The field of text-to-image models is rapidly advancing, with a focus on improving fairness and reducing biases in generated images. Recent developments have introduced novel frameworks for debiasing and evaluating text-to-image models, such as post-hoc debiasing methods and multimodal reward modeling. These advancements have the potential to significantly improve the fairness and accuracy of text-to-image generation. Noteworthy papers include FairImagen, which introduces a post-hoc debiasing framework, and FairJudge, which presents a protocol for evaluating text-to-image models. Additionally, papers like Semantic Surgery and SceneDecorator have made significant contributions to concept erasure and scene-oriented story generation, respectively. Overall, the field is moving towards more equitable and accurate text-to-image generation, with a growing emphasis on fairness, transparency, and accountability.