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# SDIT: Scalable and Diverse Cross-domain Image Translation 
### [[paper]](https://arxiv.org/abs/1908.06881)

# 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](#dependences)
- [Installation](#installtion)
- [Instructions](#instructions)
- [Framework](#Framework)
- [Results](#results)
- [Update](#Update)
- [References](#references)
- [Contact](#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:

then you can get access to folder: results to check the log and the generated images
 
# Framework 
 
<img width="100%" height='60%'src="img/framework/framework.png" />

# Results 
 
<img width="100%" height='60%'src="img/smaples/face_compara_baselines.png" />

# 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

SDIT

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