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A novel flexible identity-net with diffusion models for painting-style generation
Yifei Zhao1, Ziqi Liang2, Yingrui Qiu2
1School of New Media, Beijing Institute of Graphic Communication, Beijing, 102600, China. zhaoyifei@bigc.edu.cn.
Scientific Reports
|July 31, 2025
Summary
This study introduces PDANet, an AI for advanced painting style transformation, and Identity-Net to improve text-to-image models. These tools enhance creative design with high-quality, versatile AI-generated art.
Area of Science:
- Artificial Intelligence
- Computer Vision
- Digital Art
Background:
- Artistic style is crucial for identity and perception.
- Diffusion models have advanced AI art generation.
- Traditional creative processes face limitations in speed, cost, and scalability.
Purpose of the Study:
- To enhance painting-style creative design using deep learning.
- To introduce a novel network architecture for advanced style transformation.
- To improve large-scale text-to-image (T2I) models for diverse and consistent image generation.
Main Methods:
- Developed the Painting-Style Design Assistant Network (PDANet) for style transformation.
- Utilized the Painting-42 dataset of 4055 Chinese paintings.
- Introduced a lightweight Identity-Net to integrate image prompts into U-Net encoders for T2I models.
Main Results:
- PDANet demonstrates superior performance in style transformation.
- Identity-Net enhances T2I models for diverse and consistent image generation.
- The approach produces high-quality, versatile content applicable across creative domains.
Conclusions:
- AI-driven art generation offers a new paradigm for creative design.
- PDANet and Identity-Net advance AI capabilities in art and design.
- The developed methods provide valuable tools for enhancing artistic creation and visual identity.
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