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.
Overview of our method. (a) The denoising process of our model is decomposed into an ideation stage and an iteration stage, which are conditioned on high-level concepts and low-level attributes, respectively. (b) our editing method starts from the generated design daft \(x^C\) and produces a sequence of edited results \((x^{A_1}, x^{A_2}, \dots)\) given text prompts for different attributes \(({A_1}, {A_2}, \dots)\). (c) our UNet-based denoising network is conditioned on additional pose information.
Qualitative results of different methods for fashion draft generation from high-level design concepts.
Qualitative comparison of iterative local editing. The latest editing methods often lack alignment with low-level attribute semantics, or cause undesirable global changes. Our method can precisely edit the corresponding regions according to the attribute descriptions while keeping the other regions unchanged, which is superior to other methods. TexFit-M refers to TexFit with our body part masks rather than its predicted ones.
