Beta
A beta is a timestep-dependent diffusion coefficient that controls the amount of noise added at each diffusion step.
Grokking AI Algorithms, Second Edition
Beta is the diffusion coefficient that determines how much noise is added at a particular timestep.
- How it is used
- Controls the amount of noise added at each forward-diffusion timestep and helps calculate the matching denoising step.
- How deep it goes
- Explains beta through schedules, alpha, alpha-bar, a table, and implementation, but defers deeper mathematical derivation.
- Examples
- Uses a 0.0001-to-0.02 schedule, timestep tables, noisy-image progression, and Python-like schedule code.
- What you can do after
- Apply it via a diffusion schedule to control forward noising and provide matching constants for reverse denoising.
Read how Grokking AI Algorithms, Second Edition explains beta →
Deep Learning with PyTorch, Second Edition
Per-step noise schedule coefficients that control how much noise is added during diffusion.
- How it is used
- Controls the amount of noise added at every forward-diffusion timestep.
- How deep it goes
- Explains beta scheduling, per-step scaling, closed-form computation, and visual progression without developing schedule theory.
- Examples
- Uses PyTorch linear schedules and progressively diffused point sets, including snapshots across 1,000 timesteps.
- What you can do after
- Apply it via PyTorch to construct a noise schedule and sample progressively or directly at selected diffusion timesteps.
Read how Deep Learning with PyTorch, Second Edition explains beta →
Related concepts
- noise scheduleconfigures · A noise schedule is a prescribed sequence of noise levels or coefficients governing noise addition and removal across diffusion timesteps.
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