Update README.md

README.md

@@ -1,58 +1,87 @@
 ---
-license: apache-2.0
 language:
 - en
+license: openrail++
 pipeline_tag: depth-estimation
+library_name: diffusers
 tags:
-- monocular depth estimation
-- single image depth estimation
-- depth
+- depth estimation
+- image analysis
+- computer vision
 - in-the-wild
 - zero-shot
-- depth
+pinned: true
 ---
-# Marigold: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
 
-This model represents the internal test checkpoint of affine-invariant disparity version (train_marigold_affine_disparity_iter_24000).
+<h1 align="center">Marigold Disparity v0.1 Model Card</h1>
 
-[![Website](doc/badges/badge-website.svg)](https://marigoldmonodepth.github.io)
-[![GitHub](https://img.shields.io/github/stars/prs-eth/Marigold?style=default&label=GitHub%20★&logo=github)](https://github.com/prs-eth/Marigold)
-[![Paper](doc/badges/badge-pdf.svg)](https://arxiv.org/abs/2312.02145)
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/12G8reD13DdpMie5ZQlaFNo2WCGeNUH-u?usp=sharing)
-[![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/toshas/marigold)
-[![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0)
-<!-- [![HF Space](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Space-blue)]() -->
-<!-- [![Open In Colab](doc/badges/badge-colab.svg)]() -->
-<!-- [![Docker](doc/badges/badge-docker.svg)]() -->
-<!-- ### [Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation]() -->
+<!-- This model represents the internal test checkpoint of affine-invariant disparity version (train_marigold_affine_disparity_iter_24000). -->
 
-[Bingxin Ke](http://www.kebingxin.com/),
-[Anton Obukhov](https://www.obukhov.ai/),
-[Shengyu Huang](https://shengyuh.github.io/),
-[Nando Metzger](https://nandometzger.github.io/),
-[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.
+<p align="center">
+<a title="Image Depth" href="https://huggingface.co/spaces/prs-eth/marigold" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+    <img src="https://img.shields.io/badge/%F0%9F%A4%97%20Image%20Depth%20-Demo-yellow" alt="Image Depth">
+</a>
+<a title="diffusers" href="https://huggingface.co/docs/diffusers/using-diffusers/marigold_usage" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+    <img src="https://img.shields.io/badge/%F0%9F%A4%97%20diffusers%20-Integration%20🧨-yellow" alt="diffusers">
+</a>
+<a title="Github" href="https://github.com/prs-eth/marigold" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+    <img src="https://img.shields.io/github/stars/prs-eth/marigold?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="Github">
+</a>
+<a title="Website" href="https://marigoldcomputervision.github.io/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+    <img src="https://img.shields.io/badge/%E2%99%A5%20Project%20-Website-blue" alt="Website">
+</a>
+<a title="arXiv" href="https://arxiv.org/abs/2505.09358" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+    <img src="https://img.shields.io/badge/%F0%9F%93%84%20Read%20-Paper-AF3436" alt="arXiv">
+</a>
+<a title="Social" href="https://twitter.com/antonobukhov1" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+    <img src="https://img.shields.io/twitter/follow/:?label=Subscribe%20for%20updates!" alt="Social">
+</a>
+<a title="License" href="https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+    <img src="https://img.shields.io/badge/License-OpenRAIL++-929292" alt="License">
+</a>
+</p>
 
-![teaser](doc/teaser_collage_transparant.png)
+This is a model card for the `marigold-disparity-affine-v0-1` model for monocular depth estimation from a single image. 
+The model is fine-tuned from the `stable-diffusion-2` [model](https://huggingface.co/stabilityai/stable-diffusion-2) as 
+described in our papers, in inverse depth (disparity) space:
+- [CVPR'2024 paper](https://hf.co/papers/2312.02145) titled "Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation"
+- [Journal extension](https://hf.co/papers/2505.09358) titled "Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis"
 
+### Using the model
+- Play with the interactive [Hugging Face Spaces demo](https://huggingface.co/spaces/prs-eth/marigold): check out how the model works with example images or upload your own.
+- Use it with [diffusers](https://huggingface.co/docs/diffusers/using-diffusers/marigold_usage) to compute the results with a few lines of code.
+- Get to the bottom of things with our [official codebase](https://github.com/prs-eth/marigold).
 
-## 🎓 Citation
+## Model Details
+- **Developed by:** [Bingxin Ke](http://www.kebingxin.com/), [Kevin Qu](https://ch.linkedin.com/in/kevin-qu-b3417621b), [Tianfu Wang](https://tianfwang.github.io/), [Nando Metzger](https://nandometzger.github.io/), [Shengyu Huang](https://shengyuh.github.io/), [Bo Li](https://www.linkedin.com/in/bobboli0202), [Anton Obukhov](https://www.obukhov.ai/), [Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ).
+- **Model type:** Generative latent diffusion-based affine-invariant monocular depth estimation from a single image.
+- **Language:** English.
+- **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL).
+- **Model Description:** This model can be used to generate an estimated depth map of an input image. 
+  - **Resolution**: Even though any resolution can be processed, the model inherits the base diffusion model's effective resolution of roughly **768** pixels. 
+    This means that for optimal predictions, any larger input image should be resized to make the longer side 768 pixels before feeding it into the model.
+  - **Steps and scheduler**: This model was designed for usage with the **DDIM** scheduler and between **1 and 50** denoising steps.
+  - **Outputs**:
+    - **Affine-invariant depth map**: The predicted values are between 0 and 1, interpolating between the near and far planes of the model's choice.
+    - **Uncertainty map**: Produced only when multiple predictions are ensembled with ensemble size larger than 2.
+- **Resources for more information:** [Project Website](https://marigoldcomputervision.github.io/), [Paper](https://arxiv.org/abs/2505.09358), [Code](https://github.com/prs-eth/marigold).
+- **Cite as:**
 
 ```bibtex
-@InProceedings{ke2023repurposing,
-      title={Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation},
-      author={Bingxin Ke and Anton Obukhov and Shengyu Huang and Nando Metzger and Rodrigo Caye Daudt and Konrad Schindler},
-      booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-      year={2024}
+@misc{ke2025marigold,
+  title={Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis},
+  author={Bingxin Ke and Kevin Qu and Tianfu Wang and Nando Metzger and Shengyu Huang and Bo Li and Anton Obukhov and Konrad Schindler},
+  year={2025},
+  eprint={2505.09358},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV}
 }
-```
-
-## 🎫 License
-
-This work is licensed under the Apache License, Version 2.0 (as defined in the [LICENSE](LICENSE.txt)).
 
-By downloading and using the code and model you agree to the terms in the  [LICENSE](LICENSE.txt).
-
-[![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0)
+@InProceedings{ke2023repurposing,
+  title={Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation},
+  author={Bingxin Ke and Anton Obukhov and Shengyu Huang and Nando Metzger and Rodrigo Caye Daudt and Konrad Schindler},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2024}
+}
+```