Automated visual story generation aims to produce stories with corresponding illustrations that exhibit coherence, progression, and adherence to characters' emotional development. This work proposes a story generation pipeline to co-create visual stories with the users. The pipeline allows the user to control events and emotions on the generated content. The pipeline includes two parts: narrative and image generation. For narrative generation, the system generates the next sentence using user-specified keywords and emotion labels. For image generation, diffusion models are used to create a visually appealing image corresponding to each generated sentence. Further, object recognition is applied to the generated images to allow objects in these images to be mentioned in future story development.

As GPT-3 was released, one idea became obvious: Prompt Design is everything. GPT-3 as a language model needed a specific approach. It was a communication between humans and AI. You needed a text -- you had to inspire AI. Neither force it nor request -- but inspire, nudge the Transformer-driven model.