  Colorway creation is the task of generating textile samples in alternate
color variations maintaining an underlying pattern. The individuation of a
suitable color palette for a colorway is a complex creative task, responding to
client and market needs, stylistic and cultural specifications, and mood. We
introduce a modification of this task, the "generative colorway" creation, that
includes minimal shape modifications, and propose a framework, "ColorwAI", to
tackle this task using color disentanglement on StyleGAN and Diffusion. We
introduce a variation of the InterfaceGAN method for supervised
disentanglement, ShapleyVec. We use Shapley values to subselect a few
dimensions of the detected latent direction. Moreover, we introduce a general
framework to adopt common disentanglement methods on any architecture with a
semantic latent space and test it on Diffusion and GANs. We interpret the color
representations within the models' latent space. We find StyleGAN's W space to
be the most aligned with human notions of color. Finally, we suggest that
disentanglement can solicit a creative system for colorway creation, and
evaluate it through expert questionnaires and creativity theory.
