  Cross-modal fashion synthesis and editing offer intelligent support to
fashion designers by enabling the automatic generation and local modification
of design drafts.While current diffusion models demonstrate commendable
stability and controllability in image synthesis,they still face significant
challenges in generating fashion design from abstract design elements and
fine-grained editing.Abstract sensory expressions, \eg office, business, and
party, form the high-level design concepts, while measurable aspects like
sleeve length, collar type, and pant length are considered the low-level
attributes of clothing.Controlling and editing fashion images using lengthy
text descriptions poses a difficulty.In this paper, we propose HieraFashDiff,a
novel fashion design method using the shared multi-stage diffusion model
encompassing high-level design concepts and low-level clothing attributes in a
hierarchical structure.Specifically, we categorized the input text into
different levels and fed them in different time step to the diffusion model
according to the criteria of professional clothing designers.HieraFashDiff
allows designers to add low-level attributes after high-level prompts for
interactive editing incrementally.In addition, we design a differentiable loss
function in the sampling process with a mask to keep non-edit
areas.Comprehensive experiments performed on our newly conducted Hierarchical
fashion dataset,demonstrate that our proposed method outperforms other
state-of-the-art competitors.
