StackGAN

Text to Photo-Realistic Image Synthesis

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Dependencies

tensorflow==2.1.0
numpy==1.16.4
absl_py==0.7.0
matplotlib==2.2.3
pandas==0.23.4
Pillow==6.1.0

Downloads

• To download all the dependencies, simply execute

pip install -r requirements.txt

• To download the CUB 200 dataset, simply execute the data_download.py file

python data_download.py

• Download the Char-RNN-CNN embeddings from this link: download link and unzip it in place.

unzip birds.zip

Training

• The model.py file contains the bare minimum code to run the stage 1 and stage 2 architecture. It automatically stores the weights after the specified/default number of epochs have completed. Note that the weights will be stored at the same directory level as model.py.

python model.py

Architecture

• Stage 1
  • Text Encoder Network
    • Text description to a 1024 dimensional text embedding
    • Learning Deep Representations of Fine-Grained Visual Descriptions Arxiv Link
  • Conditioning Augmentation Network
    • Adds randomness to the network
    • Produces more image-text pairs
  • Generator Network
  • Discriminator Network
  • Embedding Compressor Network
  • Outputs a 64x64 image

• Stage 2
  • Text Encoder Network
  • Conditioning Augmentation Network
  • Generator Network
  • Discriminator Network
  • Embedding Compressor Network
  • Outputs a 256x256 image

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Reference Papers

1. StackGAN: Text to photo-realistic image synthesis [Arxiv Link]
2. Improved Techniques for Training GANs [Arxiv Link]
3. Generative Adversarial Text to Image Synthesis [Arxiv Link]
4. Learning Deep Representations of Fine-Grained Visual Descriptions [Arxiv Link]

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Note

This is the code I have submitted to TensorFlow for Google Summer of Code. Hence the attributions and the License is for "TensorFlow Authors" and not "Vishal V". This code is under the MIT License.