# Aided design of bridge aesthetics based on Stable Diffusion fine-tuning

arXiv: https://arxiv.org/abs/2409.15812 · HTML: https://mlsanity.com/paper/2409.15812/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 6 of 6)

- [An attempt to generate new bridge types from latent space of variational autoencoder](https://mlsanity.com/paper/2311.03380/) (2023)
- [An attempt to generate new bridge types from latent space of generative adversarial network](https://mlsanity.com/paper/2401.00700/) (2024)
- [An attempt to generate new bridge types from latent space of PixelCNN](https://mlsanity.com/paper/2401.05964/) (2024)
- [An attempt to generate new bridge types from latent space of generative flow](https://mlsanity.com/paper/2401.10299/) (2024)
- [An attempt to generate new bridge types from latent space of denoising diffusion Implicit model](https://mlsanity.com/paper/2402.07129/) (2024)
- [An attempt to generate new bridge types from latent space of energy-based model](https://mlsanity.com/paper/2401.17657/) (2024)

## Abstract (arXiv)

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.