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import os
import autocuda
from pyabsa.utils.pyabsa_utils import fprint
import gradio as gr
import torch
import time
from Waifu2x.magnify import ImageMagnifier
magnifier = ImageMagnifier()
start_time = time.time()
CUDA_VISIBLE_DEVICES = ''
device = autocuda.auto_cuda()
dtype = torch.float16 if device != 'cpu' else torch.float32
def magnify_image(image, scale_factor=2):
start_time = time.time()
try:
if image.size[0] > 800 or image.size[1] > 800:
message = 'Failed! Image too large, please resize to <800x800 or clone the repo and code to allow larger images on your local machine.'
else:
image = magnifier.magnify(image, scale_factor=scale_factor)
fprint(f'Inference time: {time.time() - start_time:.2f}s')
message = f'Success! Processed image with scale factor {scale_factor}...'
except Exception as e:
message = f'Error: {e}'
return image, message
with gr.Blocks() as demo:
if not os.path.exists('imgs'):
os.mkdir('imgs')
# gr.Markdown('# Free Anime Image Scale Up Demo (CPU)')
# gr.Markdown('## 免费动漫插图图片分辨率放大 （最大支持500x500,更大尺寸请clone repo本地运行）')
# gr.Markdown('## Powered by Waifu2x')
# gr.Markdown("## Author: [yangheng95](https://github.com/yangheng95) Github:[Github](https://github.com/yangheng95/SuperResolutionAnimeDiffusion)")
with gr.Row():
with gr.Column(scale=40):
with gr.Group():
image_in = gr.Image(label="Image", height=512, tool="editor", type="pil")
with gr.Row():
scale_factor = gr.Slider(1, 8, label='Scale factor (to magnify image) (1, 2, 4, 8)',
value=2,
step=1)
message = gr.TextArea(label='message', lines=1, default='')
with gr.Row():
generate = gr.Button(value="Magnify", label="Magnify")
error_output = gr.Markdown()
with gr.Column(scale=60):
gr.Markdown('## Click the right button to save the magnified image')
gr.Markdown('## 右键点击图片保存放大后的图片')
with gr.Group():
image_out = gr.Image(height=512)
inputs = [image_in, scale_factor]
outputs = [image_out, message]
generate.click(magnify_image, inputs=inputs, outputs=outputs, api_name="magnify_image")
print(f"Space built in {time.time() - start_time:.2f} seconds")
demo.launch(share=False)
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