UNO implements a less-to-more generalization approach for subject-driven image generation, allowing flexible conditioning on both single and multiple subjects. Built on diffusion transformers like FLUX, it enables customizable text-to-image synthesis with high identity consistency across subjects. The project includes model weights, a 1M sample dataset, and a Hugging Face demo space.