Undirected graphical models are a widely used class of probabilistic models in machine learning that capture prior knowledge or putative pairwise interactions between variables. Those interactions are encoded in a graph for pairwise interactions; however, generalizations such as factor graphs account for higher-degree interactions using hypergraphs. Inference on such models, which is performed by conditioning on some observed variables, is typically done approximately by optimizing a free energy, which is an instance of variational inference. The Belief Propagation algorithm is a dynamic programming algorithm that finds critical points of that free energy. Recent efforts have been made to unify and extend inference on graphical models and factor graphs to more expressive probabilistic models. A synthesis of these works shows that inference on graphical models, factor graphs, and their generalizations relies on the introduction of presheaves and associated invariants (homology and cohomology groups).We propose to study the impact of the transformation of the presheaves onto the associated message passing algorithms. We show that natural transformations between presheaves associated with graphical models and their generalizations, which can be understood as coherent binning of the set of values of the variables, induce morphisms between associated message-passing algorithms. It is, to our knowledge, the first result on functoriality of the Loopy Belief Propagation.