Generative Adversarial Network Python Packages

descript-audio-codec

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

clean-fid

PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022]

sdv

Synthetic data generation for tabular data

ctgan

Conditional GAN for generating synthetic tabular data.

pytorch-fid

Compute FID scores with PyTorch.

deepecho

Synthetic Data Generation for mixed-type, multivariate time series.

cd-fvd

[CVPR 2024] On the Content Bias in Fréchet Video Distance

prdc

Code base for the precision, recall, density, and coverage metrics for generative models. ICML 2020.

ydata-synthetic

Synthetic data generators for tabular and time-series data

stylegan2-pytorch

Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement

pytorch-pretrained-biggan

🦋A PyTorch implementation of BigGAN with pretrained weights and conversion scripts.

focal-frequency-loss

[ICCV 2021] Focal Frequency Loss for Image Reconstruction and Synthesis

sdgym

Benchmarking synthetic data generation methods.

lightweight-gan

Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two

at-gan

A Tabular GAN framework for generating synthetic tabular data from arbitrary mixed-type tabular datasets.

monai-generative

MONAI Generative Models makes it easy to train, evaluate, and deploy generative models and related applications

descript-audio-codec-unofficial

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

accelbrainbeat

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

mmedit

OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image generation, image/video restoration/enhancement, etc.

at-gan-core

A Tabular GAN framework for generating synthetic tabular data from arbitrary mixed-type tabular datasets.

avitai-artifex

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

pysummarization

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

nrel-sup3r

The Super-Resolution for Renewable Resource Data (sup3r) software uses generative adversarial networks to create synthetic high-resolution wind and solar spatiotemporal data from coarse low-resolution inputs.

mmagic

OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image generation, image/video restoration/enhancement, etc.