Normalizing Flows Python Packages

zuko

Normalizing flows in PyTorch

nflows

Normalizing flows in PyTorch

glasflow

Normalising flows implemented using nflows

nessai

nessai: Nested Sampling with Artificial Intelligence

flowmc

Normalizing-flow enhanced sampling package for probabilistic inference in JAX

pyknos

Conditional density estimation with neural networks

flowjax

Easy to use distributions, bijections and normalizing flows in JAX.

xgboostlss

An extension of XGBoost to probabilistic modelling

surjectors

Surjection layers for density estimation with normalizing flows

pythae

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

lightgbmlss

An extension of LightGBM to probabilistic modelling

avitai-artifex

A research-focused modular generative modeling library built on JAX/Flax NNX

umnn

Implementation of Unconstrained Monotonic Neural Network and the related experiments. These architectures are particularly useful for modelling monotonic transformations in normalizing flows.

linfa-vi

A Python library for inference with normalizing flow and annealing

lampe

Likelihood-free AMortized Posterior Estimation with PyTorch

pocomc

pocoMC: A Python implementation of Preconditioned Monte Carlo for accelerated Bayesian Computation

raylab

Reinforcement learning algorithms in RLlib

scyan

Biology-driven deep generative model for cell-type annotation in cytometry. Scyan is an interpretable model that also corrects batch-effect and can be used for debarcoding or population discovery.

sbiax

Fast, lightweight and parallelised simulation-based inference in JAX.

vistan

Library to run VI algorithms on Stan models.

deeprob-kit

A Python Library for Deep Probabilistic Modeling

sinflow

Sliced Iterative Normalizing Flow

causalflows

CausalFlows: A library for Causal Normalizing Flows in Pytorch

probaforms

Conditional normalizing flows (NFs), conditional GANs, and conditional variational autoencoders (CVAEs) with sklearn-like interface