text2image

This repository includes the implementation for Text to Image Generation with Semantic-Spatial Aware GAN

Network Structure

The structure of the spatial-semantic aware (SSA) block is shown as below

Main Requirements

• python 3.6+
• pytorch 1.0+
• numpy
• matplotlib
• opencv

Prepare data

1. Download the preprocessed metadata for birds and coco and save them to data/
2. Download birds dataset and extract the images to data/birds/
3. Download coco dataset and extract the images to data/coco/

Pre-trained DAMSM model

1. Download the pre-trained DAMSM for CUB and save it to DAMSMencoders/
2. Download the pre-trained DAMSM for coco and save it to DAMSMencoders/

Trained model

you can download our trained models from our onedrive repo

Start training

Run main.py file. Please adjust args in the file as your need.

Evaluation

please run IS.py and test_lpips.py (remember to change the image path) to evaluate the IS and diversity scores, respectively.

For evaluating the FID score, please use this repo https://github.com/bioinf-jku/TTUR.

Performance

You will get the scores close to below after training under xe loss for xxxxx epochs:

Qualitative Results

Some qualitative results on coco and birds dataset from different methods are shown as follows:

The predicted mask maps on different stages are shown as as follows:

Reference

If you find this repo helpful in your research, please consider citing our paper:

@article{liao2021text,
  title={Text to Image Generation with Semantic-Spatial Aware GAN},
  author={Liao, Wentong and Hu, Kai and Yang, Michael Ying and Rosenhahn, Bodo},
  journal={arXiv preprint arXiv:2104.00567},
  year={2021}
}

The code is released for academic research use only. For commercial use, please contact Wentong Liao.

Acknowledgements

This implementation borrows part of the code from DF-GAN.