CNB

 

AI image generators work by using deep learning algorithms trained on massive datasets of images. The algorithms, known as generative adversarial networks (GANs), consist of two neural networks - a generator and a discriminator.

 The generator tries to create realistic synthetic images, while the discriminator tries to determine if images are real or fake.

 During training, the two networks compete against each other in an adversarial game where the generator tries to fool the discriminator. Over many iterations, the generator learns to produce increasingly realistic images. 

To generate a new image, the user provides the generator with a text prompt describing the desired image.

 The generator network then creates an image pixel by pixel based on patterns learned from the training data. It starts with random noise and slowly modifies that noise until recognizable features start to emerge. 

The network has learned which features tend to co-occur, so as a tree starts forming, it also begins generating grass and sky around it. The generator fine-tunes the details until the image matches the text description. The result is a completely synthetic yet realistic image generated from just a short text prompt.

AI image generators represent a major advance in AI creativity. By learning holistic representations of visual concepts from large datasets, generators can now create original, highly detailed images from simple text descriptions.

 The applications for creative fields and beyond are vast, though concerns remain about potential misuse. Overall, AI image generation marks an exciting new frontier in artificial intelligence.