1. Tutorial Introduction

The UNO project is an AI image generation model released by ByteDance's intelligent creation team in April 2025. It can support both single-subject and multi-subject image generation, unifying multiple tasks with one model and demonstrating strong generalization capabilities.Less-to-More Generalization: Unlocking More Controllability by In-Context Generation".

The project supports multiple data formats and storage backends, has powerful query optimization capabilities and flexible scalability, and is suitable for large-scale data analysis scenarios. UNO is committed to helping developers easily build efficient data processing processes through simple APIs and rich functional features, and providing reliable data infrastructure support for enterprises and developers.

This tutorial uses resources for a single RTX 4090 card.

👉 This project provides a model of:

• FLUX.1-dev-fp 8: This is a 12 billion parameter rectifier transformer capable of generating images from text descriptions.

Project Examples

2. Operation steps

1. After starting the container, click the API address to enter the Web interface

If "Model" is not displayed, it means the model is being initialized. Since the model is large, please wait about 1-2 minutes and refresh the page.

2. After entering the webpage, you can start a conversation with the model

❗️Important usage tips:

• Number of steps:  Indicates the number of iterations of the model or the number of steps in the inference process, representing the number of optimization steps the model uses to produce the result. A higher number of steps generally produces more refined results, but may increase the computation time.
• Guidance:  It is used to control the influence of conditional input (such as text or image) on the generated results in the generative model. A higher guidance value will make the generated results closer to the input conditions, while a lower value will retain more randomness.
• Seed:  It is a random number seed, which is used to control the randomness of the generation process. The same Seed value can generate the same results (provided that other parameters are the same), which is very important in reproducing the results.

How to use

Exchange and discussion

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Citation Information

Thanks to Github user xxxjjjyyy1  For the production of this tutorial, the project reference information is as follows:

@article{wu2025less,
  title={Less-to-More Generalization: Unlocking More Controllability by In-Context Generation},
  author={Wu, Shaojin and Huang, Mengqi and Wu, Wenxu and Cheng, Yufeng and Ding, Fei and He, Qian},
  journal={arXiv preprint arXiv:2504.02160},
  year={2025}
}