Generation of plant dyeing stylized artwork based on diffusion model

Yan Mao; Cuicui Ye; Chengjun Yuan; Mohan Li; Lin Liu; Fengmei Wei; Qiaofei Dai

doi:10.1117/12.2673251

13 April 2023 Generation of plant dyeing stylized artwork based on diffusion model

Yan Mao, Cuicui Ye, Chengjun Yuan, Mohan Li, Lin Liu, Fengmei Wei, Qiaofei Dai

Proceedings Volume 12605, 2022 2nd Conference on High Performance Computing and Communication Engineering (HPCCE 2022); 126050L (2023) https://doi.org/10.1117/12.2673251
Event: Second Conference on High Performance Computing and Communication Engineering, 2022, Harbin, China

Abstract

With the improvement of people's living standards, people have higher clothing safety and artistry requirements. Plant dyeing artworks use plant sap to dye natural and woven fabrics, thus creating a print close to nature, gorgeous in style, and free from the harmful components of chemical dyeing, which is recognized and widely used by highly knowledgeable people. However, the type and the design of plant dyeing artworks need to incorporate the artist's creative inspiration, which is limited by human work efficiency. It constrains the creation of plant-dyeing artworks and limits the speed of innovation. Deep learning has made significant progress in the field of artistic creation. In this paper, we propose to combine the disco diffusion model with the creation of plant-dyeing artworks. The high-quality image generation capability of the diffusion model and multi-modal content description is utilized to design and generate plant dyeing artworks. The original style is firstly modeled after photographs of the original plant dyeing artwork. The style type is further expanded using text descriptions. The disco diffusion model is then used to create the final plant-dyeing piece of art. The experiment results show that generating plant dyeing paintings based on the disco diffusion model achieves better visual results. Through the controlled generation process and high-quality work generation results, the stylized plant dyeing design works is obtained automatically. It provides strong support for the creation of plant dyeing art.

Citation Download Citation

Yan Mao, Cuicui Ye, Chengjun Yuan, Mohan Li, Lin Liu, Fengmei Wei, and Qiaofei Dai "Generation of plant dyeing stylized artwork based on diffusion model", Proc. SPIE 12605, 2022 2nd Conference on High Performance Computing and Communication Engineering (HPCCE 2022), 126050L (13 April 2023); https://doi.org/10.1117/12.2673251

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KEYWORDS

Diffusion

Design and modelling

Semantics

Deep learning

Data modeling

Denoising

Image processing

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