StyleGAN-NADA adapts image generators to new domains using only natural language text prompts and CLIP guidance. The method leverages the semantic power of CLIP models to shift StyleGAN to diverse domains characterized by different styles and shapes, without collecting any images from the target domain. This zero-shot domain adaptation approach enables practitioners to repurpose pre-trained generators through natural language descriptions alone.