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.

News & Updates

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

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

Clone this repository to your ComfyUI/custom_nodes directory:

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 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

Add the “Relighting (LBM)” node from the 🧪AILab/🔆LBM category

Connect an image source to the node

Select the model file (defaults to LBM_relighting.safetensors)

Adjust the parameters as needed

Run the workflow

Depth/Normal Node

Add the “Depth / Normal (LBM)” node from the 🧪AILab/🔆LBM category

Connect an image source to the node

Select the task type (depth or normal)

Adjust the parameters as needed

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 512×512 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.