{"id":33114039,"url":"https://facebookresearch.github.io/flow_matching/","last_synced_at":"2025-12-09T10:30:14.285Z","repository":{"id":267391451,"uuid":"900093817","full_name":"facebookresearch/flow_matching","owner":"facebookresearch","description":"A PyTorch library for implementing flow matching algorithms, featuring continuous and discrete flow matching implementations. 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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).\n\n\n![](./assets/teaser.png)\n\n## Installation\n\nThis repository requires Python 3.9 and Pytorch 2.1 or greater. To install the latest version run:\n```\npip install flow_matching\n```\n\n## Repository structure\n\nThe core and example folders are structured in the following way:\n```bash\n.\n├── flow_matching                  # Core library\n│   ├── loss                       # Loss functions\n│   │   └── ...\n│   ├── path                       # Path and schedulers\n│   │   ├── ...\n│   │   └── scheduler              # Schedulers and transformations\n│   │       └── ...\n│   ├── solver                     # Solvers for continuous and discrete flows\n│   │   └── ...\n│   └── utils\n│       └── ...\n└── examples                       # Synthetic, image, and text examples\n    ├── ...\n    ├── image\n    │       └── ...\n    └── text \n            └── ...\n```\n\n## Development\n\nTo create a conda environment with all required dependencies, run:\n```\nconda env create -f environment.yml\nconda activate flow_matching\n```\n\nInstall pre-commit hook. This will ensure that all linting is done on each commit\n```\npre-commit install\n```\n\nInstall the `flow_matching` package in an editable mode:\n```\npip install -e .\n```\n\n## FAQ\n\n#### I want to train a Flow Matching model, where can I find the training code?\n\nWe 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).\n\n#### Do you release pre-trained models?\n\nIn this version, we don't release pre-trained models. All models under [examples](examples) can be trained from scratch by a single running command. \n\n#### How to contribute to this codebase?\nPlease follow the [contribution guide](CONTRIBUTING.md).\n\n## License\n\nThe code in this repository is CC BY-NC licensed. See the [LICENSE](LICENSE) for details.\n\n## Citation\n\nIf you found this repository useful, please cite the following.\n\n```\n@misc{lipman2024flowmatchingguidecode,\n      title={Flow Matching Guide and Code}, \n      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},\n      year={2024},\n      eprint={2412.06264},\n      archivePrefix={arXiv},\n      primaryClass={cs.LG},\n      url={https://arxiv.org/abs/2412.06264}, \n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/facebookresearch.github.io%2Fflow_matching%2F","html_url":"https://awesome.ecosyste.ms/projects/facebookresearch.github.io%2Fflow_matching%2F","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/facebookresearch.github.io%2Fflow_matching%2F/lists"}