This is the official Pytorch implementation of the paper Improving Text-to-Image Synthesis Using Contrastive Learning
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Linux
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Python ≥ 3.6
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PyTorch ≥ 1.4.0
Download the preprocessed datasets from AttnGAN
Alternatively, another site is from DM-GAN
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Pretrain DAMSM+CL:
- For bird dataset:
python pretrain_DAMSM.py --cfg cfg/DAMSM/bird.yml --gpu 0 - For coco dataset:
python pretrain_DAMSM.py --cfg cfg/DAMSM/coco.yml --gpu 0
- For bird dataset:
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Train AttnGAN+CL:
- For bird dataset:
python main.py --cfg cfg/bird_attn2.yml --gpu 0 - For coco dataset:
python main.py --cfg cfg/coco_attn2.yml --gpu 0
- For bird dataset:
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Train DM-GAN+CL:
- For bird dataset:
python main.py --cfg cfg/bird_DMGAN.yml --gpu 0 - For coco dataset:
python main.py --cfg cfg/coco_DMGAN.yml --gpu 0
- For bird dataset:
- DAMSM+CL for bird. Download and save it to
DAMSMencoders/ - DAMSM+CL for coco. Download and save it to
DAMSMencoders/ - AttnGAN+CL for bird. Download and save it to
models/ - AttnGAN+CL for coco. Download and save it to
models/ - DM-GAN+CL for bird. Download and save it to
models/ - DM-GAN+CL for coco. Download and save it to
models/
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Sampling and get the R-precision:
python main.py --cfg cfg/eval_bird.yml --gpu 0python main.py --cfg cfg/eval_coco.yml --gpu 0
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Inception score:
python inception_score_bird.py --image_folder fake_images_birdpython inception_score_coco.py fake_images_coco
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FID:
python fid_score.py --gpu 0 --batch-size 50 --path1 real_images_bird --path2 fake_images_birdpython fid_score.py --gpu 0 --batch-size 50 --path1 real_images_coco --path2 fake_images_coco
If you find this work useful in your research, please consider citing:
@article{ye2021improving,
title={Improving Text-to-Image Synthesis Using Contrastive Learning},
author={Ye, Hui and Yang, Xiulong and Takac, Martin and Sunderraman, Rajshekhar and Ji, Shihao},
journal={The 32nd British Machine Vision Conference (BMVC)},
year={2021}
}
Our work is based on the following works: