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import spaces
import gradio as gr
from PIL import Image
import torch
from diffusion import DiffusionPipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline(device)
def read_content(file_path: str) -> str:
"""read the content of target file
"""
with open(file_path, 'r', encoding='utf-8') as f:
content = f.read()
return content
@spaces.GPU
def predict(input, dkernel, diffusion_step, q=False):
lq = input["image"].convert("RGB")
mask = input["mask"].convert("RGB")
mask = mask.resize(lq.size, resample=Image.NEAREST)
output = pipe(lq=lq, mask=mask, dkernel=dkernel, diffusion_step=diffusion_step)
return output
@spaces.GPU
def qpredict(input, dkernel, diffusion_step, q=False):
lq = input["image"].convert("RGB")
mask = input["mask"].convert("RGB")
mask = mask.resize(lq.size, resample=Image.NEAREST)
for output in pipe.quick_solve(lq=lq, mask=mask, dkernel=dkernel, diffusion_step=diffusion_step):
yield output
css = '''
.container {max-width: 1150px;margin: auto;padding-top: 1.5rem}
#image_upload{min-height:400px}
#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px}
#mask_radio .gr-form{background:transparent; border: none}
#word_mask{margin-top: .75em !important}
#word_mask textarea:disabled{opacity: 0.3}
.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
.dark .footer {border-color: #303030}
.dark .footer>p {background: #0b0f19}
.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
#image_upload .touch-none{display: flex}
@keyframes spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
'''
image_blocks = gr.Blocks(css=css)
with image_blocks as demo:
gr.HTML(read_content("header.html"))
with gr.Group():
with gr.Box():
with gr.Row():
with gr.Column():
image = gr.Image(source='upload', tool='sketch', elem_id="image_upload", type="pil", label="Shadow Image").style(height=400)
dkernel = gr.Slider(minimum=11, maximum=55, step=2, value=11, label="Dilation Kernel Size")
diffusion_step = gr.Slider(minimum=10, maximum=200, step=5, value=20, label="Diffusion Time Step")
with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
with gr.Column():
btn = gr.Button("Removal").style(
margin=False,
full_width=True,
)
with gr.Column():
qbtn = gr.Button("Quick Removal").style(
margin=False,
full_width=True,
)
with gr.Column():
image_out = gr.Image(label="Removal Result", elem_id="output-img")
with gr.Row():
gr.Examples(examples=[
'examples/lssd2025.jpg',
'examples/web-shadow0248.jpg',
], inputs=[image])
btn.click(fn=predict, inputs=[image, dkernel, diffusion_step], outputs=[image_out])
qbtn.click(fn=qpredict, inputs=[image, dkernel, diffusion_step], outputs=[image_out])
image_blocks.launch(enable_queue=True)
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