Abstract: Generative modeling methods can generate images from textual or visual inputs. However, diversity in the generated images persists as a major challenge of the existing approaches. We address this issue head-on and demonstrating that

• the diversity of a generated batch of images is intrinsically linked to the diversity within the latent variables
• leveraging the geometry of the latent space, we can establish an effective metric for quantifying diversity; and
• employing this insight allows one to achieve a significantly enhanced diversity in image generation beyond the capabilities of traditional random independent sampling.

This advancement is consistent across a variety of generative models, including latent diffusion models and GANs. Additionally, we have integrated our contributions into a widely recognized tool for generative image modeling, ensuring that our improvements are accessible to the broader community. As a result, this work not only presents a methodological advancement in generative modeling but also significantly broadens the scope of potential applications by enhancing the diversity of generated images