FoRAGe: High-CTR Food Image Synthesis with Retrieval-Augmented Diffusion Model

High Click-Through Rate (CTR) imagery has proven commercial value for food delivery platforms, driving a need for strategies to generate visually compelling images. Our investigations reveal a positive correlation between appropriate food backgrounds and subsequent user engagement. Despite advancements in diffusion models, inpainting new backgrounds does not guarantee high CTR, and fine-tuning diffusion models for this purpose is prohibitively expensive for the fast-paced online food delivery advertising sector. Consequently, there is a lack of cost-effective, transferable generation frameworks tailored to high-CTR food images. In this paper, we propose FoRAGe, a novel high-CTR Food image Retrieval-Augmented Generation pipeline leveraging ControlNet based on Stable Diffusion. Specifically, we construct a comprehensive food image database encompassing a diverse range of background environments. During image generation, FoRAGe retrieves high-quality background exemplars featuring analogous food subjects from the database and employs the retrieved backgrounds as conditions to guide image synthesis via the ControlNet model. Subsequently, a multimodal CTR prediction model is utilized to identify and select optimal images for deployment. Extensive online experiments demonstrate a significant increase in CTR for images generated by our proposed pipeline, and ablation studies further elucidate the impact of different strategies and configurations. Code is available at https://github.com/jiaxu-feng/FoRAGe.