  We employ an inversion-based approach to examine CLIP models. Our examination
reveals that inverting CLIP models results in the generation of images that
exhibit semantic alignment with the specified target prompts. We leverage these
inverted images to gain insights into various aspects of CLIP models, such as
their ability to blend concepts and inclusion of gender biases. We notably
observe instances of NSFW (Not Safe For Work) images during model inversion.
This phenomenon occurs even for semantically innocuous prompts, like "a
beautiful landscape," as well as for prompts involving the names of
celebrities.
