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update model cards

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README.md CHANGED
@@ -4,37 +4,69 @@ language:
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  - en
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  pipeline_tag: normals-estimation
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  tags:
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- - monocular normals estimation
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- - single image normals estimation
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- - normals
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  - in-the-wild
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  - zero-shot
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  ---
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- # Marigold Normals Model Card
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- This model belongs to the family of diffusion-based Marigold models for solving various computer vision tasks.
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- The Marigold Normals model focuses on the surface normals task.
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- It takes an input image and computes surface normals in each pixel.
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- The Marigold Normals model is trained from Stable Diffusion with synthetic data.
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- Thanks to the rich visual knowledge stored in Stable Diffusion, Marigold models possess deep scene understanding and excel at solving computer vision tasks.
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- Read more about Marigold in our paper titled "Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation".
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- [![Website](doc/badges/badge-website.svg)](https://marigoldmonodepth.github.io)
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- [![GitHub](https://img.shields.io/github/stars/prs-eth/Marigold?style=default&label=GitHub%20★&logo=github)](https://github.com/prs-eth/Marigold)
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- [![Paper](doc/badges/badge-pdf.svg)](https://arxiv.org/abs/2312.02145)
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- [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/toshas/marigold)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- Developed by:
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- [Bingxin Ke](http://www.kebingxin.com/),
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- [Anton Obukhov](https://www.obukhov.ai/),
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- [Shengyu Huang](https://shengyuh.github.io/),
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- [Nando Metzger](https://nandometzger.github.io/),
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- [Rodrigo Caye Daudt](https://rcdaudt.github.io/),
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- [Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ&hl=en)
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- ![teaser](doc/teaser_collage_transparant.png)
 
 
 
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- ## 🎓 Citation
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```bibtex
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  @InProceedings{ke2023repurposing,
@@ -44,11 +76,3 @@ Developed by:
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  year={2024}
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  }
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  ```
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-
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- ## 🎫 License
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-
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- This work is licensed under the Apache License, Version 2.0 (as defined in the [LICENSE](LICENSE.txt)).
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-
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- By downloading and using the code and model you agree to the terms in the [LICENSE](LICENSE.txt).
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-
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- [![License](https://img.shields.io/badge/License-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0)
 
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  - en
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  pipeline_tag: normals-estimation
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  tags:
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+ - normals estimation
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+ - image analysis
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+ - computer vision
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  - in-the-wild
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  - zero-shot
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  ---
 
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+ <h1 align="center">Marigold Normals v0-1 Model Card</h1>
 
 
 
 
 
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+ <p align="center">
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+ <a title="Image Normals" href="https://huggingface.co/spaces/prs-eth/marigold-normals" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
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+ <img src="https://img.shields.io/badge/%F0%9F%A4%97%20Image%20Normals%20-Demo-yellow" alt="Image Normals">
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+ </a>
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+ <a title="diffusers" href="https://huggingface.co/docs/diffusers/using-diffusers/marigold_usage" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
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+ <img src="https://img.shields.io/badge/%F0%9F%A4%97%20diffusers%20-Integration%20🧨-yellow" alt="diffusers">
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+ </a>
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+ <a title="Github" href="https://github.com/prs-eth/marigold" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
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+ <img src="https://img.shields.io/github/stars/prs-eth/marigold?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="Github">
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+ </a>
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+ <a title="Website" href="https://marigoldcomputervision.github.io/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
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+ <img src="https://img.shields.io/badge/%E2%99%A5%20Project%20-Website-blue" alt="Website">
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+ </a>
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+ <a title="arXiv" href="https://arxiv.org/abs/2312.02145" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
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+ <img src="https://img.shields.io/badge/%F0%9F%93%84%20Read%20-Paper-AF3436" alt="arXiv">
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+ </a>
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+ <a title="Social" href="https://twitter.com/antonobukhov1" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
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+ <img src="https://img.shields.io/twitter/follow/:?label=Subscribe%20for%20updates!" alt="Social">
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+ </a>
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+ <a title="License" href="https://www.apache.org/licenses/LICENSE-2.0" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
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+ <img src="https://img.shields.io/badge/License-Apache--2.0-929292" alt="License">
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+ </a>
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+ </p>
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+ <h2 align="center"><span style="color: red;"><b>This model is deprecated. Use the new Marigold Normals v1-1 Model instead.</b></span></h2>
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+ <h2 align="center">
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+ <a href="https://huggingface.co/prs-eth/marigold-normals-v1-1">NEW: Marigold Normals v1-1 Model</a>
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+ </h2>
 
 
 
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+ This is a model card for the `marigold-normals-v0-1` model for monocular normals estimation from a single image.
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+ The model is fine-tuned from the `stable-diffusion-2` [model](https://huggingface.co/stabilityai/stable-diffusion-2) as
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+ described in
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+ <span style="color:red;">a follow-up of our [CVPR'2024 paper](https://arxiv.org/abs/2312.02145) titled "Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation".</span>
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+ - Play with the interactive [Hugging Face Spaces demo](https://huggingface.co/spaces/prs-eth/marigold-normals): check out how the model works with example images or upload your own.
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+ - Use it with [diffusers](https://huggingface.co/docs/diffusers/using-diffusers/marigold_usage) to compute the results with a few lines of code.
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+ - Get to the bottom of things with our [official codebase](https://github.com/prs-eth/marigold).
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+
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+ ## Model Details
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+ - **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).
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+ - **Model type:** Generative latent diffusion-based normals estimation from a single image.
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+ - **Language:** English.
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+ - **License:** [Apache License License Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
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+ - **Model Description:** This model can be used to generate an estimated surface normals map of an input image.
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+ - **Resolution**: Even though any resolution can be processed, the model inherits the base diffusion model's effective resolution of roughly **768** pixels.
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+ 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.
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+ - **Steps and scheduler**: This model was designed for usage with **DDIM** scheduler and between **10 and 50** denoising steps.
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+ - **Outputs**:
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+ - **Surface normals map**: The predicted values are 3-dimensional unit vectors in the screen space camera.
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+ - **Uncertainty map**: Produced only when multiple predictions are ensembled with ensemble size larger than 2.
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+ - **Resources for more information:** [Project Website](https://marigoldcomputervision.github.io/), [Paper](https://arxiv.org/abs/2312.02145), [Code](https://github.com/prs-eth/marigold).
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+ - **Cite as:**
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+
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+ <span style="color:red;">Placeholder for the citation block of the follow-up paper</span>
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  ```bibtex
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  @InProceedings{ke2023repurposing,
 
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  year={2024}
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  }
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  ```
 
 
 
 
 
 
 
 
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