--- license: cc-by-nc-4.0 base_model: - stabilityai/stable-diffusion-xl-base-1.0 tags: - image-to-image inference: false --- # ✨ Latent Bridge Matching for Depth Estimation ✨ Latent Bridge Matching (LBM) is a new, versatile and scalable method proposed in [LBM: Latent Bridge Matching for Fast Image-to-Image Translation](https://arxiv.org/abs/2503.07535) that relies on Bridge Matching in a latent space to achieve fast image-to-image translation. This model was trained to estimate the depth map from a given input image. See also our [live demo](https://huggingface.co/spaces/jasperai/LBM_relighting) for image relighting and official [Github repo](https://github.com/gojasper/LBM). ## How to use? To use this model you need first to install the associated `lbm` library by running the following ```bash pip install git+https://github.com/gojasper/LBM.git ``` Then, you can infer with the model on your input images ```python import torch from PIL import Image from lbm.inference import evaluate, get_model # Load model model = get_model( "jasperai/LBM_depth", torch_dtype=torch.bfloat16, device="cuda", ) # Load a source image source_image = Image.open("your_image") # Perform inference output_image = evaluate(model, source_image, num_sampling_steps=1) ``` ## Metrics This model achieves the following metrics | Metrics | AbsRel ↓ | δ1 ↑ | δ2 ↑ | |---------|-----------|------|------| | NYUv2 | 5.6 | 97.2 | 99.2 | | Kitti | 9.4 | 93.0 | 98.9 | | ETH3D | 6.3 | 96.5 | 99.3 | | ScanNet | 5.7 | 97.0 | 99.2 | | DIODE | 30.3 | 77.5 | 89.3 | ## 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 ```bibtex @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}, } ```