A text-to-image generator often uses neural networks to process your input - in the form of words - and generate an image for you. This process only takes a few seconds, so the results are immediately visible.
But for neural networks to work reliably, they need jordan number dataset to be thoroughly trained. Just imagine a small child learning to associate words and objects for the first time. This learning process is similar to the training of AI generators, only this one is much faster and uses much larger amounts of data. Over the years, different types of AI models for text-to-image have been developed:
Generative Adversarial Networks (GANs): The first neural networks
Earlier versions of AI image generators were based on GANs. These models pit two neural networks against each other. One network, the image generator, creates the image. The second network, called the discriminator, is used to determine whether an image is real or fake.
From GANs to Diffusion Models
AI image generators are increasingly using diffusion models instead of GANs. Diffusion models are trained on a large number of images, each containing an image description, to learn the association between text and image.
During training, the AI image generator's neural network develops an understanding of other conceptual information - such as color or elements that give an image a certain style.
Once trained, the models are able to create a low-resolution version of an image based on a text instruction and gradually add new details to it, thus producing a high-resolution image.
The advantages of diffusion models
Unlike other approaches, diffusion models generate images from scratch - without relying on existing imagery on the World Wide Web. This gives you more control over the generated image.
AI image generators using diffusion models have made tremendous progress and can produce very realistic images. Even if such an AI image generator has never seen a particular image before, it is able to generate a unique image based on what it has learned.