docs: update README
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README.md
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<div align="center">
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[](https://github.com/ByteDance-Seed/cryofm)
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[](https://opensource.org/licenses/Apache-2.0)
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</div>
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## Play with CryoFM2
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### Unconditional Generation (Explore Training Data Distribution)
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Generate samples from the pretrained model to explore the learned data distribution:
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## Citation
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## License
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<div align="center">
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[](https://doi.org/10.64898/2025.12.29.696802)
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[](https://github.com/ByteDance-Seed/cryofm)
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[](https://opensource.org/licenses/Apache-2.0)
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[](https://bytedance-seed.github.io/cryofm/docs/)
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</div>
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## Play with CryoFM2
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### Installation
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Before using CryoFM2, you need to set up the environment and install the package. Follow these steps to get started:
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```bash
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# Clone the repository
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git clone https://github.com/ByteDance-Seed/cryofm.git
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cd cryofm
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# Create a new conda environment for CryoFM (recommended)
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conda create -n cryofm python=3.10 -y
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conda activate cryofm
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# Install CryoFM
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pip install .
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```
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### Unconditional Generation (Explore Training Data Distribution)
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Generate samples from the pretrained model to explore the learned data distribution:
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## Citation
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If you find CryoFM2 useful, please cite:
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```bibtex
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@article{
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Li2025.12.29.696802,
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author={Li, Yilai and Yuan, Jing and Zhou, Yi and Wang, Zhenghua and Chen, Suyi and Yang, Fengyu and Ling, Haibin and Kovalsky, Shahar Z and Zheng, Xiaoqing and Gu, Quanquan},
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title={A Generative Foundation Model for Cryo-EM Densities},
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elocation-id={2025.12.29.696802},
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year={2025},
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doi={10.64898/2025.12.29.696802},
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publisher={Cold Spring Harbor Laboratory},
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URL={https://www.biorxiv.org/content/early/2025/12/29/2025.12.29.696802},
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eprint={https://www.biorxiv.org/content/early/2025/12/29/2025.12.29.696802.full.pdf},
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journal={bioRxiv}
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}
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```
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## License
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