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README.md

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+---
+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/banner.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 early access](https://aihub.qualcomm.com/sign-up).
+
+
+
+## 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
+
+