USO: Unified Style and Subject-Driven Generation via Disentangled and
  Reward Learning
Existing literature typically treats style-driven and subject-driven
generation as two disjoint tasks: the former prioritizes stylistic similarity,
whereas the latter insists on subject consistency, resulting in an apparent
antagonism. We argue that both objectives can be unified under a single
framework because they ultimately concern the disentanglement and
re-composition of content and style, a long-standing theme in style-driven
research. To this end, we present USO, a Unified Style-Subject Optimized
customization model. First, we construct a large-scale triplet dataset
consisting of content images, style images, and their corresponding stylized
content images. Second, we introduce a disentangled learning scheme that
simultaneously aligns style features and disentangles content from style
through two complementary objectives, style-alignment training and
content-style disentanglement training. Third, we incorporate a style
reward-learning paradigm denoted as SRL to further enhance the model's
performance. Finally, we release USO-Bench, the first benchmark that jointly
evaluates style similarity and subject fidelity across multiple metrics.
Extensive experiments demonstrate that USO achieves state-of-the-art
performance among open-source models along both dimensions of subject
consistency and style similarity. Code and model:
https://github.com/bytedance/USO
68ae8011364411bea07df7fd
Shaojin Wu
claimed_verified
2025-08-27T07:10:26.369Z
660114b38ae190912a61be5d
/avatars/abc4ab10d6f9769d2b5e697ccbf3fb70.svg
ShaojinWu
user
fenfan
68ae8011364411bea07df7fe
Mengqi Huang
68ae8011364411bea07df7ff
Yufeng Cheng
claimed_verified
2025-08-29T08:03:16.038Z
66a0aed210de1ccd3d409721
/avatars/bc6e6c1d60b6601dfb3fe3a697e02ce9.svg
Yufeng Cheng
user
cb1cyf
68ae8011364411bea07df800
Wenxu Wu
claimed_verified
2025-08-28T08:55:56.250Z
635634171c93c1ef4e9eb1c2
/avatars/66b31b801960612057ecfd1e26410075.svg
wuwenxu
user
wuwx
68ae8011364411bea07df801
Jiahe Tian
68ae8011364411bea07df802
Yiming Luo
68ae8011364411bea07df803
Fei Ding
68ae8011364411bea07df804
Qian He
USO: Unified Style and Subject-Driven Generation via Disentangled and
  Reward Learning
Existing literature typically treats style-driven and subject-driven
generation as two disjoint tasks: the former prioritizes stylistic similarity,
whereas the latter insists on subject consistency, resulting in an apparent
antagonism. We argue that both objectives can be unified under a single
framework because they ultimately concern the disentanglement and
re-composition of content and style, a long-standing theme in style-driven
research. To this end, we present USO, a Unified Style-Subject Optimized
customization model. First, we construct a large-scale triplet dataset
consisting of content images, style images, and their corresponding stylized
content images. Second, we introduce a disentangled learning scheme that
simultaneously aligns style features and disentangles content from style
through two complementary objectives, style-alignment training and
content-style disentanglement training. Third, we incorporate a style
reward-learning paradigm denoted as SRL to further enhance the model's
performance. Finally, we release USO-Bench, the first benchmark that jointly
evaluates style similarity and subject fidelity across multiple metrics.
Extensive experiments demonstrate that USO achieves state-of-the-art
performance among open-source models along both dimensions of subject
consistency and style similarity. Code and model:
https://github.com/bytedance/USO
USO, a unified model, achieves state-of-the-art performance in both style similarity and subject consistency by disentangling and re-composing content and style through a disentangled learning scheme and style reward-learning paradigm.
disentangled learning style-alignment training content-style disentanglement training style reward-learning USO-Bench