TSIT: A Simple and Versatile Framework for Image-to-Image Translation

This repository provides the official PyTorch implementation for the following paper:

TSIT: A Simple and Versatile Framework for Image-to-Image Translation
Liming Jiang, Changxu Zhang, Mingyang Huang, Chunxiao Liu, Jianping Shi and Chen Change Loy
In ECCV 2020 (Spotlight).
Paper

Abstract: We introduce a simple and versatile framework for image-to-image translation. We unearth the importance of normalization layers, and provide a carefully designed two-stream generative model with newly proposed feature transformations in a coarse-to-fine fashion. This allows multi-scale semantic structure information and style representation to be effectively captured and fused by the network, permitting our method to scale to various tasks in both unsupervised and supervised settings. No additional constraints (e.g., cycle consistency) are needed, contributing to a very clean and simple method. Multi-modal image synthesis with arbitrary style control is made possible. A systematic study compares the proposed method with several state-of-the-art task-specific baselines, verifying its effectiveness in both perceptual quality and quantitative evaluations.

Updates

• [01/2021] The code of TSIT is released.

• [07/2020] The paper of TSIT is accepted by ECCV 2020 (Spotlight).

Installation

After installing Anaconda, we recommend you to create a new conda environment with python 3.7.6:

conda create -n tsit python=3.7.6 -y
conda activate tsit

Clone this repo, install PyTorch 1.1.0 (newer versions may also work) and other dependencies:

git clone https://github.com/EndlessSora/TSIT.git
cd TSIT
pip install -r requirements.txt

This code also requires the Synchronized-BatchNorm-PyTorch:

cd models/networks/
git clone https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
cp -rf Synchronized-BatchNorm-PyTorch/sync_batchnorm .
rm -rf Synchronized-BatchNorm-PyTorch
cd ../../

Tasks and Datasets

The code covers 3 image-to-image translation tasks on 5 datasets. For more details, please refer to our paper.

Task Abbreviations

• Arbitrary Style Transfer (AST) on Yosemite summer → winter, BDD100K day → night, and Photo → art datasets.
• Semantic Image Synthesis (SIS) on Cityscapes and ADE20K datasets.
• Multi-Modal Image Synthesis (MMIS) on BDD100K sunny → different time/weather conditions dataset.

The abbreviations are used to specify the --task argument when training and testing.

Dataset Preparation

We provide one-click scripts to prepare datasets. The details are provided below.

• Yosemite summer → winter and Photo → art. The provided scripts will make all things ready (including the download). For example, simply run:

bash datasets/prepare_summer2winteryosemite.sh

• BDD100K. Please first download BDD100K Images on their official website. We have provided the classified lists of different weathers and times. After downloading, you only need to run:

bash datasets/prepare_bdd100k.sh [data_root]

The [data_root] should be specified, which is the path to the BDD100K root folder that contains images folder. The script will put the list to the suitable place and symlink the root folder to ./datasets.

• Cityscapes. Please follow the standard download and preparation guidelines on the official website. We recommend to symlink its root folder [data_root] to ./datasets by:

bash datasets/prepare_cityscapes.sh [data_root]

• ADE20K. The dataset can be downloaded here, which is from MIT Scene Parsing BenchMark. After unzipping the dataset, put the jpg image files ADEChallengeData2016/images/ and png label files ADEChallengeData2016/annotatoins/ in the same directory. We also recommend to symlink its root folder [data_root] to ./datasets by:

bash datasets/prepare_ade20k.sh [data_root]

Testing Pretrained Models

1. Download the pretrained models and unzip them to ./checkpoints.

2. For a quick start, we have provided all the example test scripts. After preparing the corresponding datasets, you can directly use the test scripts. For example:

bash test_scripts/ast_summer2winteryosemite.sh

3. The generated images will be saved at ./results/[experiment_name] by default.

4. You can use --results_dir to specify the output directory. --how_many will specify the maximum number of images to generate. By default, the code loads the latest checkpoint, which can be changed using --which_epoch. You can also discard --show_input to show the generated images only without the input references.

5. For MMIS sunny → different time/weather conditions, the --test_mode can be specified (optional): night | cloudy | rainy | snowy | all (default).

Training

For a quick start, we have provided all the example training scripts. After preparing the corresponding datasets, you can directly use the training scripts. For example:

bash train_scripts/ast_summer2winteryosemite.sh

Please note that you may want to change the experiment name --name or the checkpoint saving root --checkpoints_dir to prevent your newly trained models overwriting the pretrained ones (if used).

--task is given using the abbreviations. --dataset_mode specifies the dataset type. --croot and --sroot specify the content and style data root, respectively. The results may be better reproduced on NVIDIA Tesla V100 GPUs.

After training, testing the newly trained models is similar to testing pretrained models.

Citation

If you find this work useful for your research, please cite our paper:

@inproceedings{jiang2020tsit,
  title={{TSIT}: A Simple and Versatile Framework for Image-to-Image Translation},
  author={Jiang, Liming and Zhang, Changxu and Huang, Mingyang and Liu, Chunxiao and Shi, Jianping and Loy, Chen Change},
  booktitle={ECCV},
  year={2020}
}

Acknowledgments

The code is greatly inspired by SPADE, pytorch-AdaIN, and Synchronized-BatchNorm-PyTorch.

License

Copyright (c) 2020. All rights reserved.

The code is licensed under the CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike 4.0 International).