subject customization approaches encounter two critical
  challenges: the difficulty in acquiring diverse multi-subject training data,
  and attribute entanglement across different subjects. To bridge these gaps, we
  propose MUSAR - a simple yet effective framework to achieve robust
  multi-subject customization while requiring only single-subject training data.
  Firstly, to break the data limitation, we introduce debiased diptych learning.
  It constructs diptych training pairs from single-subject images to facilitate
  multi-subject learning, while actively correcting the distribution bias
  introduced by diptych construction via static attention routing and dual-branch
  LoRA. Secondly, to eliminate cross-subject entanglement, we introduce dynamic
  attention routing mechanism, which adaptively establishes bijective mappings
  between generated images and conditional subjects. This design not only
  achieves decoupling of multi-subject representations but also maintains
  scalable generalization performance with increasing reference subjects.
  Comprehensive experiments demonstrate that our MUSAR outperforms existing
  methods - even those trained on multi-subject dataset - in image quality,
  subject consistency, and interaction naturalness, despite requiring only
  single-subject dataset.
paper_ai_summary: MUSAR framework facilitates robust multi-subject customization using single-subject training data through debiased diptych learning and dynamic attention routing.
paper_ai_keywords: debiased diptych learning static attention routing dual-branch LoRA dynamic attention routing bijective mappings multi-subject representations generalization performance