Papers › Flow Matching for Generative Modeling
Flow Matching for Generative Modeling
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matt Le
We introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present the notion of Flow Matching (FM), a simulation-free approach for training CNFs based on regressing vector fields of fixed conditional probability paths. Flow Matching is compatible with a general family of Gaussian probability paths for transforming between noise and data samples -- which subsumes existing diffusion paths as specific instances. Interestingly, we find that employing FM with diffusion paths results in a more robust and stable alternative for training diffusion models. Furthermore, Flow Matching opens the door to training CNFs with other, non-diffusion probability paths. An instance of particular interest is using Optimal Transport (OT) displacement interpolation to define the conditional probability paths. These paths are more efficient than diffusion paths, provide faster training and sampling, and result in better generalization. Training CNFs using Flow Matching on ImageNet leads to consistently better performance than alternative diffusion-based methods in terms of both likelihood and sample quality, and allows fast and reliable sample generation using off-the-shelf numerical ODE solvers.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Density Estimation | CIFAR-10 | Flow matching | NLL (bits/dim) | 2.99 | #9 of 15 | Archive leaderboard | report |
| Image Generation | CIFAR-10 | FM | FID | 6.35 | #37 of 78 | Archive leaderboard | report |
| Image Generation | ImageNet 32x32 | FM | FID | 5.02 | #5 of 35 | Archive leaderboard | report |
| Image Generation | ImageNet 32x32 | FM | bpd | 3.53 | #5 of 35 | Archive leaderboard | report |
| Image Generation | ImageNet 64x64 | FM | Bits per dim | 3.31 | #28 of 65 | Archive leaderboard | report |
| Image Generation | ImageNet 64x64 | FM | FID | 14.45 | #28 of 65 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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