XLSR-Quantized: Optimized for Mobile Deployment

Upscale images in real time

XLSR is designed for lightweight real-time upscaling of images.

This model is an implementation of XLSR-Quantized found here.

This repository provides scripts to run XLSR-Quantized on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

Model Type: Super resolution
Model Stats:
- Model checkpoint: xlsr_3x_checkpoint
- Input resolution: 128x128
- Number of parameters: 22.0K
- Model size: 39.0 KB

Model	Device	Chipset	Target Runtime	Inference Time (ms)	Peak Memory Range (MB)	Precision	Primary Compute Unit	Target Model
XLSR-Quantized	Samsung Galaxy S23	Snapdragon® 8 Gen 2	TFLITE	1.076 ms	0 - 1 MB	INT8	NPU	XLSR-Quantized.tflite
XLSR-Quantized	Samsung Galaxy S23	Snapdragon® 8 Gen 2	QNN	0.652 ms	0 - 3 MB	INT8	NPU	XLSR-Quantized.so
XLSR-Quantized	Samsung Galaxy S23	Snapdragon® 8 Gen 2	ONNX	0.678 ms	0 - 1 MB	INT8	NPU	XLSR-Quantized.onnx
XLSR-Quantized	Samsung Galaxy S24	Snapdragon® 8 Gen 3	TFLITE	0.878 ms	0 - 22 MB	INT8	NPU	XLSR-Quantized.tflite
XLSR-Quantized	Samsung Galaxy S24	Snapdragon® 8 Gen 3	QNN	0.454 ms	0 - 15 MB	INT8	NPU	XLSR-Quantized.so
XLSR-Quantized	Samsung Galaxy S24	Snapdragon® 8 Gen 3	ONNX	0.499 ms	0 - 24 MB	INT8	NPU	XLSR-Quantized.onnx
XLSR-Quantized	RB3 Gen 2 (Proxy)	QCS6490 Proxy	TFLITE	2.437 ms	0 - 16 MB	INT8	NPU	XLSR-Quantized.tflite
XLSR-Quantized	RB3 Gen 2 (Proxy)	QCS6490 Proxy	QNN	1.076 ms	0 - 7 MB	INT8	NPU	Use Export Script
XLSR-Quantized	RB5 (Proxy)	QCS8250 Proxy	TFLITE	16.048 ms	4 - 28 MB	INT8	GPU	XLSR-Quantized.tflite
XLSR-Quantized	QCS8550 (Proxy)	QCS8550 Proxy	TFLITE	1.06 ms	0 - 12 MB	INT8	NPU	XLSR-Quantized.tflite
XLSR-Quantized	QCS8550 (Proxy)	QCS8550 Proxy	QNN	0.426 ms	0 - 2 MB	INT8	NPU	Use Export Script
XLSR-Quantized	SA8255 (Proxy)	SA8255P Proxy	TFLITE	1.054 ms	0 - 3 MB	INT8	NPU	XLSR-Quantized.tflite
XLSR-Quantized	SA8255 (Proxy)	SA8255P Proxy	QNN	0.429 ms	0 - 2 MB	INT8	NPU	Use Export Script
XLSR-Quantized	SA8775 (Proxy)	SA8775P Proxy	TFLITE	1.065 ms	0 - 1 MB	INT8	NPU	XLSR-Quantized.tflite
XLSR-Quantized	SA8775 (Proxy)	SA8775P Proxy	QNN	0.433 ms	0 - 1 MB	INT8	NPU	Use Export Script
XLSR-Quantized	SA8650 (Proxy)	SA8650P Proxy	TFLITE	1.077 ms	2 - 14 MB	INT8	NPU	XLSR-Quantized.tflite
XLSR-Quantized	SA8650 (Proxy)	SA8650P Proxy	QNN	0.424 ms	0 - 1 MB	INT8	NPU	Use Export Script
XLSR-Quantized	QCS8450 (Proxy)	QCS8450 Proxy	TFLITE	1.399 ms	0 - 23 MB	INT8	NPU	XLSR-Quantized.tflite
XLSR-Quantized	QCS8450 (Proxy)	QCS8450 Proxy	QNN	0.716 ms	0 - 13 MB	INT8	NPU	Use Export Script
XLSR-Quantized	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	TFLITE	0.854 ms	0 - 16 MB	INT8	NPU	XLSR-Quantized.tflite
XLSR-Quantized	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	QNN	0.404 ms	0 - 10 MB	INT8	NPU	Use Export Script
XLSR-Quantized	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite	ONNX	0.381 ms	0 - 16 MB	INT8	NPU	XLSR-Quantized.onnx
XLSR-Quantized	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN	0.536 ms	0 - 0 MB	INT8	NPU	Use Export Script
XLSR-Quantized	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	0.794 ms	3 - 3 MB	INT8	NPU	XLSR-Quantized.onnx

Installation

This model can be installed as a Python package via pip.

pip install "qai-hub-models[xlsr_quantized]"

Configure Qualcomm® AI Hub to run this model on a cloud-hosted device

Sign-in 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.xlsr_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.xlsr_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.

python -m qai_hub_models.models.xlsr_quantized.export

Profiling Results
------------------------------------------------------------
XLSR-Quantized
Device                          : Samsung Galaxy S23 (13) 
Runtime                         : TFLITE                  
Estimated inference time (ms)   : 1.1                     
Estimated peak memory usage (MB): [0, 1]                  
Total # Ops                     : 19                      
Compute Unit(s)                 : NPU (16 ops) CPU (3 ops)

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.xlsr_quantized.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.xlsr_quantized.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 app provides instructions on how to use the .so shared library in an Android application.

View on Qualcomm® AI Hub

Get more details on XLSR-Quantized's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

The license for the original implementation of XLSR-Quantized can be found here.
The license for the compiled assets for on-device deployment can be found here

References

Community

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