Diffusion Models
Diffusion Models are the technology behind DALL-E 2, Stable Diffusion, and Midjourney. They generate high-quality images by learning to reverse a gradual noising process.
Key Insight: If you can learn to denoise images step-by-step, you can generate new images by starting from pure noise.
The Process
Forward Process (Training)
Gradually add noise to real images over T steps until they become pure noise.
Image → Noisy → Noisier → Pure Noise
Reverse Process (Generation)
Learn to denoise step-by-step. Start from noise and gradually remove it to create images.
Pure Noise → Less Noisy → Clear Image
Simplified Denoising Step
This demonstrates a single denoising step (highly simplified).
python
Output:
Click "Run Code" to see output
Why Diffusion Models Excel
- Quality: Generate extremely high-quality, diverse images
- Stability: More stable training than GANs
- Controllability: Easy to guide with text prompts (CLIP guidance)
- Scalability: Performance improves with model size
Popular Models: Stable Diffusion, DALL-E 2, Imagen, Midjourney