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Flow Matching Guide and Code

About

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.

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

Related benchmarks

TaskDatasetResultRank
Trajectory PlanningExperiment Simulation 1
Trajectory Time (s)5.77
7
Probabilistic RegressionSynthetic Manifold (test)
AUROC56.5
6
Trajectory PlanningOnline replanning for object grasps Experiment 2
Success Rate15
5
Surface Temperature ForecastingERA5 (test)
MSE0.0038
4
Cross-Domain Style TransferMNIST to SVHN
FID (50K)4.102
3
Conditional GenerationSynthetic Manifold (test)
AUROC0.51
3
Extrapolation DetectionMNIST to SVHN vs FMNIST
AUROC0.529
3
Extrapolation DetectionMNIST to SVHN vs KMNIST
AUROC0.516
3
Human-robot object handoverHuman-human handover dataset (test)
Terminal Constraint Violations0.05
3
Physical Anomaly DetectionERA5 (test)
AUROC0.556
3
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