  This paper examines the limitations of advanced text-to-image models in
accurately rendering unconventional concepts which are scarcely represented or
absent in their training datasets. We identify how these limitations not only
confine the creative potential of these models but also pose risks of
reinforcing stereotypes. To address these challenges, we introduce the Inpaint
Biases framework, which employs user-defined masks and inpainting techniques to
enhance the accuracy of image generation, particularly for novel or
inaccurately rendered objects. Through experimental validation, we demonstrate
how this framework significantly improves the fidelity of generated images to
the user's intent, thereby expanding the models' creative capabilities and
mitigating the risk of perpetuating biases. Our study contributes to the
advancement of text-to-image models as unbiased, versatile tools for creative
expression.
