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AnyDesign: Versatile area fashion editing via mask-free diffusion.

Yunfang Niu1, Dong Yi2, Lingxiang Wu2

  • 1Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 28, 2026
PubMed
Summary

This study introduces AnyDesign, a novel diffusion-based method for flexible fashion image editing. It enables mask-free editing with diverse clothing types and complex backgrounds, outperforming existing methods.

Keywords:
Diffusion modelsFashion designImage editing

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Digital Image Processing

Background:

  • Current fashion image editing methods lack flexibility and a unified framework, often requiring auxiliary tools like segmenters and keypoint extractors.
  • Existing datasets and methods are limited to generic garments and simple backgrounds, restricting real-world applicability.
  • The need for versatile and user-friendly fashion editing tools is significant for both research and practical applications.

Purpose of the Study:

  • To develop a mask-free, flexible, and unified framework for fashion image editing.
  • To enhance datasets with diverse apparel and complex backgrounds for more robust training.
  • To enable users to edit fashion images using text or image prompts without manual segmentation.

Main Methods:

  • Extended an existing human generation dataset to include a wider variety of apparel (e.g., headwear, scarves, bags) and complex backgrounds.
  • Proposed AnyDesign, a diffusion-based method for mask-free fashion image editing.
  • Incorporated Fashion DiT with a Fashion-Guidance Attention (FGA) module to fuse apparel type and CLIP-encoded features.

Main Results:

  • The extended dataset provides a richer resource for training versatile fashion editing models.
  • AnyDesign successfully performs mask-free editing on diverse clothing items and complex scenes.
  • Qualitative and quantitative experiments show AnyDesign produces high-quality results and outperforms existing text-guided methods.

Conclusions:

  • AnyDesign offers a significant advancement in fashion image editing by providing a flexible, unified, and mask-free approach.
  • The method demonstrates superior performance in handling diverse fashion items and complex scenarios.
  • This work paves the way for more realistic and accessible AI-powered fashion content creation.