README.md

---
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
  - 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 | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| TextEncoder_Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 11.394 ms | 0 - 74 MB | UINT16 | NPU | [ControlNet.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/TextEncoder_Quantized.bin) |
| TextEncoder_Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 8.08 ms | 0 - 137 MB | UINT16 | NPU | [ControlNet.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/TextEncoder_Quantized.bin) |
| TextEncoder_Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 10.982 ms | 0 - 1 MB | UINT16 | NPU | Use Export Script |
| UNet_Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 262.52 ms | 11 - 17 MB | UINT16 | NPU | [ControlNet.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/UNet_Quantized.bin) |
| UNet_Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 192.789 ms | 3 - 1247 MB | UINT16 | NPU | [ControlNet.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/UNet_Quantized.bin) |
| UNet_Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 260.158 ms | 14 - 15 MB | UINT16 | NPU | Use Export Script |
| VAEDecoder_Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 390.243 ms | 0 - 36 MB | UINT16 | NPU | [ControlNet.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/VAEDecoder_Quantized.bin) |
| VAEDecoder_Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 294.404 ms | 0 - 88 MB | UINT16 | NPU | [ControlNet.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/VAEDecoder_Quantized.bin) |
| VAEDecoder_Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 379.548 ms | 0 - 1 MB | UINT16 | NPU | Use Export Script |
| ControlNet_Quantized | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 100.33 ms | 2 - 68 MB | UINT16 | NPU | [ControlNet.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/ControlNet_Quantized.bin) |
| ControlNet_Quantized | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 76.94 ms | 0 - 533 MB | UINT16 | NPU | [ControlNet.bin](https://huggingface.co/qualcomm/ControlNet/blob/main/ControlNet_Quantized.bin) |
| ControlNet_Quantized | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 103.52 ms | 2 - 3 MB | UINT16 | NPU | Use Export Script |




## Installation


Install the 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
```
```
Profiling Results
------------------------------------------------------------
TextEncoder_Quantized
Device                          : Samsung Galaxy S23 (13)
Runtime                         : QNN                    
Estimated inference time (ms)   : 11.4                   
Estimated peak memory usage (MB): [0, 74]                
Total # Ops                     : 570                    
Compute Unit(s)                 : NPU (570 ops)          

------------------------------------------------------------
UNet_Quantized
Device                          : Samsung Galaxy S23 (13)
Runtime                         : QNN                    
Estimated inference time (ms)   : 262.5                  
Estimated peak memory usage (MB): [11, 17]               
Total # Ops                     : 5434                   
Compute Unit(s)                 : NPU (5434 ops)         

------------------------------------------------------------
VAEDecoder_Quantized
Device                          : Samsung Galaxy S23 (13)
Runtime                         : QNN                    
Estimated inference time (ms)   : 390.2                  
Estimated peak memory usage (MB): [0, 36]                
Total # Ops                     : 409                    
Compute Unit(s)                 : NPU (409 ops)          

------------------------------------------------------------
ControlNet_Quantized
Device                          : Samsung Galaxy S23 (13)
Runtime                         : QNN                    
Estimated inference time (ms)   : 100.3                  
Estimated peak memory usage (MB): [2, 68]                
Total # Ops                     : 2406                   
Compute Unit(s)                 : NPU (2406 ops)         
```


## How does this work?

This [export script](https://aihub.qualcomm.com/models/controlnet_quantized/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: **Compile model for on-device deployment**

To compile a PyTorch model for on-device deployment, we first trace the model
in memory using the `jit.trace` and then call the `submit_compile_job` API.

```python
import torch

import qai_hub as hub
from qai_hub_models.models.controlnet_quantized import Model

# Load the model
model = Model.from_pretrained()
controlnet_model = model.controlnet
text_encoder_model = model.text_encoder
unet_model = model.unet
vae_decoder_model = model.vae_decoder

# Device
device = hub.Device("Samsung Galaxy S23")

# Trace model
controlnet_input_shape = controlnet_model.get_input_spec()
controlnet_sample_inputs = controlnet_model.sample_inputs()

traced_controlnet_model = torch.jit.trace(controlnet_model, [torch.tensor(data[0]) for _, data in controlnet_sample_inputs.items()])

# Compile model on a specific device
controlnet_compile_job = hub.submit_compile_job(
    model=traced_controlnet_model ,
    device=device,
    input_specs=controlnet_model.get_input_spec(),
)

# Get target model to run on-device
controlnet_target_model = controlnet_compile_job.get_target_model()
# Trace model
text_encoder_input_shape = text_encoder_model.get_input_spec()
text_encoder_sample_inputs = text_encoder_model.sample_inputs()

traced_text_encoder_model = torch.jit.trace(text_encoder_model, [torch.tensor(data[0]) for _, data in text_encoder_sample_inputs.items()])

# Compile model on a specific device
text_encoder_compile_job = hub.submit_compile_job(
    model=traced_text_encoder_model ,
    device=device,
    input_specs=text_encoder_model.get_input_spec(),
)

# Get target model to run on-device
text_encoder_target_model = text_encoder_compile_job.get_target_model()
# Trace model
unet_input_shape = unet_model.get_input_spec()
unet_sample_inputs = unet_model.sample_inputs()

traced_unet_model = torch.jit.trace(unet_model, [torch.tensor(data[0]) for _, data in unet_sample_inputs.items()])

# Compile model on a specific device
unet_compile_job = hub.submit_compile_job(
    model=traced_unet_model ,
    device=device,
    input_specs=unet_model.get_input_spec(),
)

# Get target model to run on-device
unet_target_model = unet_compile_job.get_target_model()
# Trace model
vae_decoder_input_shape = vae_decoder_model.get_input_spec()
vae_decoder_sample_inputs = vae_decoder_model.sample_inputs()

traced_vae_decoder_model = torch.jit.trace(vae_decoder_model, [torch.tensor(data[0]) for _, data in vae_decoder_sample_inputs.items()])

# Compile model on a specific device
vae_decoder_compile_job = hub.submit_compile_job(
    model=traced_vae_decoder_model ,
    device=device,
    input_specs=vae_decoder_model.get_input_spec(),
)

# Get target model to run on-device
vae_decoder_target_model = vae_decoder_compile_job.get_target_model()

```


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_controlnet_quantized = hub.submit_profile_job(
    model=model_controlnet_quantized,
    device=device,
)
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,
)

```

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_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()

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()

```
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://github.com/lllyasviel/ControlNet/blob/main/LICENSE)



## 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).


## 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