This repository implements a conditional Generative Adversarial Network trained on text embeddings to generate images matching given descriptions. The architecture extends DCGAN with training stabilization techniques including feature matching, label smoothing, minibatch discrimination, and WGAN/WGAN-GP variants. The model is trained on Caltech-UCSD Birds 200 and Flowers datasets using pre-computed text embeddings.