Generating Synthetic Data via Augmentations for Improved Facial Resemblance in DreamBooth and InstantID

Abstract: The personalization of Stable Diffusion for generating professional portraits from amateur photographs is a burgeoning area, with applications in various downstream contexts. This paper investigates the impact of augmentations on improving facial resemblance when using two prominent personalization techniques: DreamBooth and InstantID. Through a series of experiments with diverse subject datasets, we assessed the effectiveness of various augmentation strategies on the generated headshots' fidelity to the original subject. We introduce FaceDistance, a wrapper around FaceNet, to rank the generations based on facial similarity, which aided in our assessment. Ultimately, this research provides insights into the role of augmentations in enhancing facial resemblance in SDXL-generated portraits, informing strategies for their effective deployment in downstream applications.