What is Diffusion Models?
Diffusion Models — Generative AI models that learn to create data (like images) by reversing a gradual noise-adding process.
Diffusion models learn to generate data by reversing a gradual noising process. They start with pure noise and iteratively refine it into coherent images, audio, or video. Stable Diffusion, DALL-E, and Midjourney all use diffusion-based architectures.
Frequently Asked Questions
How do diffusion models create images?
They start with random noise and gradually denoise it step by step, guided by the text prompt. Each step removes a small amount of noise until a clear image emerges, typically over 20-50 steps.
Can diffusion models generate video?
Yes. Models like Sora and Runway Gen-3 extend diffusion to video generation. The process is similar but generates coherent frames across time, requiring significantly more compute.
Are diffusion models better than GANs?
For most image generation tasks, yes. Diffusion models produce higher quality and more diverse outputs with more stable training. GANs remain competitive for real-time generation due to faster inference.