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Recently, text-to-image (T2I) synthesis has undergone significantadvancements, particularly with the emergence of Large Language Models (LLM)and their enhancement in Large Vision Models (LVM), greatly enhancing theinstruction-following capabilities of traditional T2I models. Nevertheless,previous methods focus on improving generation quality but introduce unsafefactors into prompts. We explore that appending specific camera descriptions toprompts can enhance safety performance. Consequently, we propose a simple andsafe prompt engineering method (SSP) to improve image generation quality byproviding optimal camera descriptions. Specifically, we create a dataset frommulti-datasets as original prompts. To select the optimal camera, we design anoptimal camera matching approach and implement a classifier for originalprompts capable of automatically matching. Appending camera descriptions tooriginal prompts generates optimized prompts for further LVM image generation.Experiments demonstrate that SSP improves semantic consistency by an average of16% compared to others and safety metrics by 48.9%.

SSP: A Simple and Safe automatic Prompt engineering method towards realistic image synthesis on LVM