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Expand Up @@ -6,11 +6,11 @@ This repository represents the official implementation of the paper titled "Repu

[![Website](doc/badges/badge-website.svg)](https://marigoldmonodepth.github.io)
[![Paper](https://img.shields.io/badge/arXiv-PDF-b31b1b)](https://arxiv.org/abs/2312.02145)
[![Hugging Face (LCM) Space](https://img.shields.io/badge/🤗%20Hugging%20Face%20(LCM)-Space-yellow)](https://huggingface.co/spaces/prs-eth/marigold-lcm)
[![Hugging Face (LCM) Model](https://img.shields.io/badge/🤗%20Hugging%20Face%20(LCM)-Model-green)](https://huggingface.co/prs-eth/marigold-lcm-v1-0)
[![Open In Colab](doc/badges/badge-colab.svg)](https://colab.research.google.com/drive/12G8reD13DdpMie5ZQlaFNo2WCGeNUH-u?usp=sharing)
[![Hugging Face (LCM) Space](https://img.shields.io/badge/🤗%20Hugging%20Face(LCM)-Space-yellow)](https://huggingface.co/spaces/prs-eth/marigold-lcm)
[![Hugging Face (LCM) Model](https://img.shields.io/badge/🤗%20Hugging%20Face(LCM)-Model-green)](https://huggingface.co/prs-eth/marigold-lcm-v1-0)
<!-- [![Hugging Face Model](https://img.shields.io/badge/🤗%20Hugging%20Face-Model-green)](https://huggingface.co/prs-eth/marigold-v1-0) -->
[![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0)
<!-- [![Hugging Face Model](https://img.shields.io/badge/🤗%20Hugging%20Face-Model-green)](https://huggingface.co/prs-eth/marigold-v1-0) -->
<!-- [![Website](https://img.shields.io/badge/Project-Website-1081c2)](https://arxiv.org/abs/2312.02145) -->
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[Rodrigo Caye Daudt](https://rcdaudt.github.io/),
[Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ&hl=en )

We present Marigold, a diffusion model and associated fine-tuning protocol for monocular depth estimation. Its core principle is to leverage the rich visual knowledge stored in modern generative image models. Our model, derived from Stable Diffusion and fine-tuned with synthetic data, can zero-shot transfer to unseen data, offering state-of-the-art monocular depth estimation results.
We present Marigold, a diffusion model, and associated fine-tuning protocol for monocular depth estimation. Its core principle is to leverage the rich visual knowledge stored in modern generative image models. Our model, derived from Stable Diffusion and fine-tuned with synthetic data, can zero-shot transfer to unseen data, offering state-of-the-art monocular depth estimation results.

![teaser](doc/teaser_collage_transparant.png)

## 📢 News
2024-05-28: Training code is released.<br>
2024-03-23: Added [LCM v1.0](https://huggingface.co/prs-eth/marigold-lcm-v1-0) for faster inference - try it out at [![Hugging Face (LCM) Space](https://img.shields.io/badge/🤗%20Hugging%20Face(LCM)-Space-yellow)](https://huggingface.co/spaces/prs-eth/marigold-lcm)<br>
2024-03-23: Added [LCM v1.0](https://huggingface.co/prs-eth/marigold-lcm-v1-0) for faster inference - try it out at <a href="https://huggingface.co/spaces/prs-eth/marigold-lcm"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face%20(LCM)-Space-yellow" height="16"></a><br>
2024-03-04: Accepted to CVPR 2024. <br>
2023-12-22: Contributed to Diffusers [community pipeline](https://github.com/huggingface/diffusers/tree/main/examples/community#marigold-depth-estimation). <br>
2023-12-19: Updated [license](LICENSE.txt) to Apache License, Version 2.0.<br>
Expand All @@ -43,19 +43,19 @@ paper and inference code (this repository).

**We offer several ways to interact with Marigold**:

1. A free online interactive demo is available here: <a href="https://huggingface.co/spaces/prs-eth/marigold-lcm"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face(LCM)-Space-yellow" height="16"></a> (kudos to the HF team for the GPU grant)
1. We integrated [Marigold Pipelines into diffusers 🧨](https://huggingface.co/docs/diffusers/api/pipelines/marigold). Check out many exciting usage scenarios in [this diffusers tutorial](https://huggingface.co/docs/diffusers/using-diffusers/marigold_usage).

1. A free online interactive demo is available here: <a href="https://huggingface.co/spaces/prs-eth/marigold-lcm"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face%20(LCM)-Space-yellow" height="16"></a> (kudos to the HF team for the GPU grant)

1. Run the demo locally (requires a GPU and an `nvidia-docker2`, see [Installation Guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)):
1. Paper version: `docker run -it -p 7860:7860 --platform=linux/amd64 --gpus all registry.hf.space/toshas-marigold:latest python app.py`
1. LCM version: `docker run -it -p 7860:7860 --platform=linux/amd64 --gpus all registry.hf.space/prs-eth-marigold-lcm:latest python app.py`

1. Run with Diffusers [community pipeline](https://github.com/huggingface/diffusers/tree/main/examples/community#marigold-depth-estimation) (requires `diffusers >= 0.25.0`).

1. Extended demo on a Google Colab: <a href="https://colab.research.google.com/drive/12G8reD13DdpMie5ZQlaFNo2WCGeNUH-u?usp=sharing"><img src="doc/badges/badge-colab.svg" height="16"></a>

1. If you just want to see the examples, visit our gallery: <a href="https://marigoldmonodepth.github.io"><img src="doc/badges/badge-website.svg" height="16"></a>

1. Finally, local development instructions are given below.
1. Finally, local development instructions with this codebase are given below.

## 🛠️ Setup

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