analyze USO, a novel generative AI model developed by Bytedance's Intelligent Creation Lab. USO addresses the long-standing challenge of separately controlling style and subject in image generation by proposing a unified framework that synergizes these tasks. The text details USO's conceptual foundations, including cross-task co-disentanglement and style reward-learning, which allow it to effectively separate and recombine content and style information. It further explains the model's architecture, training methodology utilizing a large-scale triplet dataset, and practical capabilities such as combined style-subject generation and low VRAM inference. Finally, the sources position USO within the broader generative AI landscape, comparing it to specialized models like StyleDrop and PhotoMaker, and highlighting its potential as a step towards universal customization models.