SGDiff: A Style Guided Diffusion Model for Fashion Synthesis
Zhengwentai Sun Yanghong Zhou Honghong He P. Y. Mok
This paper reports on the development of \textbf{a novel style guided
diffusion model (SGDiff)} which overcomes certain weaknesses inherent in
existing models for image synthesis. The proposed SGDiff combines image
modality with a pretrained text-to-image diffusion model to facilitate creative
fashion image synthesis. It addresses the limitations of text-to-image
diffusion models by incorporating supplementary style guidance, substantially
reducing training costs, and overcoming the difficulties of controlling
synthesized styles with text-only inputs. This paper also introduces a new
dataset -- SG-Fashion, specifically designed for fashion image synthesis
applications, offering high-resolution images and an extensive range of garment
categories. By means of comprehensive ablation study, we examine the
application of classifier-free guidance to a variety of conditions and validate
the effectiveness of the proposed model for generating fashion images of the
desired categories, product attributes, and styles. The contributions of this
paper include a novel classifier-free guidance method for multi-modal feature
fusion, a comprehensive dataset for fashion image synthesis application, a
thorough investigation on conditioned text-to-image synthesis, and valuable
insights for future research in the text-to-image synthesis domain. The code
and dataset are available at: \url{https://github.com/taited/SGDiff}.