Extensions/ComfyUI-LBM
ComfyUI Extension

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…

By 1038lab·Created about a year ago·Updated about a year ago· 93
1038lab/ComfyUI-LBM
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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.

LBM-Relighting

News & Updates

  • 2025/05/22: Update ComfyUI-LBM to v1.1.0 ( update.md )

LBM-Depth&Normal Added Depth/Normal map generation support

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
  • Support for depth and normal map generation
  • Mask support for selective processing
  • Multiple precision options (fp32, bf16, fp16)

Installation

  1. Clone this repository to your ComfyUI/custom_nodes directory:
cd ComfyUI/custom_nodes
git clone https://github.com/1038lab/ComfyUI-LBM.git
  1. Install the required dependencies:
cd ComfyUI/custom_nodes/ComfyUI-LBM
pip install -r requirements.txt

Download Models

The models will be automatically downloaded and renamed on first use, or you can manually download them:

| Model | Description | Link | | ----- | ----------- | ---- | | LBM Relighting | Main model for image relighting | Download | | LBM Depth | Model for depth map generation | Download | | LBM Normals | Model for normal map generation | Download |

After downloading, place the model files in your ComfyUI/models/diffusion_models/LBM directory.

Basic Usage

Relighting Node

  1. Add the "Relighting (LBM)" node from the 🧪AILab/🔆LBM category
  2. Connect an image source to the node
  3. Select the model file (defaults to LBM_relighting.safetensors)
  4. Adjust the parameters as needed
  5. Run the workflow

Depth/Normal Node

  1. Add the "Depth / Normal (LBM)" node from the 🧪AILab/🔆LBM category
  2. Connect an image source to the node
  3. Select the task type (depth or normal)
  4. Adjust the parameters as needed
  5. Run the workflow

Parameters

Relighting Node

| Parameter | Description | Default | Range | | --------- | ----------- | ------- | ----- | | Model | The LBM model file to use | LBM_relighting.safetensors | - | | Steps | Number of inference steps | 28 | 1-100 | | Precision | Inference precision | bf16 | fp32, bf16, fp16 | | Bridge Noise Sigma | Controls diversity of results | 0.005 | 0.0-0.1 |

Depth/Normal Node

| Parameter | Description | Default | Range | | --------- | ----------- | ------- | ----- | | Task | Select task type | depth | depth, normal | | Steps | Number of inference steps | 28 | 1-100 | | Precision | Inference precision | bf16 | fp32, bf16, fp16 | | Bridge Noise Sigma | Controls diversity of results | 0.1 | 0.0-0.1 | | Mask | Optional mask for selective processing | None | - |

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 using fp16 precision or processing images at a lower resolution | | Performance | For batch processing, consider reducing steps to 15-20 for faster throughput | | Bridge Noise Sigma | Lower values (0.005) for relighting, higher values (0.1) for depth/normal maps |

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
  • Support for depth and normal map generation
  • Mask-based selective processing

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
    • diffusers>=0.19.0
    • accelerate>=0.20.0
    • transformers>=4.30.0
    • safetensors>=0.3.1
    • requests>=2.25.0

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.