YGO GAN
A generative adversarial network trained from scratch to synthesize trading-card-style art. Built in spring of 2022.
Try it in your browser at the demo Space, which generates fresh images from this checkpoint.
Training
- Architecture. lightweight-gan (lucidrains), 128 x 128 output
- Data. 10,000+ unique crops of trading card art
- Compute. Trained for 48 hours on a Colab GPU, saving and evaluating every 500 steps
- This checkpoint.
model_69.pt, the final checkpoint from runvioletrun1, May 2022
The original training notebook is on Colab.
Generated samples
EMA outputs from this checkpoint, uncurated beyond picking a dozen.
Usage
pip install lightweight-gan
# place the checkpoint where the CLI expects it
mkdir -p models/violetrun1
# download model_69.pt from this repo into models/violetrun1/
lightweight_gan --name violetrun1 --load-from 69 --generate
Dataset note
The training images are card art owned by Konami. The dataset is not distributed here, only the trained weights and their outputs. This model is a research and portfolio project with no affiliation to Konami, and the weights are shared for non-commercial use.
Author
Vi Ellis. More work at vi-ellis.com.