SGDiff: A Style Guided Diffusion Model for Fashion Synthesis
arXiv:2308.07605 · doi:10.1145/3581783.3613806
Abstract
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}.
Accepted by ACM MM'23
References in corpus (9)
- Diffusion Models Beat GANs on Image Synthesis
- Zero-Shot Text-to-Image Generation
- LAION-5B: An open large-scale dataset for training next generation image-text models
- Classifier-Free Diffusion Guidance
- An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
- Prompt-to-Prompt Image Editing with Cross Attention Control
- Learning Fashion Compatibility with Bidirectional LSTMs
- FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization
- ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion Design