In this video I get into Denoising Diffusion Probabilistic Models implementation ( DDPM ) and walk through the complete Denoising Diffusion Probabilistic Models code in pytorch.

I give a quick overview of math behind diffusion models before getting into DDPM implementation.
I cover the denoising diffusion probabilistic models pytorch implementation in 4 parts:
1. Noise scheduler in ddpm coding forward and reverse process of ddpm in pytorch
2. Model architecture for denoising diffusion probabilistic models Unet
3. Implementing the unet which can be used in any diffusion models code
4. Training and sampling code of ddpm
5. Results of training ddpm

Timestamps:
00:00 Intro
00:30 Denoising Diffusion Probabilistic Models Math Review
03:15 Noise Scheduler for DDPM
04:30 Noise Scheduler Pytorch Code for DDPM
07:10 Denoising Diffusion Probabilistic Models Architecture
08:10 Time embedding Block for DDPM Implementation
08:54 Overview of Unet Architecture for DDPM
09:49 Downblock of DDPM Unet
11:34 Midblock and Upblock for DDPM Unet
12:40 Code for Positional Embedding in DDPM in Pytorch
14:07 Code for Downblock in DDPM Unet
16:42 Code for Mid and Upblock in DDPM Unet
18:53 Unet class for DDPM
22:04 Code for Diffusion Model training
22:47 Code for Sampling in Denoising Diffusion Probabilistic Model
23:24 Configurable Code
24:15 Dataset for training
24:56 Results after DDPM training
25:42 Thank you

Code Repository:
Access the full implementation, along with detailed comments and explanations from GitHub repository https://github.com/explainingaicode/.... Feel free to explore, experiment, and adapt the code to suit your specific needs.

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Background Track Fruits of Life by Jimena Contreras
Email [email protected]

Related Tags:
#DDPM #DiffusionModels #DDPMImplementation #GenerativeAI