Advanced Topics
Diffusion Models — How DALL-E and Stable Diffusion Create Images
Understand the revolutionary diffusion models behind modern image generation. Learn how these models gradually denoise random noise into coherent images.
- Forward Diffusion — The process of adding noise to data
- Reverse Diffusion — Learning to denoise and generate new samples
- Conditional Generation — Guiding generation with text or other inputs
"Creation is just destruction in reverse."
Diffusion Models — Complete Guide
Diffusion models are the state-of-the-art for image generation (DALL-E, Stable Diffusion, Midjourney).
How Diffusion Works
Forward Process (fixed): Image -> Add noise -> Add noise -> ... -> Pure noise
Reverse Process (learned): Pure noise -> Remove noise -> ... -> Image
The model LEARNS to reverse the noise!
Forward and Reverse Diffusion Process
How diffusion models generate images: This diagram shows the two core processes of diffusion models. The forward process (top, flowing right) gradually adds Gaussian noise to a clean image x₀ over T timesteps, progressively destroying information until it becomes pure noise x_T ~ N(0,I). This process is fixed and non-learned — the noise schedule β_t controls how quickly noise is added. The reverse process (bottom, flowing left) is learned by a neural network: starting from random noise x_T, it iteratively denoises one step at a time, progressively recovering the clean image. At each step, the model predicts the noise ε_θ(x_t, t) that was added, and subtracts it to get a slightly cleaner version. After T denoising steps, the result is a generated image x₀. The key insight: by learning to reverse a simple, known corruption process, we get a powerful generative model that can create realistic images from pure noise.
DDPM (Denoising Diffusion Probabilistic Models)
Training:
- Take clean image
- Sample random timestep
- Add noise:
- Model predicts noise:
- Loss:
Generation:
- Start with random noise
- For :
- Return
DDPM Noise Schedule
Classifier-Free Guidance
Where = guidance scale:
- : No guidance (standard)
- : Good balance
- : Very high quality, low diversity
Classifier-Free Guidance Visualization
Latent Diffusion (Stable Diffusion)
Latent Diffusion Architecture
Key Takeaways
What to Learn Next
-> GANs — Generative Adversarial Networks Complete Guide Learn about gans — generative adversarial networks complete guide.
-> Autoencoders — Encoding, Decoding and Representation Learning Learn about autoencoders — encoding, decoding and representation learning.
-> 19-variational-autoencoders Learn about 19-variational-autoencoders.
-> Neural Networks Fundamentals — Perceptrons to Deep Learning Learn about neural networks fundamentals — perceptrons to deep learning.
-> Transformers — Attention Is All You Need Complete Guide Learn about transformers — attention is all you need complete guide.
-> Self-Supervised Learning — Pre-training Revolution Learn about self-supervised learning — pre-training revolution.