The rapid evolution of the fashion industry increasingly intersects with
technological advancements, particularly through the integration of generative
AI. This study introduces a novel generative pipeline designed to transform the
fashion design process by employing latent diffusion models. Utilizing
ControlNet and LoRA fine-tuning, our approach generates high-quality images
from multimodal inputs such as text and sketches. We leverage and enhance
state-of-the-art virtual try-on datasets, including Multimodal Dress Code and
VITON-HD, by integrating sketch data. Our evaluation, utilizing metrics like
FID, CLIP Score, and KID, demonstrates that our model significantly outperforms
traditional stable diffusion models. The results not only highlight the
effectiveness of our model in generating fashion-appropriate outputs but also
underscore the potential of diffusion models in revolutionizing fashion design
workflows. This research paves the way for more interactive, personalized, and
technologically enriched methodologies in fashion design and representation,
bridging the gap between creative vision and practical application.

这篇研究论文介绍了一种新颖的生成性流程，通过使用潜在扩散模型改变了时尚设计过程，利用 ControlNet 和 LoRA 微调生成高质量图像，从文本和草图等多模态输入生成。我们结合草图数据，对包括 Multimodal Dress Code 和 VITON-HD 在内的最先进的虚拟试穿数据集进行了整合和增强。通过使用 FID、CLIP 评分和 KID 等指标进行评估，我们的模型明显优于传统稳定扩散模型。结果不仅突显了我们模型在生成符合时尚要求的输出方面的效果，也凸显了扩散模型在改革时尚设计工作流程中的潜力。这项研究为时尚设计和表达领域提供了更具互动性、个性化和技术丰富的方法，弥合了创意愿景与实际应用之间的差距。