Diffusion Models Intro
Diffusion models are a class of generative models that have achieved state‑of‑the‑art results in image generation (DALL‑E, Stable Diffusion, Midjourney). They work by gradually adding noise to data (forward process) and then learning to reverse that process (denoising).
Diffusion models start with pure noise and iteratively denoise to produce realistic data.
Forward Process (Diffusion)
Take a real image x₀. Gradually add Gaussian noise over T steps, producing x₁, x₂, …, x_T. At large T, x_T is nearly pure noise. This process is fixed (not learned).
Reverse Process (Denoising)
Learn a neural network that predicts the noise added at each step. Starting from pure noise x_T, the model iteratively removes predicted noise to recover an image x₀. This is the generation process.
x_T (noise) → denoise step T → ... → denoise step 1 → x_0 (image)Why Diffusion Models Are Popular
- Generate higher quality and more diverse images than GANs.
- Training is more stable (no adversarial training).
- Conditioning (text‑to‑image) works very well.
- Open source implementations (Stable Diffusion).
Key Variants
- DDPM (Denoising Diffusion Probabilistic Models): original formulation.
- DDIM: faster sampling.
- Latent Diffusion Models (Stable Diffusion): run diffusion in latent space of a VAE, reducing compute.
Two Minute Drill
- Diffusion models add noise gradually, then learn to reverse.
- Generate images from pure noise by iterative denoising.
- More stable than GANs; state‑of‑the‑art quality.
- Used in DALL‑E, Stable Diffusion, Midjourney.
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