Latent Bridge Matching (LBM)

This repository is the official implementation of the paper LBM: Latent Bridge Matching for Fast Image-to-Image Translation.

DEMO space

Abstract

In this paper, we introduce Latent Bridge Matching (LBM), a new, versatile and scalable method that relies on Bridge Matching in a latent space to achieve fast image-to-image translation. We show that the method can reach state-of-the-art results for various image-to-image tasks using only a single inference step. In addition to its efficiency, we also demonstrate the versatility of the method across different image translation tasks such as object removal, normal and depth estimation, and object relighting. We also derive a conditional framework of LBM and demonstrate its effectiveness by tackling the tasks of controllable image relighting and shadow generation.

License

This code is released under the Creative Commons BY-NC 4.0 license.

Considered Use-cases

We validate the method on various use-cases such as object relighting, image restoration, object removal, depth and normal maps estimation as well as controllable object relighting and shadow generation.

Image Relighting 🔦

For object relighting, the method should translate the encoded source images created by pasting the foreground onto the target background image to the desired target relighted image.

Image Restoration 🧹

In the context of image restoration, the method shall transport the distribution of the degraded images to the distribution of the clean images.

Object Removal ✂️

For object removal, the model is trained to find a transport map from the masked images to the images without the objects

Controllable Image Relighting and Shadow Generation🕹️

We also derive a conditional framework of LBM and demonstrate its effectiveness by tackling the tasks of controllable image relighting and shadow generation

Normals and Depth Maps Estimation 🗺️

Finally, we also consider common tasks such as normal and depth estimation where the model should translate an input image into a normal or depth map

Setup

To be up and running, you need first to create a virtual env with at least python3.10 installed and activate it

With venv

python3.10 -m venv envs/lbm
source envs/lbm/bin/activate

With conda

conda create -n lbm python=3.10
conda activate lbm

Then install the required dependencies and the repo in editable mode

pip install --upgrade pip
pip install -e .

Inference

We provide in examples a simple script to perform depth and normal estimation using the proposed method.

python examples/inference/inference.py \
--model_name [depth|normals|relighting] \
--source_image path_to_your_image.jpg \
--output_path output_images

See the trained models on the HF Hub 🤗

• Surface normals Checkpoint
• Depth Checkpoint
• Relighting Checkpoint

Local Gradio Demo

To run the local gradio demo, just run the following command:

python examples/inference/gradio_demo.py

It will download the pretrained model from the HF Hub as well as example images.

Training

We provide in examples\training an example of a script to train a LBM for surface normal predictions on hypersim see this for data processing.

In examples\trainig\configs, you will find the configuration yaml associated to the training script. The only thing you need to do is to amend the SHARDS_PATH_OR_URLS section of the yaml so the model is trained on your own data.

Please note that this package uses webdataset to handle the datastream and so the urls you use should be fomatted according to the webdataset format. In particular, for this example, each sample in your .tar files needs to be composed of a jpg file containing the image, a normal.png file containing the target normals as well as a mask.png containing a mask indicating the valid pixels

sample = {
    "jpg": source_image,
    "normal.png": normals # target_image
    "mask.png": mask # mask of valid pixels
}

To train the model, you can use the following command:

python examples/training/train_lbm_surface.py examples/training/config/surface.yaml

Note: Make sure to update the relevant section of the yaml file to use your own data and log the results on your own WandB.

Citation

If you find this work useful or use it in your research, please consider citing us

@article{chadebec2025lbm,
      title={LBM: Latent Bridge Matching for Fast Image-to-Image Translation}, 
      author={Clément Chadebec and Onur Tasar and Sanjeev Sreetharan and Benjamin Aubin},
      year={2025},
      journal = {arXiv preprint arXiv:2503.07535},
}