362 lines
14 KiB
Markdown
362 lines
14 KiB
Markdown
# ComfyUI-RMBG Update Log
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## v2.0.0 (2025/03/13)
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### New Features
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- Added Load Image, Preview Image, Preview Mask, and a node that previews both the image and the mask simultaneously. This is the first phase of our toolset, with more useful tools coming in future updates.
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- Reorganized the code structure for better maintainability, making it easier to navigate and update.
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- Renamed certain node classes to prevent conflicts with other repositories.
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- Improved category organization with a new structure: 🧪AILab/🛠️UTIL/🖼️IMAGE, making tools easier to find and use.
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- Integrated predefined workflows into the ComfyUI Browse Template section, allowing users to quickly load and understand each custom node’s functionality.
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### Technical Improvements
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- Optimized utility functions for image and mask conversion
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- Improved error handling and code robustness
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- Updated and changed some variable names for consistency
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- Enhanced compatibility with the latest ComfyUI versions
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## v1.9.3 (2025/02/24)
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- Clean up the code and fix the transformers version issue `transformers>=4.35.0,<=4.48.3`
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## v1.9.2 (2025/02/21)
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### New Features
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- Added Fast Foreground Color Estimation feature
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- New `refine_foreground` option for optimizing transparent backgrounds
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- Improved edge quality and detail preservation
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- Better handling of semi-transparent regions
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### Technical Improvements
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- Added OpenCV dependency for advanced image processing
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- Enhanced foreground refinement algorithm
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- Optimized memory usage for large images
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- Improved edge detection accuracy
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## v1.9.1 (2025/02/20)
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### Technical Updates
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- Changed repository for model management to the new repository
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- Reorganized models files structure for better maintainability
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## v1.9.0 (2025/02/19)
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Add and group all BiRefNet models collections into BiRefNet node.
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### New BiRefNet Models Adds
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- Added `BiRefNet` general purpose model (balanced performance)
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- Added `BiRefNet_512x512` model (optimized for 512x512 resolution)
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- Added `BiRefNet-portrait` model (optimized for portrait/human matting)
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- Added `BiRefNet-matting` model (general purpose matting)
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- Added `BiRefNet-HR model` (high resolution up to 2560x2560)
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- Added `BiRefNet-HR-matting` model (high resolution matting)
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- Added `BiRefNet_lite` model (lightweight version for faster processing)
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- Added `BiRefNet_lite-2K` model (lightweight version for 2K resolution)
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### Technical Improvements
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- Added FP16 (half-precision) support for better performance
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- Optimized for high-resolution image processing
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- Enhanced memory efficiency
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- Maintained compatibility with existing workflows
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- Simplified model loading through Transformers pipeline
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## v1.8.0 (2025/02/07)
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** (To ensure compatibility with the old V1.8.0 workflow, we have replaced this image with the new BiRefNet Node) (2025/03/01)
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### New Model Added: BiRefNet-HR
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- Added support for BiRefNet High Resolution model
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- Trained with 2048x2048 resolution images
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- Superior performance metrics (maxFm: 0.925, MAE: 0.026)
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- Better edge detection and detail preservation
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- FP16 optimization for faster processing
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- MIT License for commercial use
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** (To ensure compatibility with the old V1.8.0 workflow, we have replaced this image with the new BiRefNet Node) (2025/03/01)
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### Technical Improvements
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- Added FP16 (half-precision) support for better performance
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- Optimized for high-resolution image processing
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- Enhanced memory efficiency
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- Maintained compatibility with existing workflows
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- Simplified model loading through Transformers pipeline
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### Performance Comparison
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- BiRefNet-HR vs other models:
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- Higher resolution support (up to 2048x2048)
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- Better edge detection accuracy
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- Improved detail preservation
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- Optimized for high-resolution images
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- More efficient memory usage with FP16 support
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## v1.7.0 (2025/02/05)
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### New Model Added: BEN2
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- Added support for BEN2 (Background Elimination Network 2)
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- Improved performance over original BEN model
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- Better edge detection and detail preservation
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- Enhanced batch processing capabilities (up to 3 images per batch)
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- Optimized memory usage and processing speed
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### Model Changes
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- Updated model repository paths for BEN and BEN2
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- Switched to 1038lab repositories for better maintenance and updates
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- Maintained full compatibility with existing workflows
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### Technical Improvements
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- Implemented efficient batch processing for BEN2
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- Optimized memory management for large batches
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- Enhanced error handling and model loading
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- Improved model switching and resource cleanup
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### Comparison with Previous Models
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- BEN2 vs BEN:
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- Better edge detection
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- Improved handling of complex backgrounds
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- More efficient batch processing
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- Enhanced detail preservation
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- Faster processing speed
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## v1.6.0 (2025/01/22)
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### New Face Segment Custom Node
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- Added a new custom node for face parsing and segmentation
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- Support for 19 facial feature categories (Skin, Nose, Eyes, Eyebrows, etc.)
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- Precise facial feature extraction and segmentation
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- Multiple feature selection for combined segmentation
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- Same parameter controls as other RMBG nodes
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- Automatic model downloading and resource management
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- Perfect for portrait editing and facial feature manipulation
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## v1.5.0 (2025/01/05)
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### New Fashion and accessories Segment Custom Node
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- Added a new custom node for fashion and accessories segmentation.
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- Capable of identifying and segmenting various fashion items such as dresses, shoes, and accessories.
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- Utilizes advanced machine learning techniques for accurate segmentation.
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- Supports real-time processing for enhanced user experience.
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- Ideal for fashion-related applications, including virtual try-ons and outfit recommendations.
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- Support for gray background color.
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## v1.4.0 (2025/01/02)
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### New Clothes Segment Node
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- Added intelligent clothes segmentation functionality
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- Support for 18 different clothing categories (Hat, Hair, Face, Sunglasses, Upper-clothes, etc.)
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- Multiple item selection for combined segmentation
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- Same parameter controls as other RMBG nodes (process_res, mask_blur, mask_offset, background options)
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- Automatic model downloading and resource management
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## v1.3.2 (2024/12/29)
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### Updates
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- Enhanced background handling to support RGBA output when "Alpha" is selected.
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- Ensured RGB output for all other background color selections.
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## v1.3.1 (2024/12/25)
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### Bug Fixes
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- Fixed an issue with mask processing when the model returns a list of masks.
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- Improved handling of image formats to prevent processing errors.
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## v1.3.0 (2024/12/23)
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### New Segment (RMBG) Node
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- Text-Prompted Intelligent Object Segmentation
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- Use natural language prompts (e.g., "a cat", "red car") to identify and segment target objects
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- Support for multiple object detection and segmentation
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- Perfect for precise object extraction and recognition tasks
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### Supported Models
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- SAM (Segment Anything Model)
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- sam_vit_h: 2.56GB - Highest accuracy
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- sam_vit_l: 1.25GB - Balanced performance
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- sam_vit_b: 375MB - Lightweight option
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- GroundingDINO
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- SwinT: 694MB - Fast and efficient
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- SwinB: 938MB - Higher precision
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### Key Features
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- Intuitive Parameter Controls
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- Threshold: Adjust detection precision
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- Mask Blur: Smooth edges
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- Mask Offset: Expand or shrink selection
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- Background Options: Alpha/Black/White/Green/Blue/Red
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- Automatic Model Management
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- Auto-download models on first use
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- Smart GPU memory handling
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### Usage Examples
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1. Tag-Style Prompts
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- Single object: "cat"
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- Multiple objects: "cat, dog, person"
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- With attributes: "red car, blue shirt"
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- Format: Use commas to separate multiple objects (e.g., "a, b, c")
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2. Natural Language Prompts
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- Simple sentence: "a person wearing a red jacket"
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- Complex scene: "a woman in a blue dress standing next to a car"
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- With location: "a cat sitting on the sofa"
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- Format: Write a natural descriptive sentence
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3. Tips for Better Results
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- For Tag Style:
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- Separate objects with commas: "chair, table, lamp"
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- Add attributes before objects: "wooden chair, glass table"
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- Keep it simple and clear
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- For Natural Language:
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- Use complete sentences
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- Include details like color, position, action
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- Be as descriptive as needed
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- Parameter Adjustments:
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- Threshold: 0.25-0.35 for broad detection, 0.45-0.55 for precision
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- Use mask blur for smoother edges
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- Adjust mask offset to fine-tune selection
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## v1.2.2 (2024/12/12)
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### Improvements
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- Changed INSPYRENET model format from .pth to .safetensors for:
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- Better security
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- Faster loading speed (2-3x faster)
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- Improved memory efficiency
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- Better cross-platform compatibility
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- Simplified node display name for better UI integration
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## v1.2.1 (2024/12/02)
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### New Features
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- ANPG (animated PNG), AWEBP (animated WebP) and GIF supported.
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https://github.com/user-attachments/assets/40ec0b27-4fa2-4c99-9aea-5afad9ca62a5
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### Bug Fixes
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- Fixed video processing issue
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### Performance Improvements
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- Enhanced batch processing in RMBG-2.0 model
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- Added support for proper batch image handling
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- Improved memory efficiency by optimizing image size handling
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### Technical Details
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- Added original size preservation for maintaining aspect ratios
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- Implemented proper batch tensor processing
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- Improved error handling and code robustness
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- Performance gains:
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- Single image processing: ~5-10% improvement
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- Batch processing: up to 30-50% improvement (depending on batch size and GPU)
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## v1.2.0 (2024/11/29)
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### Major Changes
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- Combined three background removal models into one unified node
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- Added support for RMBG-2.0, INSPYRENET, and BEN models
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- Implemented lazy loading for models (only downloads when first used)
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### Model Introduction
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- RMBG-2.0 ([Homepage](https://huggingface.co/briaai/RMBG-2.0))
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- Latest version of RMBG model
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- Excellent performance on complex backgrounds
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- High accuracy in preserving fine details
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- Best for general purpose background removal
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- INSPYRENET ([Homepage](https://github.com/plemeri/InSPyReNet))
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- Specialized in human portrait segmentation
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- Fast processing speed
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- Good edge detection capability
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- Ideal for portrait photos and human subjects
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- BEN (Background Elimination Network) ([Homepage](https://huggingface.co/PramaLLC/BEN))
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- Robust performance on various image types
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- Good balance between speed and accuracy
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- Effective on both simple and complex scenes
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- Suitable for batch processing
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### Features
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- Unified interface for all three models
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- Common parameters for all models:
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- Sensitivity adjustment
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- Processing resolution control
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- Mask blur and offset options
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- Multiple background color options
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- Invert output option
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- Model optimization toggle
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### Improvements
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- Optimized memory usage with model clearing
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- Enhanced error handling and user feedback
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- Added detailed tooltips for all parameters
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- Improved mask post-processing
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### Dependencies
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- Updated all package dependencies to latest stable versions
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- Added support for transparent-background package
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- Optimized dependency management
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## v1.1.0 (2024/11/21)
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### New Features
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- Added background color options
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- Alpha (transparent background)
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- Black, White, Green, Blue, Red
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- Improved mask processing
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- Better detail preservation
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- Enhanced edge quality
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- More accurate segmentation
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- Added video batch processing
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- Support for video file background removal
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- Maintains original video framerate and resolution
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- Multiple output format support (with Alpha channel)
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- Efficient batch processing for video frames
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https://github.com/user-attachments/assets/259220d3-c148-4030-93d6-c17dd5bccee1
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- Added model cache management
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- Cache status checking
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- Model memory cleanup
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- Better error handling
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### Parameter Updates
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- Renamed 'invert_mask' to 'invert_output' for clarity
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- Added sensitivity adjustment for mask strength
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- Updated tooltips for better clarity
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### Technical Improvements
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- Optimized image processing pipeline
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- Added proper model cache verification
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- Improved memory management
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- Better error handling and recovery
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- Enhanced batch processing performance for videos
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### Dependencies
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- Added timm>=0.6.12,<1.0.0 for model support
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- Updated requirements.txt with version constraints
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### Bug Fixes
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- Fixed mask detail preservation issues
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- Improved mask edge quality
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- Fixed memory leaks in model handling
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### Usage Notes
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- The 'Alpha' background option provides transparent background
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- Sensitivity parameter now controls mask strength
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- Model cache is checked before each operation
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- Memory is automatically cleaned when switching models
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- Video processing supports various formats and maintains quality
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