Artificial intelligence (AI) image generators can produce detailed images in a variety of styles based on large sets of training data.
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Artificial intelligence (AI) image generation is a type of generative AI for producing visual content. It uses mathematical analysis to identify and duplicate patterns within photos and illustrations. In simpler terms, AI image generators create images based on a set of examples.
AI image generators are built on a specialized type of machine learning model called a neural network. Via the use of advanced statistical analysis, along with some fine-tuning on the part of the developer, image generators can produce relevant, detailed images in a variety of styles.
Rather than an artist with a paintbrush, an AI image generator is more like a gambler placing bets on probable sporting outcomes. The content it produces is statistically likely to fulfill the request that it received. And, its created content is based on preexisting content, just as the gambler might examine athletes' past performances before placing their bet.
AI image generators can create realistic-seeming photographs. They can also edit preexisting images. Like other types of generative AI, AI image generation models can interpret natural-language prompts and create images in response. "Make an image of an elephant" is a valid prompt — although such a prompt may need to be refined before it produces the image that the prompter has in mind.
Generative AI is a category of deep learning model that produces text, images, computer code, audio, or visual content. As a type of machine learning, it relies on mathematical and statistical analysis of sample data sets in order to produce content that is statistically likely to be relevant in response to prompts. In other words, generative AI quickly makes content based on past examples it has seen.
As described above, AI image generation is constructed using machine learning, a class of advanced computer programs that can learn without definite instructions. Specifically, AI image generation is built on neural networks.
A neural network is a type of deep learning computing architecture. Essentially, neural networks aim to mimic the structure of a human brain. They are a collection of processing units called "nodes." The nodes pass data to each other, similar to the way that a human brain works, with neurons sending electrical impulses to each other.
There are many different classes of neural networks in the field of AI. The specific kind of neural network used by most image generating AI models is called a generative adversarial network (GAN). A GAN has two workstreams: one produces images, and the other compares those images to real-life examples and identifies errors. Thus a GAN-based model is able to train itself and continually improve. Think of a painter who learns by imitating famous paintings of the past and comparing their work against the real pieces.
While the same or similar algorithms may be used across types of generative AI, image generating models are trained on sets of visual images, as opposed to large amounts of text, like ChatGPT and other large language models (LLMs).
AI hallucinations can occur with any type of generative AI model, and image-generating AI is no exception. These show up as inaccuracies in the image: for instance, if asked to generate a portrait of a person, an extra finger appears on the hand of the subject. With sufficient prompting and refining, it is usually possible to remove these hallucinations.
Any human-produced creative work is under copyright, unless the creator either waives the copyright or the copyright has expired. Rights to a work can be transferred or sold to other parties via a license.
Image licenses fall into several tiers:
AI-generated images are not protected by copyright law since a human does not create them. Therefore, such images usually enter the public domain.
The problem, however, is that the training data set upon which a model draws may contain images with a range of licenses, and may also contain protected intellectual property. If an AI image generator produces an image very similar to a preexisting image by a human creator, or to a brand owned by another company, those parties may sue someone who uses the image. (For example, an AI-generated image of Superman may still be protected by the same legal protections that apply to official images of the character.)
For AI-generated images, this depends on the license and on the image-generating service used. Some AI generation services have trained their models on a curated collection of images for which they have the rights. Such services may allow for commercial use of the images generated by their services under a Creative Commons license — which means anyone else could also use the images. This complicates commercial use, as the images cannot be protected from use by competitors in the way original images, brands, or trademarks can be.
Cloudflare Workers AI provides full-stack AI building blocks, allowing developers to integrate a multitude of popular generative AI models into their applications and run them on a global network of GPUs. View this tutorial to learn how to get started with building AI image generators.