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.

通过用户定义的掩码和修复技术，引入修复偏差框架以提高图像生成的准确性，特别是对于新颖或不准确渲染的对象。通过实验验证，我们证明了该框架显著提高了生成图像的逼真度，从而扩展了模型的创造能力并减轻了偏见传承的风险。