Knit-FLUX: Simulation of Knitted Fabric Images Based on Low-Rank Adaptation of Diffusion Models

Abstract

Generative model-assisted design has become a trend, providing a new paradigm for knitted fabric image generation. The FLUX diffusion model was chosen to generate images in this study and was compared to other generative models. In order to effectively apply the large model to specialized verticals, an efficient fine-tuning method, low-rank adaptation, was used. Experiments showed that the method allows a pre-trained model to stably generate knitted fabric images in batches through easily understandable text prompts. The generated images have clear textures and correct structures, and can display the surface characteristics of knitted fabrics generated by using different yarn specifications and yarn bristles. Moreover, the unit tissue structural similarity index measure (SSIM) is 0.6528, which is very similar to real fabrics. This research expands the application of fabric generation in the field of deep learning. This method is highly efficient, low-cost, and capable of stably simulating knitted fabrics, which can be used to rapidly expand the image design materials of knitted fabrics.