ColorwAI: Generative Colorways of Textiles through GAN and Diffusion Disentanglement

TL;DR

This paper presents ColorwAI, a framework that uses disentangled generative models like StyleGAN and Diffusion to create diverse textile colorways with minimal shape changes, enhancing creative control.

Contribution

It introduces a novel supervised disentanglement method, ShapleyVec, and a general framework for applying disentanglement to various architectures, advancing generative textile design.

Findings

StyleGAN's W space aligns well with human color perception.

Disentanglement enables creative control over colorway generation.

Expert evaluations support the system's effectiveness in design.

Abstract

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.