UNO

UNO is a breakthrough AI framework that generalizes from single-subject to multi-subject image generation with unprecedented control. Developed by ByteDance, it leverages diffusion transformers and a novel universal rotary position embedding (UnoPE) to maintain consistency across complex multi-subject scenes—solving long-standing challenges in attribute preservation and scalability.

Unlike traditional models, UNO uses a two-stage training pipeline: first fine-tuning on single-subject data, then scaling to multi-subject contexts via synthetic high-consistency datasets. Open-sourced for research (Apache 2.0), it’s ideal for applications needing precise subject-driven generation, from product design to conceptual art, while adhering to ethical guidelines.
Key differentiators:

Multi-subject consistency

UnoPE for attribute control

Synthetic data pipeline

Diffusion transformer backbone

Open-source research focus