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With the rapid rise of AI over the past couple of years, most mainstream image-generation tools—such as DALL·E, MidJourney, and Stable Diffusion—now support multiple languages. However, these systems aren’t usually optimized for one specific language. While they technically work with German, users often face several challenges when compared to working in English. 1. Training Data Bias Most image-generation models are trained primarily on English datasets. As a result, they tend to understand English prompts far better than German ones. When German users provide input in their native language, the model may interpret it through an “English lens.” This can cause it to miss uniquely German cultural or stylistic elements—for example, details of German architecture, restaurant interiors, or regional artistic traditions. A prompt that makes perfect sense in German might lead to a generic or Anglicized result. 2. Semantic and Cultural Gaps Language is more than just words—it carries cu...