Enhance model card: Add pipeline tag, paper, project, code links, and usage

This PR significantly enhances the model card for CineScale, improving its discoverability and providing comprehensive documentation for users.

Key additions include:
- **Pipeline Tag**: Added `pipeline_tag: text-to-video` to ensure the model appears in the relevant category on the Hub, aligning with its capabilities in high-resolution video generation.
- **Paper Link**: Linked to the official paper, [CineScale: Free Lunch in High-Resolution Cinematic Visual Generation](https://huggingface.co/papers/2508.15774).
- **External Links**: Included clear links to the project page ([https://eyeline-labs.github.io/CineScale/](https://eyeline-labs.github.io/CineScale/)) and the GitHub repository ([https://github.com/Eyeline-Labs/CineScale](https://github.com/Eyeline-Labs/CineScale)) for easy access to code and further details.
- **Model Overview**: Added a table detailing the available CineScale models, their tuning resolutions, and capabilities.
- **Sample Usage**: Provided a "Quick Start" section with a code snippet directly from the GitHub README, guiding users on how to initiate video generation, with a clear reference to the GitHub repository for more advanced examples and setup.
- **Citation**: Included the BibTeX citation for proper academic attribution.

These changes make the model card more informative and user-friendly.

README.md

@@ -1,3 +1,46 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: text-to-video
+---
+
+# CineScale: Free Lunch in High-Resolution Cinematic Visual Generation
+
+This repository contains the CineScale models presented in the paper [CineScale: Free Lunch in High-Resolution Cinematic Visual Generation](https://huggingface.co/papers/2508.15774).
+
+CineScale proposes a novel inference paradigm to enable higher-resolution visual generation. It broadens the scope by enabling high-resolution I2V (Image-to-Video) and V2V (Video-to-Video) synthesis, built atop state-of-the-art open-source video generation frameworks, significantly improving upon existing methods which are prone to repetitive patterns in high-resolution outputs.
+
+**Project Page:** [https://eyeline-labs.github.io/CineScale/](https://eyeline-labs.github.io/CineScale/)
+**Code & Detailed Usage:** [https://github.com/Eyeline-Labs/CineScale](https://github.com/Eyeline-Labs/CineScale)
+
+## Models
+CineScale provides a family of models, including Text-to-Video (T2V) and Image-to-Video (I2V) variants, capable of generating videos up to 4K resolution.
+
+| Model                       | Tuning Resolution | Checkpoint                                                                       | Description                                   |
+| :-------------------------- | :---------------- | :------------------------------------------------------------------------------- | :-------------------------------------------- |
+| CineScale-1.3B-T2V          | 1088x1920         | [Hugging Face](https://huggingface.co/Eyeline-Labs/CineScale/blob/main/t2v_1.3b_ntk20.ckpt) | Supports 3K (1632x2880) inference on A100 x 1 |
+| CineScale-14B-T2V           | 1088x1920         | [Hugging Face](https://huggingface.co/Eyeline-Labs/CineScale/blob/main/t2v_14b_ntk20.ckpt) | Supports 4K (2176x3840) inference on A100 x 8 |
+| CineScale-14B-I2V           | 1088x1920         | [Hugging Face](https://huggingface.co/Eyeline-Labs/CineScale/blob/main/i2v_14b_ntk20.ckpt) | Supports 4K (2176x3840) inference on A100 x 8 |
+
+## Quick Start
+To get started, you will need to set up the environment and download the model checkpoints as described in the [GitHub repository](https://github.com/Eyeline-Labs/CineScale).
+
+Inference examples for various resolutions and tasks are provided in the GitHub repository's command-line scripts. For instance, to run 2K-resolution text-to-video inference:
+```bash
+# Example for 2K-Resolution Text-to-Video (Base Model Wan2.1-1.3B)
+# Single GPU
+CUDA_VISIBLE_DEVICES=0 python cinescale_t2v1.3b_single.py
+# Multiple GPUs
+torchrun --standalone --nproc_per_node=8 cinescale_t2v1.3b.py
+```
+Refer to the [GitHub repository](https://github.com/Eyeline-Labs/CineScale) for more detailed instructions and examples for 3K and 4K video generation.
+
+## Citation
+If you find our work useful, please consider citing our paper:
+```bib
+@article{qiu2025cinescale,
+  title={CineScale: Free Lunch in High-Resolution Cinematic Visual Generation},
+  author={Haonan Qiu and Ning Yu and Ziqi Huang and Paul Debevec and Ziwei Liu},
+  journal={arXiv preprint arXiv:2508.15774},
+  year={2025}
+}
+```