ControlNet with Diffusion Models | Explanation and PyTorch Implementation

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In this tutorial we get into ControlNet for diffusion models. We delve into the architecture of ControlNet for Stable Diffusion, explaining how it enhances final model performance on conditional dataset.
We cover need for controlnet and goal it tries to achieve, architecture overview of controlnet for a simple block. Then we get into how to use controlnet for controlling generation output of diffusion models(particularly using controlnet for stable diffusion).
We then implement controlnet for diffusion models and see results of controlnet on mnist and celeb faces dataset with canny edges being used as conditional control.

⏱️ Timestamps:
00:00 Intro
00:36 Objective of Controlnet
04:15 Controlnet Model Architecture Explained
07:12 Controlnet in Stable Diffusion
10:41 Diffusion Model Implementation Review
13:10 ControlNet with Canny Edges MNIST for Diffusion Models
14:00 Diffusion Model UNet Recap
16:50 Architecture for Canny Edges ControlNet for Diffusion Models
17:45 ControlNet PyTorch Implementation for Diffusion Models
26:59 ControlNet Canny Edge Results for MNIST
27:53 Control net for Unconditional Latent Diffusion Models
29:16 Unconditional Latent Diffusion Model Result on CelebHQ
29:36 ControlNet for Canny Edge Results on CelebHQ
30:05 ControlNet Implementation Settings
31:25 ControlNet Architecture Variations and Results
32:20 Thank You

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Awesome! I love how you first describe the theory and then guide us through the implementation, it makes it so much easier to understand both the concepts and pytorch in general.

rafa_br