Diffusion Models

Core ideas behind denoising-based image generation.

Diffusion models learn to reverse a noising process, turning random noise into coherent data such as images.

Key Ideas

• Forward process: gradually add noise to training data.
• Reverse process: train a model to remove noise step by step.
• Sampling quality depends on scheduler, step count, and guidance scale.

Why It Matters

Diffusion remains one of the most practical approaches for controllable image synthesis.