USO: Unified Style and Subject-Driven Generation via Disentangled and
  Reward Learning
Authors: name: Shaojin Wu; name: Mengqi Huang; name: Yufeng Cheng; name: Wenxu Wu; name: Jiahe Tian; name: Yiming Luo; name: Fei Ding; name: Qian He
Published: 2025-08-26 12:10:24

Abstract: 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