AI-powered image generation works by utilizing trained artificial neural networks to create images from scratch. These generators use a process called diffusion, which starts with a random field of noise and edits it in a series of steps to match their interpretation of the prompt. The process begins with a textual input provided in natural language, and the AI generator uses this input to create an image that matches the described concept. The generator uses a dataset of text-image pairs to learn the relationship between the text and the corresponding image.
The images created by AI image generators can range from realistic and natural-looking pictures to highly creative and abstract compositions. They can generate anthropomorphized versions of animals and objects, combine unrelated concepts in plausible ways, render text, and apply transformations to existing images.
Some of the best AI image generators in 2024 include DALLE, which is a 12-billion parameter version of GPT3 trained to generate images from text descriptions. DALLE can create original, realistic images and art from a text description, combining concepts, attributes, and styles. Another AI image generator is Stable Diffusion, which uses a process called diffusion to create images incrementally closer to the text prompt over time until no noise is left.
The use of AI image generators has the potential to revolutionize the way we create images and has numerous applications in various industries, including photography, advertising, and entertainment. However, it also raises questions about the line between AI and human creativity, and whether these technical infrastructures can be applied to other domains.
In summary, AI-powered image generation works by utilizing trained artificial neural networks to create images from scratch, using a process called diffusion to refine the image over time, and can create a wide range of images, from realistic to abstract, based on textual input provided in natural language.