The Core AI Models Behind Image Generation

AI-generated images rely on advanced machine learning models trained on massive datasets. The most prominent approaches include:

• Generative Adversarial Networks (GANs) – GANs use two competing neural networks: a generator, which creates images, and a discriminator, which evaluates their realism. Through iterative training, GANs produce increasingly realistic images.
• Diffusion Models – These models work by starting with noise and progressively refining it into a coherent image, mimicking a reverse diffusion process. **Stable Diffusion and DALL·E** use this technique for high-quality, scalable image generation.
• Variational Autoencoders (VAEs) – VAEs compress input images into latent representations and reconstruct them, enabling smooth variations in generated images.
• CLIP-Guided Models – CLIP (Contrastive Language-Image Pretraining) enables models to understand text-to-image relationships, making prompt-based image generation possible.

Each model type has its advantages: **GANs** are excellent for photorealism, **Diffusion Models** offer high flexibility and quality, and **CLIP-based models** provide better text understanding.