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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[abcd]

Great work. Interesting paper read indeed.
At 7:27 ; Bayes theorem is incorrect. P(X/Y) = P(Y/X).P(X) / P(Y) ; The rest of the math that follows is fine.

[frazuppi]

great video but is not clear how one train it, one needs to have pairs of controlnet input - image output right?