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Injection of Cultural-based Subjects into Stable Diffusion Image Generative Model


Amirah Alharbi, Reem Alluhibi, Maryam Saif, Altalhi, Alharthi


Vol. 24  No. 2  pp. 1-14


While text-to-image models have made remarkable progress in image synthesis, certain models, particularly generative diffusion models, have exhibited a noticeable bias to- wards generating images related to the culture of some developing countries. This paper introduces an empirical investigation aimed at mitigating the bias of image generative model. We achieve this by incorporating symbols representing Saudi culture into a stable diffusion model using the Dreambooth technique. CLIP score metric is used to assess the outcomes in this study. This paper also explores the impact of varying parameters for instance the quantity of training images and the learning rate. The findings reveal a substantial reduction in bias-related concerns and propose an innovative metric for evaluating cultural relevance.


Generative model; Diffusion model; Bias; Saudi culture; text-to-image