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Alpha-bar

As explained in Grokking AI Algorithms, Second Edition

Alpha-bar is the cumulative product of alpha values that represents the total original signal remaining after multiple diffusion steps.

Alpha-bar is the cumulative product of alpha values: it tells you how much of the original signal remains after t diffusion steps. Alpha itself is the signal kept at one step, while alpha-bar combines every earlier retention rate. section 12.9

Following the retained signal

At step 1, alpha-bar is 0.99990. At step 2, it becomes 0.99990 × 0.99988 = 0.99978. At step 3, multiplying by 0.99986 gives 0.99964. Later steps therefore preserve progressively less original image information and replace more of it with noise. section 12.9

The value lets forward diffusion jump directly to a chosen timestep instead of repeatedly modifying the previous noisy image. The image combines scaled original pixels with scaled random noise: the signal scale uses √alpha-bar, and the noise scale uses √(1 − alpha-bar). section 12.9

How Grokking AI Algorithms, Second Edition covers it

How deep it goes
Defines cumulative retention, works through three schedule values, and gives the image-mixing formula without deeper derivation.
Examples
Uses a three-step noise schedule, a happy-face image progression, pixel calculations, and cumulative-product code.
What it assumes
Assumes basic beta, alpha, timesteps, pixel values, and random noise, while defining alpha as one minus beta.
What you can do after
Apply it to calculate direct noisy-image mixtures from an original image, timestep, and sampled random noise.
How it connects
Connects beta, alpha, noise schedules, forward diffusion, random noise, and image denoising training.

This explainer was generated by Manning's knowledge graph from the text of Grokking AI Algorithms, Second Edition. The section links point to the parts of the book it draws on.