### Install RMBG-2.0 Dependencies

Source: https://github.com/bria-ai/rmbg-2.0/blob/dev/README.md

Lists the Python packages required to run the RMBG-2.0 model. These include PyTorch, Torchvision, Pillow, Kornia, and Transformers.

```bash
torch
torchvision
pillow
kornia
transformers
```

--------------------------------

### Load BRIA RMBG 2.0 Model from Hugging Face

Source: https://context7.com/bria-ai/rmbg-2.0/llms.txt

Loads the pre-trained BRIA RMBG 2.0 model using the Hugging Face transformers library. It configures the model for optimal performance and moves it to the GPU for evaluation. Ensure you have the 'transformers' and 'torch' libraries installed.

```python
from transformers import AutoModelForImageSegmentation
import torch

# Load the model from Hugging Face Hub
model = AutoModelForImageSegmentation.from_pretrained(
    'briaai/RMBG-2.0',
    trust_remote_code=True
)

# Configure for optimal performance
torch.set_float32_matmul_precision('high')

# Move model to GPU and set to evaluation mode
model.to('cuda')
model.eval()

# Model is now ready for inference
print(f"Model loaded successfully on device: {next(model.parameters()).device}")
```

--------------------------------

### Batch Process Multiple Images with BRIA RMBG 2.0

Source: https://context7.com/bria-ai/rmbg-2.0/llms.txt

Demonstrates setting up the BRIA RMBG 2.0 model for batch processing multiple images efficiently. This is useful for high-throughput background removal tasks. Requires 'transformers', 'torch', 'Pillow', and 'os'.

```python
from PIL import Image
import torch
from torchvision import transforms
from transformers import AutoModelForImageSegmentation
import os

# Initialize model
model = AutoModelForImageSegmentation.from_pretrained(
    'briaai/RMBG-2.0',
    trust_remote_code=True
)
torch.set_float32_matmul_precision('high')
model.to('cuda')
model.eval()


```

--------------------------------

### Configure BiRefNet Model with Custom Settings

Source: https://context7.com/bria-ai/rmbg-2.0/llms.txt

Defines and utilizes a custom configuration for the BiRefNet model using Hugging Face's PretrainedConfig. This allows for setting model-specific parameters like backbone pretraining. Requires Transformers library.

```python
from transformers import PretrainedConfig

class BiRefNetConfig(PretrainedConfig):
    """Configuration class for BiRefNet model."""
    model_type = "SegformerForSemanticSegmentation"

    def __init__(
        self,
        bb_pretrained=False,  # Use pretrained backbone weights
        **kwargs
    ):
        self.bb_pretrained = bb_pretrained
        super().__init__(**kwargs)

# Create custom configuration
config = BiRefNetConfig(bb_pretrained=True)

# Load model with custom config
from transformers import AutoModelForImageSegmentation

model = AutoModelForImageSegmentation.from_pretrained(
    'briaai/RMBG-2.0',
    config=config,
    trust_remote_code=True
)

print(f"Model type: {config.model_type}")
print(f"Backbone pretrained: {config.bb_pretrained}")
```

--------------------------------

### Image Preprocessing Pipeline for BRIA RMBG 2.0

Source: https://context7.com/bria-ai/rmbg-2.0/llms.txt

Sets up a preprocessing pipeline using torchvision transforms to resize input images to 1024x1024 and normalize them with ImageNet mean and standard deviation. This prepares images for the BRIA RMBG 2.0 model. Requires 'Pillow' and 'torchvision'.

```python
from PIL import Image
from torchvision import transforms

# Define image preprocessing pipeline
image_size = (1024, 1024)

transform_image = transforms.Compose([
    transforms.Resize(image_size),
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],  # ImageNet mean
        std=[0.229, 0.224, 0.225]     # ImageNet std
    )
])

# Load and preprocess an image
image = Image.open("input_image.jpg")
original_size = image.size  # Store for later resizing

# Transform image and add batch dimension
input_tensor = transform_image(image).unsqueeze(0).to('cuda')

print(f"Original size: {original_size}")
print(f"Tensor shape: {input_tensor.shape}")  # Expected: [1, 3, 1024, 1024]
```

--------------------------------

### Python Usage: Remove Image Background with RMBG-2.0

Source: https://github.com/bria-ai/rmbg-2.0/blob/dev/README.md

Demonstrates how to use the RMBG-2.0 model in Python to remove the background from an image. It involves loading the model, preprocessing the image, performing inference, and saving the result.

```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()

# 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])
])

image = Image.open(input_image_path)
input_images = transform_image(image).unsqueeze(0).to('cuda')

# Prediction
with torch.no_grad():
    preds = model(input_images)[-1].sigmoid().cpu()
pred = preds[0].squeeze()
pred_pil = transforms.ToPILImage()(pred)
mask = pred_pil.resize(image.size)
image.putalpha(mask)

image.save("no_bg_image.png")
```

--------------------------------

### Apply Custom Thresholds for Mask Binarization with RMBG 2.0

Source: https://context7.com/bria-ai/rmbg-2.0/llms.txt

Generates binary foreground masks by applying custom thresholds to the alpha matte predicted by the RMBG 2.0 model. This allows for fine-grained control over foreground-background separation. It requires Pillow, PyTorch, torchvision, transformers, and numpy.

```python
from PIL import Image
import torch
from torchvision import transforms
from transformers import AutoModelForImageSegmentation
import numpy as np

# Load model (assuming 'model' is globally defined or loaded elsewhere)
# model = AutoModelForImageSegmentation.from_pretrained(
#     'briaai/RMBG-2.0',
#     trust_remote_code=True
# )
# model.to('cuda')
# model.eval()

# Preprocess image
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])
])

# image = Image.open("input.jpg")
# input_tensor = transform_image(image).unsqueeze(0).to('cuda')

# Get prediction
# with torch.no_grad():
#     preds = model(input_tensor)[-1].sigmoid().cpu()

# pred = preds[0].squeeze().numpy()

def apply_threshold(prediction, threshold=0.5):
    """Apply binary threshold to create hard mask."""
    binary_mask = (prediction > threshold).astype(np.uint8) * 255
    return Image.fromarray(binary_mask, mode='L')

# Create masks with different thresholds
# soft_mask = Image.fromarray((pred * 255).astype(np.uint8), mode='L')
# hard_mask_05 = apply_threshold(pred, threshold=0.5)
# hard_mask_07 = apply_threshold(pred, threshold=0.7)

# Resize masks to original image size
# soft_mask = soft_mask.resize(image.size)
# hard_mask_05 = hard_mask_05.resize(image.size)
# hard_mask_07 = hard_mask_07.resize(image.size)

# Apply masks
# image_soft = image.copy()
# image_soft.putalpha(soft_mask)
# image_soft.save("output_soft_mask.png")

# image_hard = image.copy()
# image_hard.putalpha(hard_mask_05)
# image_hard.save("output_hard_mask_05.png")

# print("Generated masks with different thresholds!")
```

--------------------------------

### Remove Background from Image with BRIA RMBG 2.0

Source: https://context7.com/bria-ai/rmbg-2.0/llms.txt

Performs background removal by loading the BRIA RMBG 2.0 model, preprocessing an input image, running inference to generate an alpha matte, and applying this matte to the original image to create a transparent background. Saves the result as a PNG. Requires 'transformers', 'torch', and 'Pillow'.

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

# Load model
model = AutoModelForImageSegmentation.from_pretrained(
    'briaai/RMBG-2.0',
    trust_remote_code=True
)
torch.set_float32_matmul_precision('high')
model.to('cuda')
model.eval()

# Preprocessing setup
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 image
input_image_path = "input.jpg"
image = Image.open(input_image_path)
input_images = transform_image(image).unsqueeze(0).to('cuda')

# Run inference
with torch.no_grad():
    preds = model(input_images)[-1].sigmoid().cpu()

# Process prediction to create alpha mask
pred = preds[0].squeeze()
pred_pil = transforms.ToPILImage()(pred)
mask = pred_pil.resize(image.size)

# Apply alpha channel to original image
image.putalpha(mask)

# Save result with transparent background
image.save("no_bg_image.png")
print("Background removed successfully!")
```

--------------------------------

### Process Batch of Images with RMBG 2.0

Source: https://context7.com/bria-ai/rmbg-2.0/llms.txt

Processes a list of images, removes their backgrounds using the RMBG 2.0 model, and saves the results with transparent backgrounds. It requires PyTorch, torchvision, Pillow, and the transformers library. The function takes a list of image paths and an output directory as input.

```python
import os
from PIL import Image
import torch
from torchvision import transforms
from transformers import AutoModelForImageSegmentation

# Load model (assuming 'model' is globally defined or loaded elsewhere)
# model = AutoModelForImageSegmentation.from_pretrained('briaai/RMBG-2.0', trust_remote_code=True)
# model.to('cuda')
# model.eval()

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])
])

def process_batch(image_paths, output_dir):
    """Process multiple images and save with transparent backgrounds."""
    os.makedirs(output_dir, exist_ok=True)

    for path in image_paths:
        # Load and preprocess
        image = Image.open(path).convert('RGB')
        input_tensor = transform_image(image).unsqueeze(0).to('cuda')

        # Inference
        with torch.no_grad():
            preds = model(input_tensor)[-1].sigmoid().cpu()

        # Create mask and apply
        pred = preds[0].squeeze()
        mask = transforms.ToPILImage()(pred).resize(image.size)
        image.putalpha(mask)

        # Save output
        filename = os.path.splitext(os.path.basename(path))[0]
        output_path = os.path.join(output_dir, f"{filename}_nobg.png")
        image.save(output_path)
        print(f"Processed: {output_path}")

# Example usage
# image_paths = ["image1.jpg", "image2.jpg", "image3.jpg"]
# process_batch(image_paths, "output_images")
```

--------------------------------

### CPU-Only Background Removal with RMBG 2.0

Source: https://context7.com/bria-ai/rmbg-2.0/llms.txt

Performs background removal using the RMBG 2.0 model on systems without GPU support. It loads the model, preprocesses the input image, and saves the output with the background removed. Requires Pillow, PyTorch, Torchvision, and Transformers libraries.

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

# Load model for CPU
model = AutoModelForImageSegmentation.from_pretrained(
    'briaai/RMBG-2.0',
    trust_remote_code=True
)
model.to('cpu')  # Use CPU
model.eval()

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

def remove_background_cpu(image_path, output_path):
    """Remove background using CPU inference."""
    image = Image.open(image_path).convert('RGB')
    input_tensor = transform_image(image).unsqueeze(0)  # No .to('cuda')

    with torch.no_grad():
        preds = model(input_tensor)[-1].sigmoid()

    pred = preds[0].squeeze()
    mask = transforms.ToPILImage()(pred).resize(image.size)
    image.putalpha(mask)
    image.save(output_path)
    print(f"Saved: {output_path}")

# Process image on CPU
remove_background_cpu("input.jpg", "output_nobg.png")
```

--------------------------------

### Replace Background with Solid Color using RMBG 2.0

Source: https://context7.com/bria-ai/rmbg-2.0/llms.txt

Replaces the background of an image with a specified solid color using the RMBG 2.0 model for foreground extraction. This function requires Pillow, PyTorch, torchvision, transformers, and numpy. It takes an image path and an RGB tuple for the background color.

```python
from PIL import Image
import torch
from torchvision import transforms
from transformers import AutoModelForImageSegmentation
import numpy as np

# Load model (assuming 'model' is globally defined or loaded elsewhere)
# model = AutoModelForImageSegmentation.from_pretrained(
#     'briaai/RMBG-2.0',
#     trust_remote_code=True
# )
# model.to('cuda')
# model.eval()

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

def replace_background(image_path, bg_color=(255, 255, 255)):
    """Replace background with solid color."""
    image = Image.open(image_path).convert('RGB')
    input_tensor = transform_image(image).unsqueeze(0).to('cuda')

    # Get mask
    with torch.no_grad():
        preds = model(input_tensor)[-1].sigmoid().cpu()

    mask = preds[0].squeeze().numpy()
    mask = np.array(Image.fromarray((mask * 255).astype(np.uint8)).resize(image.size)) / 255.0

    # Create background
    background = Image.new('RGB', image.size, bg_color)

    # Composite foreground over background
    foreground = np.array(image).astype(float)
    background_arr = np.array(background).astype(float)
    mask_3d = np.stack([mask] * 3, axis=-1)

    result = foreground * mask_3d + background_arr * (1 - mask_3d)
    result_image = Image.fromarray(result.astype(np.uint8))

    return result_image

# Usage examples
# white_bg = replace_background("input.jpg", bg_color=(255, 255, 255))
# white_bg.save("output_white_bg.jpg")

# blue_bg = replace_background("input.jpg", bg_color=(0, 120, 215))
# blue_bg.save("output_blue_bg.jpg")

# green_screen = replace_background("input.jpg", bg_color=(0, 255, 0))
# green_screen.save("output_green_screen.jpg")

# print("Generated images with custom backgrounds!")
```

--------------------------------

### Export Alpha Mask from RMBG 2.0 Model

Source: https://context7.com/bria-ai/rmbg-2.0/llms.txt

Generates and exports the alpha mask from the RMBG 2.0 model as a separate grayscale image. This function processes an input image, extracts the mask, and saves it in various formats including PNG and 8-bit grayscale. Requires Pillow, PyTorch, Torchvision, and Transformers libraries.

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

# Load model
model = AutoModelForImageSegmentation.from_pretrained(
    'briaai/RMBG-2.0',
    trust_remote_code=True
)
model.to('cuda')
model.eval()

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

def export_mask(image_path):
    """Generate and export alpha mask as separate image."""
    image = Image.open(image_path).convert('RGB')
    input_tensor = transform_image(image).unsqueeze(0).to('cuda')

    with torch.no_grad():
        preds = model(input_tensor)[-1].sigmoid().cpu()

    # Get mask
    pred = preds[0].squeeze()
    mask_pil = transforms.ToPILImage()(pred)
    mask_resized = mask_pil.resize(image.size)

    return mask_resized

# Export mask
mask = export_mask("input.jpg")

# Save as grayscale PNG (0-255 values)
mask.save("alpha_mask.png")

# Save as 8-bit grayscale for compositing software
mask.convert('L').save("alpha_mask_8bit.png")

# Create inverted mask (background white, foreground black)
from PIL import ImageOps
inverted_mask = ImageOps.invert(mask.convert('L'))
inverted_mask.save("inverted_mask.png")

print("Exported alpha masks successfully!")
```

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