Narrative Canvas: Story-Inspired Image Synthesis

Abstract

This research proposes Narrative Canvas, a novel framework for Stable Diffusion-based story-inspired picture synthesis. Our method uses deep learning models to produce visually appealing and logical drawings from narrative inputs. Through the integration of cutting-edge text-to-image synthesis algorithms, Narrative Canvas ensures that images faithfully convey the story's central themes and maintain character consistency. The suggested technique trains and fine-tunes the model using the COYO-300M data set, allowing it to handle a variety of storytelling aspects with effectiveness. The outcomes of our experiments show that our system can generate high-quality visuals that complement the storyline and improve the storytelling experience. This work creates new opportunities for automated content generation, especially in interactive media, digital art, and children's literature. Key Words: Story-inspired image synthesis, Stable Diffusion, deep learning, text-to-image synthesis, narrative consistency, COYO-300M data set, automated content creation