SPADE-pix2pix-for-Anime

This pix2pix model is based on Generative Adversarial Networks. I implemented in order to generate from a segmentation label to an anime illust. I could not get a satisfactory result, but obtained enough for small and rough datasets.

Overview

This model generates a anime illust from a segmentation label and a color map of the hair.

The differences from the architecture of GauGAN

• The original SPADE[1] computes a weight from label at each itself layers, but this architecture has some mapping networks like the StyleGAN[2] and precompute weights. They are called to Constant-Resolution FCN used AtrousConvolution[6, 7] instead of FCN[5] in order not to do down sampling. In addition, each SPADE layer resize and share a encoded weight by a mapping network.
• I want to assign a hair color, so this Generator has double mapping networks, inputed a segmented label to one mapping network and a RGB color map, to another. The RGB color map has a hair color and positions, it is same resolution with label.
• I added NoiseAdder of StyleGAN[2].
• I selected a single patch discriminator with SelfAttention[3] instead of Multi-scale discriminator[1, 10].
• The discriminator's loss is Hinge loss [1] and Zero Centered Gradient Penalty[9].
• The generator's loss is Hinge loss, Feature Matching loss[1, 10] and Perceptual loss[1, 10].

Result

This result is obtained by training with 500 pixiv images. This is very small datasets. Additionaly, test datasets is generated by Anime-Semantic-Segmentation-GAN, and training datasets is manualy anotated. However, I cannot publish training datasets for copyright of source images.

The parameters of the upper result is almost same as default value of options.py.

pretrained weights

I prepared pre-trained weights of Generator and Discriminator and added scripts in order to get these weights.
You can get them by executing a following command.

python get_pretrained_weight.py  

Totally about 110MB, so it may take a few minutes.

How to predict

If you want pre-trained model to predict, please do a next python script.

python predict.py  

predict.py creates predicted images from predict_from directory to predict_to. Please prepare 256 x 256 png images annotated and 256 x 256 filled hair color. You have to concatenate these.

How to train

Please create dataset directory and prepare dataset. Next, you can set dataset path to option of command.

Python3 train.py --dataset_dir dataset/example

Environment

References

[1] Taesung Park, Ming-Yu Liu, Ting-Chun Wang, Jun-Yan Zhu. Semantic Image Synthesis with Spatially-Adaptive Normalization. arXiv preprint arXiv:1903.07291, 2019

[2] Tero Karras, Samuli Laine, Timo Aila. A Style-Based Generator Architecture for Generative Adversarial Networks . arXiv preprint arXiv:1812.04948, 2019

[3] Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena. Self-Attention Generative Adversarial Networks. arXiv preprint arXiv:1805.08318, 2019

[4] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio. Generative Adversarial Networks. arXiv preprint arXiv:1406.2661, 2014

[5] Jonathan Long, Evan Shelhamer, Trevor Darrell. Fully Convolutional Networks for Semantic Segmentation. arXiv preprint arXiv:1411.4038, 2015

[6] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. arXiv preprint arXiv:1606.00915, 2017 (v2)

[7] Liang-Chieh Chen, George Papandreou, Florian Schroff, Hartwig Adam. Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv preprint arXiv:1706.05587, 2017 (v3)

[8] Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida. Spectral Normalization for Generative Adversarial Networks. arXiv preprint arXiv:1802.05957, 2018

[9] Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, Zehan Wang. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. arXiv preprint arXiv:1609.05158, 2016

[10] Qi Mao, Hsin-Ying Lee, Hung-Yu Tseng, Siwei Ma, Ming-Hsuan Yang. Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis. arXiv preprint arXiv:1903.05628, 2019(v6)

[11] Lars Mescheder, Andreas Geiger, Sebastian Nowozin. Which Training Methods for GANs do actually Converge?. arXiv preprint arXiv:1801.04406, 2018

[12] Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, Bryan Catanzaro. High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs. arXiv preprint arXiv:1711.11585, 2018

[13] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros. Image-to-Image Translation with Conditional Adversarial Networks. arXiv preprint arXiv:1611.07004, 2018

Author

• pit-ray
  [E-mail] contact(at)pit-ray.com