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https://github.com/g4vrel/cfm

A minimal PyTorch implementation of Flow Matching for Generative Modeling.
https://github.com/g4vrel/cfm

conditional-flow-matching flow-matching implementation-of-research-paper paper-implementation pytorch

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A minimal PyTorch implementation of Flow Matching for Generative Modeling.

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# Flow Matching for Generative Modeling

A minimal PyTorch implementation of [Flow Matching for Generative Modeling](https://arxiv.org/abs/2210.02747).

This repository implements conditional flow matching with optimal transport probability paths using a U-Net architecture.

Here are some images generated by the model trained on CIFAR-10 for 400K steps:

![image](samples/0.png)

A helpful reference for my theoretical understanding was the paper [Flow Matching Guide and Code](https://arxiv.org/abs/2412.06264).

---

#### Dependencies

- `pytorch`
- [`einops`](https://einops.rocks/)
- [`torchdiffeq`](https://github.com/rtqichen/torchdiffeq)

---

## References

```bibtex
@misc{lipman2023flowmatchinggenerativemodeling,
title={Flow Matching for Generative Modeling},
author={Yaron Lipman and Ricky T. Q. Chen and Heli Ben-Hamu and Maximilian Nickel and Matt Le},
year={2023},
eprint={2210.02747},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2210.02747},
}
```

```bibtex
@misc{lipman2024flowmatchingguidecode,
title={Flow Matching Guide and Code},
author={Yaron Lipman and Marton Havasi and Peter Holderrieth and Neta Shaul and Matt Le and Brian Karrer and Ricky T. Q. Chen and David Lopez-Paz and Heli Ben-Hamu and Itai Gat},
year={2024},
eprint={2412.06264},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2412.06264},
}
```
```bibtex
@misc{torchdiffeq,
author={Chen, Ricky T. Q.},
title={torchdiffeq},
year={2018},
url={https://github.com/rtqichen/torchdiffeq},
}
```