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README.md

StackGAN-v2

Pytorch implementation for reproducing StackGAN_v2 results in the paper StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks by Han Zhang*, Tao Xu*, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, Dimitris Metaxas.

Dependencies

python 2.7

Pytorch

In addition, please add the project folder to PYTHONPATH and pip install the following packages:

  • tensorboard
  • python-dateutil
  • easydict
  • pandas
  • torchfile

Data

  1. Download our preprocessed char-CNN-RNN text embeddings for birds and save them to data/
  • [Optional] Follow the instructions reedscot/icml2016 to download the pretrained char-CNN-RNN text encoders and extract text embeddings.
  1. Download the birds image data. Extract them to data/birds/
  2. Download ImageNet dataset and extract the images to data/imagenet/
  3. Download LSUN dataset and save the images to data/lsun

Training

  • Train a StackGAN-v2 model on the bird (CUB) dataset using our preprocessed embeddings:
    • python main.py --cfg cfg/birds_3stages.yml --gpu 0
  • Train a StackGAN-v2 model on the ImageNet dog subset:
    • python main.py --cfg cfg/dog_3stages_color.yml --gpu 0
  • Train a StackGAN-v2 model on the ImageNet cat subset:
    • python main.py --cfg cfg/cat_3stages_color.yml --gpu 0
  • Train a StackGAN-v2 model on the lsun bedroom subset:
    • python main.py --cfg cfg/bedroom_3stages_color.yml --gpu 0
  • Train a StackGAN-v2 model on the lsun church subset:
    • python main.py --cfg cfg/church_3stages_color.yml --gpu 0
  • *.yml files are example configuration files for training/evaluation our models.
  • If you want to try your own datasets, here are some good tips about how to train GAN. Also, we encourage to try different hyper-parameters and architectures, especially for more complex datasets.

Pretrained Model

Evaluating

  • Run python main.py --cfg cfg/eval_birds.yml --gpu 1 to generate samples from captions in birds validation set.
  • Change the eval_*.yml files to generate images from other pre-trained models.

Examples generated by StackGAN-v2

Tsne visualization of randomly generated birds, dogs, cats, churchs and bedrooms

Citing StackGAN++

If you find StackGAN useful in your research, please consider citing:

@article{Han17stackgan2,
  author    = {Han Zhang and Tao Xu and Hongsheng Li and Shaoting Zhang and Xiaogang Wang and Xiaolei Huang and Dimitris Metaxas},
  title     = {StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks},
  journal   = {arXiv: 1710.10916},
  year      = {2017},
}
@inproceedings{han2017stackgan,
Author = {Han Zhang and Tao Xu and Hongsheng Li and Shaoting Zhang and Xiaogang Wang and Xiaolei Huang and Dimitris Metaxas},
Title = {StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks},
Year = {2017},
booktitle = {{ICCV}},
}

Our follow-up work

References

  • Generative Adversarial Text-to-Image Synthesis Paper Code
  • Learning Deep Representations of Fine-grained Visual Descriptions Paper Code

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