Improve model card with paper links, metadata and usage

This PR enhances the model card for rCM. It adds:
- Links to the research paper, official project page, and GitHub repository.
- Descriptive metadata tags for better discoverability.
- A sample usage section based on the official inference script provided in the repository.
- A clear overview of the model's capabilities in few-step video generation.

README.md

@@ -1,20 +1,42 @@
 ---
-license: apache-2.0
 base_model:
 - Wan-AI/Wan2.1-T2V-1.3B
 - Wan-AI/Wan2.1-T2V-14B
+license: apache-2.0
 pipeline_tag: text-to-video
+tags:
+- rcm
+- consistency-models
+- diffusion-distillation
+- video-generation
 ---
+
 # rCM: Score-Regularized Continuous-Time Consistency Model
+
 [**Paper**](https://arxiv.org/abs/2510.08431) | [**Website**](https://research.nvidia.com/labs/dir/rcm) | [**Code**](https://github.com/NVlabs/rcm)
 
-This repo holds unofficial rCM models for Wan, reproduced by Tsinghua University.
+rCM is a framework for scaling up continuous-time consistency distillation to large-scale video diffusion models (up to 14B parameters). It enables high-fidelity video generation in only 1–4 steps, accelerating diffusion sampling by $15\times \sim 50\times$. This repository contains unofficial rCM models for Wan, reproduced by Tsinghua University.
 
-The Wan2.2 rCM checkpoints are obtained by merging Wan2.1 rCM weights to Wan2.2 checkpoints, no extra training included. Should have the same effect as directly using the Wan2.1 rCM LoRAs (https://huggingface.co/Kijai/WanVideo_comfy/tree/main/LoRAs/rCM) and adjusting the strength.
+The Wan2.2 rCM checkpoints are obtained by merging Wan2.1 rCM weights to Wan2.2 checkpoints, no extra training included. This should have the same effect as directly using the Wan2.1 rCM LoRAs and adjusting the strength.
 
-## Citation
+## Inference
 
+To run the models, please refer to the environment setup in the [official GitHub repository](https://github.com/NVlabs/rcm). 
+
+Below is an example inference script for running rCM on T2V as found in the documentation:
+
+```bash
+# Example for Wan2.1 T2V 1.3B
+PYTHONPATH=. python rcm/inference/wan2pt1_t2v_rcm_infer.py \
+    --model_size 1.3B \
+    --dit_path assets/checkpoints/rCM_Wan2.1_T2V_1.3B_480p.pt \
+    --num_samples 5 \
+    --prompt "A cinematic shot of a snowy mountain at sunrise"
 ```
+
+## Citation
+
+```bibtex
 @article{zheng2025rcm,
   title={Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency},
   author={Zheng, Kaiwen and Wang, Yuji and Ma, Qianli and Chen, Huayu and Zhang, Jintao and Balaji, Yogesh and Chen, Jianfei and Liu, Ming-Yu and Zhu, Jun and Zhang, Qinsheng},