Semantically Multi-modal Image Synthesis
Project page / Paper / Demo
Semantically Multi-modal Image Synthesis(CVPR2020).
Zhen Zhu, Zhiliang Xu, Ansheng You, Xiang Bai
Requirements
- torch>=1.0.0
- torchvision
- dominate
- dill
- scikit-image
- tqdm
- opencv-python
Getting Started
Data Preperation
DeepFashion
Note: We provide an example of the DeepFashion dataset. That is slightly different from the DeepFashion used in our paper due to the impact of the COVID-19.
Cityscapes
The Cityscapes dataset can be downloaded at here
ADE20K
The ADE20K dataset can be downloaded at here
Test/Train the models
Download the tar of the pretrained models from the Google Drive Folder. Save it in checkpoints/ and unzip it.
There are deepfashion.sh, cityscapes.sh and ade20k.sh in the scripts folder. Change the parameters like --dataroot and so on, then comment or uncomment some code to test/train model.
And you can specify the --test_mask for SMIS test.
Acknowledgments
Our code is based on the popular SPADE