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SESR-M5

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SESR-M5: Optimized for Mobile Deployment

Upscale images in real time

SESR M5 performs efficient on-device upscaling of images.

This model is an implementation of SESR-M5 found here.

This repository provides scripts to run SESR-M5 on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Model_use_case.super_resolution
  • Model Stats:
    • Model checkpoint: sesr_m5_3x_checkpoint
    • Input resolution: 128x128
    • Number of parameters: 343K
    • Model size (float): 1.32 MB
    • Model size (w8a8): 395 KB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
SESR-M5 float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 10.94 ms 3 - 19 MB NPU SESR-M5.tflite
SESR-M5 float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 10.509 ms 0 - 16 MB NPU SESR-M5.dlc
SESR-M5 float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 3.155 ms 0 - 29 MB NPU SESR-M5.tflite
SESR-M5 float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 2.78 ms 0 - 29 MB NPU SESR-M5.dlc
SESR-M5 float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 2.022 ms 0 - 6 MB NPU SESR-M5.tflite
SESR-M5 float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 1.868 ms 0 - 5 MB NPU SESR-M5.dlc
SESR-M5 float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) ONNX 2.293 ms 0 - 4 MB NPU SESR-M5.onnx.zip
SESR-M5 float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 3.219 ms 0 - 16 MB NPU SESR-M5.tflite
SESR-M5 float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 3.019 ms 0 - 17 MB NPU SESR-M5.dlc
SESR-M5 float SA7255P ADP Qualcomm® SA7255P TFLITE 10.94 ms 3 - 19 MB NPU SESR-M5.tflite
SESR-M5 float SA7255P ADP Qualcomm® SA7255P QNN_DLC 10.509 ms 0 - 16 MB NPU SESR-M5.dlc
SESR-M5 float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 2.032 ms 0 - 6 MB NPU SESR-M5.tflite
SESR-M5 float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 1.872 ms 0 - 10 MB NPU SESR-M5.dlc
SESR-M5 float SA8295P ADP Qualcomm® SA8295P TFLITE 3.973 ms 0 - 22 MB NPU SESR-M5.tflite
SESR-M5 float SA8295P ADP Qualcomm® SA8295P QNN_DLC 3.448 ms 0 - 22 MB NPU SESR-M5.dlc
SESR-M5 float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 2.028 ms 0 - 7 MB NPU SESR-M5.tflite
SESR-M5 float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 1.871 ms 0 - 5 MB NPU SESR-M5.dlc
SESR-M5 float SA8775P ADP Qualcomm® SA8775P TFLITE 3.219 ms 0 - 16 MB NPU SESR-M5.tflite
SESR-M5 float SA8775P ADP Qualcomm® SA8775P QNN_DLC 3.019 ms 0 - 17 MB NPU SESR-M5.dlc
SESR-M5 float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 1.388 ms 0 - 31 MB NPU SESR-M5.tflite
SESR-M5 float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 1.265 ms 0 - 26 MB NPU SESR-M5.dlc
SESR-M5 float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 1.588 ms 0 - 26 MB NPU SESR-M5.onnx.zip
SESR-M5 float Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile TFLITE 1.238 ms 0 - 20 MB NPU SESR-M5.tflite
SESR-M5 float Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile QNN_DLC 1.034 ms 0 - 24 MB NPU SESR-M5.dlc
SESR-M5 float Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile ONNX 1.263 ms 0 - 20 MB NPU SESR-M5.onnx.zip
SESR-M5 float Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile TFLITE 0.962 ms 0 - 20 MB NPU SESR-M5.tflite
SESR-M5 float Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile QNN_DLC 0.878 ms 0 - 21 MB NPU SESR-M5.dlc
SESR-M5 float Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile ONNX 1.086 ms 0 - 23 MB NPU SESR-M5.onnx.zip
SESR-M5 float Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 2.083 ms 0 - 0 MB NPU SESR-M5.dlc
SESR-M5 float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 2.273 ms 8 - 8 MB NPU SESR-M5.onnx.zip
SESR-M5 w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 2.249 ms 1 - 17 MB NPU SESR-M5.tflite
SESR-M5 w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN_DLC 1.938 ms 0 - 17 MB NPU SESR-M5.dlc
SESR-M5 w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 0.921 ms 1 - 27 MB NPU SESR-M5.tflite
SESR-M5 w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN_DLC 0.942 ms 0 - 28 MB NPU SESR-M5.dlc
SESR-M5 w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 0.77 ms 0 - 10 MB NPU SESR-M5.tflite
SESR-M5 w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN_DLC 0.59 ms 0 - 9 MB NPU SESR-M5.dlc
SESR-M5 w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) ONNX 1.584 ms 0 - 6 MB NPU SESR-M5.onnx.zip
SESR-M5 w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 1.023 ms 0 - 17 MB NPU SESR-M5.tflite
SESR-M5 w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN_DLC 0.859 ms 0 - 16 MB NPU SESR-M5.dlc
SESR-M5 w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) TFLITE 2.649 ms 1 - 21 MB NPU SESR-M5.tflite
SESR-M5 w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) QNN_DLC 3.056 ms 0 - 21 MB NPU SESR-M5.dlc
SESR-M5 w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) ONNX 44.629 ms 63 - 74 MB CPU SESR-M5.onnx.zip
SESR-M5 w8a8 RB5 (Proxy) Qualcomm® QCS8250 (Proxy) TFLITE 21.725 ms 1 - 3 MB NPU SESR-M5.tflite
SESR-M5 w8a8 RB5 (Proxy) Qualcomm® QCS8250 (Proxy) ONNX 37.999 ms 63 - 66 MB CPU SESR-M5.onnx.zip
SESR-M5 w8a8 SA7255P ADP Qualcomm® SA7255P TFLITE 2.249 ms 1 - 17 MB NPU SESR-M5.tflite
SESR-M5 w8a8 SA7255P ADP Qualcomm® SA7255P QNN_DLC 1.938 ms 0 - 17 MB NPU SESR-M5.dlc
SESR-M5 w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 0.776 ms 0 - 10 MB NPU SESR-M5.tflite
SESR-M5 w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN_DLC 0.616 ms 0 - 9 MB NPU SESR-M5.dlc
SESR-M5 w8a8 SA8295P ADP Qualcomm® SA8295P TFLITE 1.484 ms 0 - 24 MB NPU SESR-M5.tflite
SESR-M5 w8a8 SA8295P ADP Qualcomm® SA8295P QNN_DLC 1.386 ms 0 - 22 MB NPU SESR-M5.dlc
SESR-M5 w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 0.778 ms 0 - 10 MB NPU SESR-M5.tflite
SESR-M5 w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN_DLC 0.599 ms 0 - 9 MB NPU SESR-M5.dlc
SESR-M5 w8a8 SA8775P ADP Qualcomm® SA8775P TFLITE 1.023 ms 0 - 17 MB NPU SESR-M5.tflite
SESR-M5 w8a8 SA8775P ADP Qualcomm® SA8775P QNN_DLC 0.859 ms 0 - 16 MB NPU SESR-M5.dlc
SESR-M5 w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 0.56 ms 0 - 26 MB NPU SESR-M5.tflite
SESR-M5 w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN_DLC 0.471 ms 0 - 23 MB NPU SESR-M5.dlc
SESR-M5 w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 1.022 ms 0 - 30 MB NPU SESR-M5.onnx.zip
SESR-M5 w8a8 Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile TFLITE 0.496 ms 0 - 24 MB NPU SESR-M5.tflite
SESR-M5 w8a8 Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile QNN_DLC 0.377 ms 0 - 20 MB NPU SESR-M5.dlc
SESR-M5 w8a8 Samsung Galaxy S25 Snapdragon® 8 Elite For Galaxy Mobile ONNX 0.791 ms 0 - 23 MB NPU SESR-M5.onnx.zip
SESR-M5 w8a8 Snapdragon 7 Gen 5 QRD Snapdragon® 7 Gen 5 Mobile TFLITE 0.925 ms 0 - 23 MB NPU SESR-M5.tflite
SESR-M5 w8a8 Snapdragon 7 Gen 5 QRD Snapdragon® 7 Gen 5 Mobile QNN_DLC 0.793 ms 0 - 22 MB NPU SESR-M5.dlc
SESR-M5 w8a8 Snapdragon 7 Gen 5 QRD Snapdragon® 7 Gen 5 Mobile ONNX 31.494 ms 65 - 80 MB CPU SESR-M5.onnx.zip
SESR-M5 w8a8 Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile TFLITE 0.423 ms 1 - 16 MB NPU SESR-M5.tflite
SESR-M5 w8a8 Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile QNN_DLC 0.316 ms 0 - 16 MB NPU SESR-M5.dlc
SESR-M5 w8a8 Snapdragon 8 Elite Gen 5 QRD Snapdragon® 8 Elite Gen5 Mobile ONNX 0.721 ms 2 - 25 MB NPU SESR-M5.onnx.zip
SESR-M5 w8a8 Snapdragon X Elite CRD Snapdragon® X Elite QNN_DLC 0.743 ms 0 - 0 MB NPU SESR-M5.dlc
SESR-M5 w8a8 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 1.469 ms 8 - 8 MB NPU SESR-M5.onnx.zip

Installation

Install the package via pip:

pip install qai-hub-models

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.sesr_m5.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.sesr_m5.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.sesr_m5.export

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.sesr_m5 import Model

# Load the model
torch_model = Model.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S25")

# 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(
    model=pt_model,
    device=device,
    input_specs=torch_model.get_input_spec(),
)

# 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(
    model=target_model,
    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.

input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
    model=target_model,
    device=device,
    inputs=input_data,
)
    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.sesr_m5.demo --eval-mode 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.sesr_m5.demo -- --eval-mode 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 SESR-M5's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of SESR-M5 can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

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