DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis (CVPR 2022 Oral)
Official Pytorch implementation for our paper DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis by Ming Tao, Hao Tang, Fei Wu, Xiao-Yuan Jing, Bing-Kun Bao, Changsheng Xu.
News!
[CVPR2023]Our new simple and effective model GALIP (paper link, code link) achieves comparable results to Large Pretrained Diffusion Models! Furthermore, our GALIP is training-efficient which only requires 3% training data, 6% learnable parameters. Our GALIP achieves ~120 x faster synthesis speed and can be inferred on CPU.
GALIP significantly lowers the hardware threshold for training and inference. We hope that more users can find the interesting of AIGC.
Requirements
- python 3.8
- Pytorch 1.9
- At least 1x12GB NVIDIA GPU
Installation
Clone this repo.
git clone https://github.com/tobran/DF-GAN
pip install -r requirements.txt
cd DF-GAN/code/Preparation
Datasets
- Download the preprocessed metadata for birds coco and extract them to
data/ - Download the birds image data. Extract them to
data/birds/ - Download coco2014 dataset and extract the images to
data/coco/images/
Training
cd DF-GAN/code/Train the DF-GAN model
- For bird dataset:
bash scripts/train.sh ./cfg/bird.yml - For coco dataset:
bash scripts/train.sh ./cfg/coco.ymlResume training process
If your training process is interrupted unexpectedly, set resume_epoch and resume_model_path in train.sh to resume training.
TensorBoard
Our code supports automate FID evaluation during training, the results are stored in TensorBoard files under ./logs. You can change the test interval by changing test_interval in the YAML file.
- For bird dataset:
tensorboard --logdir=./code/logs/bird/train --port 8166 - For coco dataset:
tensorboard --logdir=./code/logs/coco/train --port 8177
Evaluation
Download Pretrained Model
- DF-GAN for bird. Download and save it to
./code/saved_models/bird/ - DF-GAN for coco. Download and save it to
./code/saved_models/coco/
Evaluate DF-GAN models
We synthesize about 3w images from the test descriptions and evaluate the FID between synthesized images and test images of each dataset.
cd DF-GAN/code/- For bird dataset:
bash scripts/calc_FID.sh ./cfg/bird.yml - For coco dataset:
bash scripts/calc_FID.sh ./cfg/coco.yml - We compute inception score for models trained on birds using StackGAN-inception-model.
Some tips
- Our evaluation codes do not save the synthesized images (about 3w images). If you want to save them, set save_image: True in the YAML file.
- Since we find that the IS can be overfitted heavily through Inception-V3 jointed training, we do not recommend the IS metric for text-to-image synthesis.
Performance
The released model achieves better performance than the CVPR paper version.
| Model | CUB-FID↓ | COCO-FID↓ | NOP↓ |
|---|---|---|---|
| DF-GAN(paper) | 14.81 | 19.32 | 19M |
| DF-GAN(pretrained model) | 12.10 | 15.41 | 18M |
Sampling
cd DF-GAN/code/Synthesize images from example captions
- For bird dataset:
bash scripts/sample.sh ./cfg/bird.yml - For coco dataset:
bash scripts/sample.sh ./cfg/coco.yml
Synthesize images from your text descriptions
- Replace your text descriptions into the ./code/example_captions/dataset_name.txt
- For bird dataset:
bash scripts/sample.sh ./cfg/bird.yml - For coco dataset:
bash scripts/sample.sh ./cfg/coco.yml
The synthesized images are saved at ./code/samples.
Citing DF-GAN
If you find DF-GAN useful in your research, please consider citing our paper:
@inproceedings{tao2022df,
title={DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis},
author={Tao, Ming and Tang, Hao and Wu, Fei and Jing, Xiao-Yuan and Bao, Bing-Kun and Xu, Changsheng},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={16515--16525},
year={2022}
}The code is released for academic research use only. For commercial use, please contact Ming Tao.
Reference