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AI for Science · Structural Biology
A Generative Foundation Model for Cryo-Electron Microscopy
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| **Model Weights** | 
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| **User Guide** | 
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## Getting started (For end users)
### Installation
CryoFM was developed and tested on Debian GNU/Linux 11 (bullseye). GPU is required; we have tested on both NVIDIA V100 and A100 GPUs. The installation usually takes a few minutes.
```bash
# Clone the repository
git clone https://github.com/ByteDance-Seed/cryofm.git
cd cryofm
# Create a new conda environment for CryoFM (recommended)
conda create -n cryofm python=3.10 -y
conda activate cryofm
# Install CryoFM
pip install .
```
For detailed installation instructions and troubleshooting, see the [Installation Guide](https://bytedance-seed.github.io/cryofm/docs/getting-started/installation.html).
### Quick Start
CryoFM2 is recommended for most practical applications. It supports density map denoising, inpainting, and style enhancement.
#### CryoFM2 - Density Map Modification and Enhancement
CryoFM2 supports density map denoising, inpainting, anisotropy correction, and style enhancement.
**Example: Denoising a density map**
```bash
cfm denoise -i1 half_map_1.mrc -i2 half_map_2.mrc -o ./output \
--model-dir path/to/cryofm-v2/cryofm2-pretrain \
--op denoise --norm-grad --use-lamb-w
```
**Example: Style enhancement**
```bash
# EMhancer style
cfm enhance -i input_map.mrc -o ./output_emhancer \
--model-dir path/to/cryofm-v2/cryofm2-emhancer --output-tag 1
# EMReady style
cfm enhance -i input_map.mrc -o ./output_emready \
--model-dir path/to/cryofm-v2/cryofm2-emready --output-tag 0 --cfg-weight 0.5
```
As a reference, processing six 64³ patches with a batch size of 6 takes ~44 seconds on a single NVIDIA V100 GPU. Runtime scales with the input map size: larger maps are split into more patches and therefore take proportionally longer.
For more examples and advanced options, refer to the [CryoFM2 Quick Start Guide](https://bytedance-seed.github.io/cryofm/docs/model-guides/cryofm2/quick-start.html).
## Getting Started (For Developers)
This section provides a quick start guide for developers who wish to pretrain, fine-tune, or test CryoFM models. Please refer to the [documentation](https://bytedance-seed.github.io/cryofm/docs/) for further details and customization.
### CryoFM2
For unconditional generation, conditional generation, and likelihood control, see:
- [CryoFM2 Unconditional Sampling](https://bytedance-seed.github.io/cryofm/docs/model-guides/cryofm2/unconditional-sampling.html)
- [CryoFM2 Likelihood Control](https://bytedance-seed.github.io/cryofm/docs/model-guides/cryofm2/likelihood-control.html)
### CryoFM1
For sampling and downstream tasks (denoising, anisotropy correction, missing wedge restoration), see:
- [CryoFM1 Sampling Guide](https://bytedance-seed.github.io/cryofm/docs/model-guides/cryofm1/sampling.html)
- [CryoFM1 Downstream Tasks](https://bytedance-seed.github.io/cryofm/docs/model-guides/cryofm1/downstream-tasks.html)
---
For more details on data preparation, model customization, and advanced usage, please refer to the official documentation or contact the maintainers.
## License
This project is licensed under the Apache License 2.0. See the [LICENSE](./LICENSE) file for details.
## Citation
If you use CryoFM in your research, please cite the relevant paper(s):
**CryoFM2:**
```bibtex
@article{
Li2025.12.29.696802,
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},
title={A Generative Foundation Model for Cryo-EM Densities},
elocation-id={2025.12.29.696802},
year={2025},
doi={10.64898/2025.12.29.696802},
publisher={Cold Spring Harbor Laboratory},
URL={https://www.biorxiv.org/content/early/2025/12/29/2025.12.29.696802},
eprint={https://www.biorxiv.org/content/early/2025/12/29/2025.12.29.696802.full.pdf},
journal={bioRxiv}
}
```
**CryoFM1:**
```bibtex
@inproceedings{
zhou2025cryofm,
title={Cryo{FM}: A Flow-based Foundation Model for Cryo-{EM} Densities},
author={Yi Zhou and Yilai Li and Jing Yuan and Quanquan Gu},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=T4sMzjy7fO}
}
```
## About [ByteDance Seed Team](https://seed.bytedance.com/)
Founded in 2023, ByteDance Seed Team is dedicated to crafting the industry's most advanced AI foundation models. The team aspires to become a world-class research team and make significant contributions to the advancement of science and society. You can get to know Bytedance Seed better through the following channels👇
