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unify cards

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  1. README.md +3 -5
README.md CHANGED
@@ -5,7 +5,6 @@ license: apache-2.0
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  pipeline_tag: image-to-image
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  library_name: diffusers
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  tags:
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- - normals-estimation
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  - normals estimation
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  - latent consistency model
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  - image analysis
@@ -22,7 +21,7 @@ new_version: prs-eth/marigold-normals-v1-1
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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%E2%9B%B3-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">
@@ -50,11 +49,10 @@ This is a model card for the `marigold-normals-lcm-v0-1` model for monocular nor
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  The model is fine-tuned from the `marigold-normals-v0-1` [model](https://huggingface.co/prs-eth/marigold-normals-v0-1)
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  using the latent consistency distillation method, as
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  described in our papers:
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- - [CVPR'2024 paper](https://arxiv.org/abs/2312.02145) titled "Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation"
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- - [Jounal extension](https://www.arxiv.org/abs/2505.09358) titled "Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis"
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  ### Using the model
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-
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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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  pipeline_tag: image-to-image
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  library_name: diffusers
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  tags:
 
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  - normals estimation
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  - latent consistency model
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  - image analysis
 
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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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  The model is fine-tuned from the `marigold-normals-v0-1` [model](https://huggingface.co/prs-eth/marigold-normals-v0-1)
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  using the latent consistency distillation method, as
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  described in our papers:
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+ - [CVPR'2024 paper](https://hf.co/papers/2312.02145) titled "Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation"
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+ - [Journal extension](https://hf.co/papers/2505.09358) titled "Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis"
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  ### Using the model
 
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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).