In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) has been redirecting to new domain since Jan. 1st, 2025.
See More
App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 8

Method for generating clothing model images based on stable diffusion and adaptive enhancement techniques

Liu Dawei1,2
Yu Bihui1,2
Shi Jiawei1,2
Wei Jingxuan1,2
Shi Huiyang2,3
Jin Hexuan1,2
Sun Linzhuang1,2
1. Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang Liaoning 110168, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. School of Computer Science & Technology, University of Chinese Academy of Sciences, Beijing 101408, China
DOI: 10.19734/j.issn.1001-3695.2025.01.0008

Abstract

With the advancement of computer vision and generative models, image generation technology has made significant strides, particularly in e-commerce product displays, enhancing user interaction. Realistic fashion model generation has become an innovative application, deeply integrating generative technology with e-commerce. However, challenges remain, especially in generating high-quality, realistic clothing images that capture details, texture, and consistency. Current models often struggle with accurately representing the factual consistency of clothing and maintaining naturalness and coherence compared to real images. To improve the performance of clothing mannequin generation technology in e-commerce applications, this study presents LoRA-DAE, an improved stable diffusion generative model that integrates LoRA for optimized weight adjustment in attention and convolution layers. Additionally, an adaptive enhancement module is added to the generation process, dynamically adjusting texture and detail distribution, addressing issues like texture blurring and edge distortion. Experiments show that LoRA-DAE outperforms mainstream methods on the Fashion Mannequin dataset, achieving notable improvements in perceived quality (user evaluation) , quantitative metrics (FID, IS, PSNR, SSIM) , and multi-modal large model VQA evaluation.

Foundation Support

沈阳市科技计划项目(23-407-3-29)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.01.0008
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 8

Publish History

[2025-04-17] Accepted Paper

Cite This Article

刘大伟, 于碧辉, 石珈维, 等. 基于稳定扩散与自适应增强技术的服装模特图像生成方法 [J]. 计算机应用研究, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2025.01.0008. (Liu Dawei, Yu Bihui, Shi Jiawei, et al. Method for generating clothing model images based on stable diffusion and adaptive enhancement techniques [J]. Application Research of Computers, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2025.01.0008. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)