The burgeoning landscape of text-to-image models, exemplified by innovations such as Midjourney and DALLE 3, has revolutionized content creation across diverse sectors. However, these advancements bring forth critical ethical concerns, particularly with the misuse of open-source models to generate content that violates societal norms. Addressing this, we introduce Ethical-Lens, a framework designed to facilitate the value-aligned usage of text-to-image tools without necessitating internal model revision. Ethical-Lens ensures value alignment in text-to-image models across toxicity and bias dimensions by refining user commands and rectifying model outputs. Systematic evaluation metrics, combining GPT4-V, HEIM, and FairFace scores, assess alignment capability. Our experiments reveal that Ethical-Lens enhances alignment capabilities to levels comparable with or superior to commercial models like DALLE 3, ensuring user-generated content adheres to ethical standards while maintaining image quality. This study indicates the potential of Ethical-Lens to ensure the sustainable development of open-source text-to-image tools and their beneficial integration into society. Our code is available at https://github.com/yuzhu-cai/Ethical-Lens.

翻译：以Midjourney和DALLE 3为代表的文本转图像模型蓬勃发展，已在多个领域彻底革新了内容创作方式。然而，这些进步也引发了关键伦理问题，特别是开源模型被滥用于生成违反社会规范的内容。针对这一问题，我们提出伦理透镜（Ethical-Lens）框架，旨在促进文本转图像工具的价值对齐使用，而无需修改模型内部结构。伦理透镜通过优化用户指令和纠正模型输出，确保文本转图像模型在毒性及偏见维度上的价值对齐。我们结合GPT4-V、HEIM和FairFace评分建立了系统性评估指标，用于衡量对齐能力。实验表明，伦理透镜可将对齐能力提升至与DALLE 3等商业模型相当甚至更优的水平，在保障图像质量的同时确保用户生成内容符合伦理标准。本研究揭示了伦理透镜在确保开源文本转图像工具可持续发展及其良性融入社会方面的潜力。我们的代码已开源在https://github.com/yuzhu-cai/Ethical-Lens。