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]

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.

sdv

Synthetic data generation for tabular data

cd-fvd

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

avitai-artifex

Generative modeling for JAX/Flax NNX: VAEs, GANs, DDPM/DDIM/score/DiT/latent diffusion, normalizing flows, energy-based, autoregressive and geometric (SE(3), protein, point cloud, mesh) models; image, text, audio, molecular, protein, tabular and time-series modalities; a shared Trainer with callbacks and extensions; evaluation through calibrax.

ydata-synthetic

Synthetic data generators for tabular and time-series data

sdgym

Benchmarking synthetic data generation methods.

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.

descript-audio-codec-unofficial

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

prdc

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

focal-frequency-loss

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

orion-ml

Unsupervised time series anomaly detection library

monai-generative

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

stylegan2-pytorch

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

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.

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

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.

pytorch-pretrained-biggan

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

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.

at-gan

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

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.