Diverse Diffusion: Enhancing Image Diversity in Text-to-Image Generation
Mariia Zameshina Olivier Teytaud Laurent Najman
Latent diffusion models excel at producing high-quality images from text.
Yet, concerns appear about the lack of diversity in the generated imagery. To
tackle this, we introduce Diverse Diffusion, a method for boosting image
diversity beyond gender and ethnicity, spanning into richer realms, including
color diversity.Diverse Diffusion is a general unsupervised technique that can
be applied to existing text-to-image models. Our approach focuses on finding
vectors in the Stable Diffusion latent space that are distant from each other.
We generate multiple vectors in the latent space until we find a set of vectors
that meets the desired distance requirements and the required batch size.To
evaluate the effectiveness of our diversity methods, we conduct experiments
examining various characteristics, including color diversity, LPIPS metric, and
ethnicity/gender representation in images featuring humans.The results of our
experiments emphasize the significance of diversity in generating realistic and
varied images, offering valuable insights for improving text-to-image models.
Through the enhancement of image diversity, our approach contributes to the
creation of more inclusive and representative AI-generated art.