  For the last decade, there has been a push to use multi-dimensional (latent)
spaces to represent concepts; and yet how to manipulate these concepts or
reason with them remains largely unclear. Some recent methods exploit multiple
latent representations and their connection, making this research question even
more entangled. Our goal is to understand how operations in the latent space
affect the underlying concepts. To that end, we explore the task of concept
blending through diffusion models. Diffusion models are based on a connection
between a latent representation of textual prompts and a latent space that
enables image reconstruction and generation. This task allows us to try
different text-based combination strategies, and evaluate easily through a
visual analysis. Our conclusion is that concept blending through space
manipulation is possible, although the best strategy depends on the context of
the blend.
