Easy impelementation of stylegans2. You can literally train a stylegan2 in less than 10 lines of code.
| Name | Notebook |
|---|---|
| Training |
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| Visualization |
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In just a few lines you can use style tranfer or train a stylegan from scratch.
from datasets import Dataset
from sgan import SGAN
dataset = Dataset('/path/to/dataset')
dataset.prepare('path/to/records')
model = SGAN()
model.train(data_path = 'path/to/records', out_dir = 'path/to/out')A set of functions for vis, interpolation and animation. Mostly tested in colab notebooks.
from sgan import SGAN
model = SGAN(pkl_path = '/path/to/pkl')sgan.generate_randomly()model.generate_grid()model.generate_animation(size = 2, steps = 20)
- Gan-surgery: https://github.com/aydao/stylegan2-surgery
- WikiArt model: https://github.com/pbaylies/stylegan2
- Starter-Notebook: https://github.com/Hephyrius/Stylegan2-Ada-Google-Colab-Starter-Notebook/