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ControlNet-Canny

Unconditional Image Generation
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android
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metadata
library_name: pytorch
license: other
tags:
  - generative_ai
  - android
pipeline_tag: unconditional-image-generation

ControlNet-Canny: Optimized for Mobile Deployment

Generating visual arts from text prompt and input guiding image

On-device, high-resolution image synthesis from text and image prompts. ControlNet guides Stable-diffusion with provided input image to generate accurate images from given input prompt.

This model is an implementation of ControlNet-Canny found here.

This repository provides scripts to run ControlNet-Canny on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Model_use_case.image_generation
  • Model Stats:
    • Input: Text prompt and input image as a reference
    • Conditioning Input: Canny-Edge
    • Text Encoder Number of parameters: 340M
    • UNet Number of parameters: 865M
    • VAE Decoder Number of parameters: 83M
    • ControlNet Number of parameters: 361M
    • Model size: 1.4GB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
text_encoder w8a16 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) PRECOMPILED_QNN_ONNX 5.498 ms 0 - 162 MB NPU Use Export Script
text_encoder w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile PRECOMPILED_QNN_ONNX 3.956 ms 0 - 19 MB NPU Use Export Script
text_encoder w8a16 Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile PRECOMPILED_QNN_ONNX 3.113 ms 0 - 15 MB NPU Use Export Script
text_encoder w8a16 Snapdragon 7 Gen 4 QRD Snapdragon® 7 Gen 4 Mobile PRECOMPILED_QNN_ONNX 5.77 ms 0 - 14 MB NPU Use Export Script
text_encoder w8a16 Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile PRECOMPILED_QNN_ONNX 2.67 ms 0 - 10 MB NPU Use Export Script
text_encoder w8a16 Snapdragon X Elite CRD Snapdragon® X Elite PRECOMPILED_QNN_ONNX 5.629 ms 157 - 157 MB NPU Use Export Script
unet w8a16 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) PRECOMPILED_QNN_ONNX 118.597 ms 0 - 884 MB NPU Use Export Script
unet w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile PRECOMPILED_QNN_ONNX 84.656 ms 13 - 30 MB NPU Use Export Script
unet w8a16 Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile PRECOMPILED_QNN_ONNX 66.542 ms 5 - 16 MB NPU Use Export Script
unet w8a16 Snapdragon 7 Gen 4 QRD Snapdragon® 7 Gen 4 Mobile PRECOMPILED_QNN_ONNX 178.134 ms 13 - 28 MB NPU Use Export Script
unet w8a16 Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile PRECOMPILED_QNN_ONNX 46.089 ms 13 - 27 MB NPU Use Export Script
unet w8a16 Snapdragon X Elite CRD Snapdragon® X Elite PRECOMPILED_QNN_ONNX 115.82 ms 829 - 829 MB NPU Use Export Script
vae w8a16 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) PRECOMPILED_QNN_ONNX 218.452 ms 0 - 67 MB NPU Use Export Script
vae w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile PRECOMPILED_QNN_ONNX 162.809 ms 3 - 21 MB NPU Use Export Script
vae w8a16 Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile PRECOMPILED_QNN_ONNX 146.805 ms 3 - 19 MB NPU Use Export Script
vae w8a16 Snapdragon 7 Gen 4 QRD Snapdragon® 7 Gen 4 Mobile PRECOMPILED_QNN_ONNX 445.931 ms 3 - 17 MB NPU Use Export Script
vae w8a16 Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile PRECOMPILED_QNN_ONNX 94.664 ms 5 - 15 MB NPU Use Export Script
vae w8a16 Snapdragon X Elite CRD Snapdragon® X Elite PRECOMPILED_QNN_ONNX 219.693 ms 59 - 59 MB NPU Use Export Script
controlnet w8a16 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) PRECOMPILED_QNN_ONNX 59.385 ms 0 - 385 MB NPU Use Export Script
controlnet w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile PRECOMPILED_QNN_ONNX 45.318 ms 31 - 49 MB NPU Use Export Script
controlnet w8a16 Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile PRECOMPILED_QNN_ONNX 33.035 ms 33 - 44 MB NPU Use Export Script
controlnet w8a16 Snapdragon 7 Gen 4 QRD Snapdragon® 7 Gen 4 Mobile PRECOMPILED_QNN_ONNX 112.564 ms 34 - 48 MB NPU Use Export Script
controlnet w8a16 Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile PRECOMPILED_QNN_ONNX 30.002 ms 32 - 46 MB NPU Use Export Script
controlnet w8a16 Snapdragon X Elite CRD Snapdragon® X Elite PRECOMPILED_QNN_ONNX 59.108 ms 352 - 352 MB NPU Use Export Script

Installation

Install the package via pip:

# NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported.
pip install "qai-hub-models[controlnet-canny]"

Configure Qualcomm® AI Hub Workbench to run this model on a cloud-hosted device

Sign-in to Qualcomm® AI Hub Workbench with your Qualcomm® ID. Once signed in navigate to Account -> Settings -> API Token.

With this API token, you can configure your client to run models on the cloud hosted devices.

qai-hub configure --api_token API_TOKEN

Navigate to docs for more information.

Demo off target

The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input.

python -m qai_hub_models.models.controlnet_canny.demo

The above demo runs a reference implementation of pre-processing, model inference, and post processing.

NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).

%run -m qai_hub_models.models.controlnet_canny.demo

Run model on a cloud-hosted device

In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following:

  • Performance check on-device on a cloud-hosted device
  • Downloads compiled assets that can be deployed on-device for Android.
  • Accuracy check between PyTorch and on-device outputs.
python -m qai_hub_models.models.controlnet_canny.export

Deploying compiled model to Android

The models can be deployed using multiple runtimes:

  • TensorFlow Lite (.tflite export): This tutorial provides a guide to deploy the .tflite model in an Android application.

  • QNN (.so export ): This sample app provides instructions on how to use the .so shared library in an Android application.

View on Qualcomm® AI Hub

Get more details on ControlNet-Canny's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of ControlNet-Canny can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

Community