  Image generation techniques, particularly latent diffusion models, have
exploded in popularity in recent years. Many techniques have been developed to
manipulate and clarify the semantic concepts these large-scale models learn,
offering crucial insights into biases and concept relationships. However, these
techniques are often only validated in conventional realms of human or animal
faces and artistic style transitions. The food domain offers unique challenges
through complex compositions and regional biases, which can shed light on the
limitations and opportunities within existing methods. Through the lens of food
imagery, we analyze both qualitative and quantitative patterns within a concept
traversal technique. We reveal measurable insights into the model's ability to
capture and represent the nuances of culinary diversity, while also identifying
areas where the model's biases and limitations emerge.
