How does Generative AI create images?

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Generative AI creates images by using advanced machine learning models that learn patterns from a large dataset of existing images and then generate new, original images based on that learning. Here's a simplified breakdown of how it works:

1. Training on Image Data

• The AI is trained on millions of images and their features (shapes, colors, textures, objects, etc.).

• It learns how images are structured and how different elements relate to one another.

• This process typically uses deep learning, especially neural networks like GANs or diffusion models.

2. Core Models Used

GANs (Generative Adversarial Networks)

• Consist of two networks:

  • Generator: Creates fake images.

  • Discriminator: Evaluates whether an image is real or generated.

• They compete with each other, improving over time until the generator creates highly realistic images.

🔹 Diffusion Models (used in tools like DALL·E, Midjourney, and Stable Diffusion)

• Start with random noise and gradually “denoise” it to form a clear image.

• The model learns to reverse the process of adding noise to images.

 3. Image Generation Process

• You provide an input (text prompt, sketch, or reference image).

• The AI converts that input into a latent representation—a compressed mathematical version of what the image should be.

• It then decodes that representation into a full image using the trained model.

 4. Customization and Control

• You can guide image generation using:

• Text prompts ("A cat wearing sunglasses on a beach").

• Style cues ("in the style of Van Gogh").

• Conditioning images (e.g., editing or inpainting).

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