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import gradio as gr
import requests
from PIL import Image
import os
import torch
import numpy as np
from transformers import AutoImageProcessor, Swin2SRForImageSuperResolution
torch.hub.download_url_to_file('https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/00003.jpg', '00003.jpg')
torch.hub.download_url_to_file('https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/0855.jpg', '0855.jpg')
torch.hub.download_url_to_file('https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/ali_eye.jpg', 'ali_eye.jpg')
torch.hub.download_url_to_file('https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/butterfly.jpg', 'butterfly.jpg')
torch.hub.download_url_to_file('https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/chain-eye.jpg', 'chain-eye.jpg')
torch.hub.download_url_to_file('https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/gojou-eyes.jpg', 'gojou-eyes.jpg')
torch.hub.download_url_to_file('https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/shanghai.jpg', 'shanghai.jpg')
torch.hub.download_url_to_file('https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/vagabond.jpg', 'vagabond.jpg')
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 = "Demo: Swin2SR for Image Super-Resolution 🚀🚀🔥"
description = '''
**This demo expects low-quality and low-resolution JPEG compressed images.**
**Demo notebook can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Swin2SR/Perform_image_super_resolution_with_Swin2SR.ipynb).
'''
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://huggingface.co/docs/transformers/main/model_doc/swin2sr' target='_blank'>HuggingFace docs</a></p>"
examples = [['00003.jpg'], ['0855.jpg'], ['ali_eye.jpg'], ['butterfly.jpg'], ['chain-eye.jpg'], ['gojou-eyes.jpg'], ['shanghai.jpg'], ['vagabond.jpg']]
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,
examples=examples,
).launch(enable_queue=True) |