  Neural Cellular Automata (NCA) have proven to be effective in a variety of
fields, with numerous biologically inspired applications. One of the fields, in
which NCAs perform well is the generation of textures, modelling global
patterns from local interactions governed by uniform and coherent rules. This
paper aims to enhance the usability of NCAs in texture synthesis by addressing
a shortcoming of current NCA architectures for texture generation, which
requires separately trained NCA for each individual texture. In this work, we
train a single NCA for the evolution of multiple textures, based on individual
examples. Our solution provides texture information in the state of each cell,
in the form of an internally coded genomic signal, which enables the NCA to
generate the expected texture. Such a neural cellular automaton not only
maintains its regenerative capability but also allows for interpolation between
learned textures and supports grafting techniques. This demonstrates the
ability to edit generated textures and the potential for them to merge and
coexist within the same automaton. We also address questions related to the
influence of the genomic information and the cost function on the evolution of
the NCA.
