Understanding AI Image Generation for Beginners
AI image generators are powered by machine learning algorithms, which have been developed with AI model training best practices to generate creative graphics and artworks, depending on the inputs provided–usually a sentence, selection of words, or phrase.
Learning how to train AI image models is a little more complex because developers use large, sometimes vast datasets, which educate the model in different types of imagery, differentiating between colours, art creation techniques, styles, and the topic of the graphic itself. The more diverse the datasets used in AI image training, the more advanced and interesting the outputs a custom AI generator is capable of, meaning the more creative and experimental users can be.
What Is an AI Image Generator, and How Does It Work?
Image generators, once deployed and available to use, have already been extensively trained, which means you can provide any phrase or input text you can imagine–and sometimes multimedia inputs–to produce unique graphics, gaming assets, artworks, designs, or even images that replicate highly intricate paintings.
Behind the interface, the image generator uses machine learning algorithms, which have been taught to define things like pattern, shape, texture, colour, and features. As a result of in-depth AI model training, the generator knows the difference between a circle and a square, a natural landscape and a futuristic city skyline, and what makes a watercolour look different from pop art.
When you type in a text prompt, the AI defines the words, and the order of the words, verifying which are contextual, and interprets your instruction to pick specific attributes from its training to piece together, layer by layer, the finished product. From there, you can edit, save, tweak, or start over, depending on how closely the finished image matches your expectations and how much additional detail or specificity you wish to provide.
Why Do Image Generators Sometimes Produce Unexpected Results?
A lot depends on how comprehensively AI models have been trained, because if they rely on smaller or narrower datasets, they might not have the knowledge to understand every word you choose to input or might have limited data to draw on. In many cases, using an AI image generator is a fun, creative outlet, and it can be interesting to see how the model understands, recognises, and translates your prompts.
However, it’s also important to recognise that even the most powerful image generation tool is reliant on the quality of the training, the pre-trained images used, and the extent of the datasets. You can test different image generators backed by alternative machine learning algorithms to compare strengths and weaknesses, with some of the most popular features including style transfer and Generative Adversarial Network (GAN) generators.
These tools respectively replicate a style onto a new image, transferring the graphic design onto a new output and create realistic or photorealistic images.
What Are AI Image Generators Used For?
Image creation through artificial intelligence has multiple applications and uses, from testing designs or artwork concepts to creating quick design illustrations to represent an idea within gaming, web publication, or graphic design sectors. Users can create almost anything they can think of, from backgrounds, environments, landscapes, characters, creatures, and artworks, based on input text strings that represent the theme or idea they wish to base the output on.
There are multiple benefits, including:
- Reducing the time spent on creating an image from scratch, with most artwork generators needing just a few minutes to identify prompts and return an image
- Low costs, without any pricing barriers that make them inaccessible to individuals or small businesses
- Design consistency, with the ability to create a series of images that capture the same style, theme, or texture–ideal for artists, creators, or companies that want to quickly develop a sequence of graphics that have the same aesthetic
- Versatility, where one AI image generator with high-level training can create a huge array of outputs
The technical AI training process and development behind an image generator may be complex and involved, but from a user’s perspective, it is anything but. Instead, a user, even without any prior experience creating AI-generated graphics, can test ideas, experiment with different inputs, and see what results they generate with endless possibilities.