briaai/VRMBG-2.0

Requirements

torch
torchvision
pillow
kornia
transformers

Usage

from PIL import Image
import torch
from torchvision import transforms
from transformers import AutoModelForImageSegmentation

# load model
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = AutoModelForImageSegmentation.from_pretrained('briaai/VRMBG-2.0', trust_remote_code=True).eval().half().to(device)
model = torch.compile(model, dynamic=False, backend="inductor")

# Data settings
image_size = (1024, 1024)
transform_image = transforms.Compose([
    transforms.Resize(image_size),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# load inputs
image_prev_frame_path = "" # fill path
image_current_frame_path = "" # fill path
image_prev_frame = Image.open(image_prev_frame_path)
image_current_frame = Image.open(image_current_frame_path)
input_frames = torch.cat([transform_image(image_prev_frame), transform_image(image_current_frame)], dim=0)
input_frames = input_frames.unsqueeze(0).to(device).half()

# Prediction
with torch.no_grad():
    preds = model(input_frames).cpu()
pred = preds[0].squeeze()
pred_pil = transforms.ToPILImage()(pred)
mask = pred_pil.resize(image_current_frame.size)

# save result
image_current_frame.putalpha(mask)
image_current_frame.save("image_current_frame_no_background.png")