qualcomm
/

ControlNet

@@ -1,258 +1,10 @@
 ---
 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/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).
-### 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
-|---|---|---|---|---|---|---|---|---|
-| TextEncoder_Quantized | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 10.874 ms | 0 - 3 MB | NPU | [ControlNet.dlc](https://huggingface.co/qualcomm/ControlNet/blob/main/ControlNet_w8a16.dlc) |
-| TextEncoder_Quantized | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 7.918 ms | 0 - 18 MB | NPU | [ControlNet.dlc](https://huggingface.co/qualcomm/ControlNet/blob/main/ControlNet_w8a16.dlc) |
-| TextEncoder_Quantized | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 10.875 ms | 0 - 3 MB | NPU | [ControlNet.dlc](https://huggingface.co/qualcomm/ControlNet/blob/main/ControlNet_w8a16.dlc) |
-| UNet_Quantized | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 258.151 ms | 13 - 15 MB | NPU | [ControlNet.dlc](https://huggingface.co/qualcomm/ControlNet/blob/main/ControlNet_w8a16.dlc) |
-| UNet_Quantized | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 197.629 ms | 13 - 31 MB | NPU | [ControlNet.dlc](https://huggingface.co/qualcomm/ControlNet/blob/main/ControlNet_w8a16.dlc) |
-| UNet_Quantized | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 256.936 ms | 13 - 16 MB | NPU | [ControlNet.dlc](https://huggingface.co/qualcomm/ControlNet/blob/main/ControlNet_w8a16.dlc) |
-| VAEDecoder_Quantized | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 397.625 ms | 0 - 2 MB | NPU | [ControlNet.dlc](https://huggingface.co/qualcomm/ControlNet/blob/main/ControlNet_w8a16.dlc) |
-| VAEDecoder_Quantized | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 300.627 ms | 0 - 21 MB | NPU | [ControlNet.dlc](https://huggingface.co/qualcomm/ControlNet/blob/main/ControlNet_w8a16.dlc) |
-| VAEDecoder_Quantized | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 395.006 ms | 0 - 3 MB | NPU | [ControlNet.dlc](https://huggingface.co/qualcomm/ControlNet/blob/main/ControlNet_w8a16.dlc) |
-| ControlNet_Quantized | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 104.668 ms | 2 - 9 MB | NPU | [ControlNet.dlc](https://huggingface.co/qualcomm/ControlNet/blob/main/ControlNet_w8a16.dlc) |
-| ControlNet_Quantized | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 77.289 ms | 2 - 23 MB | NPU | [ControlNet.dlc](https://huggingface.co/qualcomm/ControlNet/blob/main/ControlNet_w8a16.dlc) |
-| ControlNet_Quantized | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 103.817 ms | 2 - 5 MB | NPU | [ControlNet.dlc](https://huggingface.co/qualcomm/ControlNet/blob/main/ControlNet_w8a16.dlc) |
-## Installation
-Install the package via pip:
-```bash
-pip install "qai-hub-models[controlnet]"
-```
-## 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.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.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.export
-```
-```
-Profiling Results
-------------------------------------------------------------
-TextEncoder_Quantized
-Device                          : cs_8_gen_2 (ANDROID 13)
-Runtime                         : QNN_DLC
-Estimated inference time (ms)   : 10.9
-Estimated peak memory usage (MB): [0, 3]
-Total # Ops                     : 569
-Compute Unit(s)                 : npu (569 ops) gpu (0 ops) cpu (0 ops)
-------------------------------------------------------------
-UNet_Quantized
-Device                          : cs_8_gen_2 (ANDROID 13)
-Runtime                         : QNN_DLC
-Estimated inference time (ms)   : 258.2
-Estimated peak memory usage (MB): [13, 15]
-Total # Ops                     : 5433
-Compute Unit(s)                 : npu (5433 ops) gpu (0 ops) cpu (0 ops)
-------------------------------------------------------------
-VAEDecoder_Quantized
-Device                          : cs_8_gen_2 (ANDROID 13)
-Runtime                         : QNN_DLC
-Estimated inference time (ms)   : 397.6
-Estimated peak memory usage (MB): [0, 2]
-Total # Ops                     : 408
-Compute Unit(s)                 : npu (408 ops) gpu (0 ops) cpu (0 ops)
-------------------------------------------------------------
-ControlNet_Quantized
-Device                          : cs_8_gen_2 (ANDROID 13)
-Runtime                         : QNN_DLC
-Estimated inference time (ms)   : 104.7
-Estimated peak memory usage (MB): [2, 9]
-Total # Ops                     : 2405
-Compute Unit(s)                 : npu (2405 ops) gpu (0 ops) cpu (0 ops)
-```
-## How does this work?
-This [export script](https://aihub.qualcomm.com/models/controlnet/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` on hub.
-```python
-import torch
-import qai_hub as hub
-from qai_hub_models.models.controlnet import Model
-# Load the model
-model = Model.from_precompiled()
-```
-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")
-```
-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
-```
-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).
-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://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:[email protected]).
-## Usage and Limitations
-This 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

 ---
 library_name: pytorch
 license: other
 tags:
+- deprecated
+pipeline_tag: other
 ---
+This model is deprecated. Please refer to https://aihub.qualcomm.com for the latest models and updates.