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import torch
from PIL import Image
import numpy as np
from RealESRGAN import RealESRGAN
import gradio as gr
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = None
scale = 4
def upscale_image(image, scale):
global device
global model
if model is None or model.scale != scale:
model = RealESRGAN(device, scale=scale)
model.load_weights('weights/RealESRGAN_x{}.pth'.format(scale), download=True)
sr_image = model.predict(image)
return sr_image
inputs = [
gr.Image(label="Input Image", type="pil"),
gr.Slider(minimum=2, maximum=8, value=4, label="Upscale Scale", step=2)
]
output = gr.Image(label="Upscaled Image")
examples = [
['groot.jpg', '4'],
['woman.jpg', '8']
]
title = "Image upscaler 2k, 4k, 8k"
gr.Interface(fn=upscale_image, title=title inputs=inputs, outputs=output, title="Image Upscaler",
examples=examples, theme="soft").launch()
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