ControlNet-Canny / README.md

v0.32.0

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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) \| QNN_CONTEXT_BINARY \| 5.37 ms \| 0 - 3 MB \| NPU \| Use Export Script \|
	\| text_encoder \| w8a16 \| QCS9075 (Proxy) \| Qualcomm® QCS9075 (Proxy) \| QNN_CONTEXT_BINARY \| 5.903 ms \| 0 - 10 MB \| NPU \| Use Export Script \|
	\| text_encoder \| w8a16 \| SA8255 (Proxy) \| Qualcomm® SA8255P (Proxy) \| QNN_CONTEXT_BINARY \| 5.395 ms \| 0 - 2 MB \| NPU \| Use Export Script \|
	\| text_encoder \| w8a16 \| SA8650 (Proxy) \| Qualcomm® SA8650P (Proxy) \| QNN_CONTEXT_BINARY \| 5.412 ms \| 0 - 2 MB \| NPU \| Use Export Script \|
	\| text_encoder \| w8a16 \| SA8775P ADP \| Qualcomm® SA8775P \| QNN_CONTEXT_BINARY \| 5.903 ms \| 0 - 10 MB \| NPU \| Use Export Script \|
	\| text_encoder \| w8a16 \| Samsung Galaxy S23 \| Snapdragon® 8 Gen 2 Mobile \| QNN_CONTEXT_BINARY \| 5.432 ms \| 0 - 3 MB \| NPU \| Use Export Script \|
	\| text_encoder \| w8a16 \| Samsung Galaxy S23 \| Snapdragon® 8 Gen 2 Mobile \| PRECOMPILED_QNN_ONNX \| 5.743 ms \| 0 - 3 MB \| NPU \| Use Export Script \|
	\| text_encoder \| w8a16 \| Samsung Galaxy S24 \| Snapdragon® 8 Gen 3 Mobile \| QNN_CONTEXT_BINARY \| 3.872 ms \| 0 - 18 MB \| NPU \| Use Export Script \|
	\| text_encoder \| w8a16 \| Samsung Galaxy S24 \| Snapdragon® 8 Gen 3 Mobile \| PRECOMPILED_QNN_ONNX \| 4.067 ms \| 0 - 20 MB \| NPU \| Use Export Script \|
	\| text_encoder \| w8a16 \| Snapdragon 8 Elite QRD \| Snapdragon® 8 Elite Mobile \| QNN_CONTEXT_BINARY \| 3.481 ms \| 0 - 14 MB \| NPU \| Use Export Script \|
	\| text_encoder \| w8a16 \| Snapdragon 8 Elite QRD \| Snapdragon® 8 Elite Mobile \| PRECOMPILED_QNN_ONNX \| 3.255 ms \| 0 - 13 MB \| NPU \| Use Export Script \|
	\| text_encoder \| w8a16 \| Snapdragon X Elite CRD \| Snapdragon® X Elite \| QNN_CONTEXT_BINARY \| 5.792 ms \| 1 - 1 MB \| NPU \| Use Export Script \|
	\| text_encoder \| w8a16 \| Snapdragon X Elite CRD \| Snapdragon® X Elite \| PRECOMPILED_QNN_ONNX \| 5.958 ms \| 158 - 158 MB \| NPU \| Use Export Script \|
	\| unet \| w8a16 \| QCS8550 (Proxy) \| Qualcomm® QCS8550 (Proxy) \| QNN_CONTEXT_BINARY \| 110.879 ms \| 13 - 15 MB \| NPU \| Use Export Script \|
	\| unet \| w8a16 \| QCS9075 (Proxy) \| Qualcomm® QCS9075 (Proxy) \| QNN_CONTEXT_BINARY \| 107.956 ms \| 6 - 13 MB \| NPU \| Use Export Script \|
	\| unet \| w8a16 \| SA8255 (Proxy) \| Qualcomm® SA8255P (Proxy) \| QNN_CONTEXT_BINARY \| 116.595 ms \| 13 - 15 MB \| NPU \| Use Export Script \|
	\| unet \| w8a16 \| SA8650 (Proxy) \| Qualcomm® SA8650P (Proxy) \| QNN_CONTEXT_BINARY \| 115.724 ms \| 13 - 16 MB \| NPU \| Use Export Script \|
	\| unet \| w8a16 \| SA8775P ADP \| Qualcomm® SA8775P \| QNN_CONTEXT_BINARY \| 107.956 ms \| 6 - 13 MB \| NPU \| Use Export Script \|
	\| unet \| w8a16 \| Samsung Galaxy S23 \| Snapdragon® 8 Gen 2 Mobile \| QNN_CONTEXT_BINARY \| 117.156 ms \| 13 - 16 MB \| NPU \| Use Export Script \|
	\| unet \| w8a16 \| Samsung Galaxy S23 \| Snapdragon® 8 Gen 2 Mobile \| PRECOMPILED_QNN_ONNX \| 116.818 ms \| 0 - 883 MB \| NPU \| Use Export Script \|
	\| unet \| w8a16 \| Samsung Galaxy S24 \| Snapdragon® 8 Gen 3 Mobile \| QNN_CONTEXT_BINARY \| 81.085 ms \| 13 - 31 MB \| NPU \| Use Export Script \|
	\| unet \| w8a16 \| Samsung Galaxy S24 \| Snapdragon® 8 Gen 3 Mobile \| PRECOMPILED_QNN_ONNX \| 84.025 ms \| 13 - 32 MB \| NPU \| Use Export Script \|
	\| unet \| w8a16 \| Snapdragon 8 Elite QRD \| Snapdragon® 8 Elite Mobile \| QNN_CONTEXT_BINARY \| 70.612 ms \| 13 - 27 MB \| NPU \| Use Export Script \|
	\| unet \| w8a16 \| Snapdragon 8 Elite QRD \| Snapdragon® 8 Elite Mobile \| PRECOMPILED_QNN_ONNX \| 70.807 ms \| 13 - 28 MB \| NPU \| Use Export Script \|
	\| unet \| w8a16 \| Snapdragon X Elite CRD \| Snapdragon® X Elite \| QNN_CONTEXT_BINARY \| 116.726 ms \| 13 - 13 MB \| NPU \| Use Export Script \|
	\| unet \| w8a16 \| Snapdragon X Elite CRD \| Snapdragon® X Elite \| PRECOMPILED_QNN_ONNX \| 117.502 ms \| 829 - 829 MB \| NPU \| Use Export Script \|
	\| vae \| w8a16 \| QCS8550 (Proxy) \| Qualcomm® QCS8550 (Proxy) \| QNN_CONTEXT_BINARY \| 268.758 ms \| 0 - 3 MB \| NPU \| Use Export Script \|
	\| vae \| w8a16 \| QCS9075 (Proxy) \| Qualcomm® QCS9075 (Proxy) \| QNN_CONTEXT_BINARY \| 248.983 ms \| 0 - 10 MB \| NPU \| Use Export Script \|
	\| vae \| w8a16 \| SA8255 (Proxy) \| Qualcomm® SA8255P (Proxy) \| QNN_CONTEXT_BINARY \| 272.989 ms \| 0 - 2 MB \| NPU \| Use Export Script \|
	\| vae \| w8a16 \| SA8650 (Proxy) \| Qualcomm® SA8650P (Proxy) \| QNN_CONTEXT_BINARY \| 284.628 ms \| 0 - 2 MB \| NPU \| Use Export Script \|
	\| vae \| w8a16 \| SA8775P ADP \| Qualcomm® SA8775P \| QNN_CONTEXT_BINARY \| 248.983 ms \| 0 - 10 MB \| NPU \| Use Export Script \|
	\| vae \| w8a16 \| Samsung Galaxy S23 \| Snapdragon® 8 Gen 2 Mobile \| QNN_CONTEXT_BINARY \| 270.831 ms \| 0 - 3 MB \| NPU \| Use Export Script \|
	\| vae \| w8a16 \| Samsung Galaxy S23 \| Snapdragon® 8 Gen 2 Mobile \| PRECOMPILED_QNN_ONNX \| 273.364 ms \| 0 - 66 MB \| NPU \| Use Export Script \|
	\| vae \| w8a16 \| Samsung Galaxy S24 \| Snapdragon® 8 Gen 3 Mobile \| QNN_CONTEXT_BINARY \| 205.993 ms \| 0 - 18 MB \| NPU \| Use Export Script \|
	\| vae \| w8a16 \| Samsung Galaxy S24 \| Snapdragon® 8 Gen 3 Mobile \| PRECOMPILED_QNN_ONNX \| 204.786 ms \| 3 - 22 MB \| NPU \| Use Export Script \|
	\| vae \| w8a16 \| Snapdragon 8 Elite QRD \| Snapdragon® 8 Elite Mobile \| QNN_CONTEXT_BINARY \| 194.607 ms \| 0 - 14 MB \| NPU \| Use Export Script \|
	\| vae \| w8a16 \| Snapdragon 8 Elite QRD \| Snapdragon® 8 Elite Mobile \| PRECOMPILED_QNN_ONNX \| 193.998 ms \| 3 - 17 MB \| NPU \| Use Export Script \|
	\| vae \| w8a16 \| Snapdragon X Elite CRD \| Snapdragon® X Elite \| QNN_CONTEXT_BINARY \| 266.935 ms \| 0 - 0 MB \| NPU \| Use Export Script \|
	\| vae \| w8a16 \| Snapdragon X Elite CRD \| Snapdragon® X Elite \| PRECOMPILED_QNN_ONNX \| 266.448 ms \| 63 - 63 MB \| NPU \| Use Export Script \|
	\| controlnet \| w8a16 \| QCS8550 (Proxy) \| Qualcomm® QCS8550 (Proxy) \| QNN_CONTEXT_BINARY \| 83.197 ms \| 2 - 4 MB \| NPU \| Use Export Script \|
	\| controlnet \| w8a16 \| QCS9075 (Proxy) \| Qualcomm® QCS9075 (Proxy) \| QNN_CONTEXT_BINARY \| 81.755 ms \| 2 - 11 MB \| NPU \| Use Export Script \|
	\| controlnet \| w8a16 \| SA8255 (Proxy) \| Qualcomm® SA8255P (Proxy) \| QNN_CONTEXT_BINARY \| 83.451 ms \| 2 - 5 MB \| NPU \| Use Export Script \|
	\| controlnet \| w8a16 \| SA8650 (Proxy) \| Qualcomm® SA8650P (Proxy) \| QNN_CONTEXT_BINARY \| 83.565 ms \| 2 - 4 MB \| NPU \| Use Export Script \|
	\| controlnet \| w8a16 \| SA8775P ADP \| Qualcomm® SA8775P \| QNN_CONTEXT_BINARY \| 81.755 ms \| 2 - 11 MB \| NPU \| Use Export Script \|
	\| controlnet \| w8a16 \| Samsung Galaxy S23 \| Snapdragon® 8 Gen 2 Mobile \| QNN_CONTEXT_BINARY \| 83.39 ms \| 2 - 5 MB \| NPU \| Use Export Script \|
	\| controlnet \| w8a16 \| Samsung Galaxy S23 \| Snapdragon® 8 Gen 2 Mobile \| PRECOMPILED_QNN_ONNX \| 86.158 ms \| 0 - 384 MB \| NPU \| Use Export Script \|
	\| controlnet \| w8a16 \| Samsung Galaxy S24 \| Snapdragon® 8 Gen 3 Mobile \| QNN_CONTEXT_BINARY \| 58.723 ms \| 2 - 21 MB \| NPU \| Use Export Script \|
	\| controlnet \| w8a16 \| Samsung Galaxy S24 \| Snapdragon® 8 Gen 3 Mobile \| PRECOMPILED_QNN_ONNX \| 59.623 ms \| 32 - 50 MB \| NPU \| Use Export Script \|
	\| controlnet \| w8a16 \| Snapdragon 8 Elite QRD \| Snapdragon® 8 Elite Mobile \| QNN_CONTEXT_BINARY \| 56.385 ms \| 2 - 16 MB \| NPU \| Use Export Script \|
	\| controlnet \| w8a16 \| Snapdragon 8 Elite QRD \| Snapdragon® 8 Elite Mobile \| PRECOMPILED_QNN_ONNX \| 57.339 ms \| 31 - 45 MB \| NPU \| Use Export Script \|
	\| controlnet \| w8a16 \| Snapdragon X Elite CRD \| Snapdragon® X Elite \| QNN_CONTEXT_BINARY \| 85.054 ms \| 2 - 2 MB \| NPU \| Use Export Script \|
	\| controlnet \| w8a16 \| Snapdragon X Elite CRD \| Snapdragon® X Elite \| PRECOMPILED_QNN_ONNX \| 80.108 ms \| 351 - 351 MB \| NPU \| Use Export Script \|




	## Installation


	Install the package via pip:
	```bash
	pip install "qai-hub-models[controlnet-canny]"
	```


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

	Sign-in to [Qualcomm® AI Hub](https://app.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://app.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
	```
	```
	Profiling Results
	------------------------------------------------------------
	text_encoder
	Device : cs_8550 (ANDROID 12)
	Runtime : QNN_CONTEXT_BINARY
	Estimated inference time (ms) : 5.4
	Estimated peak memory usage (MB): [0, 3]
	Total # Ops : 438
	Compute Unit(s) : npu (438 ops) gpu (0 ops) cpu (0 ops)

	------------------------------------------------------------
	unet
	Device : cs_8550 (ANDROID 12)
	Runtime : QNN_CONTEXT_BINARY
	Estimated inference time (ms) : 110.9
	Estimated peak memory usage (MB): [13, 15]
	Total # Ops : 4055
	Compute Unit(s) : npu (4055 ops) gpu (0 ops) cpu (0 ops)

	------------------------------------------------------------
	vae
	Device : cs_8550 (ANDROID 12)
	Runtime : QNN_CONTEXT_BINARY
	Estimated inference time (ms) : 268.8
	Estimated peak memory usage (MB): [0, 3]
	Total # Ops : 175
	Compute Unit(s) : npu (175 ops) gpu (0 ops) cpu (0 ops)

	------------------------------------------------------------
	controlnet
	Device : cs_8550 (ANDROID 12)
	Runtime : QNN_CONTEXT_BINARY
	Estimated inference time (ms) : 83.2
	Estimated peak memory usage (MB): [2, 4]
	Total # Ops : 664
	Compute Unit(s) : npu (664 ops) gpu (0 ops) cpu (0 ops)
	```





	## 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).