Ultimate Guide to Diffusion Models | ML Coding Series | Denoising Diffusion Probabilistic Models
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In this 3rd video of my ML coding series, we do a deep dive into diffusion models! Diffusion is the powerhouse behind recent text-to-image generation models such as OpenAI's DALL-E 2, Google's Imagen, etc.
I first give you some context by going over 2 seminal diffusion papers:
Denoising Diffusion Probabilistic Models
Improved Denoising Diffusion Probabilistic Models
And then we dig deep into the actual code analysis comparing mathematical formulas behind diffusion with actual code implementation.
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✅ DDPM paper: https://arxiv.org/abs/2006.11239
✅ Improved DDPM paper: https://arxiv.org/abs/2102.09672
✅ Improved DDPM code: https://github.com/openai/improved-di...
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⌚️ Timetable:
00:00:00 (Paper) Denoising Diffusion Probabilistic Models
00:16:00 (Paper) Improved DDPMs
00:23:10 (Coding starts) Training DDPMs
00:24:50 UNet model creation walk-through
00:35:05 Gaussian Diffusion model creation walk-through
00:43:50 Training loop
00:56:08 Computing noise and variance (forward prop through UNet)
01:04:00 Variational lower bound loss
01:17:25 MSE loss
01:19:23 Sampling from diffusion models
01:26:50 Sampling an actual image
01:28:03 Outro
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#diffusion #ddpm #coding