Diffusion Process Visualizer

Start from a clean synthetic image, add Gaussian noise step-by-step (forward diffusion), then reverse it with a toy denoiser (reverse process). Explore β-schedules and a simple 2D latent trajectory.

Visualization β-Schedule Latent 2D Guide
Image Diffusion
Use the right panel to choose t, T, schedule, and strength.
Forward: xt = √ᾱt·x0 + √(1−ᾱt)·ε. Reverse (toy): blend toward xt−1 computed with same ε (oracle), scaled by strength.
β-Schedule
αt = 1−βt, ᾱt = ∏s=1..t αs. The schedule controls how fast noise is added.
Latent 2D Trajectory (toy)
A 2D sample diffused with the same schedule: forward adds noise, reverse removes it.
Controls
Total steps T100
Step t0
Denoise strength0.50
Base Image (synthetic)
Generates simple shapes (smiley, checkerboard, stripes) for a clear noise/denoise effect.
What’s happening — and why it matters
The diffusion idea
  • Forward process (q): Gradually add Gaussian noise to a clean sample x0, producing xt until it becomes nearly pure noise.
  • Reverse process (pθ): A learned model predicts noise (or x0) at each step so we can denoise from xT → x0.
  • β‑schedule: Controls how fast noise grows. Linear is simple; cosine is gentler at the start and often helps quality.
This demo
  • Forward diffusion is exact: xt = √ᾱt·x0 + √(1−ᾱt)·ε with a fixed ε.
  • Reverse is a toy visualization: we blend toward the oracle xt−1 (uses the same ε) to show the denoising trajectory.
  • The 2D latent shows the same schedule in a simple space: forward adds noise, reverse removes it.
Why this matters
  • Intuition: “Noise in → structure out” is at the heart of modern generative models (e.g., Stable Diffusion).
  • Design choices: β‑schedules, steps T, and denoising strength affect quality/speed and failure modes.
  • Education & debugging: Visualizing forward noise growth and reverse trajectories makes the math tangible and helps spot schedule or strength issues.
  • Latent diffusion: Real systems run diffusion in a compact latent space for efficiency; the 2D view hints at this idea.
Search terms: diffusion model explained • denoising diffusion visualization • latent diffusion tutorial.

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About This Tool & Methodology

Illustrates forward diffusion (noise addition over steps) and an intuitive denoising process. Uses simplified schedules and visualizations to convey the idea behind diffusion models; not a full generative model implementation.

Learning Outcomes

  • Understand progressive noise schedules and their visual effect.
  • Build intuition for reverse denoising and sampling.
  • Recognize differences between toy demos and real diffusion models.

Authorship & Review

  • Author: 8gwifi.org engineering team
  • Reviewed by: Anish Nath
  • Last updated: 2025-11-19

Trust & Privacy

  • Runs entirely in your browser with synthetic visuals.
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