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import torch
from PIL import Image
from RealESRGAN import RealESRGAN
import gradio as gr
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model2 = RealESRGAN(device, scale=2)
model2.load_weights('weights/RealESRGAN_x2.pth', download=True)
model4 = RealESRGAN(device, scale=4)
model4.load_weights('weights/RealESRGAN_x4.pth', download=True)
model8 = RealESRGAN(device, scale=8)
model8.load_weights('weights/RealESRGAN_x8.pth', download=True)
def inference(image, size):
global model2
global model4
global model8
if image is None:
raise gr.Error("Image not uploaded")
width, height = image.size
if width >= 5000 or height >= 5000:
raise gr.Error("The image is too large.")
if torch.cuda.is_available():
torch.cuda.empty_cache()
if size == '2x':
try:
result = model2.predict(image.convert('RGB'))
except torch.cuda.OutOfMemoryError as e:
print(e)
model2 = RealESRGAN(device, scale=2)
model2.load_weights('weights/RealESRGAN_x2.pth', download=False)
result = model2.predict(image.convert('RGB'))
elif size == '4x':
try:
result = model4.predict(image.convert('RGB'))
except torch.cuda.OutOfMemoryError as e:
print(e)
model4 = RealESRGAN(device, scale=4)
model4.load_weights('weights/RealESRGAN_x4.pth', download=False)
result = model2.predict(image.convert('RGB'))
else:
try:
result = model8.predict(image.convert('RGB'))
except torch.cuda.OutOfMemoryError as e:
print(e)
model8 = RealESRGAN(device, scale=8)
model8.load_weights('weights/RealESRGAN_x8.pth', download=False)
result = model2.predict(image.convert('RGB'))
print(f"Image size ({device}): {size} ... OK")
return result
title = "RealESRGAN UpScale Model: 2x 4x 8x"
description = "This model running on cpu so it takes bit time,so pls be patient :)"
gr.Interface(inference,
[gr.Image(type="pil"),
gr.Radio(['2x', '4x', '8x'],
type="value",
value='2x',
label='Resolution model')],
gr.Image(type="pil", label="Output"),
title=title,
description=description,
allow_flagging='never',
cache_examples=False,
).queue(api_open=False).launch(show_error=True, show_api=False)
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