Despite the fact that popular text-to-image generation models cope well with international and general cultural queries, they have a significant knowledge gap regarding individual cultures. This is due to the content of existing large training datasets collected on the Internet, which are predominantly based on Western European or American popular culture. Meanwhile, the lack of cultural adaptation of the model can lead to incorrect results, a decrease in the generation quality, and the spread of stereotypes and offensive content. In an effort to address this issue, we examine the concept of cultural code and recognize the critical importance of its understanding by modern image generation models, an issue that has not been sufficiently addressed in the research community to date. We propose the methodology for collecting and processing the data necessary to form a dataset based on the cultural code, in particular the Russian one. We explore how the collected data affects the quality of generations in the national domain and analyze the effectiveness of our approach using the Kandinsky 3.1 text-to-image model. Human evaluation results demonstrate an increase in the level of awareness of Russian culture in the model.

本研究探讨了当前文本到图像生成模型在文化适应性方面的不足，特别是缺乏对个别文化的理解所导致的生成质量下降和刻板印象的传播。我们提出了一种基于文化代码（尤其是俄罗斯文化）的数据集收集和处理方法，实验证明该方法能够有效提高模型对俄罗斯文化的认知水平，从而改善生成质量。

CRAFT：文化俄罗斯导向的数据集适应用于聚焦的文本到图像生成