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The fusion of AI and fashion design has emerged as a promising research area.However, the lack of extensive, interrelated data on clothing and try-on stageshas hindered the full potential of AI in this domain. Addressing this, wepresent the Fashion-Diffusion dataset, a product of multiple years' rigorouseffort. This dataset, the first of its kind, comprises over a millionhigh-quality fashion images, paired with detailed text descriptions. Sourcedfrom a diverse range of geographical locations and cultural backgrounds, thedataset encapsulates global fashion trends. The images have been meticulouslyannotated with fine-grained attributes related to clothing and humans,simplifying the fashion design process into a Text-to-Image (T2I) task. TheFashion-Diffusion dataset not only provides high-quality text-image pairs anddiverse human-garment pairs but also serves as a large-scale resource abouthumans, thereby facilitating research in T2I generation. Moreover, to fosterstandardization in the T2I-based fashion design field, we propose a newbenchmark comprising multiple datasets for evaluating the performance offashion design models. This work represents a significant leap forward in therealm of AI-driven fashion design, setting a new standard for future researchin this field.

Quality and Quantity: Unveiling a Million High-Quality Images for Text-to-Image Synthesis in Fashion Design