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

clip2latent: Text driven sampling of a pre-trained StyleGAN using denoising diffusion and CLIP