X-Omni-En

X-Omni-En (support English text rendering)

<p align="left"> <a href="https://x-omni-team.github.io">🏠 Project Page</a> | <a href="https://arxiv.org/pdf/2507.22058">📄 Paper</a> | <a href="https://github.com/X-Omni-Team/X-Omni">💻​ Code</a> | <a href="https://huggingface.co/collections/X-Omni/x-omni-spaces-6888c64f38446f1efc402de7">🚀 HuggingFace Space</a> </p>

🌟 Highlights

• Unified Modeling Approach: A discrete autoregressive model handling image and language modalities.
• Superior Instruction Following: Exceptional capability to follow complex instructions.
• Superior Text Rendering: Accurately render text in English.
• Arbitrary resolutions: Produces aesthetically pleasing images at arbitrary resolutions.

<p align="left"> <img src="assets/fig2-1.png" alt="" width="600" /> <img src="assets/fig5-1.png" alt="" width="600" /> </p>

📖 Citation

If you find this project helpful for your research or use it in your own work, please cite our paper:

@article{geng2025xomni,
      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},
      title        = {X-Omni: Reinforcement Learning Makes Discrete Autoregressive Image Generative Models Great Again},
      journal      = {CoRR},
      volume       = {abs/2507.22058},
      year         = {2025},
}