app.py

import gradio as gr
import requests
from PIL import Image
import os
import torch
from transformers import AutoImageProcessor, Swin2SRForImageSuperResolution

processor = AutoImageProcessor.from_pretrained("caidas/swin2SR-classical-sr-x2-64")
model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64")

def enhance(image):
    # prepare image for the model
    inputs = processor(image, return_tensors="pt")

    # forward pass
    with torch.no_grad():
        outputs = model(**inputs)

    # postprocess
    output = outputs.reconstruction.data.squeeze().float().cpu().clamp_(0, 1).numpy()
    output = np.moveaxis(output, source=0, destination=-1)
    output = (output * 255.0).round().astype(np.uint8)  # float32 to uint8
    
    return Image.fromarray(output)

title = "Swin2SR demo for Image Super-Resolution 🚀🚀🔥"
description = ''' 

**This Demo expects low-quality and low-resolution JPEG compressed images, in the near future we will support any kind of input**

**We are looking for collaborators! Collaborator를 찾고 있습니다!** 🇬🇧 🇪🇸 🇰🇷 🇫🇷 🇷🇴 🇩🇪 🇨🇳

**Please check our github project: https://github.com/mv-lab/swin2sr or paper: https://arxiv.org/abs/2209.11345 feel free to contact us**

**Demos also available at [google colab](https://colab.research.google.com/drive/1paPrt62ydwLv2U2eZqfcFsePI4X4WRR1?usp=sharing) and [Kaggle](https://www.kaggle.com/code/jesucristo/super-resolution-demo-swin2sr-official/)**
</br>
'''
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2209.11345' target='_blank'>Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration</a> | <a href='https://github.com/mv-lab/swin2sr' target='_blank'>Github Repo</a></p>"

gr.Interface(
    enhance, 
    gr.inputs.Image(type="pil", label="Input").style(height=260),
    gr.inputs.Image(type="pil", label="Ouput").style(height=240),
    title=title,
    description=description,
    article=article,
    ).launch(enable_queue=True)