controlnet paper explained - Adding Conditional Control to Text-to-Image Diffusion Models

ControlNets is the first paper to enable precise spatial control of the generated outputs of image generation models. It won the best prize in the prestigious ICCV 2023 conference.

This video covers the architecture of ControlNets, the idea of classifier-free guidance, and how it has been modified for resolution reweighting. It also covers the qualitative results and ablation studies.


⌚️ ⌚️ ⌚️ TIMESTAMPS ⌚️ ⌚️ ⌚️
0:00 Introduction to ControlNet
1:45 Neural Network Blocks
2:04 ControlNet Architecture
3:02 ControlNet with Stable Diffusion
5:05 ControlNet Training
6:39 Classifier-free Guidance Resolution Weighting
6:56 Classifier Guidance
8:58 Classifier-free Guidance
9:46 Classifier-free Guidance Resolution Weighting
11:08 Ablation Studies


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📚 📚 📚 BOOKS I HAVE READ, REFER AND RECOMMEND 📚 📚 📚
📖 Deep Learning by Ian Goodfellow - https://amzn.to/3Wnyixv
📙 Pattern Recognition and Machine Learning by Christopher M. Bishop - https://amzn.to/3ZVnQQA
📗 Machine Learning: A Probabilistic Perspective by Kevin Murphy - https://amzn.to/3kAqThb
📘 Multiple View Geometry in Computer Vision by R Hartley and A Zisserman - https://amzn.to/3XKVOWi

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WHO AM I?
I am a Machine Learning researcher/practitioner who has seen the grind of academia and start-ups equally. I started my career as a software engineer 15 years ago. Because of my love for Mathematics (coupled with a glimmer of luck), I graduated with a Master's in Computer Vision and Robotics in 2016 when the now happening AI revolution just started. Life has changed for the better ever since.

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