https://github.com/byrkbrk/synthesizing-super-resolution-by-experts

Synthesize fast (4x upscaled) super-resolution images, in PyTorch & Gradio
https://github.com/byrkbrk/synthesizing-super-resolution-by-experts

gradio image-upscaling pytorch seemore super-resolution

Last synced: 3 months ago
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Synthesize fast (4x upscaled) super-resolution images, in PyTorch & Gradio

• Host: GitHub
• URL: https://github.com/byrkbrk/synthesizing-super-resolution-by-experts
• Owner: byrkbrk
• Created: 2024-08-05T18:27:05.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-08-14T23:00:33.000Z (about 1 year ago)
• Last Synced: 2025-03-02T16:18:00.894Z (7 months ago)
• Topics: gradio, image-upscaling, pytorch, seemore, super-resolution
• Language: Python
• Homepage:
• Size: 9.7 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Synthesize Super Resolution Image by Experts Mining

## Introduction

  We build a module that synthesizes super-resolution images by 4x upscaling. While preparing, we utilize the pretrained model [SeemoRe](https://arxiv.org/abs/2402.03412) provided by [eduardzamfir at HuggingFace](https://huggingface.co/eduardzamfir/SeemoRe-T/tree/main). The demo is accessible at [the HuggingFace space](https://huggingface.co/spaces/byrkbrk/Synthesize-super-resolution-images).

## Setting Up the Environment

### Using Conda (recommended)

1. Install [Conda](https://conda.io/projects/conda/en/latest/user-guide/install/index.html), if not already installed.

2. Clone the repository:

    ~~~

    git clone https://github.com/byrkbrk/synthesizing-super-resolution-by-experts.git

    ~~~

3. Change the directory:

    ~~~

    cd synthesizing-super-resolution-by-experts

    ~~~

4. Create the environment:

    ~~~

    conda env create -f synthesizing-sr-by-experts.yaml

    ~~~

5. Activate the environment:

    ~~~

    conda activate synthesizing-sr-by-experts

    ~~~

### Using Pip

1. Download & install [Python](https://www.python.org/downloads/) (version==3.11)

2. Clone the repository:

    ~~~

    git clone https://github.com/byrkbrk/synthesizing-super-resolution-by-experts.git

    ~~~

3. Change the directory:

    ~~~

    cd synthesizing-super-resolution-by-experts

    ~~~

4. Install packages using `pip`:

    ~~~

    pip install -r requirements.txt

    ~~~

## Synthesizing SR Image

Check it out how to use:

~~~

python3 synthesize.py --help

~~~

Output:

~~~

Synthesize (4x upscaled) super-resolution images by SeemoRe

positional arguments:

  image_name            Name of the image that be upscaled. Note image that be

                        processed must be in `low-res-images` directory

options:

  -h, --help            show this help message and exit

  --device {cuda,mps,cpu}

                        Name of the GPU device that be used during inference.

                        Default: None

~~~

### Example usages

Execute the followings to obtain super-resolved images:

~~~

python3 synthesize.py building.png

~~~

~~~

python3 synthesize.py plant.png

~~~

The output images seen below (left: Original, right: Super-resolved) will be saved into `./synthesized-images` folder.



  

  





  

  



## Synthesizing by using Gradio

To run the gradio app on your local computer, execute:

~~~

python3 app.py

~~~

Then, visit the url [http://127.0.0.1:7860](http://127.0.0.1:7860) to open the interface.

### Example usage

See the display below for an example usage of the module via Gradio.