Diffusion Models, Explained Simply

Diffusion models are a core technique in generative AI, especially for image creation. They use forward diffusion to add random noise to an image and reverse diffusion to reconstruct the original image from the noisy version. Key components include the U-Net architecture, which preserves image dimensions and facilitates precise image reconstruction. The diffusion process involves training neural networks across multiple iterations, enabling effective image synthesis while balancing computational costs.