Advertising image generation has increasingly focused on online metrics like Click-Through Rate (CTR), yet existing approaches adopt a ``one-size-fits-all" strategy that optimizes for overall CTR while neglecting preference diversity among user groups. This leads to suboptimal performance for specific groups, limiting targeted marketing effectiveness. To bridge this gap, we present \textit{One Size, Many Fits} (OSMF), a unified framework that aligns diverse group-wise click preferences in large-scale advertising image generation. OSMF begins with product-aware adaptive grouping, which dynamically organizes users based on their attributes and product characteristics, representing each group with rich collective preference features. Building on these groups, preference-conditioned image generation employs a Group-aware Multimodal Large Language Model (G-MLLM) to generate tailored images for each group. The G-MLLM is pre-trained to simultaneously comprehend group features and generate advertising images. Subsequently, we fine-tune the G-MLLM using our proposed Group-DPO for group-wise preference alignment, which effectively enhances each group's CTR on the generated images. To further advance this field, we introduce the Grouped Advertising Image Preference Dataset (GAIP), the first large-scale public dataset of group-wise image preferences, including around 600K groups built from 40M users. Extensive experiments demonstrate that our framework achieves the state-of-the-art performance in both offline and online settings. Our code and datasets will be released at https://github.com/JD-GenX/OSMF.

翻译：广告图像生成日益关注点击率等在线指标，然而现有方法采用“一刀切”策略，仅优化整体点击率而忽视了用户群体间的偏好多样性。这导致针对特定群体的性能欠佳，限制了定向营销的效果。为弥补这一差距，我们提出\textit{一种尺寸，多种适配}框架，这是一个在大规模广告图像生成中对齐多样化群体点击偏好的统一框架。OSMF始于产品感知的自适应分组，该模块根据用户属性与产品特征动态组织用户，并用丰富的集体偏好特征表征每个群体。基于这些分组，偏好条件图像生成采用群体感知多模态大语言模型为每个群体生成定制化图像。G-MLLM经过预训练，可同时理解群体特征并生成广告图像。随后，我们使用提出的Group-DPO对G-MLLM进行微调以实现群体偏好对齐，从而有效提升各群体对生成图像的点击率。为推进该领域发展，我们构建了首个大规模公开群体图像偏好数据集，包含从4000万用户构建的约60万个群体。大量实验表明，我们的框架在离线与在线场景中均实现了最先进的性能。我们的代码与数据集将在https://github.com/JD-GenX/OSMF发布。