ai-models/RMBG-2.0
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Cherrytest
2025-09-22 07:12:51 +00:00
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README.md
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@ -146,15 +146,12 @@ transformers
```python
from PIL import Image
import matplotlib.pyplot as plt
import torch
from torchvision import transforms
from transformers import AutoModelForImageSegmentation
model = AutoModelForImageSegmentation.from_pretrained('briaai/RMBG-2.0', trust_remote_code=True)
torch.set_float32_matmul_precision(['high', 'highest'][0])
model.to('cuda')
model.eval()
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = AutoModelForImageSegmentation.from_pretrained('briaai/RMBG-2.0', trust_remote_code=True).eval().to(device)
# Data settings
image_size = (1024, 1024)
@ -165,7 +162,7 @@ transform_image = transforms.Compose([
])
image = Image.open(input_image_path)
input_images = transform_image(image).unsqueeze(0).to('cuda')
input_images = transform_image(image).unsqueeze(0).to(device)
# Prediction
with torch.no_grad():

1
birefnet.py
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@ -6,6 +6,7 @@ from transformers import PretrainedConfig
class Config(PretrainedConfig):
def __init__(self) -> None:
super().__init__()
# PATH settings
self.sys_home_dir = os.path.expanduser('~') # Make up your file system as: SYS_HOME_DIR/codes/dis/BiRefNet, SYS_HOME_DIR/datasets/dis/xx, SYS_HOME_DIR/weights/xx