bitmind/SyntheticFacesHighQualitypart4

Synthetic Faces High Quality (SFHQ) part 4 This dataset consists of 125,754 high quality 1024x1024 curated face images, and was created by first creating large amount of "text to image" generations (most from stable diffusion v2.1, some from stable diffusion v1.4) model and then creating several photo-realistic candidate images using a process similar to what is described in this short twitter thread which involve encoding the images into StyleGAN2 latent space and performing a small manipulation that turns each image into a high quality photo-realistic image candidate. Finally, we then sift through the resulting candidate images and keep only the good ones for the dataset.

Summary Overall, the SFHQ dataset contains ~425,000 high quality and curated synthetic face images that have no privacy issues or license issues surrounding them.

This dataset contains a high degree of variability on the axes of identity, ethnicity, age, pose, expression, lighting conditions, hair-style, hair-color, facial hair. It lacks variability in accessories axes such as hats or earphones as well as various jewelry. It also doesn't contain any occlusions except the self-occlusion of hair occluding the forehead, the ears and rarely the eyes. This dataset naturally inherits all the biases of it's original datasets (FFHQ, AAHQ, Close-Up Humans, Face Synthetics, LAION-5B) and the StyleGAN2 and Stable Diffusion models.

The purpose of this dataset is to be of sufficiently high quality that new machine learning models can be trained using this data, including even generative face models such as StyleGAN. The dataset may be extended from time to time with additional supervision labels (e.g. text descriptions), but no promises.