Generative Model Python Packages

copulas

A library to model multivariate data using copulas.

sdv

Synthetic data generation for tabular data

image-reward

[NeurIPS 2023] ImageReward: Learning and Evaluating Human Preferences for Text-to-image Generation

zuko

Normalizing flows in PyTorch

nflows

Normalizing flows in PyTorch

denoising-diffusion-pytorch

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

miditok

MIDI / symbolic music tokenizers for Deep Learning models 🎶

stylegan2-pytorch

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

azula

Diffusion models in PyTorch

genqc

Generative Quantum Circuits

prdc

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

scyan

Biology-driven deep generative model for cell-type annotation in cytometry. Scyan is an interpretable model that also corrects batch-effect and can be used for debarcoding or population discovery.

bayesianflow-for-chem

ChemBFN: Bayesian Flow Network Framework for Chemistry Tasks. Developed in Hiroshima University.

causalegm

A General Causal Inference Framework by Encoding Generative Modeling

mimikit

Music Modeling Kit

torchgan

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

scgen

Single cell perturbation prediction

torchflows

Modern normalizing flows in Python. Simple to use and easily extensible.

gpt2lm

A implimentation of GPT2 varient.

deepgen

A Collection of modern Deep Generative Models

evodiff

Python package for generation of protein sequences and evolutionary alignments via discrete diffusion models

texturize

🤖🖌️ Generate photo-realistic textures based on source images or (soon) PBR materials. Remix, remake, mashup! Useful if you want to create variations on a theme or elaborate on an existing texture.

lamda-ssl

30 Semi-Supervised Learning Algorithms

causaltorch

CausalTorch is a PyTorch library for building generative models with explicit causal constraints. It integrates graph-based causal reasoning with deep learning to create AI systems that respect logical causal relationships.