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Installation

System requirements

  • This implementation support running on CPU, Nvidia GPU, and Apple's m1/m2 chips.
  • When using with GPU, 8 GB memory is required for 1024 models. 6 GB is recommended for 512 models.

Install with PyPI

📑 Step by Step Tutorial | 中文部署教程

We recommend to use Conda to install requirements.

conda create -n draggan python=3.7
conda activate draggan

Install PyTorch following the official instructions

conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

Install DragGAN

pip install draggan
# If you meet ERROR: Could not find a version that satisfies the requirement draggan (from versions: none), use
pip install draggan -i https://pypi.org/simple/

Launch the Gradio demo

# if you have a Nvidia GPU
python -m draggan.web
# if you use m1/m2 mac
python -m draggan.web --device mps
# otherwise
python -m draggan.web --device cpu

Install Manually

Ensure you have a GPU and CUDA installed. We use Python 3.7 for testing, other versions (>= 3.7) of Python should work too, but not tested. We recommend to use Conda to prepare all the requirements.

For Windows users, you might encounter some issues caused by StyleGAN custom ops, youd could find some solutions from the issues pannel. We are also working on a more friendly package without setup.

git clone https://github.com/Zeqiang-Lai/DragGAN.git
cd DragGAN
conda create -n draggan python=3.7
conda activate draggan
pip install -r requirements.txt

Launch the Gradio demo

# if you have a Nvidia GPU
python gradio_app.py
# if you use m1/m2 mac
python gradio_app.py --device mps
# otherwise
python gradio_app.py --device cpu

If you have any issue for downloading the checkpoint, you could manually download it from here and put it into the folder checkpoints.

Install with Docker

Follow these steps to run DragGAN using Docker:

Prerequisites

  1. Install Docker on your system from the official Docker website.
  2. Ensure that your system has NVIDIA Docker support if you are using GPUs.

Run using docker Hub image

  # For GPU
  docker run -t -p 7860:7860 --gpus all baydarov/draggan
  # For CPU only (not recommended)
  docker run -t -p 7860:7860 baydarov/draggan --device cpu

Step-by-step Guide with building image locally

  1. Clone the DragGAN repository and build the Docker image:
   git clone https://github.com/Zeqiang-Lai/DragGAN.git # clone repo
   cd DragGAN                                           # change into the repo directory
   docker build -t draggan .                            # build image
  1. Run the DragGAN Docker container:
  # For GPU
  docker run -t -p 7860:7860 --gpus all draggan
  # For CPU (not recommended)
  docker run -t -p 7860:7860 draggan --device cpu
  1. The DragGAN Web UI will be accessible once you see the following output in your console:
  ...
  Running on local URL: http://0.0.0.0:7860
  ...

Visit http://localhost:7860 to access the Web UI.

That's it! You're now running DragGAN in a Docker container.