ComfyUI-LBM
A ComfyUI implementation of Latent Bridge Matching (LBM) for efficient image relighting. This node utilizes the LBM algorithm to perform single-step image-to-image translation specifically for relighting tasks.
Features
- Fast image relighting with a single inference step
- Simplified workflow with just one node
- Optimized memory usage
- Automatic model download - the model will be downloaded automatically and properly renamed on first use
- Extensible architecture - support for depth and normal map processing coming soon
Installation
- Clone this repository to your
ComfyUI/custom_nodesdirectory:
cd ComfyUI/custom_nodes
git clone https://github.com/1038lab/ComfyUI-LBM.git
- Install the required dependencies:
cd ComfyUI/custom_nodes/ComfyUI-LBM
pip install -r requirements.txt
Download Models
The model will be automatically downloaded and renamed on first use, or you can manually download it:
| Model | Description | Link |
|---|---|---|
| LBM Relighting | Main model for image relighting | Download |
After downloading, place the model file in your ComfyUI/models/diffusion_models directory and rename it to LBM_relighting.safetensors
Basic Usage
- Add the "LBM Relighting" node from the
🧪AILab/🔆LBMcategory - Connect an image source to the "LBM Relighting" node
- Select the model file (defaults to
LBM_relighting.safetensors) - Adjust the steps parameter as needed (default: 30)
- Run the workflow
Parameters
| Parameter | Description | Recommendation |
|---|---|---|
| Model | The LBM model file to use | Default is LBM_relighting.safetensors |
| Steps | Number of inference steps | Default is 30. More steps may improve quality at the cost of processing time |
Setting Tips
| Setting | Recommendation |
|---|---|
| Steps | For most images, 20-30 steps provides a good balance between quality and speed |
| Input Resolution | The model works best with images of 512x512 or higher resolution |
| Memory Usage | If you encounter memory issues, try processing images at a lower resolution |
| Performance | For batch processing, consider reducing steps to 15-20 for faster throughput |
About Model
This implementation uses the Latent Bridge Matching (LBM) method from the paper "LBM: Latent Bridge Matching for Fast Image-to-Image Translation". The model is designed for fast image relighting, transforming the lighting of objects in an image.
LBM offers:
- Fast processing with a single inference step
- High-quality relighting effects
- Memory-efficient operation
- Consistent results across various image types
The model is trained on a diverse dataset of images with different lighting conditions, ensuring:
- Balanced representation across different image types
- High accuracy in various scenarios
- Robust performance with complex lighting
Roadmap
Future plans for this repository include:
- LBM Depth - for depth map estimation
- LBM Normal - for normal map generation
- Additional optimization options
Requirements
- ComfyUI
- Python 3.10+
- Required packages (automatically installed via requirements.txt):
- torch>=2.0.0
- torchvision>=0.15.0
- Pillow>=9.0.0
- numpy>=1.22.0
- huggingface-hub>=0.19.0
- tqdm>=4.65.0
Credits
- LBM Model: Hugging Face Model
- Original Implementation: GitHub Repository
- Paper: "LBM: Latent Bridge Matching for Fast Image-to-Image Translation" by Clément Chadebec, Onur Tasar, Sanjeev Sreetharan, and Benjamin Aubin
- Created by: 1038lab
License
This repository's code is released under the GNU General Public License v3.0 (GPL-3.0).
The LBM model itself is released under the Creative Commons BY-NC 4.0 license, following the original LBM implementation. Please refer to the original repository for more details on model usage restrictions.