Aided design of bridge aesthetics based on Stable Diffusion fine-tuning
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909324053315584 |
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| author | Zhang, Leye Tian, Xiangxiang Zhang, Chengli Zhang, Hongjun |
| author_facet | Zhang, Leye Tian, Xiangxiang Zhang, Chengli Zhang, Hongjun |
| contents | Stable Diffusion fine-tuning technique is tried to assist bridge-type innovation. The bridge real photo dataset is built, and Stable Diffusion is fine tuned by using four methods that are Textual Inversion, Dreambooth, Hypernetwork and Lora. All of them can capture the main characteristics of dataset images and realize the personalized customization of Stable Diffusion. Through fine-tuning, Stable Diffusion is not only a drawing tool, but also has the designer's innovative thinking ability. The fine tuned model can generate a large number of innovative new bridge types, which can provide rich inspiration for human designers. The result shows that this technology can be used as an engine of creativity and a power multiplier for human designers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15812 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Aided design of bridge aesthetics based on Stable Diffusion fine-tuning Zhang, Leye Tian, Xiangxiang Zhang, Chengli Zhang, Hongjun Machine Learning Computer Vision and Pattern Recognition Stable Diffusion fine-tuning technique is tried to assist bridge-type innovation. The bridge real photo dataset is built, and Stable Diffusion is fine tuned by using four methods that are Textual Inversion, Dreambooth, Hypernetwork and Lora. All of them can capture the main characteristics of dataset images and realize the personalized customization of Stable Diffusion. Through fine-tuning, Stable Diffusion is not only a drawing tool, but also has the designer's innovative thinking ability. The fine tuned model can generate a large number of innovative new bridge types, which can provide rich inspiration for human designers. The result shows that this technology can be used as an engine of creativity and a power multiplier for human designers. |
| title | Aided design of bridge aesthetics based on Stable Diffusion fine-tuning |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.15812 |