bayesiains/nflows

Health v2 · maintenance only

Adoption not part of the score

1019 stars · 135 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

nflows is a comprehensive PyTorch library of normalizing flows for density estimation and generative modeling. It provides invertible transforms, base distributions, and flow objects for computing log probabilities and sampling.

Use cases

• estimate probability densities with normalizing flows
• build generative models in PyTorch
• sample from learned distributions
• compute log probabilities of data
• implement neural spline flows and autoregressive transforms

When to choose

• you need normalizing flows in PyTorch with a clean, composable API
• you want well-tested implementations of flows like masked autoregressive and spline transforms
• you need density estimation or sampling from invertible transformations

When to avoid

• you need diffusion models or GANs rather than normalizing flows
• you want a framework-agnostic or JAX/TF implementation
• you need large-scale training features like distributed training out of the box

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning deep-learning data-science python normalizing-flows pytorch density-estimation generative-models probabilistic-modeling

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