内容风格解用于使用隐性扩散架构生成多种风格图像
Kaiyan Chu1,2, Yu Shang3,4, Lingrui Zhang5
1School of Design and Art, College of Science & Technology Ningbo University, Ningbo, 315300, China.
Scientific reports
|January 29, 2026
概括
本研究介绍了双条件轻量级风格扩散模型 (DCLSDM),用于高效的多样化图像生成. DCLSDM增强了内容式解,提供了更好的控制和更低的计算成本.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 多种风格图像生成方法与内容风格脱,对高分辨率输出的高计算需求以及风格传输过程中的结构扭曲作斗争.
- 现有的模型往往无法独立控制内容结构和风格元素,从而限制了它们的有效性.
研究的目的:
- 提出一种新的双条件轻量级风格扩散模型 (DCLSDM),解决当前多样风格图像生成技术的局限性.
- 增强内容风格脱,以改善风格转移应用程序的控制.
- 开发一种高效,资源友好的解决方案,用于生成高质量的图像.
主要方法:
- 开发了一种双条件轻量级风格扩散模型 (DCLSDM),采用双条件控制机制.
- 该机制独立地管理内容结构和风格表达,以便在风格转移过程中精确控制.
- 在WikiArt和Summer2Winter Yosemite数据集上对模型进行了评估.
主要成果:
- 与现有模型相比,DCLSDM表现出优越的性能,由改进的SSIM,LPIPS和FID得分证明.
- 在推断时间,内存使用和参数规模方面实现了显著的减少.
- 该模型在资源有限的环境中被证明有效.
结论:
- DCLSDM为多种风格的图像生成提供了一种高效可控的解决方案.
- 它的轻量级设计和增强的脱功能使其适用于各种应用,包括内容创作和数字艺术制作.
- 该模型克服了风格转移和高分辨率图像合成方面的关键挑战.
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