TITLE: Evaluating a Synthetic Image Dataset Generated with Stable Diffusion 

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.
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.
Evaluating a Synthetic Image Dataset Generated with Stable Diffusion
http://arxiv.org/abs/2211.01777v2