Originally, we use Chinese for environment generation. But during experiment, there appear to be some inaccuracy. I wonder if it has somthing to do with language.
After research, it appears language will affact the model to some extent.
Language and Prompting in Image Generation
- Language can influence image results.
Even if two prompts have the same meaning, different languages may lead to slightly different styles, details, or levels of accuracy. - English style keywords are often more stable.
Many image models respond well to common English visual terms such as painterly brushstrokes, hand-painted texture, low saturation, and dusty atmosphere. - Chinese is still very useful.
Chinese can express mood, intention, and subtle creative ideas clearly, especially if the user is more comfortable writing in Chinese. - Direct translation is not always enough.
A word like “笔触感” can mean brushstroke texture, painterly texture, or rough brushwork, and each may produce a different result. - A mixed-language workflow works best.
The user can first describe the idea in Chinese, then refine the prompt with specific English visual keywords. - Overall, prompting is about clarification.
The goal is not just to translate, but to turn creative ideas into clear visual instructions that the model can follow.