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SDIT: Scalable and Diverse Cross-domain Image Translation

[paper]

Abstract:

Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfer, these properties have not been combined into a single method. To address this limitation, we propose SDIT: Scalable and Diverse image-to-image translation. These properties are combined into a single generator. The diversity is determined by a latent variable which is randomly sampled from a normal distribution. The scalability is obtained by conditioning the network on the domain attributes. Additionally, we also exploit an attention mechanism that permits the generator to focus on the domain-specific attribute. We empirically demonstrate the performance of the proposed method on face mapping and other datasets beyond faces.

Overview

• Dependences
• Installation
• Instructions
• Framework
• Results
• Update
• References
• Contact

Dependences

• Python2.7, NumPy, SciPy, NVIDIA GPU
• Pytorch: the version is 0.4 (https://pytorch.org/)
• Dataset: CelebA or your dataset

Installation

• Install pytorch

Instructions

• Using 'git clone https://github.com/yaxingwang/SDIT.git'

  You will get new folder whose name is 'SDIT' in your current path, then use 'cd SDIT' to enter the downloaded new folder

• Download dataset or use your dataset.

  The data path is data/celeba/images, and the attribute is data/celeba/list_attr_celeba.txt

• Run:

  python main.py --mode train --dataset CelebA --celeba_image_dir data/celeba/images --attr_path data/celeba/list_attr_celeba.txt --image_size 128 --c_dim 5 --sample_dir results/samples --log_dir results/logs --model_save_dir results/models --result_dir results/results --selected_attrs Sideburns Bangs Blond_Hair Brown_Hair Male Eyeglasses --lambda_noise 800 --attention True

  If you want to get insight quickly, try:

  python main.py --mode train --dataset CelebA --celeba_image_dir data/celeba/images --attr_path data/celeba/list_attr_celeba.txt --image_size 128 --c_dim 5 --sample_dir results/samples --log_dir results/logs --model_save_dir results/models --result_dir results/results --selected_attrs Sideburns Bangs Blond_Hair Brown_Hair Male Eyeglasses --lambda_noise 0 --attention True

  then you can get access to folder: results to check the log and the generated images

Framework

Results

Update

There is one type error. In paper we mention the hyper-parameters: Image_rec = 800, Lat_rec=10, which should be opposite. Thank you @taki0112(https://github.com/taki0112)

References

Our code heavily rely on the following projects:

• [1] 'StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation' by Choi et. al, https://arxiv.org/abs/1711.09020, (https://github.com/yunjey/stargan)[code]
• [2] 'MUNIT: Multimodal UNsupervised Image-to-image Translation' by Huang et. al, https://arxiv.org/abs/1804.04732, (https://github.com/NVlabs/MUNIT)

It would be helpful to understand this project if you are familiar with the above projects.

Contact

If you run into any problems with this code, please submit a bug report on the Github site of the project. For another inquries pleace contact with me: yaxing@cvc.uab.es