Real-ESRGAN-x4plus is a high-performance image super-resolution model based on the ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks) architecture. It can upscale low-resolution images by 4 times while significantly enhancing details and visual quality. The model uses an improved GAN structure to optimize detail restoration and noise reduction, making it suitable for photo restoration, video enhancement, and other applications. Real-ESRGAN-x4plus supports various hardware accelerations for efficient inference on mobile and edge computing platforms.

Source model

• Input shape: 1x3x128x128
• Number of parameters: 15.92M
• Model size: 67.76M
• Output shape: 1x3x512x512

The source model can be found here

When the user has fine-tuned the source model, the model conversion process must be performed again.

Users can refer to either of the following two methods to complete the model conversion:

• Using AIMO for model conversion: Click Model Conversion Reference in the Performance Reference section on the right to view the conversion steps.

• Using Qualcomm QNN for model conversion: Please refer to the Qualcomm QNN Documentation.

The model performance benchmarks and example code provided by Model Farm are all implemented based on the AIDLux AidLite SDK.

For models in .bin format, you can use either of the following two inference engines to run inference on Qualcomm chips:

Inference using APLUX AidLite: please refer to the APLUX AidLite Developer Documentation

Inference using Qualcomm QNN: Please refer to the Qualcomm QNN Documentation

Inference Example Code

The inference example code is implemented using the AidLite SDK.

Click Model & Code to download the model files and the inference code package. The file structure is as follows:

/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