A Survey on Generation of Graphic Design Models using Stable Diffusion Models

Abstract

AI image generation has grown significantly more accessible over the past few months and is expanding into other industries where the creation of assets is involved. AI picture generators may produce breath-taking photorealistic images from challenging challenges, albeit their capabilities are currently fairly limited. Diffusion models can take in some user input, and some noise, and create content similar to what it’s been trained on. In this case, these models were trained on images, meaning you can pass in a prompt, and have the model produce an image related to that prompt. Fundamentally, generation models that work on the diffusion method can generate images by first randomizing the training data by adding Gaussian noise and then recovering the data by reversing the noise process. The diffusion probabilistic model, sometimes known as the "diffusion model," is a parameterized Markov chain trained using various assumptions to generate images that, after a predetermined amount of time, match the data. Using this technology and resources, we infer to create our model that will make use of all these prompts.