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An implementation of "CAN: Creative Adversarial Networks" capable of high-resolution image learning

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GANforArt

An implementation of "CAN: Creative Adversarial Networks"

The aim of this repo is to implement the CAN model that can handle high-resolution images.

Data

Download wikiart data. wikiart dataset available here. Using the dataset is subject to wikiart's terms of use

mkdir data
cd data
mv DOWNLOADED_DATA_PATH ./
unzip wikiart.zip

Installation

Install packages required to execute the code.

$ pip install -r requirements.txt

Train

You can train the model with the command below.

$ python main.py

Test

You can test the model with the command below. The trained models are saved in the "checkpoints" directory.

$ python main.py --test --load-from TRAINED_MODEL_PATH

Experiments

References

Creative Adversarial Networks
AEGeAN

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An implementation of "CAN: Creative Adversarial Networks" capable of high-resolution image learning

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