使用扩散模型的纺织品图案生成
Textile Pattern Generation Using Diffusion Models
AI总结：
本文针对文本引导生成纺织品图案效果不佳的问题，提出一种基于新构建数据集微调的扩散模型，实验证明其在图案生成质量和效率上均优于基线模型，有望简化纺织行业设计流程。
摘要：
文本引导的图像生成是计算机视觉中的一项复杂任务，具有各种应用，包括创作视觉上吸引人的艺术品和逼真的产品图像。广泛用于此任务的一种流行解决方案是扩散模型，这是一种通过迭代过程生成图像的生成模型。尽管扩散模型在各种图像生成任务中展现了有前景的结果，但当应用于更特定的领域（例如基于文本引导的纺织品图案生成）时，它们可能仅在某些时候产生令人满意的结果。本研究提出了一种专门为通过文本引导生成纺织品图案而训练的微调扩散模型，以解决这一问题。该研究涉及收集各种纺织品图案图像，并在另一个AI模型的帮助下为其生成描述。使用这个新创建的数据集对微调扩散模型进行训练，并将其结果与基线模型在视觉和数值上进行比较。结果表明，所提出的微调扩散模型在通过文本引导生成纺织品图案方面，在图案质量和效率上均优于基线模型。本研究为文本引导的纺织品图案生成问题提出了一种有前景的解决方案，并具有简化纺织行业设计过程的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
