How to Generate Better Synthetic Image Datasets with Stable Diffusion

This article explores the art of prompt engineering for generating useful image datasets, using Stable Diffusion as a text-to-image model. The complexity of creating diverse and convincing images that mimic real-world scenarios is highlighted. A quantitative framework to score the quality of any synthetic dataset is introduced, which can guide prompt engineering efforts to generate better synthetic datasets. Cleanlab Studio offers an automated way to quantitatively assess the quality of synthetic datasets by computing four scores: unrealistic, unrepresentative, unvaried, and unoriginal. These scores help compare different synthetic data generators (i.e., prompt templates) and can be computed for image/text/tabular data. The Snacks dataset is used as an example to demonstrate the process of generating images from prompts and evaluating their quality using these scores.

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