  Fashion attribute editing is a task that aims to convert the semantic
attributes of a given fashion image while preserving the irrelevant regions.
Previous works typically employ conditional GANs where the generator explicitly
learns the target attributes and directly execute the conversion. These
approaches, however, are neither scalable nor generic as they operate only with
few limited attributes and a separate generator is required for each dataset or
attribute set. Inspired by the recent advancement of diffusion models, we
explore the classifier-guided diffusion that leverages the off-the-shelf
diffusion model pretrained on general visual semantics such as Imagenet. In
order to achieve a generic editing pipeline, we pose this as multi-attribute
image manipulation task, where the attribute ranges from item category, fabric,
pattern to collar and neckline. We empirically show that conventional methods
fail in our challenging setting, and study efficient adaptation scheme that
involves recently introduced attention-pooling technique to obtain a
multi-attribute classifier guidance. Based on this, we present a mask-free
fashion attribute editing framework that leverages the classifier logits and
the cross-attention map for manipulation. We empirically demonstrate that our
framework achieves convincing sample quality and attribute alignments.
