Unified Customized Generation by Disentangled Reward Modeling

Existing literature typically treats various customized generation tasks (e.g., subject-customized generation, style-customized generation) as distinct and disjoint problems, with each task focusing solely on customizing a specific aspect of the reference image. However, we argue that the objectives of these different customization tasks are inherently complementary and can be mutually enhanced within a unified framework, as they fundamentally involve the disentanglement of multiple feature aspects from the reference image. To this end, we introduce USO , a U nified S imultaneous O ptimization framework to simultaneously unify different customized tasks (i.e., subject and style). Specifically, USO introduces a cyclical data-model framework that connects these two tasks by a subject-for-style data curation pipeline and a style-for-subject model training pipeline. The subject-for-style data curation pipeline leverages a state-of-the-art subject-customized model to generate high-quality triplet data comprising content images, style images, and their corresponding stylized content images. Building on this foundation, the style-for-subject model training pipeline introduces an auxiliary style reward to simultaneously align style and content features, thereby reinforcing the model’s ability to extract the desired style or content features from the reference image. Extensive experiments demonstrate that USO achieves state-of-the-art performance among open-source models, excelling in both subject consistency and style similarity.