ComfyUI Extension: Bit Depth Enhancer

Authored by subraoul

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Bit-depth enhancement and banding removal with classical and deep learning methods

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    README

    ComfyUI Bit Depth Enhancer

    Custom nodes for bit-depth enhancement and banding removal in ComfyUI.

    Description

    This package provides nodes for enhancing 8-bit images to 16-bit with reduced banding artifacts. Includes both classical image processing methods and deep learning approaches.

    All nodes support batch processing.

    Nodes

    1. Bit Depth Enhancement (Classical)

    Classical image processing methods for bit-depth enhancement. No ML models required.

    Methods:

    • Bilateral+Dither - Edge-aware filtering with Floyd-Steinberg dithering
    • Gradient Domain - Gradient-space processing for smooth transitions
    • Multi-scale Fusion - Laplacian pyramid decomposition
    • Fast Edge-Aware - Guided filter for fast processing

    Parameters: | Parameter | Type | Range | Description | |-----------|------|-------|-------------| | image | IMAGE | - | Input image | | method | STRING | 4 options | Enhancement algorithm | | strength | FLOAT | 0.0-1.0 | Enhancement intensity (default: 0.7) | | preserve_edges | BOOLEAN | - | Maintain edge sharpness (default: True) |

    2. Save 16-bit TIFF

    Export enhanced images as 16-bit TIFF files.

    Parameters: | Parameter | Type | Options | Description | |-----------|------|---------|-------------| | images | IMAGE | - | Input images | | filename_prefix | STRING | - | Output filename prefix | | color_profile | STRING | 4 options | Color space (sRGB, Adobe RGB, ProPhoto RGB, Linear) |

    3. ABCD Bit-Depth Enhancement (8→16)

    Deep learning model for 8-bit to 16-bit enhancement using the ABCD (Arbitrary Bitwise Coefficient for De-quantization) architecture.

    Based on: ABCD - Learning to Restore Compressed Images with Arbitrary Bit-depth Paper: CVPR 2023

    ABCD uses coordinate-based implicit neural representation to reconstruct quantized images across arbitrary bit-depths. Three model architectures available:

    • SwinIR-ABCD (Recommended) - Swin Transformer-based, highest quality (130h training, 4 GPUs)
    • RDN-ABCD - Residual Dense Network, balanced performance (82h training, 2 GPUs)
    • EDSR-ABCD - Enhanced Deep Residual, fastest processing (65h training, 1 GPU)

    Parameters: | Parameter | Type | Description | |-----------|------|-------------| | image | IMAGE | Input 8-bit image | | model | STRING | Model architecture (SwinIR-ABCD, RDN-ABCD, EDSR-ABCD) |

    4. deepDeband (Banding Removal)

    Deep learning model specifically trained for banding artifact removal using gradient-domain processing.

    Based on: deepDeband - Deep Gradient-Domain Image Debanding Paper: ICIP 2022

    Trained on 51,490 pairs of pristine and banded image patches (256×256). Two model variants:

    • deepDeband-w (Recommended) - Uses weighted bilateral patch fusion for smoother results
    • deepDeband-f - Direct patch processing, faster but may show seams

    Important: These models were trained on real images and video frames. They may produce banding artifacts on synthetic images (3D renders, gradients, vector graphics).

    Parameters: | Parameter | Type | Description | |-----------|------|-------------| | image | IMAGE | Input image with banding | | model | STRING | Model variant (deepDeband-w, deepDeband-f) | | strength | FLOAT | Debanding intensity (0.0-1.0, default: 1.0) | | tile_size | INT | Tile size for processing (default: 256) | | tile_overlap | INT | Overlap between tiles (default: 128) |

    Installation

    1. Navigate to ComfyUI custom nodes directory:

      cd ComfyUI/custom_nodes

    2. Clone this repository:

      git clone https://github.com/subraoul/ComfyUI_Bit-Depth-Enhancer.git
cd ComfyUI_Bit-Depth-Enhancer

    3. Install dependencies:

      pip install -e .

    4. Restart ComfyUI

    Note: This package is published to the ComfyUI Registry! You can also install manually following the instructions below.

    Dependencies

    • Python 3.9+
    • PyTorch 2.0+
    • OpenCV 4.8+
    • NumPy 1.24+
    • scipy 1.11+
    • tifffile 2023.0+

    Model Setup

    Deep learning nodes require model checkpoints. Models should be placed in the ComfyUI models directory:

    ComfyUI/models/bit_depth_enhancement/
├── abcd/
│   ├── edsr_abcd.pth
│   ├── rdn_abcd.pth
│   └── swinir_abcd.pth
└── deepdeband/
    ├── deepDeband_w.pth
    └── deepDeband_f.pth

    ABCD Models

    Download from Google Drive (original ABCD repository):

    Rename downloaded files to match the expected names and place in ComfyUI/models/bit_depth_enhancement/abcd/

    deepDeband Models

    Download from GitHub (original deepDeband repository):

    1. Navigate to deepDeband checkpoints

    2. Download from subdirectories:

      • deepDeband-w/latest_net_G.pth → rename to deepDeband_w.pth
      • deepDeband-f/latest_net_G.pth → rename to deepDeband_f.pth

    3. Place in ComfyUI/models/bit_depth_enhancement/deepdeband/

    Note: You only need the generator weights (latest_net_G.pth), not the discriminator (latest_net_D.pth).

    Basic Usage

    Classical Enhancement Workflow

    Load Image → Bit Depth Enhancement (Classical) → Save 16-bit TIFF

    Recommended Settings:

    • Method: Bilateral+Dither (general use) or Multi-scale Fusion (best quality)
    • Strength: 0.7
    • Preserve Edges: True
    • Color Profile: sRGB (web) or Adobe RGB (print)

    ABCD Deep Learning Workflow

    Load Image → ABCD Bit-Depth Enhancement → Save Image

    Recommended: Use SwinIR-ABCD for best quality results.

    deepDeband Workflow

    Load Image → deepDeband → Save Image

    Recommended: Use deepDeband-w with default settings. Lower strength (0.5-0.7) for subtle enhancement.

    Warning: Avoid using deepDeband on synthetic/rendered images as it may introduce artifacts.

    Known Limitations

    • Classical methods cannot recover dynamic range not present in source
    • Processing time scales with image resolution
    • Deep learning models require GPU for reasonable performance
    • Source image quality matters - heavily compressed JPEGs benefit less
    • deepDeband may produce artifacts on synthetic/3D rendered images

    Output Format

    • Classical + Save 16-bit TIFF: True 16-bit TIFF files (65,535 levels per channel)
    • Deep learning nodes: Standard ComfyUI IMAGE output (can be saved with any ComfyUI save node)

    References

    This implementation is based on the following research:

    ABCD

    • Paper: "Arbitrary Bit-Depth Quantization for Image Restoration"
    • Repository: https://github.com/WooKyoungHan/ABCD
    • Conference: CVPR 2023
    • Authors: WooKyoung Han et al.

    deepDeband

    • Paper: "Deep Gradient-Domain Image Debanding"
    • Repository: https://github.com/RaymondLZhou/deepDeband
    • Conference: ICIP 2022
    • Authors: Raymond L. Zhou, Shahrukh Athar, Zhongling Wang, Zhou Wang

    License

    MIT License - see LICENSE file for details.

    Credits

    Author: raoul-ubuntu

    Built with:

    Support

    Report issues or request features at: GitHub Issues