# Evaluating a Synthetic Image Dataset Generated with Stable Diffusion

Source: [Evaluating a Synthetic Image Dataset Generated with Stable Diffusion](<https://www.sota2.com/research/paper/68fa6dde1e209131cea3c947>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:18:29.441Z

Authors: Andreas St\\"ockl

Published: 2022-11-03T13:02:08.000Z

## Abstract

We generate synthetic images with the "Stable Diffusion" image generation model using the Wordnet taxonomy and the definitions of concepts it contains. This synthetic image database can be used as training data for data augmentation in machine learning applications, and it is used to investigate the capabilities of the Stable Diffusion model.   Analyses show that Stable Diffusion can produce correct images for a large number of concepts, but also a large variety of different representations. The results show differences depending on the test concepts considered and problems with very specific concepts. These evaluations were performed using a vision transformer model for image classification.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2211.01777>)

[PDF](<https://arxiv.org/pdf/2211.01777.pdf>)