  Recently, text-to-image (T2I) synthesis has undergone significant
advancements, particularly with the emergence of Large Language Models (LLM)
and their enhancement in Large Vision Models (LVM), greatly enhancing the
instruction-following capabilities of traditional T2I models. Nevertheless,
previous methods focus on improving generation quality but introduce unsafe
factors into prompts. We explore that appending specific camera descriptions to
prompts can enhance safety performance. Consequently, we propose a simple and
safe prompt engineering method (SSP) to improve image 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 LVM image generation.
Experiments demonstrate that SSP improves semantic consistency by an average of
16% compared to others and safety metrics by 48.9%.

Computer Vision and Pattern Recognition