This thesis work presents a comprehensive overview of recent advancements in image synthesis models, exploring the recent developments of Diffusion Models and their finetuning. The primary contribution consists in a novel approach that utilizes recently released techniques to tackle a relatively unexplored area in the literature: generating cinema-like shots to assist in the storyboarding process. Starting from the intuition that shot types can be learned as an artistic style, a fine-tuned version of Stable Diffusion is leveraged to tailor the generation process specifically for this purpose. By utilizing a limited number of movie frames labelled with shot types and accompanied by brief descriptions, I use Dreambooth along with Low Rank Adaptation to teach a pre-trained model three specific shot types: close shot, medium shot, and long shot.