# janosh/awesome-normalizing-flows

Awesome resources on normalizing flows.

Repository: https://github.com/janosh/awesome-normalizing-flows
Canonical: https://ross.abutalabs.com/products/awesome-normalizing-flows
Language: Python
License: MIT
License Family: permissive
Topics: normalizing-flows, bayesian-neural-networks, variational-inference, density-estimation, generative-modeling, autoregressive, machine-learning, awesome-list, bayesian-inference
Last push: 2026-07-31T13:27:19+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 95, release rhythm 8, longevity 100
- inputs: {"age_days": 2457, "days_push": 33, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1637, forks 130 (observed 2026-08-28T04:05:14.857611+00:00)

## What it is
A curated awesome-list of resources on normalizing flows, including publications, videos, packages, repos, and blog posts. It serves as a reference hub for understanding and applying flow-based generative models and density estimation techniques.

## Use cases
- find papers on normalizing flows
- learn about flow-based generative models
- discover python libraries for density estimation
- find tutorials on variational inference with flows
- compare normalizing flow implementations in pytorch tensorflow jax
- research resources on bayesian neural networks

## When to choose
- you need a curated starting point for learning normalizing flows
- you want an index of NF packages across pytorch, tensorflow, jax, and julia
- you are surveying the research literature on flow-based models

## When to avoid
- you need a runnable normalizing flow implementation rather than links
- you want a maintained software library with API guarantees
- you need resources on other generative model families like GANs or diffusion models

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, documentation
- domain: machine-learning, deep-learning, artificial-intelligence, awesome-lists, tutorials
- platform: python
- tags: normalizing-flows, awesome-list, generative-modeling, density-estimation, variational-inference, bayesian-inference, curated-list

## Member repositories
- janosh/awesome-normalizing-flows (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:14.857611+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:46:41.343738+00:00, confidence not recorded.
  - readme: https://github.com/janosh/awesome-normalizing-flows (fetched 2026-08-28T04:05:14.857611+00:00, sha d4c42cdc1795)
- Data as of 2026-08-30T08:39:29.467469+00:00.