  Text-to-image diffusion models show great potential in synthesizing a large
variety of concepts in new compositions and scenarios. However, the latent
space of initial seeds is still not well understood and its structure was shown
to impact the generation of various concepts. Specifically, simple operations
like interpolation and finding the centroid of a set of seeds perform poorly
when using standard Euclidean or spherical metrics in the latent space. This
paper makes the observation that, in current training procedures, diffusion
models observed inputs with a narrow range of norm values. This has strong
implications for methods that rely on seed manipulation for image generation,
with applications to few-shot and long-tail learning tasks. To address this
issue, we propose a novel method for interpolating between two seeds and
demonstrate that it defines a new non-Euclidean metric that takes into account
a norm-based prior on seeds. We describe a simple yet efficient algorithm for
approximating this interpolation procedure and use it to further define
centroids in the latent seed space. We show that our new interpolation and
centroid techniques significantly enhance the generation of rare concept
images. This further leads to state-of-the-art performance on few-shot and
long-tail benchmarks, improving prior approaches in terms of generation speed,
image quality, and semantic content.
