Generative Adversarial Network Python Packages

clean-fid

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

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

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

monai-generative

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

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

prdc

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

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

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

avitai-artifex

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

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.

sdgym

Benchmarking synthetic data generation methods.

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.

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.

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.

orion-ml

Unsupervised time series anomaly detection library

supercongan

GAN trained on superconductivity data

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.