Real-ESRGAN-General-x4v3 is a lightweight image super-resolution model based on the Real-ESRGAN architecture, optimized for mobile devices and edge computing platforms. It can upscale low-resolution images by 4× while effectively removing noise, enhancing image quality. With approximately 1.21 million parameters and a model size of 4.66 MB, it is designed for input resolutions of 128×128 pixels. The model supports various deployment formats, including TFLite and ONNX, enabling efficient inference on NPUs of chips like Qualcomm Snapdragon. It is suitable for applications in virtual reality, augmented reality, gaming, and mobile apps.

Source model

• Input shape: 1x3x128x128
• Number of parameters: 1.16M
• Model size: 4.79M
• Output shape: 1x3x512x512

The source model can be found here

Click Model Conversion Reference in the Performance Reference panel on the right to view the model conversion steps.

The model performance benchmarks and inference example code provided on Model Farm are all based on the APLUX AidLite SDK

SDK installation

For details, please refer to the AidLite Developer Documentation

• Install AidLite SDK

# install aidlite sdk c++ api
sudo aid-pkg -i aidlite-sdk

# install aidlite sdk python api
python3 -m pip install pyaidlite -i https://mirrors.aidlux.com --trusted-host mirrors.aidlux.com

• Verify AidLite SDK

# aidlite sdk c++ check
python3 -c "import aidlite; print(aidlite.get_library_version())"

# aidlite sdk python check
python3 -c "import aidlite; print(aidlite.get_py_library_version())"

Inference example

• Click Model & Test Code to download model files and inference codes. The file structure showed below:

/model_farm_{model_name}_aidlite
    
    |__ models # folder where model files are stored

    |__ python # aidlite python model inference example

    |__ cpp # aidlite cpp model inference example

    |__ README.md