HieraFashDiff: Hierarchical Fashion Design with Multi-stage Diffusion Models
This work addresses the problem of practical fashion design generation and editing for designers, offering a tailored solution that improves usability over existing methods.
The paper tackles the challenge of generating and editing fashion designs by proposing HieraFashDiff, a hierarchical diffusion-based framework that mimics the practical fashion design workflow, resulting in higher fidelity and better prompt adherence compared to prior approaches.
Fashion design is a challenging and complex process.Recent works on fashion generation and editing are all agnostic of the actual fashion design process, which limits their usage in practice.In this paper, we propose a novel hierarchical diffusion-based framework tailored for fashion design, coined as HieraFashDiff. Our model is designed to mimic the practical fashion design workflow, by unraveling the denosing process into two successive stages: 1) an ideation stage that generates design proposals given high-level concepts and 2) an iteration stage that continuously refines the proposals using low-level attributes. Our model supports fashion design generation and fine-grained local editing in a single framework. To train our model, we contribute a new dataset of full-body fashion images annotated with hierarchical text descriptions. Extensive evaluations show that, as compared to prior approaches, our method can generate fashion designs and edited results with higher fidelity and better prompt adherence, showing its promising potential to augment the practical fashion design workflow. Code and Dataset are available at https://github.com/haoli-zbdbc/hierafashdiff.