USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

• URL: http://arxiv.org/abs/2508.18966v1
• Date: Tue, 26 Aug 2025 12:10:24 GMT
• Title: USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning
• Authors: Shaojin Wu, Mengqi Huang, Yufeng Cheng, Wenxu Wu, Jiahe Tian, Yiming Luo, Fei Ding, Qian He,
• Abstract summary: We present U.S.O, a Unified Style-Subject Optimized customization model.<n>First, we construct a large-scale triplet dataset consisting of content images, style images, and their corresponding stylized content images.<n>Second, we introduce a disentangled learning scheme that simultaneously aligns style features and disentangles content from style.<n>Third, we incorporate a style reward-learning paradigm denoted as SRL to further enhance the model's performance.
• Score: 17.75580298339048
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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