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README.md

Text To Image Synthesis

This is an experimental tensorflow implementation of synthesizing images. The images are synthesized using the GAN-CLS Algorithm from the paper Generative Adversarial Text-to-Image Synthesis. This implementation is built on top of the excellent DCGAN in Tensorflow.

Model architecture

Image Source : Generative Adversarial Text-to-Image Synthesis Paper

Requirements

Datasets

  • The model is currently trained on the flowers dataset. Download the images from here and save them in 102flowers/102flowers/*.jpg. Also download the captions from this link. Extract the archive, copy the text_c10 folder and paste it in 102flowers/text_c10/class_*.

N.B You can downloads all data files needed manually or simply run the downloads.py and put the correct files to the right directories.

python downloads.py

Codes

  • downloads.py download Oxford-102 flower dataset and caption files(run this first).
  • data_loader.py load data for further processing.
  • train_txt2im.py train a text to image model.
  • utils.py helper functions.
  • model.py models.

Deployment of Web Application

  • Upload all the trained (npz) and web app files to web server or domain
  • input.php run the input.php file to give input.
  • Give input and submit get desired output .

References

Results

  • these white flowers have petals that start off white in color and end in a white towards the tips.

License

Apache 2.0

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