  We propose Fast text2StyleGAN, a natural language interface that adapts
pre-trained GANs for text-guided human face synthesis. Leveraging the recent
advances in Contrastive Language-Image Pre-training (CLIP), no text data is
required during training. Fast text2StyleGAN is formulated as a conditional
variational autoencoder (CVAE) that provides extra control and diversity to the
generated images at test time. Our model does not require re-training or
fine-tuning of the GANs or CLIP when encountering new text prompts. In contrast
to prior work, we do not rely on optimization at test time, making our method
orders of magnitude faster than prior work. Empirically, on FFHQ dataset, our
method offers faster and more accurate generation of images from natural
language descriptions with varying levels of detail compared to prior work.
