Aided design of bridge aesthetics based on Stable Diffusion fine-tuning
This work addresses bridge aesthetics design for engineers and architects, but it is incremental as it applies existing fine-tuning techniques to a new domain.
The researchers tackled the problem of bridge design innovation by fine-tuning Stable Diffusion on a bridge photo dataset using four methods, enabling the model to generate numerous new bridge types and serve as a creative engine for human designers.
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.