Artist
Motivation
- An easy to edit codebase for Progressive GAN originally published by this research paper and other GANs
- Supplement my personal projects
- Resume builder
- For fun and to understand state-of-the-art AI
Installation
CUDA Installation
Ensure you have a GPU if you want to train in any reasonable amount of time.
Project Installation
pip install Artist-Engineering-Geek
# Don't forget to install your specific pytorch and torchvision libraries for your gpu
# in my case, I have the NVIDIA RTX 3090 so this is my version
pip install --no-cache-dir --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu112/torch_nightly.html
Running the program
To train the program, run the "Train.ipynb" notebook and alter your parameters at will on GitHub. There should be sufficient in-code documentation for you to understand what the hell is going on
1 maintainer
Metadata
1 maintainer
Release files for Artist-Engineering-Geek 0.1.0
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Source distribution (sdist)
| File | Size | Uploaded | |
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| Artist-Engineering_Geek-0.1.0.tar.gz | 10.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| Artist_Engineering_Geek-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 22.9 kB
Release files / Artist-Engineering_Geek-0.1.0.tar.gz
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| Size | 10.5 kB |
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Release files / Artist_Engineering_Geek-0.1.0-py3-none-any.whl
| Download URL | Artist_Engineering_Geek-0.1.0-py3-none-any.whl |
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| Size | 12.3 kB |
| Tags | Python 3 |
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