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StyleGAN install und usage instructions

Setup Remote Jupyterhub Notebook

1. Signing into Jupyterhub

Login to JupyterHub with your cluster credentials here 

Choose an XS or S slice

make sure to choose cuda 11.7 from the dropdown

cluster_01.gif

2. Installing Stylegan3 
conda init bash
source ~/.bashrc
git clone https://github.com/NVlabs/stylegan3.git
cd stylegan3
conda env create -f environment.yml


conda activate stylegan3
conda install cudatoolkit
downloading models

make 'pretrained' directory

mkdir pretrained

ffhq flicker faces

wget --no-check-certificate --content-disposition https://th-koeln.sciebo.de/s/j06LuPxYHRRtnQE/download -O pretrained/ffhq_faces.pkl

Wikiart

wget --no-check-certificate --content-disposition https://th-koeln.sciebo.de/s/tbjJS7XBezbAC3B/download -O pretrained/wikiart.pkl

Metfaces

wget --no-check-certificate --content-disposition https://th-koeln.sciebo.de/s/eFZAmR6dDLelSo7/download -O pretrained/metfaces.pkl


Setup Local Stylegan

1. Refer to the Github Page

For major installation process refer to the stylegan3 GitHub Page.

This is an in-depth YouTube tutorial on how to install stylegan3 locally

2. Installing Stylegan3 
conda init bash
source ~/.bashrc
git clone https://github.com/NVlabs/stylegan3.git
cd stylegan3
conda env create -f environment.yml


conda activate stylegan3
conda install cudatoolkit
downloading models

make 'pretrained' directory

mkdir pretrained

ffhq flicker faces

wget --no-check-certificate --content-disposition https://th-koeln.sciebo.de/s/j06LuPxYHRRtnQE/download -O pretrained/ffhq_faces.pkl

Wikiart

wget --no-check-certificate --content-disposition https://th-koeln.sciebo.de/s/tbjJS7XBezbAC3B/download -O pretrained/wikiart.pkl

Metfaces

wget --no-check-certificate --content-disposition https://th-koeln.sciebo.de/s/eFZAmR6dDLelSo7/download -O pretrained/metfaces.pkl

Inference

For generating single images and videos, you may follow these steps.

activating conda environment

this needs to be done before every session if you want to use stylegan

conda init bash
source ~/.bashrc
conda activate stylegan3

alternatively if you are not able to activate stylegan3 through the terminal on our workstations, you can use the anaconda Navigator and start the terminal with the environment activated:

image.png

inference images 
python gen_images.py --outdir=out --trunc=1 --seeds=2 --network=https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-r-afhqv2-512x512.pkl
inference video 
python gen_video.py --output=out/wikiart.mp4 --trunc=1 --seeds=0-31 --network=pretrained/wikiart.pkl

Training

For training your own datasets, you can follow these steps.

For your own dataset make sure that your training data has the correct resolution. You may use either 1024x1024, 512x512 or 256x256 resolution. The chosen resolution has to match with the pre-existing dataset that you want to train on.
You may start a dataset from scratch, just be aware that generally training your collected images on a pre-existing dataset will usually give better results (and faster ones too).

activating conda environment

this needs to be done before every session if you want to use stylegan

 

conda init bash
source ~/.bashrc
conda activate stylegan3

alternatively if you are not able to activate stylegan3 through the terminal on our workstations, you can use the anaconda Navigator and start the terminal with the environment activated:

image.png


Dowload Training data

If not done before, set up a new directory for the training data

mkdir trainingdata

If you want to download your trainingdata from a sciebo folder, you may use this code. Instead of the given link you may use your own one.

wget --no-check-certificate --content-disposition https://th-koeln.sciebo.de/s/7SzJ55ZroKPf5zY/download -O trainingdata/group01.zip
wget --no-check-certificate --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-afhqv2-512x512.pkl -O datasets/stylegan3-t-afhqv2-512x512.pkl

https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-afhqv2-512x512.pkl

Prepare training data

Before training, it is highly recommended to check your dataset through the given stylegan3 check-up. It can resize your images too, although it is usually better to do it before on your own. (Adobe Bridge is a great tool for batch processing.)

python dataset_tool.py --source=trainingdata/group01.zip --dest=trainingdata/group01.zip --resolution=512x512

--source= your directory with the given files
--destination= the output directory, dont forget to add .zip at the end
--resolution= resolution you want your images to be saved in. (1024x1024, 512x512 or 256x256 resolution)

start training 
python train.py --outdir=~/training-runs --cfg=stylegan3-t --data=~/datasets/afhqv2-512x512.zip --gpus=8 --batch=32 --gamma=8.2 --mirror=1

--outdir= direction where you want to save the trained data
--data= your dataset you want to use
--gpus= depends on the gpus you have. usually 1 will be 
--batch= 

 

python train.py --outdir=~/training-runs --cfg=stylegan3-t --data=~/trainingdata/(...)  --gpus=1 --batch=8 --gamma=2 --snap=20 --resume=~/datasets/afhqv2-512x512.zip

 

Opening the Python Visualizer

Python Visualizer

The following script will open the python Visualizer

python visualizer.py

image.png

You can open one of the .pkl files that NVIDIA provided or paste a local URL to your own trainingdata to visualize and play around.

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