  This work presents a differentiable rendering approach that allows latent
fractal flame parameters to be learned from image supervision. The approach
extends the state-of-the-art in differentiable fractal rendering through
support for color images, non-linear generator functions, and multi-fractal
compositions. With these additions, differentiable rendering is now a viable
tool for the generation of fractal artwork.
