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ControlGAN

Pytorch implementation for Controllable Text-to-Image Generation. The goal is to generate images from text, and also allow the user to manipulate synthetic images using natural language descriptions, in one framework.

Overview

Controllable Text-to-Image Generation.
Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, Philip H. S. Torr.<br> University of Oxford <br> In Neural Information Processing Systems, 2019. <br>

Data

1. Download the preprocessed metadata for bird and coco, and save both into data/
2. Download bird dataset and extract the images to data/birds/
3. Download coco dataset and extract the images to data/coco/

Training

All code was developed and tested on CentOS 7 with Python 3.7 (Anaconda) and PyTorch 1.1.

DAMSM model includes text encoder and image encoder

• Pre-train DAMSM model for bird dataset:

python pretrain_DAMSM.py --cfg cfg/DAMSM/bird.yml --gpu 0

• Pre-train DAMSM model for coco dataset:

python pretrain_DAMSM.py --cfg cfg/DAMSM/coco.yml --gpu 1

ControlGAN model

• Train ControlGAN model for bird dataset:

python main.py --cfg cfg/train_bird.yml --gpu 2

• Train ControlGAN model for coco dataset:

python main.py --cfg cfg/train_coco.yml --gpu 3

*.yml files include configuration for training and testing.

Pretrained DAMSM Model

• DAMSM for bird. Download and save it to DAMSMencoders/
• DAMSM for coco. Download and save it to DAMSMencoders/

Pretrained ControlGAN Model

• ControlGAN for bird. Download and save it to models/

Testing

• Test ControlGAN model for bird dataset:

python main.py --cfg cfg/eval_bird.yml --gpu 4

• Test ControlGAN model for coco dataset:

python main.py --cfg cfg/eval_coco.yml --gpu 5

Evaluation

• To generate images for all captions in the testing dataset, change B_VALIDATION to True in the eval_*.yml.
• Inception Score for bird dataset: StackGAN-inception-model.
• Inception Score for coco dataset: improved-gan/inception_score.

Code Structure

• code/main.py: the entry point for training and testing.
• code/trainer.py: creates the main networks, harnesses and reports the progress of training.
• code/model.py: defines the architecture of ControlGAN.
• code/attention.py: defines the spatial and channel-wise attentions.
• code/VGGFeatureLoss.py: defines the architecture of the VGG-16.
• code/datasets.py: defines the class for loading images and captions.
• code/pretrain_DAMSM.py: creates the text and image encoders, harnesses and reports the progress of training.
• code/miscc/losses.py: defines and computes the losses.
• code/miscc/config.py: creates the option list.
• code/miscc/utils.py: additional functions.

Citation

If you find this useful for your research, please use the following.

@article{li2019control,
  title={Controllable text-to-image generation},
  author={Li, Bowen and Qi, Xiaojuan and Lukasiewicz, Thomas and H.~S.~Torr, Philip},
  journal={arXiv preprint arXiv:1909.07083},
  year={2019}
}

Acknowledgements

This code borrows heavily from AttnGAN repository. Many thanks.