ControlNet-Canny / README.md

v0.42.0

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	---
	library_name: pytorch
	license: other
	tags:
	- generative_ai
	- android
	pipeline_tag: unconditional-image-generation

	---

	![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/controlnet_canny/web-assets/model_demo.png)

	# 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](https://github.com/lllyasviel/ControlNet).


	This repository provides scripts to run ControlNet-Canny on Qualcomm® devices.
	More details on model performance across various devices, can be found
	[here](https://aihub.qualcomm.com/models/controlnet_canny).



	### 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:
	```bash
	# 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](https://workbench.aihub.qualcomm.com/) 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.
	```bash
	qai-hub configure --api_token API_TOKEN
	```
	Navigate to [docs](https://workbench.aihub.qualcomm.com/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.

	```bash
	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.

	```bash
	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](https://www.tensorflow.org/lite/android/quickstart) provides a
	guide to deploy the .tflite model in an Android application.


	- QNN (`.so` export ): This [sample
	app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
	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](https://aihub.qualcomm.com/models/controlnet_canny).
	Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)


	## License
	* The license for the original implementation of ControlNet-Canny can be found
	[here](https://github.com/lllyasviel/ControlNet/blob/main/LICENSE).
	* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)



	## References
	* [Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543)
	* [Source Model Implementation](https://github.com/lllyasviel/ControlNet)



	## Community
	* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
	* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).