AdsPrompter: Bridging User and Designer Preferences for Multimodal Advertising Design Generation

Generative image modeling has shown remarkable advances recently, yet generating advertising designs with multimodal elements remains an enduring challenge. Existing works optimize either design aesthetics according to design criteria or user behaviors measured by click-through rates, overlooking the inherent discrepancy between user and designer preferences. We observe that visually appealing advertisements do not necessarily yield high user engagement, while solely maximizing user engagement may negatively affect brand perception. To bridge this gap, we introduce AdsPrompter, a new framework that aligns designer intent with user engagement for multimodal advertisement generation. Given a product image and taglines as inputs, AdsPrompter learns to create aesthetically pleasing advertising designs of varying sizes while faithfully preserving the product semantics, textual messages, and intended engagement goals. At the core of our framework is User-Designer Preference Optimization (UDPO), a coarse-to-fine learning strategy with complementary reward models for prompt refinement. The refined prompt guides the inputs to a diffusion-based model for preference-aligned advertisement generation. Furthermore, we introduce a large-scale advertisement dataset with 200k samples featuring designer and user preferences, along with a comprehensive benchmark. Experimental results show that AdsPrompter consistently outperforms existing methods in user engagement and design aesthetics.