  We introduce a new method to efficiently create text-to-image models from a
pre-trained CLIP and StyleGAN. It enables text driven sampling with an existing
generative model without any external data or fine-tuning. This is achieved by
training a diffusion model conditioned on CLIP embeddings to sample latent
vectors of a pre-trained StyleGAN, which we call clip2latent. We leverage the
alignment between CLIP's image and text embeddings to avoid the need for any
text labelled data for training the conditional diffusion model. We demonstrate
that clip2latent allows us to generate high-resolution (1024x1024 pixels)
images based on text prompts with fast sampling, high image quality, and low
training compute and data requirements. We also show that the use of the well
studied StyleGAN architecture, without further fine-tuning, allows us to
directly apply existing methods to control and modify the generated images
adding a further layer of control to our text-to-image pipeline.
