Noise Schedules: Linear vs Cosine β and the Running Product ᾱ
This lesson is part of the course
In this lesson
You build two noise schedules for a 20-step toy run: a linear β with its running product ᾱ, and a cosine ᾱ with the β that belongs to it.
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Lessons in this course
- A Toy Image in Numbers: Pixel Values, Scaling to −1…1 and Back Locked
- Noise Schedules: Linear vs Cosine β and the Running Product ᾱ Locked
- Forward Noising: Mixing Image and Noise at t = 0, 5, 10 and 20 Locked
- Signal-to-Noise: SNR per Step, Decibels and Where the Image Disappears Locked
- Denoising: From Predicted Noise to an Estimated Image and One Reverse Step Locked
- Guidance: Mixing Prompt and No-Prompt Predictions with a Guidance Scale Locked
- Steps vs Time: Seconds per Image, Cost per 1,000 Images & Diminishing Returns Locked
- Latent Space: Pixel vs Latent Tensor Sizes, Compression Factor & Memory per Batch Locked