Gans Python Packages

descript-audio-codec

State-of-the-art audio codec with 90x compression factor. Supports 44.1kHz, 24kHz, and 16kHz mono/stereo audio.

sdv

Synthetic data generation for tabular data

ydata-synthetic

Synthetic data generators for tabular and time-series data

descript-audio-codec-unofficial

State-of-the-art audio codec with 90x compression factor. Supports 44.1kHz, 24kHz, and 16kHz mono/stereo audio.

avitai-artifex

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

torchgan

Research Framework for easy and efficient training of GANs based on Pytorch

torch-mimicry

[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.

gansforvirtualeye

This package provides an implementation of Generative Adversarial Networks (GANs) for time series generation, with flexible architecture options. Users can select different combinations of generator and discriminator models, including Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM), to suit their specific needs.

torchflare

TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly.

fg-data-synthetic

Synthetic data generators for tabular and time-series data

ganetic

A collection of highly customizable GANs implemented in PyTorch.

dpsdv

Creating a Differential Privacy securing Synthetic Data Generation for tabular, relational and time series data.

simpliml

SimpliML is a versatile machine learning library designed to be a one-stop solution for the entire data lifecycle. Whether you're preparing raw data or deploying advanced predictive models, SimpliML simplifies every step of the machine learning process

easygan

DCGAN for everyone

medigan

medigan is a modular open-source Python library that provides an interface to multiple generative models and automates synthetic dataset generation.

tnagen

TNAgen is a python package that allows users to generate transient noise artifacts in gravitational-wave detector data using a GAN model.

sige

[NeurIPS 2022, T-PAMI 2023] Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models

ganpdfs

Generative Adversarial Networks (GANs) for MC PDF replicas.

evan

Evaluation framework for GANs for video generation