MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset
  via Attention Routing
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.
6819893117007d963b997a0b
Zinan Guo
admin_assigned
2025-05-06T09:14:34.748Z
66b2e5f5523bf90aa7057467
/avatars/ccdb58c2e56cf861e9dcec50c85d7778.svg
Guo
user
Zinan123212
6819893117007d963b997a0c
Pengze Zhang
6819893117007d963b997a0d
Yanze Wu
admin_assigned
2025-05-06T09:15:02.034Z
639709c2be8a14bb9eeea8f6
/avatars/c142d71b541dccff91fcfd08a2cc0ce0.svg
Yanze Wu
user
yanze
6819893117007d963b997a0e
Chong Mou
6819893117007d963b997a0f
Songtao Zhao
6819893117007d963b997a10
Qian He
admin_assigned
2025-05-06T09:15:28.996Z
645dcad7a19f3e64bbf35e6c
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/rV1uHDSnZv7jAvFq4ftj4.jpeg
Qian He
user
heqian
MUSAR: Exploring Multi-Subject Customization from Single-Subject Dataset
  via Attention Routing
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.
MUSAR framework facilitates robust multi-subject customization using single-subject training data through debiased diptych learning and dynamic attention routing.
debiased diptych learning static attention routing dual-branch LoRA dynamic attention routing bijective mappings multi-subject representations generalization performance