---
title: Text-to-Image Synthesis with AttnGAN
emoji: 🤖
colorFrom: blue
colorTo: gray
sdk: streamlit
sdk_version: 1.40.1
app_file: app.py
pinned: false
---

#### Python 3.7+ and Pytorch 1.x

Referenced from: https://github.com/taoxugit/AttnGAN

## Play with this model: [Demo Link](https://share.streamlit.io/gladiator07/text-to-image-synthesis-with-attngan/main/app.py)


## Sneak-peek into the webapp

![](https://github.com/Gladiator07/Text-to-image-synthesis-with-AttnGAN/blob/main/img/home.png)

![](https://github.com/Gladiator07/Text-to-image-synthesis-with-AttnGAN/blob/main/img/demo-1.png)


# AttnGAN

Pytorch implementation for reproducing AttnGAN results in the paper [AttnGAN: Fine-Grained Text to Image Generation
with Attentional Generative Adversarial Networks](http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_AttnGAN_Fine-Grained_Text_CVPR_2018_paper.pdf) by Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, Xiaodong He. (This work was performed when Tao was an intern with Microsoft Research). 

![](https://github.com/taoxugit/AttnGAN/blob/master/framework.png)


**Data**

1. Download preprocessed metadata for [birds](https://drive.google.com/open?id=1O_LtUP9sch09QH3s_EBAgLEctBQ5JBSJ) [coco](https://drive.google.com/open?id=1rSnbIGNDGZeHlsUlLdahj0RJ9oo6lgH9) and save them to `data/`
2. Download the [birds](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html) image data. Extract them to `data/birds/`
3. Download [coco](http://cocodataset.org/#download) dataset and extract the images to `data/coco/`



**Training**
- Pre-train DAMSM models:
  - For bird dataset: `python pretrain_DAMSM.py --cfg cfg/DAMSM/bird.yml --gpu 0`
  - For coco dataset: `python pretrain_DAMSM.py --cfg cfg/DAMSM/coco.yml --gpu 1`
 
- Train AttnGAN models:
  - For bird dataset: `python main.py --cfg cfg/bird_attn2.yml --gpu 2`
  - For coco dataset: `python main.py --cfg cfg/coco_attn2.yml --gpu 3`

- `*.yml` files are example configuration files for training/evaluation our models.



**Pretrained Model**
- [DAMSM for bird](https://drive.google.com/open?id=1GNUKjVeyWYBJ8hEU-yrfYQpDOkxEyP3V). Download and save it to `DAMSMencoders/`
- [DAMSM for coco](https://drive.google.com/open?id=1zIrXCE9F6yfbEJIbNP5-YrEe2pZcPSGJ). Download and save it to `DAMSMencoders/`
- [AttnGAN for bird](https://drive.google.com/open?id=1lqNG75suOuR_8gjoEPYNp8VyT_ufPPig). Download and save it to `models/`
- [AttnGAN for coco](https://drive.google.com/open?id=1i9Xkg9nU74RAvkcqKE-rJYhjvzKAMnCi). Download and save it to `models/`

- [AttnDCGAN for bird](https://drive.google.com/open?id=19TG0JUoXurxsmZLaJ82Yo6O0UJ6aDBpg). Download and save it to `models/`
  - This is an variant of AttnGAN which applies the proposed attention mechanisms to DCGAN framework. 

**Sampling**
- Run `python main.py --cfg cfg/eval_bird.yml --gpu 1` to generate examples from captions in files listed in "./data/birds/example_filenames.txt". Results are saved to `DAMSMencoders/`. 
- Change the `eval_*.yml` files to generate images from other pre-trained models. 
- Input your own sentence in "./data/birds/example_captions.txt" if you wannt to generate images from customized sentences. 

**Validation**
- To generate images for all captions in the validation dataset, change B_VALIDATION to True in the eval_*.yml. and then run `python main.py --cfg cfg/eval_bird.yml --gpu 1`
- We compute inception score for models trained on birds using [StackGAN-inception-model](https://github.com/hanzhanggit/StackGAN-inception-model).
- We compute inception score for models trained on coco using [improved-gan/inception_score](https://github.com/openai/improved-gan/tree/master/inception_score).


### Creating an API
[Evaluation code](eval) embedded into a callable containerized API is included in the `eval\` folder.

### Citing AttnGAN
If you find AttnGAN useful in your research, please consider citing:

```
@article{Tao18attngan,
  author    = {Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, Xiaodong He},
  title     = {AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks},
  Year = {2018},
  booktitle = {{CVPR}}
}
```

**Reference**

- [StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks](https://arxiv.org/abs/1710.10916) [[code]](https://github.com/hanzhanggit/StackGAN-v2)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434) [[code]](https://github.com/carpedm20/DCGAN-tensorflow)


### References
- [Research Paper](https://arxiv.org/abs/1711.10485)
- [Explanation of the paper](https://www.youtube.com/watch?v=Epvh4EvznUA)
- [Python 3.x implementation - Tensorflow](https://github.com/taki0112/AttnGAN-Tensorflow)
- [Python 2.x implementation - PyTorch](https://github.com/taoxugit/AttnGAN)


#### Note: This is a rough Readme as I am quite overloaded with work right now, this Readme will be updated soon with all the details (results, benchmarks, training hardware, model configurations, etc)