Denoising Diffusion Implicit Models (DDIM) Explained

In this video, we dive deep into Denoising Diffusion Implicit Models (DDIM) and how they improve upon Denoising Diffusion Probabilistic Models (DDPM) by enabling faster sampling while preserving high-quality results. We break down the DDIM paper, discuss its Non-Markovian forward process, how it allows us to do faster sampling and impact of changing the variance of the diffusion process of DDIM. In the end we also explore how it connects to score matching and stochastic differential equations in diffusion models.

DDIM enabled significantly faster image generation compared to standard DDPM. Most of the image and video models use DDIM sampling whenever smaller latency of generation is required.
This video attempts to go in detail of everything regarding it.

⏱️ Timestamps
00:00 Intro
00:22 Topics covered in Video
00:46 Recap of Denoising Diffusion Probabilistic Models
07:58 Non-Markovian Diffusion Process of DDIM
22:38 Sampling in Denoising Diffusion Implicit Models
24:19 DDPM as a special case of Denoising Diffusion Implicit Models
28:36 Accelerated Sampling in DDIM
35:38 DDIM Results
38:20 Score Matching Connection to Diffusion Models
45:39 Stochastic Differential Equation Connection to Diffusion Models
51:50 Videos to watch on score matching and sde connection
52:32 Thank You

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*Useful Resources*:
Paper Link - https://tinyurl.com/exai-ddim-paper
Prof. Ernest K. Ryu Course - https://ernestryu.com/courses/FM.html
Video Tutorial on Denoising Diffusion-based Generative Modeling -    • Tutorial on Denoising Diffusion-based Gene...  
Prof. Stefano Ermon Stanford CS236 Course Playlist -    • Stanford CS236: Deep Generative Models I 2...  

📌 Keywords:
#diffusionmodels