Update model card with paper, project, and code links

This PR improves the documentation of the ScriptAgent model by:
- Linking the research paper: [The Script is All You Need: An Agentic Framework for Long-Horizon Dialogue-to-Cinematic Video Generation](https://huggingface.co/papers/2601.17737).
- Adding the project page and GitHub repository links for easier access to resources.
- Updating the metadata with the appropriate `arxiv` ID.
- Fixing the syntax highlighting for the `ms-swift` inference code snippet.
- Adding a citation section.

README.md

@@ -6,33 +6,38 @@ tags:
 - base_model:adapter:XD-MU/ScriptAgent
 - lora
 - transformers
+arxiv: 2601.17737
 ---
 
-
 # ScriptAgent: Dialogue-to-Shooting-Script Generation Model
 
-This model is a fine-tuned adapter (LoRA) on top of the `XD-MU/ScriptAgent` base model, designed to **generate detailed shooting scripts from dialogue inputs**. It is trained to transform conversational text into structured screenplay formats suitable for film or video production.
+This model is a fine-tuned adapter (LoRA) designed to **generate detailed shooting scripts from dialogue inputs**. It is the implementation of **ScripterAgent** as described in the paper: [The Script is All You Need: An Agentic Framework for Long-Horizon Dialogue-to-Cinematic Video Generation](https://huggingface.co/papers/2601.17737).
+
+[**Project Page**](https://xd-mu.github.io/ScriptIsAllYouNeed/) | [**Code**](https://github.com/Tencent/digitalhuman/tree/main/ScriptAgent) | [**Demo**](https://huggingface.co/spaces/XD-MU/ScriptAgent)
+
+## Model Description
+ScriptAgent transforms conversational text (coarse dialogue) into structured, fine-grained, and executable cinematic scripts. It bridges the "semantic gap" between a creative idea and its cinematic execution, providing necessary context for video generation models, including character descriptions, scene settings, positions, and dialogue cues.
 
 The model is compatible with [ms-swift](https://github.com/modelscope/swift) and supports efficient inference via the **vLLM backend**.
 
-> 💡 Note: This repository contains a **PEFT adapter** (e.g., LoRA). To use it, you must merge it with the original base model or load it via `ms-swift`.
+> 💡 Note: This repository contains a **PEFT adapter** (LoRA). To use it, you must merge it with the original base model or load it via `ms-swift`.
 
 ## ▶️ Inference with ms-swift (vLLM Backend)
 
-To generate shooting scripts from dialogue inputs, use the following command with **ms-swift**:
+To generate shooting scripts from dialogue inputs, use the following snippet with **ms-swift**. You can find **DialoguePrompts** [here](https://huggingface.co/datasets/XD-MU/DialoguePrompts).
 
-You can find **DialoguePrompts** here: https://huggingface.co/datasets/XD-MU/DialoguePrompts
-```bash
+```python
 import os
 from huggingface_hub import snapshot_download
+from swift.llm import PtEngine, RequestConfig, InferRequest
 
 os.environ['CUDA_VISIBLE_DEVICES'] = '0'
 
 model_name = "XD-MU/ScriptAgent"
 local_path = "./models/ScriptAgent"
 
-# 下载整个仓库的所有文件
-print("下载模型所有文件...")
+# Download the model files
+print("Downloading model...")
 snapshot_download(
     repo_id=model_name,
     local_dir=local_path,
@@ -40,20 +45,31 @@ snapshot_download(
     resume_download=True
 )
 
-print(f"模型已完整下载到: {local_path}")
-
-# 使用 SWIFT 加载
-from swift.llm import PtEngine, RequestConfig, InferRequest
-
+# Load using SWIFT
 engine = PtEngine(local_path, max_batch_size=1)
 request_config = RequestConfig(max_tokens=8192, temperature=0.7)
 
 infer_request = InferRequest(messages=[
-    {"role": "user", "content": "你的对话上下文(Your Dialogue)"}
+    {"role": "user", "content": "Your Dialogue Here"}
 ])
 response = engine.infer([infer_request], request_config)[0]
 
 print(response.choices[0].message.content)
 ```
 
+## Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@article{directing2026,
+  title={The Script is All You Need: An Agentic Framework for Long-Horizon Dialogue-to-Cinematic Video Generation},
+  author={Mu, Chenyu and He, Xin and Yang, Qu and Chen, Wanshun and Yao, Jiadi and Liu, Huang and Yi, Zihao and Zhao, Bo and Chen, Xingyu and Ma, Ruotian and others},
+  journal={arXiv preprint arXiv:2601.17737},
+  year={2026}
+}
+```
 
+## Acknowledgments
+- Thanks to [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) for the SFT training framework.
+- Thanks to [ms-swift](https://github.com/modelscope/ms-swift) for the GRPO training framework.