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Recent progress in text-to-image (TTI) systems, such as StableDiffusion,Imagen, and DALL-E 2, have made it possible to create realistic images withsimple text prompts. It is tempting to use these systems to eliminate themanual task of obtaining natural images for training a new machine learningclassifier. However, in all of the experiments performed to date, classifierstrained solely with synthetic images perform poorly at inference, despite theimages used for training appearing realistic. Examining this apparentincongruity in detail gives insight into the limitations of the underlyingimage generation processes. Through the lens of diversity in image creationvs.accuracy of what is created, we dissect the differences in semanticmismatches in what is modeled in synthetic vs. natural images. This willelucidate the roles of the image-languag emodel, CLIP, and the image generationmodel, diffusion. We find four issues that limit the usefulness of TTI systemsfor this task: ambiguity, adherence to prompt, lack of diversity, and inabilityto represent the underlying concept. We further present surprising insightsinto the geometry of CLIP embeddings.

Diversity and Diffusion: Observations on Synthetic Image Distributions with Stable Diffusion