bytedance/UNO

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1362 stars · 79 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

UNO is a research framework from ByteDance for subject-driven image generation with diffusion transformers, supporting both single- and multi-subject customization. It builds on text-to-image models like FLUX using progressive cross-modal alignment and universal rotary position embedding (UnoPE).

Use cases

• generate images of a specific subject from reference photos
• combine multiple subjects into one generated image
• customize a text-to-image model with my product or character
• create consistent character images across scenes
• subject-driven image generation with FLUX
• synthesize multi-subject training data for image generation

When to choose

• you need high-fidelity single- or multi-subject image customization
• you want a research-grade diffusion transformer pipeline with pretrained checkpoints
• you are reproducing or extending the ICCV 2025 UNO paper

When to avoid

• you need a polished end-user image editing app
• you lack a GPU or cannot run large diffusion models
• you need non-image modalities or lightweight inference on CPU

Facets

library · maturity active

image-processing machine-learning deep-learning llm-training image-processing artificial-intelligence deep-learning computer-vision python diffusion-transformer text-to-image subject-driven-generation flux in-context-learning image-customization research-code iccv-2025 gpu linux

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