UMO: Scaling Multi-Identity Consistency for Image Customization via
  Matching Reward
Authors: name: Yufeng Cheng, name: Wenxu Wu, name: Shaojin Wu, name: Mengqi Huang, name: Fei Ding, name: Qian He

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