The problem of text-guided image generation is a complex task in Computer
Vision, with various applications, including creating visually appealing
artwork and realistic product images. One popular solution widely used for this
task is the diffusion model, a generative model that generates images through
an iterative process. Although diffusion models have demonstrated promising
results for various image generation tasks, they may only sometimes produce
satisfactory results when applied to more specific domains, such as the
generation of textile patterns based on text guidance. This study presents a
fine-tuned diffusion model specifically trained for textile pattern generation
by text guidance to address this issue. The study involves the collection of
various textile pattern images and their captioning with the help of another AI
model. The fine-tuned diffusion model is trained with this newly created
dataset, and its results are compared with the baseline models visually and
numerically. The results demonstrate that the proposed fine-tuned diffusion
model outperforms the baseline models in terms of pattern quality and
efficiency in textile pattern generation by text guidance. This study presents
a promising solution to the problem of text-guided textile pattern generation
and has the potential to simplify the design process within the textile
industry.

本研究使用 fine-tuned 的 diffusion model 结合 AI 模型通过文本指导来生成纺织花样，并且在视觉和数值上与基准模型进行了比较，证明了该模型在纺织花样生成方面具有更好的质量和效率，并有望在纺织行业中简化设计过程。