This thesis presents a framework for AI-assisted storyboarding that leverages state-of-the-art text-to-image diffusion models and efficient fine-tuning techniques to generate visually coherent and narrative-consistent storyboards. The work begins with a comprehensive review of image synthesis architectures—from VAEs, to GANs to diffusion models—and explores critical components such as attention mechanisms, latent diffusion, and CLIP-based conditioning, establishing a solid technical foundation for the study. Building on this background, the thesis surveys contemporary text-to-image systems (Stable Diffusion, GLIDE, DALL-E, Imagen, MidJourney) and fine-tuning methodologies such as Dreambooth, LoRA, Textual Inversion, Custom Diffusion, ControlNet. The work then delves into storyboarding by investigating how shot types shape visual narratives and by synthesizing insights from recent approaches like StoryGAN, AR-LDM and StoryDALL-E.