app.py

import gradio as gr
import spaces
import torch
from PIL import Image
from RealESRGAN import RealESRGAN
import time
from datetime import timedelta as td

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)


@spaces.GPU(duration=13)
def inference(image, size):
    start_load = time.time()
    global model2
    global model4
    global model8
    if image is None:
        raise gr.Error("Image not uploaded")
        

    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}, time: {td(seconds=int(time.time() - start_load))} ... OK")
    return result


title = ""
description = ""
article = ""


gr.Interface(inference,
    [gr.Image(type="pil"), 
    gr.Radio(["2x", "4x", "8x"], 
    type="value",
    value="4x",
    label="Resolution model")], 
    gr.Image(type="pil", label="Output"),
    title=title,
    description=description,
    article=article,
    examples=[],
    flagging_mode="never",
    cache_mode="lazy",
    ).queue(api_open=True).launch(show_error=True, show_api=True, mcp_server=False)