In this chapter, we have learned about and implemented a StackGAN network to generate high-resolution images from text descriptions. We started with a basic introduction to StackGAN, in which we explored the architectural details of a StackGAN and discovered the losses used for the training of StackGAN. Then, we downloaded and prepared the dataset. After that, we started implementing the StackGAN in the Keras framework. After the implementation, we trained the Stage-I and Stage-II StackGANS sequentially. After successfully training the network, we evaluated the model and saved it for further use.
In the next chapter, we will work with CycleGAN, a network that can convert paintings into photos.
