SSP: A Simple and Safe Prompt Engineering for Visual Generation

Weijin Cheng, Jianzhi Liu, Ziyun Jiao, Jiawen Deng, Fuji Ren

Abstract: Prompt engineering aims to adapt an AI foundation model on the token level without weight updating. Recently, with the development of visual models, many researchers have begun to study visual generation quality improvement using prompt engineering. However, while those studies mainly aim to improve visual quality, they overlook the safe factors in prompts. We find that adding specific camera descriptions not only prevents these issues but also enhances visual quality. Consequently, we propose a simple and safe prompt engineering method (SSP) to improve visual generation quality by providing optimal camera descriptions. Specifically, we create a dataset from multi-datasets as original prompts. To select the optimal camera, we design an optimal camera matching approach and implement a classifier for original prompts capable of automatically matching. Appending camera descriptions to original prompts generates optimized prompts for further visual generation. Experiments demonstrate that SSP improves semantic consistency by an average of 16 % compared to others and safety metrics by 35.8%.