Diffusion Models Beat GANs on Image Synthesis | ML Coding Series | Part 2
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4th video in the ML coding series! In this one I continue explaining diffusion models! I cover the “Diffusion Models Beat GANs on Image Synthesis“ paper and the code behind it.
I focus on how classifier guidance works. I cover both the training of the noise-aware classifier as well as the actual sampling (the mean shift method). I also walk you through a minor bug in their code.
Let me know how you find this format!
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✅ GitHub:
✅ My issue:
✅ Paper:
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⌚️ Timetable:
00:00:00 Intro
00:01:30 Paper overview part - U-Net architecture improvements
00:05:38 Classifier guidance explained
00:15:18 Intuition behind classifier guidance
00:20:10 Scaling classifier guidance
00:24:10 Diversity vs quality tradeoff and future work
00:26:15 Coding part - training a noise-aware classifier
00:35:35 Main training loop
00:44:26 Visualizing timestep conditioning
00:46:00 Sampling using classifier guidance
00:52:35 Core of the sampling logic
00:59:20 Shifting the mean - classifier guidance
01:05:03 Minor bug in their code and my GitHub issue
01:07:53 Outro
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#diffusion #generativemodeling #coding
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