Custom nodes for bit-depth enhancement and banding removal in ComfyUI.
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.
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) |
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) |
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) |
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) |
-
Navigate to ComfyUI custom nodes directory:
cd ComfyUI/custom_nodes -
Clone this repository:
git clone https://github.com/subraoul/ComfyUI_Bit-Depth-Enhancer.git cd ComfyUI_Bit-Depth-Enhancer -
Install dependencies:
pip install -e . -
Restart ComfyUI
Note: This package is published to the ComfyUI Registry! You can also install manually following the instructions below.
- Python 3.9+
- PyTorch 2.0+
- OpenCV 4.8+
- NumPy 1.24+
- scipy 1.11+
- tifffile 2023.0+
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
Download from Google Drive (original ABCD repository):
Rename downloaded files to match the expected names and place in ComfyUI/models/bit_depth_enhancement/abcd/
Download from GitHub (original deepDeband repository):
-
Navigate to deepDeband checkpoints
-
Download from subdirectories:
deepDeband-w/latest_net_G.pth→ rename todeepDeband_w.pthdeepDeband-f/latest_net_G.pth→ rename todeepDeband_f.pth
-
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).
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)
Load Image → ABCD Bit-Depth Enhancement → Save Image
Recommended: Use SwinIR-ABCD for best quality results.
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.
- 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
- 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)
This implementation is based on the following research:
- Paper: "Arbitrary Bit-Depth Quantization for Image Restoration"
- Repository: https://github.com/WooKyoungHan/ABCD
- Conference: CVPR 2023
- Authors: WooKyoung Han et al.
- 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
MIT License - see LICENSE file for details.
Author: raoul-ubuntu
Built with:
Report issues or request features at: GitHub Issues