Abstract
One of the main areas where generative AI models thrive is image synthesis or generation. This work highlights the importance of quality prompts in generating compelling artworks and delves into four principal methodologies for generating prompt recommendations: text embeddings, ensemble models, text with image embeddings and object detection for feature extraction. Multiple traditional and neural network-based models are explored for feature vector representation. Furthermore, the study explores the incorporation of image embeddings, the user’s preferred art styles for tailored recommendations, and the inherent challenges in evaluating these systems. We also propose a novel methodology for evaluating such systems, in the absence of ratings or preference scores, using graph analysis and community detection algorithms. This work distinctly contributes to the prompt recommendation domain and complements previous works in the AI art generation landscape.