Color prediction and color management for digital printed fabrics remain a challenging task. Accurate prediction of color appearances of digital printed fabrics would enable designers and manufacturers to better fulfill their design requirements and creative visions. We propose a color prediction method for silk digital printing utilizing a Pix2Pix Generative Adversarial Network (GAN) framework. This method aims to generate predicted images that possess the same stylistic and color characteristics as their actual fabrics after production. To develop and validate the method, color data and images are collected and processed from 5252 sets of paired original Pantone TPX color card and actual print sample fabrics. The results of this study demonstrate that the method can predict the colors of silk digital print samples while effectively reproducing the effects of inkjet printing in silk fabrics including silk crepe satin and silk twill. The method exhibits high prediction accuracy to an average CIEDE2000 value of 2.372 for silk crepe satin and 1.846 for silk twill. The findings of this research not only enhance the efficiency and accuracy of color management in fabric digital printing technology but also contribute to the exploration and development of high-fidelity color prediction techniques within the textile industry.

Colored yarns interweaving in accordance with specific weave structure provides the opportunity for textile designers to create an enormous number of mixing color effects on fabric surface. The key point of this idea is to search an appropriate model to match the optical mixing color of a fabric with the targeted or desired color. Based on the present prediction models such as K/S, Log (K/S), S-N and Friele model for colored yarn mixing, this study proposes a new color mixing model with a variable parameter to predict optical mixing color values of yarn-dyed fabrics. In order to optimize the model and improve the adaptive capacity for different kinds of yarn materials, a constant σ is introduced. Using a fitting approximation algorithm, the optimal value of constant σof the model can be found per different yarns and interweave density of fabrics. Compared with previous models, the new model has much lower color differences.