Improve model card with metadata and links

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+pipeline_tag: depth-estimation
+library_name: diffusers
+---
+
+# NormalCrafter: Learning Temporally Consistent Normals from Video Diffusion Priors
+
+[NormalCrafter](https://normalcrafter.github.io/) generates temporally consistent normal sequences with fine-grained details from open-world videos of arbitrary lengths.  This model is based on the paper [NormalCrafter: Learning Temporally Consistent Normals from Video Diffusion Priors](https://huggingface.co/papers/2504.11427).
+
+
+## 🚀 Quick Start
+
+### 🤖 Gradio Demo
+- Online demo: [NormalCrafter](https://huggingface.co/spaces/Yanrui95/NormalCrafter) 
+- Local demo:
+    ```bash
+    gradio app.py
+    ``` 
+
+### 🛠️ Installation
+1. Clone this repo:
+```bash
+git clone git@github.com:Binyr/NormalCrafter.git
+```
+2. Install dependencies (please refer to [requirements.txt](requirements.txt)):
+```bash
+pip install -r requirements.txt
+```
+
+
+
+### 🤗 Model Zoo
+[NormalCrafter](https://huggingface.co/Yanrui95/NormalCrafter) is available in the Hugging Face Model Hub.
+
+### 🏃‍♂️ Inference
+#### 1. High-resolution inference, requires a GPU with ~20GB memory for 1024x576 resolution:
+```bash
+python run.py  --video-path examples/example_01.mp4
+```
+
+#### 2. Low-resolution inference requires a GPU with ~6GB memory for 512x256 resolution:
+```bash
+python run.py  --video-path examples/example_01.mp4 --max-res 512
+```