--- 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 | 5.842 ms | 0 - 162 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 | 4.092 ms | 0 - 14 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 | 3.833 ms | 0 - 14 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 | 157 - 157 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 | 116.814 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 | 83.313 ms | 13 - 34 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 | 70.96 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 | 117.905 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 | 276.425 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 | 204.277 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 | 194.151 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 | 266.572 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 | 85.359 ms | 0 - 385 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 | 59.616 ms | 32 - 52 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 | 57.177 ms | 33 - 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 | 80.095 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).