--- library_name: pytorch license: apache-2.0 pipeline_tag: unconditional-image-generation tags: - generative_ai - quantized - android --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/controlnet_quantized/web-assets/model_demo.png) # ControlNet: 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 found [here](https://github.com/lllyasviel/ControlNet). This repository provides scripts to run ControlNet on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/controlnet_quantized). ### Model Details - **Model Type:** Image generation - **Model Stats:** - Input: Text prompt and input image as a reference - QNN-SDK: 2.19 - 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 | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model | ---|---|---|---|---|---|---|---| | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Binary | 11.369 ms | 0 - 33 MB | UINT16 | NPU | [TextEncoder_Quantized.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/TextEncoder_Quantized.bin) | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Binary | 386.746 ms | 0 - 4 MB | UINT16 | NPU | [VAEDecoder_Quantized.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/VAEDecoder_Quantized.bin) | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Binary | 259.981 ms | 12 - 14 MB | UINT16 | NPU | [UNet_Quantized.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/UNet_Quantized.bin) | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Binary | 103.748 ms | 0 - 22 MB | UINT16 | NPU | [ControlNet_Quantized.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/ControlNet_Quantized.bin) ## Installation This model can be installed as a Python package via pip. ```bash pip install "qai-hub-models[controlnet_quantized]" ``` ## 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 on-device 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_quantized.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_quantized.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_quantized.export ``` ``` Profile Job summary of TextEncoder_Quantized -------------------------------------------------- Device: Samsung Galaxy S23 Ultra (13) Estimated Inference Time: 11.37 ms Estimated Peak Memory Range: 0.05-33.25 MB Compute Units: NPU (570) | Total (570) Profile Job summary of VAEDecoder_Quantized -------------------------------------------------- Device: Samsung Galaxy S23 Ultra (13) Estimated Inference Time: 386.75 ms Estimated Peak Memory Range: 0.12-4.28 MB Compute Units: NPU (409) | Total (409) Profile Job summary of UNet_Quantized -------------------------------------------------- Device: Samsung Galaxy S23 Ultra (13) Estimated Inference Time: 259.98 ms Estimated Peak Memory Range: 12.45-14.35 MB Compute Units: NPU (5434) | Total (5434) Profile Job summary of ControlNet_Quantized -------------------------------------------------- Device: Samsung Galaxy S23 Ultra (13) Estimated Inference Time: 103.75 ms Estimated Peak Memory Range: 0.19-22.20 MB Compute Units: NPU (2406) | Total (2406) ``` ## How does this work? This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/ControlNet/export.py) leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model on-device. Lets go through each step below in detail: Step 1: **Upload compiled model** Upload compiled models from `qai_hub_models.models.controlnet_quantized` on hub. ```python import torch import qai_hub as hub from qai_hub_models.models.controlnet_quantized import Model # Load the model model = Model.from_precompiled() model_textencoder_quantized = hub.upload_model(model.text_encoder.get_target_model_path()) model_unet_quantized = hub.upload_model(model.unet.get_target_model_path()) model_vaedecoder_quantized = hub.upload_model(model.vae_decoder.get_target_model_path()) model_controlnet_quantized = hub.upload_model(model.controlnet.get_target_model_path()) ``` Step 2: **Performance profiling on cloud-hosted device** After uploading compiled models from step 1. Models can be profiled model on-device using the `target_model`. Note that this scripts runs the model on a device automatically provisioned in the cloud. Once the job is submitted, you can navigate to a provided job URL to view a variety of on-device performance metrics. ```python # Device device = hub.Device("Samsung Galaxy S23") profile_job_textencoder_quantized = hub.submit_profile_job( model=model_textencoder_quantized, device=device, ) profile_job_unet_quantized = hub.submit_profile_job( model=model_unet_quantized, device=device, ) profile_job_vaedecoder_quantized = hub.submit_profile_job( model=model_vaedecoder_quantized, device=device, ) profile_job_controlnet_quantized = hub.submit_profile_job( model=model_controlnet_quantized, device=device, ) ``` Step 3: **Verify on-device accuracy** To verify the accuracy of the model on-device, you can run on-device inference on sample input data on the same cloud hosted device. ```python input_data_textencoder_quantized = model.text_encoder.sample_inputs() inference_job_textencoder_quantized = hub.submit_inference_job( model=model_textencoder_quantized, device=device, inputs=input_data_textencoder_quantized, ) on_device_output_textencoder_quantized = inference_job_textencoder_quantized.download_output_data() input_data_unet_quantized = model.unet.sample_inputs() inference_job_unet_quantized = hub.submit_inference_job( model=model_unet_quantized, device=device, inputs=input_data_unet_quantized, ) on_device_output_unet_quantized = inference_job_unet_quantized.download_output_data() input_data_vaedecoder_quantized = model.vae_decoder.sample_inputs() inference_job_vaedecoder_quantized = hub.submit_inference_job( model=model_vaedecoder_quantized, device=device, inputs=input_data_vaedecoder_quantized, ) on_device_output_vaedecoder_quantized = inference_job_vaedecoder_quantized.download_output_data() input_data_controlnet_quantized = model.controlnet.sample_inputs() inference_job_controlnet_quantized = hub.submit_inference_job( model=model_controlnet_quantized, device=device, inputs=input_data_controlnet_quantized, ) on_device_output_controlnet_quantized = inference_job_controlnet_quantized.download_output_data() ``` With the output of the model, you can compute like PSNR, relative errors or spot check the output with expected output. **Note**: This on-device profiling and inference requires access to Qualcomm® AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup). ## 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` / `.bin` 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 or `.bin` context binary in an Android application. ## View on Qualcomm® AI Hub Get more details on ControlNet's performance across various devices [here](https://aihub.qualcomm.com/models/controlnet_quantized). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License - The license for the original implementation of ControlNet 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://join.slack.com/t/qualcomm-ai-hub/shared_invite/zt-2dgf95loi-CXHTDRR1rvPgQWPO~ZZZJg) 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). ## Usage and Limitations Model may not be used for or in connection with any of the following applications: - 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, including categorization of persons based on sensitive characteristics; - 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); and/or - Subliminal manipulation