StyleGAN install und usage instructions

Setup Remote Jupyterhub Notebook

1. Signing into Jupyterhub via keycloak

key in your keycloak credentials here

Choose an XS slice

make sure to choose cuda 11.7 from the dropdown

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

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

Dowload Training data

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

mkdir trainingdata

~~Group~~If 01you 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

~~Group 02~~

wget --no-check-certificate --content-disposition https://th-koeln.sciebo.de/s/7SzJ55ZroKPf5zY/download -O trainingdata/group02.zip

~~Group 03~~

wget --no-check-certificate --content-disposition https://th-koeln.sciebo.de/s/YGR7BaeoIBSePNl/download -O trainingdata/group03.zip

~~Group 04~~

wget --no-check-certificate --content-disposition https://th-koeln.sciebo.de/s/SsrPKyPcyswd8z2/download -O trainingdata/group04.zip

~~Group 05~~

wget --no-check-certificate --content-disposition https://th-koeln.sciebo.de/s/b1I0rgEaPcyaP44/download -O trainingdata/group05

~~Group 06~~

wget --no-check-certificate --content-disposition https://th-koeln.sciebo.de/s/Orv9FDqqKwtBMlB/download -O trainingdata/group06

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 --destination=trainingdata/group01 --resolution=512x512

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

start training

python train.py --help

DownloadingOpening the DangerzonesGANspython Visualizer

~~downloading~~Python ~~dangerzones models~~Visualizer

~~go into directory pretrained:~~

cdpython pretrainedvisualizer.py

~~Download our trained models from the KISD Modelzoo:~~

~~Group 01 - androids gynoids~~

wget --no-check-certificate --content-disposition https://th-koeln.sciebo.de/s/B7rOZIRzPN5rev1/download -O pretrained/group_01_220.pkl

~~Group 02 TBF - dataset error~~

~~Group 03 - grayscale faces~~

wget --no-check-certificate --content-disposition https://th-koeln.sciebo.de/s/SRcjw6DPv9AIacn/download -O pretrained/group_03_500.pkl

~~Group 04 -future cities~~

wget --no-check-certificate --content-disposition https://th-koeln.sciebo.de/s/CW8uQ2dsQbVOiXa/download -O pretrained/group_04_20.pkl

~~Group 05 - lamppost and sunflowers~~

wget --no-check-certificate --content-disposition https://th-koeln.sciebo.de/s/fb0skqUV9ypIOEl/download -O pretrained/group_05.pkl

~~Group 05 - encoded data~~