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The design of printed patterns is a crucial aspect of garment manufacturing. However, manually designed patterns often suffer from content similarity and low design efficiency. Therefore, a printed pattern generation method based on a diffusion model was designed and implemented. This method employs deep learning technology to extract and expand an existing dataset of printed patterns, then generated textual descriptions of these patterns from the dimensions of color and category to complete the dataset. Fine-tune diffusion model using pre-made dataset, and flatten processing was applied to the feature space of the images, as required in the textile industry. Studies were analyzed on the characteristics of local diffusion, achieving a image generation effect with variable detail from images and text. Experimental results demonstrate that our designed pattern generation method is capable of producing high-quality patterns, and its feature space tiling method provides smooth transitions at pattern edges.