Flow Matching Guide and Code

Yaron Lipman; Marton Havasi; Peter Holderrieth; Neta Shaul; Matt Le; Brian Karrer; Ricky T. Q. Chen; David Lopez-Paz; Heli Ben-Hamu; Itai Gat

arXiv · 2412.06264

Flow Matching Guide and Code

Yaron Lipman·Marton Havasi·Peter Holderrieth·Neta Shaul·Matt Le·Brian Karrer·Ricky T. Q. Chen·David Lopez-Paz·Heli Ben-Hamu·Itai Gat

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Abstract

Flow Matching (FM) is a recent framework for generative modeling that has achieved state-of-the-art performance across various domains, including image, video, audio, speech, and biological structures. This guide offers a comprehensive and self-contained review of FM, covering its mathematical foundations, design choices, and extensions. By also providing a PyTorch package featuring relevant examples (e.g., image and text generation), this work aims to serve as a resource for both novice and experienced researchers interested in understanding, applying and further developing FM.

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Yaron Lipman, Marton Havasi, Peter Holderrieth, Neta Shaul, Matt Le, Brian Karrer, Ricky T. Q. Chen, David Lopez-Paz, Heli Ben-Hamu, Itai Gat. 2024-12-09. Flow Matching Guide and Code. https://arxiv.org/abs/2412.06264

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