Completed
Core of the sampling logic
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Classroom Contents
Diffusion Models Beat GANs on Image Synthesis - ML Coding Series - Part 2
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- 1 Intro
- 2 Paper overview part - U-Net architecture improvements
- 3 Classifier guidance explained
- 4 Intuition behind classifier guidance
- 5 Scaling classifier guidance
- 6 Diversity vs quality tradeoff and future work
- 7 Coding part - training a noise-aware classifier
- 8 Main training loop
- 9 Visualizing timestep conditioning
- 10 Sampling using classifier guidance
- 11 Core of the sampling logic
- 12 Shifting the mean - classifier guidance
- 13 Minor bug in their code and my GitHub issue
- 14 Outro
