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import gradio as gr
import spaces
import torch
from loadimg import load_img
from torchvision import transforms
from transformers import AutoModelForImageSegmentation
torch.set_float32_matmul_precision(["high", "highest"][0])
birefnet = AutoModelForImageSegmentation.from_pretrained(
"ZhengPeng7/BiRefNet", trust_remote_code=True
)
birefnet.to("cuda")
transform_image = transforms.Compose(
[
transforms.Resize((1024, 1024)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
)
@spaces.GPU
def rmbg(image,url):
if image is None :
image = url
image = load_img(image).convert("RGB")
image_size = image.size
input_images = transform_image(image).unsqueeze(0).to("cuda")
# Prediction
with torch.no_grad():
preds = birefnet(input_images)[-1].sigmoid().cpu()
pred = preds[0].squeeze()
pred_pil = transforms.ToPILImage()(pred)
mask = pred_pil.resize(image_size)
image.putalpha(mask)
return image
rmbg_tab = gr.Interface(fn=rmbg, inputs=["image","text"], outputs=["image"], api_name="rmbg")
demo = gr.TabbedInterface(
[rmbg_tab],
["remove background"],
title="Utilities that require GPU",
)
demo.launch()
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