  Can a generative model be trained to produce images from a specific domain,
guided by a text prompt only, without seeing any image? In other words: can an
image generator be trained "blindly"? Leveraging the semantic power of large
scale Contrastive-Language-Image-Pre-training (CLIP) models, we present a
text-driven method that allows shifting a generative model to new domains,
without having to collect even a single image. We show that through natural
language prompts and a few minutes of training, our method can adapt a
generator across a multitude of domains characterized by diverse styles and
shapes. Notably, many of these modifications would be difficult or outright
impossible to reach with existing methods. We conduct an extensive set of
experiments and comparisons across a wide range of domains. These demonstrate
the effectiveness of our approach and show that our shifted models maintain the
latent-space properties that make generative models appealing for downstream
tasks.
