LBM_relighting

✨ Latent Bridge Matching for Image Relighting ✨

Latent Bridge Matching (LBM) is a new, versatile and scalable method proposed in LBM: Latent Bridge Matching for Fast Image-to-Image Translation that relies on Bridge Matching in a latent space to achieve fast image-to-image translation. This model was trained to relight a foreground object according to a provided background. See our live demo and official Github repo.

<p align="center"> <img style="width:700px;" src="assets/relight.jpg"> </p>

How to use?

To use this model you need first to install the associated lbm library by running the following

pip install git+https://github.com/gojasper/LBM.git

Then, you can infer with the model on your input images

import torch
from diffusers.utils import load_image
from lbm.inference import evaluate, get_model

# Load model
model = get_model(
  "jasperai/LBM_relighting",
  torch_dtype=torch.bfloat16,
  device="cuda",
)

# Load a source image
source_image = load_image(
  "https://huggingface.co/jasperai/LBM_relighting/resolve/main/assets/source_image.jpg"
)

# Perform inference
output_image = evaluate(model, source_image, num_sampling_steps=1)
output_image

<p align="center"> <img style="width:500px;" src="assets/output.jpg"> </p>

License

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

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},
}