https://github.com/reedscot/icml2016

Generative Adversarial Text-to-Image Synthesis
https://github.com/reedscot/icml2016

Last synced: about 2 months ago
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Generative Adversarial Text-to-Image Synthesis

• Host: GitHub
• URL: https://github.com/reedscot/icml2016
• Owner: reedscot
• License: mit
• Created: 2016-06-02T21:58:41.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2018-09-05T16:45:56.000Z (about 6 years ago)
• Last Synced: 2024-07-12T05:32:36.598Z (2 months ago)
• Language: Lua
• Homepage: http://arxiv.org/abs/1605.05396
• Size: 552 KB
• Stars: 906
• Watchers: 40
• Forks: 212
• Open Issues: 31
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
###Generative Adversarial Text-to-Image Synthesis

Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee

This is the code for our ICML 2016 paper on text-to-image synthesis using conditional GANs. You can use it to train and sample from  text-to-image models. The code is adapted from the excellent [dcgan.torch](https://github.com/soumith/dcgan.torch).



####Setup Instructions

You will need to install [Torch](http://torch.ch/docs/getting-started.html), CuDNN, and the [display](https://github.com/szym/display) package.

####How to train a text to image model:

1. Download the [birds](https://drive.google.com/file/d/0B0ywwgffWnLLLUc2WHYzM0Q2eWc/view?usp=sharing) and [flowers](https://drive.google.com/file/d/0B0ywwgffWnLLMl9uOU91MV80cVU/view?usp=sharing) and [COCO](https://drive.google.com/open?id=0B0ywwgffWnLLamltREhDRjlaT3M) caption data in Torch format.

2. Download the [birds](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html) and [flowers](http://www.robots.ox.ac.uk/~vgg/data/flowers/102) and [COCO](http://mscoco.org/dataset/#download) image data.

3. Download the text encoders for [birds](https://drive.google.com/open?id=0B0ywwgffWnLLU0F3UHA3NzFTNEE) and [flowers](https://drive.google.com/open?id=0B0ywwgffWnLLZUt0UmQ1LU1oWlU) and [COCO](https://drive.google.com/open?id=0B0ywwgffWnLLeVNmVVV6OHBDUFE) descriptions.

4. Modify the `CONFIG` file to point to your data and text encoder paths.

5. Run one of the training scripts, e.g. `./scripts/train_cub.sh`

####How to generate samples:

* For flowers: `./scripts/demo_flowers.sh`. Add text descriptions to `scripts/flowers_queries.txt`.

* For birds: `./scripts/demo_cub.sh`.

* For COCO (more general images): `./scripts/demo_coco.sh`. 

* An html file will be generated with the results:







####Pretrained models:

* [CUB GAN-INT-CLS](https://drive.google.com/open?id=0B0ywwgffWnLLSW84ZXRjdXhObzQ)

* [Flowers GAN-INT-CLS](https://drive.google.com/open?id=0B0ywwgffWnLLV0U4MGwzZ2JKT3c)

* [COCO GAN-CLS](https://drive.google.com/open?id=0B0ywwgffWnLLT0JqcEFrOG1iVVk)

####How to train a text encoder from scratch:

* You may want to do this if you have your own new dataset of text descriptions.

* For flowers and birds: follow the instructions [here](https://github.com/reedscot/cvpr2016).

* For MS-COCO: `./scripts/train_coco_txt.sh`.

####Citation

If you find this useful, please cite our work as follows:

```

@inproceedings{reed2016generative,

  title={Generative Adversarial Text-to-Image Synthesis},

  author={Scott Reed and Zeynep Akata and Xinchen Yan and Lajanugen Logeswaran and Bernt Schiele and Honglak Lee},

  booktitle={Proceedings of The 33rd International Conference on Machine Learning},

  year={2016}

}

```