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

Text-Free Learning of a Natural Language Interface for Pretrained Face Generators