https://facebookresearch.github.io/flow_matching/
A PyTorch library for implementing flow matching algorithms, featuring continuous and discrete flow matching implementations. It includes practical examples for both text and image modalities.
https://facebookresearch.github.io/flow_matching/
Last synced: about 22 hours ago
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A PyTorch library for implementing flow matching algorithms, featuring continuous and discrete flow matching implementations. It includes practical examples for both text and image modalities.
- Host: GitHub
- URL: https://facebookresearch.github.io/flow_matching/
- Owner: facebookresearch
- License: other
- Created: 2024-12-07T21:02:50.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-09-11T09:25:02.000Z (3 months ago)
- Last Synced: 2025-09-25T11:35:52.524Z (3 months ago)
- Language: Python
- Homepage: http://facebookresearch.github.io/flow_matching
- Size: 6.87 MB
- Stars: 3,431
- Watchers: 31
- Forks: 225
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-python - facebookresearch.github.io/flow_matching
README
# Flow Matching
[](https://arxiv.org/abs/2412.06264)
[](https://github.com/facebookresearch/flow_matching/actions/workflows/ci.yaml)
[](https://stunning-potato-4k4z71e.pages.github.io/coverage/)
[](https://creativecommons.org/licenses/by-nc/4.0/)
[](https://pypi.org/project/flow-matching/)
`flow_matching` is a PyTorch library for Flow Matching algorithms, featuring continuous and discrete implementations. It includes examples for both text and image modalities. This repository is part of [Flow Matching Guide and Codebase](https://arxiv.org/abs/2412.06264).
## Installation
This repository requires Python 3.9 and Pytorch 2.1 or greater. To install the latest version run:
```
pip install flow_matching
```
## Repository structure
The core and example folders are structured in the following way:
```bash
.
├── flow_matching # Core library
│ ├── loss # Loss functions
│ │ └── ...
│ ├── path # Path and schedulers
│ │ ├── ...
│ │ └── scheduler # Schedulers and transformations
│ │ └── ...
│ ├── solver # Solvers for continuous and discrete flows
│ │ └── ...
│ └── utils
│ └── ...
└── examples # Synthetic, image, and text examples
├── ...
├── image
│ └── ...
└── text
└── ...
```
## Development
To create a conda environment with all required dependencies, run:
```
conda env create -f environment.yml
conda activate flow_matching
```
Install pre-commit hook. This will ensure that all linting is done on each commit
```
pre-commit install
```
Install the `flow_matching` package in an editable mode:
```
pip install -e .
```
## FAQ
#### I want to train a Flow Matching model, where can I find the training code?
We provide [training examples](examples). Under this folder, you can find synthetic data for [continuous](examples/2d_flow_matching.ipynb), [discrete](examples/2d_discrete_flow_matching.ipynb), and [Riemannian](examples/2d_riemannian_flow_matching_flat_torus.ipynb) Flow Matching. We also provide full training [examples](examples/image) (continuous and discrete) on CIFAR10 and face-blurred ImageNet, and a scalable discrete Flow Matching example for [text modeling](examples/text).
#### Do you release pre-trained models?
In this version, we don't release pre-trained models. All models under [examples](examples) can be trained from scratch by a single running command.
#### How to contribute to this codebase?
Please follow the [contribution guide](CONTRIBUTING.md).
## License
The code in this repository is CC BY-NC licensed. See the [LICENSE](LICENSE) for details.
## Citation
If you found this repository useful, please cite the following.
```
@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},
}
```