EugenHotaj/pytorch-generative
Easy generative modeling in PyTorch
This library helps machine learning researchers and practitioners easily experiment with and implement generative models in PyTorch. It provides pre-built state-of-the-art models and common building blocks. Users input their datasets, train these models, and get out new synthetic data samples or learn complex data distributions.
438 stars. No commits in the last 6 months.
Use this if you are a machine learning researcher or engineer working with PyTorch and need to quickly implement, train, or reproduce results for various generative models like ImageGPT or PixelCNN.
Not ideal if you are looking for a high-level, no-code solution for generating data or if you are not familiar with deep learning frameworks like PyTorch.
Stars
438
Forks
70
Language
Python
License
MIT
Category
Last pushed
Sep 11, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/EugenHotaj/pytorch-generative"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
zhangqianhui/Conditional-GAN
Tensorflow implementation for Conditional Convolutional Adversarial Networks.
mit-han-lab/data-efficient-gans
[NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
kundan2510/pixelCNN
Theano reimplementation of pixelCNN architecture
shaohua0116/DCGAN-Tensorflow
A Tensorflow implementation of Deep Convolutional Generative Adversarial Networks trained on...
pytti-tools/pytti-notebook
Start here