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library_name: pytorch
license: bsd-3-clause
pipeline_tag: image-to-image
- android
# Real-ESRGAN-x4plus: Optimized for Mobile Deployment
## Upscale images and remove image noise
Real-ESRGAN is a machine learning model that upscales an image with minimal loss in quality. The implementation is a derivative of the Real-ESRGAN-x4plus architecture, a larger and more powerful version compared to the Real-ESRGAN-general-x4v3 architecture.
This model is an implementation of Real-ESRGAN-x4plus found [here](
This repository provides scripts to run Real-ESRGAN-x4plus on Qualcomm® devices.
More details on model performance across various devices, can be found
### Model Details
- **Model Type:** Super resolution
- **Model Stats:**
- Number of parameters: 16.7M
- Model size: 67.1 MB
- Model checkpoint: RealESRGAN_x4plus
- Input resolution: 128x128
| 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 | TFLite | 73.185 ms | 5 - 8 MB | FP16 | NPU | [Real-ESRGAN-x4plus.tflite](
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 67.019 ms | 0 - 54 MB | FP16 | NPU | [](
## Installation
This model can be installed as a Python package via pip.
pip install "qai-hub-models[real_esrgan_x4plus]"
## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
to [Qualcomm® AI Hub]( 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.
qai-hub configure --api_token API_TOKEN
Navigate to [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.
python -m qai_hub_models.models.real_esrgan_x4plus.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.real_esrgan_x4plus.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.
python -m qai_hub_models.models.real_esrgan_x4plus.export
Profile Job summary of Real-ESRGAN-x4plus
Device: SA8255 (Proxy) (13)
Estimated Inference Time: 70.75 ms
Estimated Peak Memory Range: 0.14-54.23 MB
Compute Units: NPU (1031) | Total (1031)
## How does this work?
This [export script](
leverages [Qualcomm® AI Hub]( 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.
import torch
import qai_hub as hub
from qai_hub_models.models.real_esrgan_x4plus import Model
# Load the model
torch_model = Model.from_pretrained()
# Device
device = hub.Device("Samsung Galaxy S23")
# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()
pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
# Compile model on a specific device
compile_job = hub.submit_compile_job(
# Get target model to run on-device
target_model = compile_job.get_target_model()
Step 2: **Performance profiling on cloud-hosted device**
After compiling 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.
profile_job = hub.submit_profile_job(
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.
input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
on_device_output = inference_job.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](
## Run demo on a cloud-hosted device
You can also run the demo on-device.
python -m qai_hub_models.models.real_esrgan_x4plus.demo --on-device
**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.real_esrgan_x4plus.demo -- --on-device
## Deploying compiled model to Android
The models can be deployed using multiple runtimes:
- TensorFlow Lite (`.tflite` export): [This
tutorial]( provides a
guide to deploy the .tflite model in an Android application.
- QNN (`.so` export ): This [sample
provides instructions on how to use the `.so` shared library in an Android application.
## View on Qualcomm® AI Hub
Get more details on Real-ESRGAN-x4plus's performance across various devices [here](
Explore all available models on [Qualcomm® AI Hub](
## License
- The license for the original implementation of Real-ESRGAN-x4plus can be found
- The license for the compiled assets for on-device deployment can be found [here](
## References
* [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](
* [Source Model Implementation](
## Community
* Join [our AI Hub Slack community]( to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](