GAN (Generative Adversarial Network)
In a GAN, a generator tries to produce convincing samples while a discriminator tries to catch them, and both improve through the contest. GANs dominated image generation before diffusion and remain useful where speed matters, though they are notoriously unstable to train. In practice: The ’this person does not exist’ faces were GAN output.