Version 1
: Received: 27 August 2024 / Approved: 28 August 2024 / Online: 28 August 2024 (15:37:59 CEST)
How to cite:
Zhao, Y.; Liang, Z.; Qiu, Y.; Wang, X.; Yang, W. A Novel Flexible Identity-Net with Diffusion Models for Painting-Style Generation. Preprints2024, 2024082071. https://doi.org/10.20944/preprints202408.2071.v1
Zhao, Y.; Liang, Z.; Qiu, Y.; Wang, X.; Yang, W. A Novel Flexible Identity-Net with Diffusion Models for Painting-Style Generation. Preprints 2024, 2024082071. https://doi.org/10.20944/preprints202408.2071.v1
Zhao, Y.; Liang, Z.; Qiu, Y.; Wang, X.; Yang, W. A Novel Flexible Identity-Net with Diffusion Models for Painting-Style Generation. Preprints2024, 2024082071. https://doi.org/10.20944/preprints202408.2071.v1
APA Style
Zhao, Y., Liang, Z., Qiu, Y., Wang, X., & Yang, W. (2024). A Novel Flexible Identity-Net with Diffusion Models for Painting-Style Generation. Preprints. https://doi.org/10.20944/preprints202408.2071.v1
Chicago/Turabian Style
Zhao, Y., Xiaona Wang and Wanggong Yang. 2024 "A Novel Flexible Identity-Net with Diffusion Models for Painting-Style Generation" Preprints. https://doi.org/10.20944/preprints202408.2071.v1
Abstract
Art's unique style and creativity are essential in defining a work's identity, conveying emotions, and shaping audience perception. Recent advancements in diffusion models have revolutionized art design, animation, and gaming, particularly in generating original artwork and visual identities. However, traditional creative processes face challenges such as slow innovation, high costs, and limited scalability. Consequently, deep learning has emerged as a promising solution for enhancing painting-style creative design.
In this paper, we present the Painting-Style Design Assistant Network (PDANet), a groundbreaking network architecture designed for advanced style transformation. Our work is supported by the Painting-42 dataset, a meticulously curated collection of 4,055 artworks from 42 illustrious Chinese painters, capturing the aesthetic nuances of Chinese painting and offering invaluable design references. Additionally, we introduce a lightweight Identity-Net, designed to enhance large-scale text-to-image (T2I) models by aligning internal knowledge with external control signals. This innovative Identity-Net seamlessly integrates image prompts into the U-Net encoder, enabling the generation of diverse and consistent images.
Through extensive quantitative and qualitative evaluations, our approach has demonstrated superior performance compared to existing methods, producing high-quality, versatile content with broad applicability across various creative domains. Our work not only advances the field of AI-driven art but also offers a new paradigm for the future of creative design.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
