Diffusion Models Through a Global Lens: Are They Culturally Inclusive?

Understand

Text-to-image diffusion models have recently enabled the creation of visually compelling, detailed images from textual prompts.

• However, their ability to accurately represent various cultural nuances remains an open question.
• In our work, we introduce CultDiff benchmark, evaluating state-of-the-art diffusion models whether they can generate culturally specific images spanning ten countries.
• We show that these models often fail to generate cultural artifacts in architecture, clothing, and food, especially for underrepresented country regions, by conducting a fine-grained analysis of different similarity aspects, revealing significant disparities in cultural relevance, description fidelity, and realism compared to real-world reference images.