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.

翻译：配色方案创建任务旨在生成保持基础图案的纺织品样本的替代颜色变体。为配色方案确定合适的调色板是一项复杂的创造性任务，需响应客户与市场需求、风格与文化规范以及情绪氛围。我们引入该任务的一个变体，即“生成式配色方案”创建，其中包含微小的形状修改，并提出了一个框架“ColorwAI”，利用StyleGAN和扩散模型上的颜色解耦来处理此任务。我们提出了InterfaceGAN方法的一种变体用于监督解耦，称为ShapleyVec。我们使用沙普利值对检测到的潜在方向的部分维度进行子选择。此外，我们引入了一个通用框架，可在任何具有语义潜在空间的架构上采用常见的解耦方法，并在扩散模型和GAN上进行了测试。我们解释了模型潜在空间内的颜色表征，发现StyleGAN的W空间与人类颜色概念最为契合。最后，我们提出解耦方法可为配色方案创建激发创造性系统，并通过专家问卷和创造力理论对其进行了评估。