  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 stages
has hindered the full potential of AI in this domain. Addressing this, we
present the Fashion-Diffusion dataset, a product of multiple years' rigorous
effort. This dataset, the first of its kind, comprises over a million
high-quality fashion images, paired with detailed text descriptions. Sourced
from a diverse range of geographical locations and cultural backgrounds, the
dataset encapsulates global fashion trends. The images have been meticulously
annotated with fine-grained attributes related to clothing and humans,
simplifying the fashion design process into a Text-to-Image (T2I) task. The
Fashion-Diffusion dataset not only provides high-quality text-image pairs and
diverse human-garment pairs but also serves as a large-scale resource about
humans, thereby facilitating research in T2I generation. Moreover, to foster
standardization in the T2I-based fashion design field, we propose a new
benchmark comprising multiple datasets for evaluating the performance of
fashion design models. This work represents a significant leap forward in the
realm of AI-driven fashion design, setting a new standard for future research
in this field.
