# normflows: A PyTorch Package for Normalizing Flows

## TL;DR

normflows is a comprehensive PyTorch package that facilitates building and integrating various normalizing flow models for density estimation and generative modeling tasks.

## Contribution

It provides an easy-to-use, extensible library supporting multiple flow architectures, streamlining their application in machine learning workflows.

## Key findings

- Supports diverse flow architectures like Real NVP, Glow, and Neural Spline Flows.
- Simplifies integration of normalizing flows into larger models.
- Open-source package available on GitHub.

## Abstract

Normalizing flows model probability distributions through an expressive tractable density. They transform a simple base distribution, such as a Gaussian, through a sequence of invertible functions, which are referred to as layers. These layers typically use neural networks to become very expressive. Flows are ubiquitous in machine learning and have been applied to image generation, text modeling, variational inference, approximating Boltzmann distributions, and many other problems. Here, we present normflows, a Python package for normalizing flows. It allows to build normalizing flow models from a suite of base distributions, flow layers, and neural networks. The package is implemented in the popular deep learning framework PyTorch, which simplifies the integration of flows in larger machine learning models or pipelines. It supports most of the common normalizing flow architectures, such as Real NVP, Glow, Masked Autoregressive Flows, Neural Spline Flows, Residual Flows, and many more. The package can be easily installed via pip and the code is publicly available on GitHub.