AI-Driven Comic Generation: Exploring the Creative Potential of Generative AI in Digital Storytelling

Abstract

Creative sectors have accepted AI into their industry for establishing modern methods of generating digital content. This study introduces a novel AI-based framework based on AI technology, using GANs along with Stable Diffusion models to automate comic development. This study looks at juxtaposing narrative outlining through text with automated visual generation toward an integrated system that produces adaptable comic panels with appropriate visual structure. The research methodology that the project has followed can be built on three cornerstones: advanced GAN schemes for text generation and pre-processing, followed by image synthesis through Stable Diffusion. A specially developed algorithm for speech bubbles determined the optimal placement of that text, so it would function well and maintain a semblance of beauty. By iteratively refining and tuning the model, this system was evaluated. Initial observations regarding visual coherence and narrative alignment were hopeful, but further tests using quantitative metrics-for instance, FID for images and BLEU for text, as well as broader user feedback-would be needed to validate the efficacy of the model entirely. The impediments were, however, GAN mode collapse, irregular speech-bubble layout, and inconsistent artistic styles. Further research would uncover the potential role of AI systems in easing the comic generation process for creators, educators, and designers of digital content to enhance accessibility and efficiency. This method shows promising applicability in various domains like automated narratives, customizable comics, and educational material. Further along the way, the scientists plan to improve panel storytelling, create an intuitive interface, and expand the dataset to include more artistic styles. Such enhancements could maximize the gain from automated comic conception