PaperSummary07 : ControlNet

Poonam Saini
1 min readJan 7, 2025

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This paper is introduced as a solution to enhance the control over spatial composition in text-to-image diffusion models like Stable Diffusion. Existing text-to-image models require extensive prompt engineering for specific layouts, which ControlNet addresses by integrating spatial conditions (such as edges, poses) into the image generation process.

The core concept is as follows:

  1. It combines pretrained diffusion model with a trainable branch that processes spatial conditions. It uses zero-initialized convolution layers to ensure noise free learning start.
  2. Fine-tuning is scalable and effective even with limited datasets.
  3. It introduces a weighting mechanism for resolution-specific guidance to control image fidelity. It supports multiple conditions and compositions of diverse inputs like combining pose and depth.

ControlNet provides fine-grained spatial control over image generation with efficient training on standard GPUs with small datasets. It retains the strength of the original pretrained model while learning new conditions. It outperforms prior methods in tasks like depth-to-image and pose-guided generation. This brings new ways for integrating more complex spatial conditions into large pretrained models.

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Poonam Saini
Poonam Saini

Written by Poonam Saini

PhD Student, Research Associate @ Ulm University

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