AI-generated images are primarily created using two major deep learning approaches: Diffusion Models and Generative Adversarial Networks (GANs). While both methods can generate high-quality visuals, they work in fundamentally different ways and have distinct advantages and limitations.
How GANs Work
GANs consist of two competing neural networks:
The Generator – Creates images from random noise, attempting to mimic real-world images.
The Discriminator – Evaluates images and determines whether they are AI-generated or real.
Through an adversarial training process, the generator continuously improves its ability to produce realistic images while the discriminator becomes better at detecting fakes. This competition leads to improved image quality, making GANs highly effective for tasks such as deepfake generation, image upscaling, and face synthesis.
However, GANs have some drawbacks:
Mode Collapse – The generator may produce limited variations of images, reducing diversity.
Training Complexity – GANs require extensive computational resources and careful fine-tuning.
Lower Image Resolution – GAN-generated images often contain artifacts or inconsistencies.
How Diffusion Models Work
Diffusion models operate through a gradual denoising process. They start with pure random noise and systematically refine it step by step to create a high-resolution image. These models learn the statistical distribution of real-world images and can generate highly detailed and diverse visuals.