Guide · Open source
How to use PyTorch-GAN: clone, install and run GAN models
Updated · written with AI from the project's own documentation and checked against our catalogue data
PyTorch-GAN is an open-source collection of PyTorch implementations of Generative Adversarial Network variants taken from research papers. This guide shows how to clone the repository, install its requirements and run one of the included models. By the end you will know how the project is laid out and how to start a script for a GAN variant you want to study.
PyTorch-GANeriklindernoren/PyTorch-GAN · ★ 17.5k · MIT Install options & downloads Quick start
- 1Check prerequisites. Make sure Python 3 and pip are installed on your machine.
- 2Clone the repository. Run git clone on the PyTorch-GAN repository to download the code.
- 3Enter the project folder. Change into the PyTorch-GAN directory with cd.
- 4Install requirements. Install the dependencies with sudo pip3 install -r requirements.txt.
- 5Pick a GAN variant. Choose an implementation from the README table of contents.
- 6Run the script. Change into that model's folder and run its script with python3.
What PyTorch-GAN contains
The repository holds implementations of many GAN variants, including Auxiliary Classifier GAN, BEGAN, CycleGAN, Pix2Pix, StarGAN and Wasserstein GAN. Each model lives in its own folder with a runnable script, so you can compare how different methods are coded.
The author notes that model architectures will not always mirror the papers, because the focus was on core ideas rather than every layer configuration.
- Each implementation is listed with its paper, authors, abstract and a link to its code file.
- Implementations are stored under the implementations/ folder.
Install PyTorch-GAN on your computer
You need Python 3 and pip. Clone the repository, enter the project folder and install the requirements listed in requirements.txt.
$ git clone https://github.com/eriklindernoren/PyTorch-GAN
$ cd PyTorch-GAN/
$ sudo pip3 install -r requirements.txtRun a GAN model example
Each model has a Run Example in the README. Change into that model's folder and run its script with python3. For example, the Auxiliary Classifier GAN lives in implementations/acgan/ and is started with acgan.py.
- Pick a model from the table of contents in the README.
- Open the folder named in its Run Example or Code link.
- Run the script with python3 from that folder.
$ cd implementations/acgan/
$ python3 acgan.pyRun an image translation model with a dataset
Some models need data first. For BicycleGAN, the README downloads the edges2shoes dataset with a script in the data folder before running the model.
$ cd data/
$ bash download_pix2pix_dataset.sh edges2shoes
$ cd ../implementations/bicyclegan/
$ python3 bicyclegan.pyCheck the project status before you build on it
The README states that the repository has gone stale and is no longer maintained by the author. Treat the code as a learning reference and check it works with your own setup before relying on it.
- Do not use it as production-ready code.
- Do not expect exact replications of paper architectures.
What people use it for
- Students comparing how different GAN variants are written in code.
- Developers who want a runnable PyTorch starting point for a specific GAN paper.
- Researchers comparing core ideas across several GAN papers.
- Experimenting with image translation styles such as Pix2Pix, CycleGAN or StarGAN.
Frequently asked questions
Is PyTorch-GAN free to use?
PyTorch-GAN is released under the MIT licence. The README does not list any paid tier.
What do I need to run PyTorch-GAN?
You need Python 3 and pip. The project installs its dependencies from requirements.txt and runs as Python scripts.
Is PyTorch-GAN still maintained?
No. The README says the repository has gone stale and the author does not have time to maintain it. The author invites collaborators to contact them by email.
Do the models match the research papers exactly?
Not always. The author says architectures may differ from the papers because the focus was on core ideas, not every layer configuration.
What are alternatives to PyTorch-GAN?
The README points to Keras-GAN as a related project. Other comparable projects include pytorch-CycleGAN-and-pix2pix and PyTorch-GAN-Zoo.
Can I use PyTorch-GAN for production?
The README warns against this because the repository is stale and not maintained. Pick an actively maintained project for production work.
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Our verdict
PyTorch-GAN suits students, learners and researchers who want short, runnable PyTorch examples of many GAN papers side by side. It is a poor choice for production work or exact paper replication, since the author says it is no longer maintained and architectures may differ from the papers.
Install options & downloads