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通过文本到图像生成人工智能模型生成概念性景观设计.
Xinyue Ye1, Tianchen Huang1, Yang Song1
1Texas A&M University, USA.
概括
这项研究表明,使用ControlNet和LoRA的稳定扩散,生成性AI如何可以创建高效和详细的景观设计. 精心调整的AI模型为概念景观建筑产生了卓越的结果.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 景观建筑 景观建筑 建筑
背景情况:
- 传统的景观设计耗时,创造力有限.
- 探索各种设计解决方案缺乏效率.
- 人工智能为设计创新提供了潜力.
研究的目的:
- 在概念性景观设计中探索生成性AI集成.
- 展示一个高效的AI驱动的工作流程,用于景观可视化.
- 评估微调的人工智能模型对设计质量的影响.
主要方法:
- 使用稳定扩散,控制网和LoRA来生成文本到图像.
- 开发了一个概念景观设计生成工作流程.
- 对人工智能产生的设计进行了定性和定量评估.
主要成果:
- 人工智能工作流程有效地生成了详细和连贯的景观设计 (公园,广场,花园).
- 微调模型显著优于非微调模型.
- 人工智能产生的设计保持了空间的一致性,尺寸控制和相关元素.
结论:
- 生成型人工智能提高了概念景观设计中的效率和创造力.
- 精心调整的AI模型对于卓越的设计结果至关重要.
- 人工智能集成对景观建筑创新的巨大潜力.
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