Finetuning Stable Diffusion Models for Email Marketing Text-to-Image Generation

The integration of diffusion models in digital marketing, in particular in email marketing, can be a real game-changer, in terms of the generated images’ quality and creativity. Combined with the use of knowledge graphs and vector embeddings that enhances contextual relevance, it can lead to more personalized and engaging email campaigns. This, in return, can improve customer engagement, which inevitably increases conversion rates. This paper presents a novel approach to enhancing Stable Diffusion models by integrating knowledge graphs and vector embeddings, in a digital marketing context, more specifically email marketing. Stable Diffusion models, which generate data through iterative noise refinement, are improved by the structure information from knowledge graphs and the rich semantic representations of vector embeddings. The concept is to make use of the relational data from knowledge graphs to get a better contextual understanding of the data, while employing the dense, informative representations offered by vector embeddings. Generating email marketing creatives with the finetuned model leads to experimental results that demonstrate how this combined approach result in more accurate and coherent generated outputs. Future prospects and potential advancements in combining these techniques in text-to-image generation across domains -specifically, digital marketing- are highlighted in the discussion. The following research will concentrate on investigating more extensive applications, and establishing the role of knowledge graphs and vector embeddings in Stable Diffusion models.