Skip to content
No description, website, or topics provided.
Branch: master
Clone or download
Latest commit e72ef88 Feb 25, 2018
Type Name Latest commit message Commit time
Failed to load latest commit information.
code first commit Oct 3, 2017
data first commit Oct 3, 2017
examples first commit Oct 3, 2017
models first commit Oct 3, 2017
.gitignore first commit Oct 3, 2017
LICENSE first commit Oct 3, 2017 Update Feb 26, 2018


Pytorch implementation for reproducing COCO results in the paper StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks by Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, Dimitris Metaxas. The network structure is slightly different from the tensorflow implementation.


python 2.7


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

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


  1. Download our preprocessed char-CNN-RNN text embeddings for training coco and evaluating coco, save them to data/coco.
  • [Optional] Follow the instructions reedscot/icml2016 to download the pretrained char-CNN-RNN text encoders and extract text embeddings.
  1. Download the coco image data. Extract them to data/coco/.


  • The steps to train a StackGAN model on the COCO dataset using our preprocessed embeddings.
    • Step 1: train Stage-I GAN (e.g., for 120 epochs) python --cfg cfg/coco_s1.yml --gpu 0
    • Step 2: train Stage-II GAN (e.g., for another 120 epochs) python --cfg cfg/coco_s2.yml --gpu 1
  • *.yml files are example configuration files for training/evaluating 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

  • StackGAN for coco. Download and save it to models/coco.
  • Our current implementation has a higher inception score(10.62±0.19) than reported in the StackGAN paper


  • Run python --cfg cfg/coco_eval.yml --gpu 2 to generate samples from captions in COCO validation set.

Examples for COCO:

Save your favorite pictures generated by our models since the randomness from noise z and conditioning augmentation makes them creative enough to generate objects with different poses and viewpoints from the same discription 😃

Citing StackGAN

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

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


  • Generative Adversarial Text-to-Image Synthesis Paper Code
  • Learning Deep Representations of Fine-grained Visual Descriptions Paper Code
You can’t perform that action at this time.