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Research Paper Appendix Examples on AI Methods

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17 February 2023 20h57

we encourage you to check the appendix from the original paper for more examples: https://
arxiv.org/pdf/2302.05543
.pdf
…

Zero Convolutions Training: Sudden Convergence Phenomenon Explained

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17 February 2023 20h57

the use of zero convolutions while training results in a funny behavior that the authors name “sudden convergence phenomenon”, where the model is suddenly able to follow the input conditions, as depicted in the following image:

Cloned U-Net Networks in Encoder-Decoder Architecture

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17 February 2023 20h57

in this case, the cloned networks are the U-Net encoder/middle layers, and the results of the each trainable copy are fed in each middle/decoder block.

ControlNet Architecture Clones Diffusion Model Weights

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17 February 2023 20h57

the following is a general representation of ControlNet's architecture. first, it clones the weights of a diffusion model. Then, it trains the cloned weights to control the original model with the task from the input condition.

Zero Convolutions Enable Progressive Control Learning in AI Models

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17 February 2023 20h57

the goal of this architecture is to keep as much as possible all the knowledge learned by the original model. the trainable network learns how to perform the control in a progressive way thanks to the use of zero convolutions.

Zero Convolutions in ControlNet: Progressive Training Influence

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17 February 2023 20h57

zero convolutions are 1D convolutions with weights and biases initialized to 0s. note how at the beginning of the training ControlNet will not affect the original network at all, but as it gets trained it will progressively start influencing the generation with the condition.

ControlNet Architecture with Stable Diffusion Explained

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17 February 2023 20h57

the following is a representation of ControlNet's architecture when used with Stable Diffusion

ControlNet: Conditioning Diffusion Models on Arbitrary Input Features

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17 February 2023 20h57

ControlNet is a method that can be used to condition diffusion models on arbitrary input features, such as image edges, segmentation maps, or human poses.