A diffusion model is a type of generative AI that creates images, video, or audio by learning to reverse a gradual noising process. During training, the model learns to remove noise from data step by step. During generation, it starts with pure random noise and iteratively denoises it into coherent output. Diffusion models power leading image generators like Stable Diffusion, DALL-E 3, Midjourney, and Flux, as well as video generators like Sora and Runway Gen-4. Key innovations include classifier-free guidance, latent diffusion (operating in compressed latent space for efficiency), and ControlNet for precise control over generated outputs.
Frequently Asked Questions
What is a diffusion model?
A diffusion model generates images or video by learning to remove noise from data. It starts with random noise and progressively refines it into a coherent output, step by step.
How is a diffusion model different from a GAN?
Diffusion models generate images through iterative denoising, producing more diverse and controllable results than GANs. They are slower but generally produce higher quality outputs.