Abstract
While recent text-to-image models excel at generating realistic content, they struggle to capture the nuanced visual characteristics that define a brand’s distinctive style—such as lighting preferences, photography genres, color palettes, and compositional choices. This work introduces BrandFusion, a novel framework that automatically generates brand-aligned promotional images by decoupling brand style learning from image generation. Our approach consists of two components: a Brand-aware Vision-Language Model (BrandVLM) that predicts brand-relevant style characteristics and corresponding visual embeddings from marketer-provided contextual information, and a Brand-aware Diffusion Model (BrandDM) that generates images conditioned on these learned style representations. Unlike existing personalization methods that require separate fine-tuning for each brand, BrandFusion maintains scalability while preserving interpretability through textual style characteristics. Our method generalizes effectively to unseen brands by leveraging common industry sector-level visual patterns. Extensive evaluation demonstrates consistent improvements over existing approaches across multiple brand alignment metrics, with a 66.11% preference rate in human evaluation study. This work paves the way for AI-assisted on-brand content creation in marketing workflows.
Parul
Machine Learning Research Scientist
Varun Khurana
Research Associate
Yaman Kumar Singla
Senior Research Scientist
