6.6 KiB
6.6 KiB
ComfyUI-RMBG Update Log
v1.3.1 (2024/12/25)
Bug Fixes
- Fixed an issue with mask processing when the model returns a list of masks.
- Improved handling of image formats to prevent processing errors.
v1.3.0 (2024/12/23)
New Segment (RMBG) Node
- Text-Prompted Intelligent Object Segmentation
- Use natural language prompts (e.g., "a cat", "red car") to identify and segment target objects
- Support for multiple object detection and segmentation
- Perfect for precise object extraction and recognition tasks
Supported Models
- SAM (Segment Anything Model)
- sam_vit_h: 2.56GB - Highest accuracy
- sam_vit_l: 1.25GB - Balanced performance
- sam_vit_b: 375MB - Lightweight option
- GroundingDINO
- SwinT: 694MB - Fast and efficient
- SwinB: 938MB - Higher precision
Key Features
- Intuitive Parameter Controls
- Threshold: Adjust detection precision
- Mask Blur: Smooth edges
- Mask Offset: Expand or shrink selection
- Background Options: Alpha/Black/White/Green/Blue/Red
- Automatic Model Management
- Auto-download models on first use
- Smart GPU memory handling
Usage Examples
-
Tag-Style Prompts
- Single object: "cat"
- Multiple objects: "cat, dog, person"
- With attributes: "red car, blue shirt"
- Format: Use commas to separate multiple objects (e.g., "a, b, c")
-
Natural Language Prompts
- Simple sentence: "a person wearing a red jacket"
- Complex scene: "a woman in a blue dress standing next to a car"
- With location: "a cat sitting on the sofa"
- Format: Write a natural descriptive sentence
-
Tips for Better Results
- For Tag Style:
- Separate objects with commas: "chair, table, lamp"
- Add attributes before objects: "wooden chair, glass table"
- Keep it simple and clear
- For Natural Language:
- Use complete sentences
- Include details like color, position, action
- Be as descriptive as needed
- Parameter Adjustments:
- Threshold: 0.25-0.35 for broad detection, 0.45-0.55 for precision
- Use mask blur for smoother edges
- Adjust mask offset to fine-tune selection
- For Tag Style:
v1.2.2 (2024/12/12)
Improvements
- Changed INSPYRENET model format from .pth to .safetensors for:
- Better security
- Faster loading speed (2-3x faster)
- Improved memory efficiency
- Better cross-platform compatibility
- Simplified node display name for better UI integration
v1.2.1 (2024/12/02)
New Features
- ANPG (animated PNG), AWEBP (animated WebP) and GIF supported.
https://github.com/user-attachments/assets/40ec0b27-4fa2-4c99-9aea-5afad9ca62a5
Bug Fixes
- Fixed video processing issue
Performance Improvements
- Enhanced batch processing in RMBG-2.0 model
- Added support for proper batch image handling
- Improved memory efficiency by optimizing image size handling
Technical Details
- Added original size preservation for maintaining aspect ratios
- Implemented proper batch tensor processing
- Improved error handling and code robustness
- Performance gains:
- Single image processing: ~5-10% improvement
- Batch processing: up to 30-50% improvement (depending on batch size and GPU)
v1.2.0 (2024/11/29)
Major Changes
- Combined three background removal models into one unified node
- Added support for RMBG-2.0, INSPYRENET, and BEN models
- Implemented lazy loading for models (only downloads when first used)
Model Introduction
-
RMBG-2.0 (Homepage)
- Latest version of RMBG model
- Excellent performance on complex backgrounds
- High accuracy in preserving fine details
- Best for general purpose background removal
-
INSPYRENET (Homepage)
- Specialized in human portrait segmentation
- Fast processing speed
- Good edge detection capability
- Ideal for portrait photos and human subjects
-
BEN (Background Elimination Network) (Homepage)
- Robust performance on various image types
- Good balance between speed and accuracy
- Effective on both simple and complex scenes
- Suitable for batch processing
Features
- Unified interface for all three models
- Common parameters for all models:
- Sensitivity adjustment
- Processing resolution control
- Mask blur and offset options
- Multiple background color options
- Invert output option
- Model optimization toggle
Improvements
- Optimized memory usage with model clearing
- Enhanced error handling and user feedback
- Added detailed tooltips for all parameters
- Improved mask post-processing
Dependencies
- Updated all package dependencies to latest stable versions
- Added support for transparent-background package
- Optimized dependency management
v1.1.0 (2024/11/21)
New Features
- Added background color options
- Alpha (transparent background)
- Black, White, Green, Blue, Red
- Improved mask processing
- Better detail preservation
- Enhanced edge quality
- More accurate segmentation
- Added video batch processing
- Support for video file background removal
- Maintains original video framerate and resolution
- Multiple output format support (with Alpha channel)
- Efficient batch processing for video frames
https://github.com/user-attachments/assets/259220d3-c148-4030-93d6-c17dd5bccee1
- Added model cache management
- Cache status checking
- Model memory cleanup
- Better error handling
Parameter Updates
- Renamed 'invert_mask' to 'invert_output' for clarity
- Added sensitivity adjustment for mask strength
- Updated tooltips for better clarity
Technical Improvements
- Optimized image processing pipeline
- Added proper model cache verification
- Improved memory management
- Better error handling and recovery
- Enhanced batch processing performance for videos
Dependencies
- Added timm>=0.6.12,<1.0.0 for model support
- Updated requirements.txt with version constraints
Bug Fixes
- Fixed mask detail preservation issues
- Improved mask edge quality
- Fixed memory leaks in model handling
Usage Notes
- The 'Alpha' background option provides transparent background
- Sensitivity parameter now controls mask strength
- Model cache is checked before each operation
- Memory is automatically cleaned when switching models
- Video processing supports various formats and maintains quality