Current artificial intelligence (AI) research focuses on how generative AI capabilities can be improved across various sectors and use cases. The power of generative AI lies in the ability to create various ad copy variations, generate lifelike images, improve low-quality visuals, and more. Generative AI is primarily driven by diffusion models, which push the limits of AI innovation. In this article, we'll learn what diffusion models are and explore their benefits, challenges, and possible solutions.
Diffusion Models: An Overview
A diffusion model is a parameterized probabilistic framework, which produces entirely new data by drawing from its training dataset. In other words, if the model is trained on images of existing global landmarks, it can generate images of architectural marvels and monuments that are purely imaginative. Leading AI tools like DALL-E and Midjourney harness the power of diffusion models, particularly the stable diffusion variant, to create images entirely from scratch.