The field of image synthesis has made great strides in the last couple of years. Recent models are capable of generating images with astonishing quality. Fine-grained evaluation of these models on some interesting categories such as faces is still missing. Here, we conduct a quantitative comparison of three popular systems including Stable Diffusion, Midjourney, and DALL-E 2 in their ability to generate photorealistic faces in the wild. We find that Stable Diffusion generates better faces than the other systems, according to the FID score. We also introduce a dataset of generated faces in the wild dubbed GFW, including a total of 15,076 faces. Furthermore, we hope that our study spurs follow-up research in assessing the generative models and improving them. Data and code are available at data and code, respectively.
Cite
@article{arxiv.2210.00586,
title = {Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2},
author = {Ali Borji},
journal= {arXiv preprint arXiv:2210.00586},
year = {2023}
}