We investigate bias trends in text-to-image generative models over time, focusing on the increasing availability of models through open platforms like Hugging Face. While these platforms democratize AI, they also facilitate the spread of inherently biased models, often shaped by task-specific fine-tuning. Ensuring ethical and transparent AI deployment requires robust evaluation frameworks and quantifiable bias metrics. To this end, we assess bias across three key dimensions: (i) distribution bias, (ii) generative hallucination, and (iii) generative miss-rate. Analyzing over 100 models, we reveal how bias patterns evolve over time and across generative tasks. Our findings indicate that artistic and style-transferred models exhibit significant bias, whereas foundation models, benefiting from broader training distributions, are becoming progressively less biased. By identifying these systemic trends, we contribute a large-scale evaluation corpus to inform bias research and mitigation strategies, fostering more responsible AI development. Keywords: Bias, Ethical AI, Text-to-Image, Generative Models, Open-Source Models

本研究针对文本到图像生成模型中的偏见问题进行了深入探讨，特别关注开放平台上模型可用性的增加。通过评估分布偏见、生成幻觉和生成漏率等三个关键维度，我们发现在艺术和风格转移模型中存在显著偏见，而基础模型由于训练分布更广泛而逐渐减少偏见。这项工作提供了一个大规模评估语料库，有助于推动偏见研究和缓解策略的发展，促进更加负责任的人工智能发展。

探索100多个文本到图像生成模型中的偏见