Improve dataset card: Add library, tags, project page, abstract, and sample usage (#2)

- Improve dataset card: Add library, tags, project page, abstract, and sample usage (761dea76e0e5a2ec0f0f32556f5a910278f26608)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,9 +1,14 @@
 ---
 license: apache-2.0
-task_categories:
-- text-to-video
 size_categories:
 - 10K<n<100K
+task_categories:
+- text-to-video
+library_name: fastvideo
+tags:
+- fastvideo
+- synthetic
+- video-diffusion
 ---
 
 # FastVideo Synthetic Wan2.2 720P dataset
@@ -16,12 +21,14 @@ size_categories:
   </div>
 
   <div align="center">
-    <a href="https://arxiv.org/pdf/2505.13389">Paper</a> | 
-    <a href="https://github.com/hao-ai-lab/FastVideo">Github</a>
+    <a href="https://arxiv.org/abs/2505.13389">Paper</a> | 
+    <a href="https://github.com/hao-ai-lab/FastVideo">Github</a> |
+    <a href="https://hao-ai-lab.github.io/FastVideo">Project Page</a>
   </div>
 </div>
 
-
+## Abstract
+Scaling video diffusion transformers (DiTs) is limited by their quadratic 3D attention, even though most of the attention mass concentrates on a small subset of positions. We turn this observation into VSA, a trainable, hardware-efficient sparse attention that replaces full attention at \emph{both} training and inference. In VSA, a lightweight coarse stage pools tokens into tiles and identifies high-weight \emph{critical tokens}; a fine stage computes token-level attention only inside those tiles subjecting to block computing layout to ensure hard efficiency. This leads to a single differentiable kernel that trains end-to-end, requires no post-hoc profiling, and sustains 85% of FlashAttention3 MFU. We perform a large sweep of ablation studies and scaling-law experiments by pretraining DiTs from 60M to 1.4B parameters. VSA reaches a Pareto point that cuts training FLOPS by 2.53$\times$ with no drop in diffusion loss. Retrofitting the open-source Wan-2.1 model speeds up attention time by 6$\times$ and lowers end-to-end generation time from 31s to 18s with comparable quality. These results establish trainable sparse attention as a practical alternative to full attention and a key enabler for further scaling of video diffusion models.
 
 ## Dataset Overview
 - The prompts were randomly sampled from the [Vchitect_T2V_DataVerse](https://huggingface.co/datasets/Vchitect/Vchitect_T2V_DataVerse) dataset.
@@ -30,6 +37,13 @@ size_categories:
 - It includes all preprocessed latents required for **Text-to-Video (T2V)** task (Also include the first frame Image).
 - The dataset is fully compatible with the [FastVideo](https://github.com/hao-ai-lab/FastVideo) repository and can be directly loaded and used without any additional preprocessing.
 
+## Sample Usage
+To download this dataset, ensure you have Git LFS installed, then clone the repository:
+```bash
+git lfs install
+git clone https://huggingface.co/datasets/FastVideo/Wan2.2-Syn-121x704x1280_32k
+```
+This dataset contains preprocessed latents ready for Text-to-Video (T2V) tasks and is designed to be directly used with the [FastVideo repository](https://github.com/hao-ai-lab/FastVideo) without further preprocessing. Refer to the FastVideo [documentation](https://hao-ai-lab.github.io/FastVideo) for detailed instructions on how to load and use the dataset for training or finetuning.
 
 If you use FastVideo Synthetic Wan2.1 dataset for your research, please cite our paper:
 ```