Text-to-image diffusion models in artificial intelligence

Stable Diffusion is a captivating text-to-image model that generates images based on text input. However, a major challenge is that it is pretrained on a specific dataset, limiting its ability to generate images outside of the given data. In this paper, we propose to harness two models based on neural networks, Hypernetworks and DreamBooth, to allow the introduction of any image into Stable Diffusion, addressing versatility with minimal additional training data. This work targets AI applications such as augmenting next-generation multipurpose robots, enhancing human-robot collaboration, feeding intelligent tutoring systems, training autonomous cars, injecting subjects for photo personalization, producing high quality movie animations etc.. It can contribute to AI in smart cities for facets such as smart living and mobility.