Clothing pattern-making is an important part of transforming design concepts into finished products; however, the traditional manual pattern-making process is not only time-consuming, but also suffers from inefficiency, which seriously restricts the automation and precision of clothing production. This study proposes an automated clothing pattern-making method, the core of which lies in the organic combination of an improved Pix2Pix model and a conditional diffusion model. The improved Pix2Pix model effectively captures the complex structural information in clothing patterns by introducing a multi-scale discriminator and a new composite loss function. Due to limited data, the improved Pix2Pix falls short in terms of image generation quality, so a conditional diffusion model was introduced to enhance the detail and overall integrity of the generated images. Experiments were conducted on pattern-making tasks for the sleeves and back panels of various typical clothing styles. The sleeve components primarily validated the model’s basic generation capabilities. The results showed that the improved Pix2Pix-generated initial template could capture the basic contour structure, and after diffusion model repair, the lines became clearer and the details more complete; the back panels components validated the model’s robustness. Quantitative results showed that the proposed method achieved SSIM, PSNR, and LPIPS values of 0.869, 22.31, and 0.1318, respectively. Compared with the results of other advanced models, the proposed method exhibits the highest accuracy and clarity in the generated images, confirming its practicality and effectiveness in automated apparel pattern-making.
Zheng et al. (Tue,) studied this question.