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--- |
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license: apache-2.0 |
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--- |
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## X-Omni-En (support English text rendering) |
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<p align="left"> |
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<a href="https://x-omni-team.github.io">🏠 Project Page</a> | |
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<a href="https://arxiv.org/pdf/2507.22058">📄 Paper</a> | |
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<a href="https://github.com/X-Omni-Team/X-Omni">💻 Code</a> | |
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<a href="https://huggingface.co/collections/X-Omni/x-omni-spaces-6888c64f38446f1efc402de7">🚀 HuggingFace Space</a> |
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</p> |
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## 🌟 Highlights |
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- **Unified Modeling Approach**: A discrete autoregressive model handling image and language modalities. |
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- **Superior Instruction Following**: Exceptional capability to follow complex instructions. |
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- **Superior Text Rendering**: Accurately render text in English. |
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- **Arbitrary resolutions**: Produces aesthetically pleasing images at arbitrary resolutions. |
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<p align="left"> |
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<img src="assets/fig2-1.png" alt="" width="600" /> |
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<img src="assets/fig5-1.png" alt="" width="600" /> |
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</p> |
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## 📖 Citation |
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If you find this project helpful for your research or use it in your own work, please cite our paper: |
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```bibtex |
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@article{geng2025xomni, |
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author = {Zigang Geng, Yibing Wang, Yeyao Ma, Chen Li, Yongming Rao, Shuyang Gu, Zhao Zhong, Qinglin Lu, Han Hu, Xiaosong Zhang, Linus, Di Wang and Jie Jiang}, |
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title = {X-Omni: Reinforcement Learning Makes Discrete Autoregressive Image Generative Models Great Again}, |
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journal = {CoRR}, |
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volume = {abs/None}, |
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year = {2025}, |
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} |
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``` |
