# Adding Conditional Control to Text-to-Image Diffusion Models

**Authors:** Lvmin Zhang, Maneesh Agrawala  
**arXiv:** [2302.05543](https://arxiv.org/abs/2302.05543)  
**Date:** 2023-02-10  
**Summary page:** https://www.summarizepaper.com/en/paper/2302.05543v1/

## Key points

- Authors Lvmin Zhang and Maneesh Agrawala present a novel neural network structure called ControlNet
- ControlNet can be used to control pretrained large diffusion models to support additional input conditions
- ControlNet is capable of learning task-specific conditions in an end-to-end manner and remains robust even when the training dataset is small (less than 50k)
- Training a ControlNet is as fast as fine-tuning a diffusion model and can be done on personal devices
- Large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs such as edge maps, segmentation maps, keypoints, etc.
- This approach enriches methods for controlling large diffusion models and further facilitates related applications
- The authors note that their work has particular relevance for text-to-image generation tasks where it is necessary to condition image generation on specific textual input.

## Summary

In their paper "Adding Conditional Control to Text-to-Image Diffusion Models," authors Lvmin Zhang and Maneesh Agrawala present a novel neural network structure called ControlNet. This structure can be used to control pretrained large diffusion models in order to support additional input conditions. The ControlNet is capable of learning task-specific conditions in an end-to-end manner and remains robust even when the training dataset is small (less than 50k). Additionally, the authors report that training a ControlNet is as fast as fine-tuning a diffusion model and can be done on personal devices. If powerful computation clusters are available, the model can scale up to handle large amounts of data (millions to billions). The authors demonstrate that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs such as edge maps, segmentation maps, keypoints, etc. This approach enriches methods for controlling large diffusion models and further facilitates related applications. The authors note that their work has particular relevance for text-to-image generation tasks where it is necessary to condition image generation on specific textual input. Overall, this paper presents an innovative solution for controlling large diffusion models using task-specific conditions learned by a neural network structure. By enabling conditional inputs like edge maps and segmentation maps, this approach has significant potential for advancing text-to-image generation tasks and other related applications.