DIG-In is a library for evaluating the quality, diversity, and consistency differences of text-to-image generation models across geographical regions. It utilizes GeoDE and DollarStreet as reference datasets and computes metrics such as accuracy, coverage, and diversity of generated images. It also employs the CLIPScore metric to measure model consistency. This library empowers researchers and developers to conduct geographical diversity audits of their image generation models, ensuring fairness and inclusivity on a global scale.