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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- black-forest-labs/FLUX.1-dev |
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library_name: transformers |
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pipeline_tag: image-to-image |
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tags: |
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- image-generation |
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- subject-personalization |
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- style-transfer |
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- Diffusion-Transformer |
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--- |
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<h3 align="center"> |
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<img src="assets/uso.webp" alt="Logo" style="vertical-align: middle; width: 95px; height: auto;"> |
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</br> |
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Unified Style and Subject-Driven Generation via Disentangled and Reward Learning |
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</h3> |
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<p align="center"> |
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<a href="https://github.com/bytedance/USO"><img alt="Build" src="https://img.shields.io/github/stars/bytedance/USO"></a> |
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<a href="https://bytedance.github.io/USO/"><img alt="Build" src="https://img.shields.io/badge/Project%20Page-USO-blue"></a> |
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<a href="https://arxiv.org/abs/2508.18966"><img alt="Build" src="https://img.shields.io/badge/Tech%20Report-USO-b31b1b.svg"></a> |
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<a href="https://huggingface.co/bytedance-research/USO"><img src="https://img.shields.io/static/v1?label=%F0%9F%A4%97%20Hugging%20Face&message=Model&color=green"></a> |
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</p> |
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## 📖 Introduction |
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Existing literature typically treats style-driven and subject-driven generation as two disjoint tasks: the former prioritizes stylistic similarity, whereas the latter insists on subject consistency, resulting in an apparent antagonism. We argue that both objectives can be unified under a single framework because they ultimately concern the disentanglement and re-composition of “content” and “style”, a long-standing theme in style-driven research. To this end, we present USO, a Unified framework for Style driven and subject-driven GeneratiOn. First, we construct a large-scale triplet dataset consisting of content images, style images, and their corresponding stylized content images. Second, we introduce a disentangled learning scheme that simultaneously aligns style features and disentangles content from style through two complementary objectives, style-alignment training and content–style disentanglement training. Third, we incorporate a style reward-learning paradigm to further enhance the model’s performance. |
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## ⚡️ Quick Start |
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### 🔧 Requirements and Installation |
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Clone our [Github repo](https://github.com/bytedance/UNO) |
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Install the requirements |
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```bash |
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## create a virtual environment with python >= 3.10 <= 3.12, like |
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# python -m venv uso_env |
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# source uso_env/bin/activate |
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# then install |
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pip install -r requirements.txt |
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``` |
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then download checkpoints in one of the three ways: |
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1. Directly run the inference scripts, the checkpoints will be downloaded automatically by the `hf_hub_download` function in the code to your `$HF_HOME`(the default value is `~/.cache/huggingface`). |
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2. use `huggingface-cli download <repo name>` to download `black-forest-labs/FLUX.1-dev`, `xlabs-ai/xflux_text_encoders`, `openai/clip-vit-large-patch14`, `TODO UNO hf model`, then run the inference scripts. |
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3. use `huggingface-cli download <repo name> --local-dir <LOCAL_DIR>` to download all the checkpoints menthioned in 2. to the directories your want. Then set the environment variable `TODO`. Finally, run the inference scripts. |
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### 🌟 Gradio Demo |
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```bash |
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python app.py |
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``` |
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## 📄 Disclaimer |
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<p> |
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We open-source this project for academic research. The vast majority of images |
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used in this project are either generated or from open-source datasets. If you have any concerns, |
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please contact us, and we will promptly remove any inappropriate content. |
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Our project is released under the Apache 2.0 License. If you apply to other base models, |
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please ensure that you comply with the original licensing terms. |
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<br><br>This research aims to advance the field of generative AI. Users are free to |
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create images using this tool, provided they comply with local laws and exercise |
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responsible usage. The developers are not liable for any misuse of the tool by users.</p> |
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## Citation |
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We also appreciate it if you could give a star ⭐ to our [Github repository](https://github.com/bytedance/USO). Thanks a lot! |
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If you find this project useful for your research, please consider citing our paper: |
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```bibtex |
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@article{wu2025uso, |
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title={USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning}, |
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author={Shaojin Wu and Mengqi Huang and Yufeng Cheng and Wenxu Wu and Jiahe Tian and Yiming Luo and Fei Ding and Qian He}, |
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year={2025}, |
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eprint={2508.18966}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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} |
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``` |
