Generative-Adversarial-Networks-Based-Text-to-Image-Generation

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To view the literature survey, the model architecture details, the background mathematics and the outputs - Report.pdf

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To test the code

1. Open cmd/terminal in cwd
2. run the command 'pip install -r requirements.txt'
3. run the command 'python downloader.py' to download the dataset
   • If this does not work then download the dataset from the link and add the dataset in 'dataset' directory.
   • The size of dataset is 1.87GB.
4. run the command 'python tester.py' to get results for both baseline models

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Directory Structure

1. dataset → has the flower dataset in hdf5 format
2. modelState → Stores the trained models
   • dcgan-cls.pth → DCGAN-CLS model
   • gan-cls-int.pth → GAN-CLS-INT model
3. results → stores sample results i.e. expected v/s generated images
4. src → the training process

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Note :

• This code uses pytorch for deep learning based implementations.
• Please install pytorch according to the specification of your machine.
• The code will only run on device which has 'cuda' enabled.
• Do not alter directory structure as paths are relative for downloading and extraction of dataset.

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