PokeGAN

A tensorflow/keras implementation of StyleGAN to generate images of new Pokemon.

Dataset

The model has been trained on dataset that includes 819 pokémon.
You can download dataset from this kaggle link.

Dependencies

I have used the following versions for code work:

• python==3.8.8
• tensorflow==2.4.1
• tensorflow-gpu==2.4.1
• numpy==1.19.1
• h5py==2.10.0

Note

There are several difficulties in pokemon generation using GAN :

• The difficulty of GAN training is well known; changing a hyperparameter can greatly change the results.
• The dataset size is too small! 819 different pokemon images are not enough. For this reason, I applied data augmentation on the data; these are the transformations applied :

img_transf = tf.keras.Sequential([
            	tf.keras.layers.experimental.preprocessing.RandomContrast(factor=(0.05, 0.15)),
                image_aug.RandomBrightness(brightness_delta=(-0.15, 0.15)),
                image_aug.PowerLawTransform(gamma=(0.8,1.2)),
                image_aug.RandomSaturation(sat=(0, 2)),
                image_aug.RandomHue(hue=(0, 0.15)),
                tf.keras.layers.experimental.preprocessing.RandomFlip("horizontal"),
	    	tf.keras.layers.experimental.preprocessing.RandomTranslation(height_factor=(-0.10, 0.10), width_factor=(-0.10, 0.10)),
		tf.keras.layers.experimental.preprocessing.RandomZoom(height_factor=(-0.10, 0.10), width_factor=(-0.10, 0.10)),
		tf.keras.layers.experimental.preprocessing.RandomRotation(factor=(-0.10, 0.10))])

• StyleGAN training is very expensive! I trained the model starting from a 4x4 resolution up to the final resolution of 256x256. The model was trained for 8 days using a Tesla V100 32GB SXM2.
  To get better results you need to use higher resolutions and train for longer time.

Results

These are some examples of new pokémon generated by the model :

New Generated Pokémon

More results

You can see hundreds of new pokemon here.
I repeat again it : to get better results (better details in pokemon) is necessary to train for more time.

References

This code implementation is inspired by the unofficial keras implementation of styleGAN.