  We study the problem of example-based procedural texture synthesis using
highly compact models. Given a sample image, we use differentiable programming
to train a generative process, parameterised by a recurrent Neural Cellular
Automata (NCA) rule. Contrary to the common belief that neural networks should
be significantly over-parameterised, we demonstrate that our model architecture
and training procedure allows for representing complex texture patterns using
just a few hundred learned parameters, making their expressivity comparable to
hand-engineered procedural texture generating programs. The smallest models
from the proposed $\mu$NCA family scale down to 68 parameters. When using
quantisation to one byte per parameter, proposed models can be shrunk to a size
range between 588 and 68 bytes. Implementation of a texture generator that uses
these parameters to produce images is possible with just a few lines of GLSL or
C code.
