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Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation

Pytorch implementation for Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation. The goal is to introduce a lightweight generative adversarial network for efficient image manipulation using natural language descriptions.

Overview

Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation.
Bowen Li, Xiaojuan Qi, Philip H. S. Torr, Thomas Lukasiewicz.<br> University of Oxford, University of Hong Kong <br> NeurIPS 2020 <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 a text encoder and an 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

Our Model

• Train the model for bird dataset:

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

• Train the model for coco dataset:

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

*.yml files include configuration for training and testing. To reduce the number of parameters used in the model, please edit DF_DIM and/or GF_DIM values in the corresponding *.yml files.

Pretrained DAMSM Model

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

Pretrained Lightweight Model

• Bird. Download and save it to models/

• COCO. Download and save it to models/

Testing

• Test our model on bird dataset:

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

• Test our model on 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.
• Fréchet Inception Distance.

Code Structure

• code/main.py: the entry point for training and testing.
• code/trainer.py: creates the networks, harnesses and reports the progress of training.
• code/model.py: defines the architecture.
• 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: trains 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{li2020lightweight,
  title={Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation},
  author={Li, Bowen and Qi, Xiaojuan and Torr, Philip and Lukasiewicz, Thomas},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

Acknowledgements

This code borrows heavily from ManiGAN and ControlGAN repositories. Many thanks.