Instructions to use bytedance-research/UNO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bytedance-research/UNO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="bytedance-research/UNO")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bytedance-research/UNO", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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><p align="center"> <span style="color:#137cf3; font-family: Gill Sans">Shaojin Wu,</span><sup></sup></a> <span style="color:#137cf3; font-family: Gill Sans">Mengqi Huang</span><sup>*</sup>,</a> <span style="color:#137cf3; font-family: Gill Sans">Wenxu Wu,</span><sup></sup></a> <span style="color:#137cf3; font-family: Gill Sans">Yufeng Cheng,</span><sup></sup> </a> <span style="color:#137cf3; font-family: Gill Sans">Fei Ding</span><sup>+</sup>,</a> <span style="color:#137cf3; font-family: Gill Sans">Qian He</span></a> <br>
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><span style="font-size: 16px">Intelligent Creation Team, ByteDance</span></p>
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## 🔥 News
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- [04/2025] 🔥 The [training code](https://github.com/bytedance/UNO), [inference code](https://github.com/bytedance/UNO), and [model](https://huggingface.co/bytedance-research/UNO) of UNO are released. The [demo](https://huggingface.co/spaces/bytedance-research/UNO-FLUX) will coming soon.
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## 🎨 Application Scenarios
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## 📄 Disclaimer
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><p align="center"> <span style="color:#137cf3; font-family: Gill Sans">Shaojin Wu,</span><sup></sup></a> <span style="color:#137cf3; font-family: Gill Sans">Mengqi Huang</span><sup>*</sup>,</a> <span style="color:#137cf3; font-family: Gill Sans">Wenxu Wu,</span><sup></sup></a> <span style="color:#137cf3; font-family: Gill Sans">Yufeng Cheng,</span><sup></sup> </a> <span style="color:#137cf3; font-family: Gill Sans">Fei Ding</span><sup>+</sup>,</a> <span style="color:#137cf3; font-family: Gill Sans">Qian He</span></a> <br>
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## 🔥 News
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- [04/2025] 🔥 The [training code](https://github.com/bytedance/UNO), [inference code](https://github.com/bytedance/UNO), and [model](https://huggingface.co/bytedance-research/UNO) of UNO are released. The [demo](https://huggingface.co/spaces/bytedance-research/UNO-FLUX) will coming soon.
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## 🎨 Application Scenarios
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## 📄 Disclaimer
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