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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],['miku1.jpg', "up2x-latest-no-denoise.pth", 2],['miku2.jpg', "up2x-latest-no-denoise.pth", 2]], | |
article='感谢大佬开源的项目') | |
iface.launch() | |