AOT-GAN: Optimized for Mobile Deployment

High resolution image in-painting on-device

AOT-GAN is a machine learning model that allows to erase and in-paint part of given input image.

This model is an implementation of AOT-GAN found here.

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

Model Details

Model Type: Model_use_case.image_editing
Model Stats:
- Model checkpoint: CelebAHQ
- Input resolution: 512x512
- Number of parameters: 15.2M
- Model size (float): 58.0 MB

Model	Precision	Device	Chipset	Target Runtime	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit	Target Model
AOT-GAN	float	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	TFLITE	542.141 ms	4 - 502 MB	NPU	AOT-GAN.tflite
AOT-GAN	float	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	QNN_DLC	543.56 ms	1 - 496 MB	NPU	AOT-GAN.dlc
AOT-GAN	float	QCS8450 (Proxy)	Qualcomm® QCS8450 (Proxy)	TFLITE	193.616 ms	3 - 602 MB	NPU	AOT-GAN.tflite
AOT-GAN	float	QCS8450 (Proxy)	Qualcomm® QCS8450 (Proxy)	QNN_DLC	198.055 ms	4 - 571 MB	NPU	AOT-GAN.dlc
AOT-GAN	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	TFLITE	116.076 ms	3 - 7 MB	NPU	AOT-GAN.tflite
AOT-GAN	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	QNN_DLC	116.738 ms	4 - 7 MB	NPU	AOT-GAN.dlc
AOT-GAN	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	ONNX	130.519 ms	0 - 39 MB	NPU	AOT-GAN.onnx.zip
AOT-GAN	float	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	TFLITE	161.662 ms	3 - 504 MB	NPU	AOT-GAN.tflite
AOT-GAN	float	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	QNN_DLC	161.148 ms	1 - 494 MB	NPU	AOT-GAN.dlc
AOT-GAN	float	SA7255P ADP	Qualcomm® SA7255P	TFLITE	542.141 ms	4 - 502 MB	NPU	AOT-GAN.tflite
AOT-GAN	float	SA7255P ADP	Qualcomm® SA7255P	QNN_DLC	543.56 ms	1 - 496 MB	NPU	AOT-GAN.dlc
AOT-GAN	float	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	TFLITE	117.569 ms	0 - 3 MB	NPU	AOT-GAN.tflite
AOT-GAN	float	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	QNN_DLC	117.726 ms	4 - 7 MB	NPU	AOT-GAN.dlc
AOT-GAN	float	SA8295P ADP	Qualcomm® SA8295P	TFLITE	174.044 ms	1 - 430 MB	NPU	AOT-GAN.tflite
AOT-GAN	float	SA8295P ADP	Qualcomm® SA8295P	QNN_DLC	174.855 ms	1 - 417 MB	NPU	AOT-GAN.dlc
AOT-GAN	float	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	TFLITE	117.876 ms	3 - 5 MB	NPU	AOT-GAN.tflite
AOT-GAN	float	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	QNN_DLC	117.424 ms	4 - 7 MB	NPU	AOT-GAN.dlc
AOT-GAN	float	SA8775P ADP	Qualcomm® SA8775P	TFLITE	161.662 ms	3 - 504 MB	NPU	AOT-GAN.tflite
AOT-GAN	float	SA8775P ADP	Qualcomm® SA8775P	QNN_DLC	161.148 ms	1 - 494 MB	NPU	AOT-GAN.dlc
AOT-GAN	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	TFLITE	86.492 ms	2 - 706 MB	NPU	AOT-GAN.tflite
AOT-GAN	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	QNN_DLC	86.43 ms	4 - 682 MB	NPU	AOT-GAN.dlc
AOT-GAN	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	ONNX	95.488 ms	12 - 626 MB	NPU	AOT-GAN.onnx.zip
AOT-GAN	float	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	TFLITE	66.59 ms	2 - 507 MB	NPU	AOT-GAN.tflite
AOT-GAN	float	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	QNN_DLC	66.503 ms	3 - 495 MB	NPU	AOT-GAN.dlc
AOT-GAN	float	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	ONNX	73.814 ms	8 - 470 MB	NPU	AOT-GAN.onnx.zip
AOT-GAN	float	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	TFLITE	45.815 ms	2 - 437 MB	NPU	AOT-GAN.tflite
AOT-GAN	float	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	QNN_DLC	44.874 ms	4 - 410 MB	NPU	AOT-GAN.dlc
AOT-GAN	float	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	ONNX	52.104 ms	10 - 377 MB	NPU	AOT-GAN.onnx.zip
AOT-GAN	float	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN_DLC	120.341 ms	4 - 4 MB	NPU	AOT-GAN.dlc
AOT-GAN	float	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	134.943 ms	33 - 33 MB	NPU	AOT-GAN.onnx.zip

Installation

Install the package via pip:

pip install qai-hub-models

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

Sign-in to Qualcomm® AI Hub Workbench 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.aotgan.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.aotgan.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.aotgan.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.aotgan 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 Workbench. 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.aotgan.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.aotgan.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 AOT-GAN's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

The license for the original implementation of AOT-GAN can be found here.

References

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.

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Paper for qualcomm/AOT-GAN

Aggregated Contextual Transformations for High-Resolution Image Inpainting

Paper • 2104.01431 • Published Apr 3, 2021