app.py

from upcunet_v3 import RealWaifuUpScaler
import gradio as gr
import time
import logging
import os
from PIL import ImageOps
import numpy as np
import math


def greet(input_img, input_model_name, input_tile_mode):
    input_img = np.array(input_img)
    if input_model_name not in model_cache:
        t1 = time.time()
        upscaler = RealWaifuUpScaler(input_model_name[2], ModelPath + input_model_name, half=False, device="cpu")
        t2 = time.time()
        logger.info(f'load model time, {t2 - t1}')
        model_cache[input_model_name] = upscaler
    else:
        upscaler = model_cache[input_model_name]
        logger.info(f'load model from cache')

    start = time.time()
    result = upscaler(input_img, tile_mode=input_tile_mode)
    end = time.time()
    logger.info(f'input_model_name, {input_model_name}')
    logger.info(f'input_tile_mode, {input_tile_mode}')
    logger.info(f'input shape, {input_img.shape}')
    logger.info(f'output shape, {result.shape}')
    logger.info(f'speed time, {end - start}')
    return result


if __name__ == '__main__':
    logging.basicConfig(level=logging.INFO, format="[%(asctime)s] [%(process)d] [%(levelname)s] %(message)s")
    logger = logging.getLogger()

    ModelPath = "weights_v3/"
    model_cache = {}

    input_model_name = gr.inputs.Dropdown(os.listdir(ModelPath), default="up2x-latest-no-denoise.pth", label='选择model')
    input_tile_mode = gr.inputs.Dropdown([0, 1, 2, 3, 4], default=2, label='选择tile_mode')
    input_img = gr.inputs.Image(label='image', type='pil')

    inputs = [input_img, input_model_name, input_tile_mode]
    outputs = "image"
    iface = gr.Interface(fn=greet,
                         inputs=inputs,
                         outputs=outputs,
                         allow_screenshot=False,
                         allow_flagging='never',
                         examples=[['test-img.jpg', "up2x-latest-no-denoise.pth", 2]],
                         article='感谢大佬开源的项目')
    iface.launch()