Artificial Intelligence image generation techniques use different approaches to create striking and realistic visual works.
- GAN
Generative Adversarial Network consist of two competing neural networks: the generator, which acts like a forger that makes fake images and tries to pass them off as real, and the discriminator, which acts like a detective trying to figure out if the generator’s images are real or fake. The generator is asked to create an output of an image that does not exist, which it then tasks the discriminator to figuratively fact-check. The discriminator has been trained on datasets of lots of real images, so it has an idea of what to look out for. When it identifies a fake image, it tells the generator that the generator’s images are real or fake. This process is repeated in a loop until the discriminator can no longer differentiate the generated image from the real thing.
An example is the website This Person Does Not Exist developed in 2018. It produces an image of a random fake face that you can download.
- NST
Another method of creating AI-generated images is through Neural Style Transfer. These applications merge the content of one image with the artistic style of another, by utilizing Deep Learning and large datasets.
A Neural Style Transfer works by taking both a Content Image and a Style Image, which are then merged into a generated output. So the resultant image retains the content of the Content Image while adopting the artistic style of the Style Image.
A convolutional neural network, a type of AI that mimics human visual capabilities, is used to extract the features and multiple layers of the content image.
- DIFFUSION MODEL
Diffusion Model is used by the most modern image generators applications, such as Stable Diffusion and Midjourney, and it is able to generate high-resolution images. It works by gradually adding Gaussian noise to the original data in the forward diffusion process and then learning to remove the noise in the reverse diffusion process
When we enter a text input into a generative AI app, the software will use Natural Language Processing (NLP) and Machine Learning to understand the prompt. It will then scour a massive library of stock photos and gather images that have relevant text descriptions. Then the AI takes existing images and diffuses the pixels into noise. The AI model is then tasked with rebuilding the original images using the diffused noise blocks, which will result in a new image.
