UMO: Scaling Multi-Identity Consistency for Image Customization via
  Matching Reward
Recent advancements in image customization exhibit a wide range of
application prospects due to stronger customization capabilities. However,
since we humans are more sensitive to faces, a significant challenge remains in
preserving consistent identity while avoiding identity confusion with
multi-reference images, limiting the identity scalability of customization
models. To address this, we present UMO, a Unified Multi-identity Optimization
framework, designed to maintain high-fidelity identity preservation and
alleviate identity confusion with scalability. With "multi-to-multi matching"
paradigm, UMO reformulates multi-identity generation as a global assignment
optimization problem and unleashes multi-identity consistency for existing
image customization methods generally through reinforcement learning on
diffusion models. To facilitate the training of UMO, we develop a scalable
customization dataset with multi-reference images, consisting of both
synthesised and real parts. Additionally, we propose a new metric to measure
identity confusion. Extensive experiments demonstrate that UMO not only
improves identity consistency significantly, but also reduces identity
confusion on several image customization methods, setting a new
state-of-the-art among open-source methods along the dimension of identity
preserving. Code and model: https://github.com/bytedance/UMO
68bfa87b207285de11b07c7e
Yufeng Cheng
claimed_verified
2025-09-09T13:47:18.629Z
66a0aed210de1ccd3d409721
/avatars/bc6e6c1d60b6601dfb3fe3a697e02ce9.svg
Yufeng Cheng
user
cb1cyf
68bfa87b207285de11b07c7f
Wenxu Wu
claimed_verified
2025-09-12T16:19:57.062Z
635634171c93c1ef4e9eb1c2
/avatars/66b31b801960612057ecfd1e26410075.svg
wuwenxu
user
wuwx
68bfa87b207285de11b07c80
Shaojin Wu
claimed_verified
2025-09-09T13:47:20.947Z
660114b38ae190912a61be5d
/avatars/abc4ab10d6f9769d2b5e697ccbf3fb70.svg
ShaojinWu
user
fenfan
68bfa87b207285de11b07c81
Mengqi Huang
68bfa87b207285de11b07c82
Fei Ding
68bfa87b207285de11b07c83
Qian He
UMO: Scaling Multi-Identity Consistency for Image Customization via
  Matching Reward
Recent advancements in image customization exhibit a wide range of
application prospects due to stronger customization capabilities. However,
since we humans are more sensitive to faces, a significant challenge remains in
preserving consistent identity while avoiding identity confusion with
multi-reference images, limiting the identity scalability of customization
models. To address this, we present UMO, a Unified Multi-identity Optimization
framework, designed to maintain high-fidelity identity preservation and
alleviate identity confusion with scalability. With "multi-to-multi matching"
paradigm, UMO reformulates multi-identity generation as a global assignment
optimization problem and unleashes multi-identity consistency for existing
image customization methods generally through reinforcement learning on
diffusion models. To facilitate the training of UMO, we develop a scalable
customization dataset with multi-reference images, consisting of both
synthesised and real parts. Additionally, we propose a new metric to measure
identity confusion. Extensive experiments demonstrate that UMO not only
improves identity consistency significantly, but also reduces identity
confusion on several image customization methods, setting a new
state-of-the-art among open-source methods along the dimension of identity
preserving. Code and model: https://github.com/bytedance/UMO
UMO, a Unified Multi-identity Optimization framework, enhances identity consistency and reduces confusion in multi-reference image customization using reinforcement learning on diffusion models.
Unified Multi-identity Optimization multi-to-multi matching global assignment optimization reinforcement learning diffusion models identity consistency identity confusion