AI-based Personalization of Social Media Thumbnails Using the Stacked ID Embedding Method

Abstract

Social media content creators find it hard to make thumbnails shine on Instagram, YouTube, and TikTok, where graphic design skills happen to be a significant bottleneck. To address this issue, scientists developed a text-based image generation model, PotionPix, that allows users to generate thumbnails on the fly based on text prompts and relevant images via a "Stacked ID Embedding" method. This method combines multiple identity embeddings—e.g., user interests, platform context, and content genre—into one vector representation to guide the AI to create more personalized and contextually appealing thumbnails. The system integrates a diffusion-based image generator with the stacked embedding vectors to enable dynamic adaptation to different user intents. In tests, it was observed that how relevant and good the generated thumbnails were very much a function of how specific the input image was and how clear the prompt was. However, since the AI model used was not fine-tuned on the task of thumbnail generation specifically, the visual outputs sometimes were generic and lacked the strong call-to-action elements usually found in high-performing thumbnails. Despite this constraint, the usability test conducted with 120 respondents showed promising results—83.8% of the participants confirmed that PotionPix was indeed assistive in the thumbnail design process, particularly in terms of time and effort savings. The findings show the promise of AI-driven tools in enabling the democratization of design tasks for social media content creators, as well as suggesting future work in model fine-tuning for more domain-specific outcome.