  Generating and editing images from open domain text prompts is a challenging
task that heretofore has required expensive and specially trained models. We
demonstrate a novel methodology for both tasks which is capable of producing
images of high visual quality from text prompts of significant semantic
complexity without any training by using a multimodal encoder to guide image
generations. We demonstrate on a variety of tasks how using CLIP [37] to guide
VQGAN [11] produces higher visual quality outputs than prior, less flexible
approaches like DALL-E [38], GLIDE [33] and Open-Edit [24], despite not being
trained for the tasks presented. Our code is available in a public repository.
