ControlNet

Table of Contents

Model Overview

The ControlNet model is a type of AI model that generates images from text prompts and input guiding images. It’s optimized for mobile deployment, which means it can run on devices like smartphones.

Here’s how it works:

• You give the model a text prompt and an input image as a reference.
• The model uses a technique called “Canny-Edge” to condition the input image.
• The model then generates a high-resolution image based on the text prompt and the input image.

Tasks and rank

Style Transfer 1438th

Image-to-Image Translation 3061st

Image Manipulation 1630th

Image Generation 2905th

Image Editing 1704th

Learn More About the Ranking System

Capabilities

The ControlNet model is capable of generating visual arts from text prompts and input guiding images. It can synthesize high-resolution images from text and image prompts on-device.

Primary Tasks

• Generating visual arts from text prompts and input guiding images
• Synthesizing high-resolution images from text and image prompts on-device

Strengths

• Can generate accurate images from given input prompts
• Can run on-device, making it suitable for mobile deployment
• Can be optimized for various devices, including Qualcomm Snapdragon devices

Unique Features

• Guides Stable-diffusion with the provided input image to generate accurate images
• Can be used for on-device image synthesis
• Can be optimized for various devices using Qualcomm AI Hub

Model Stats

Model	Number of Parameters
Text Encoder	340M
UNet	865M
VAE Decoder	83M
ControlNet	361M
Total	1.4GB

Alternatives

Grounded SAM 13900 Visual task model

Stable Diffusion V1 4 6520 Text-to-image model

FLUX.1 Dev 5413 Text-to-image model

OrangeMixs 3754 Anime style generator

Openjourney 3084 Fine-tuned Stable Diffusion

Performance

The ControlNet model is designed to run on mobile devices, making it a powerful tool for on-device image generation.

Speed

How fast can the ControlNet model process images? The model’s speed is measured in milliseconds (ms). Here are some results:

Device	Inference Time (ms)
Samsung Galaxy S23	11.394 ms
Samsung Galaxy S24	8.08 ms
QCS8550 (Proxy)	10.982 ms

Accuracy

But how accurate is the ControlNet model? The model’s accuracy is measured by its ability to generate high-quality images from text prompts. While we don’t have exact accuracy metrics, we can look at the model’s performance on various devices.

Device	Peak Memory Range (MB)
Samsung Galaxy S23	0 - 74 MB
Samsung Galaxy S24	0 - 137 MB
QCS8550 (Proxy)	0 - 1 MB

Efficiency

Is the ControlNet model efficient in its use of resources? Let’s look at the model’s total number of parameters:

Model Component	Number of Parameters
Text Encoder	340M
UNet	865M
VAE Decoder	83M
ControlNet	361M

Examples

Generate an image of a futuristic cityscape with a prominent skyscraper and a flying car, based on this reference image. Image: A futuristic cityscape with a sleek skyscraper in the center, surrounded by towering buildings and flying cars zipping by. The skyscraper has a unique, curved design and is illuminated by neon lights. The flying cars are sleek and silver, with glowing blue engines. The cityscape is set against a backdrop of a deep blue sky with a few fluffy white clouds.

Create an image of a serene mountain lake at sunset, with a few trees and a small boat in the distance, based on this reference image. Image: A peaceful mountain lake at sunset, with the sky painted in hues of orange and pink. The lake's surface is calm and reflective, with a few trees surrounding it. A small wooden boat is anchored in the distance, and the surrounding mountains are covered in a lush green forest. The atmosphere is serene and tranquil.

Generate an image of a futuristic robot standing in front of a city skyline at night, based on this reference image. Image: A futuristic robot standing proudly in front of a city skyline at night. The robot has a sleek, metallic body with glowing blue circuits and a distinctive fin on its head. The city skyline behind it is a bustling metropolis with towering skyscrapers and neon lights illuminating the night sky. The robot's eyes glow bright red as it gazes out over the city.

Limitations

The ControlNet model has some limitations that you should be aware of. Here are a few:

Limited Generalization

While the ControlNet model can generate high-quality images from text prompts, it may not always generalize well to new, unseen data. This means that it might not perform as well on images or prompts that are significantly different from the ones it was trained on.

Dependence on Input Quality

The quality of the input image and text prompt can greatly affect the output of the ControlNet model. If the input image is low-quality or the text prompt is unclear, the generated image may not be accurate or coherent.

Limited Control

The ControlNet model uses a guiding image to generate images from text prompts. However, the model may not always be able to accurately follow the guiding image, which can result in inconsistent or unexpected outputs.

Performance Variations

The performance of the ControlNet model can vary depending on the device and hardware it is running on. This means that the model may not perform as well on certain devices or in certain environments.

Potential Biases

Like all AI models, the ControlNet model may reflect biases present in the data it was trained on. This can result in generated images that perpetuate existing social biases or stereotypes.

Limited Explainability

The ControlNet model is a complex AI model, and its decision-making process can be difficult to understand or interpret. This can make it challenging to identify and address potential issues or biases in the model’s outputs.

Comparison to Other Models

The ControlNet model is designed for on-device deployment and is optimized for mobile devices. In comparison, ==Other Models== may have different strengths and weaknesses, and may be more suitable for certain applications or use cases.

Potential Misuse

The ControlNet model should not be used for certain applications, such as:

• Accessing essential private and public services and benefits
• Administration of justice and democratic processes
• Assessing or recognizing the emotional state of a person
• Biometric and biometrics-based systems
• Education and vocational training
• Employment and workers management
• Exploitation of the vulnerabilities of persons resulting in harmful behavior
• General purpose social scoring
• Law enforcement
• Management and operation of critical infrastructure
• Migration, asylum and border control management
• Predictive policing
• Real-time remote biometric identification in public spaces
• Recommender systems of social media platforms
• Scraping of facial images (from the internet or otherwise)
• Subliminal manipulation

It’s essential to use the ControlNet model responsibly and in accordance with its intended use case.