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Paper page - Adding Conditional Control to Text-to-Image Diffusion Models
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\n\t\n\t\t\n\t\n\t\n\t\tUnlocking Precision: ControlNet in Text-to-Image Models\n\t\n

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Please change the black coat to white t-shirt

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arXiv explained breakdown of this paper ๐Ÿ‘‰ https://arxivexplained.com/papers/adding-conditional-control-to-text-to-image-diffusion-models

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nice work๏ผ

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The neural\narchitecture is connected with \"zero convolutions\" (zero-initialized\nconvolution layers) that progressively grow the parameters from zero and ensure\nthat no harmful noise could affect the finetuning. We test various conditioning\ncontrols, eg, edges, depth, segmentation, human pose, etc, with Stable\nDiffusion, using single or multiple conditions, with or without prompts. We\nshow that the training of ControlNets is robust with small (<50k) and large\n(>1m) datasets. 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Papers
arxiv:2302.05543

Adding Conditional Control to Text-to-Image Diffusion Models

Published on Feb 10, 2023

Abstract

ControlNet adds spatial conditioning to pretrained text-to-image diffusion models using zero-initialized convolutions, enabling diverse applications with small or large datasets.

AI-generated summary

We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.

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Unlocking Precision: ControlNet in Text-to-Image Models

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