README.md

---
license: cc-by-nc-4.0
task_categories:
- text-to-video
- text-to-image
language:
- en
pretty_name: VidProM
size_categories:
- 1M<n<10M
source_datasets:
  - original
tags:
- prompts
- text-to-video
- text-to-image
- Pika
- VideoCraft2
- Text2Video-Zero
- ModelScope
- Video Generative Model Evaluation
- Text-to-Video Diffusion Model Development
- Text-to-Video Prompt Engineering
- Efficient Video Generation
- Fake Video Detection
- Video Copy Detection for Diffusion Models
configs:
- config_name: VidProM_unique
  data_files: VidProM_unique.csv
---


<p align="center">
  <img src="https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/teasor.png" width="800">
</p>


# Summary
This is the dataset proposed in our paper "[**VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models**](https://arxiv.org/abs/2403.06098)"

VidProM is the first dataset featuring 1.67 million unique text-to-video prompts and 6.69 million videos generated from 4 different state-of-the-art diffusion models.
It inspires many exciting new research areas, such as Text-to-Video Prompt Engineering, Efficient Video Generation, Fake Video Detection, and Video Copy Detection for Diffusion Models.

# Directory
```
*DATA_PATH
    *VidProM_unique.csv
    *VidProM_semantic_unique.csv
    *VidProM_embed.hdf5
	*original_files
		*generate_1_ori.html
		*generate_2_ori.html
        ...
	*pika_videos
		*pika_videos_1.tar
		*pika_videos_2.tar
		...
    *vc2_videos
        *vc2_videos_1.tar
		*vc2_videos_2.tar
		...
    *t2vz_videos
        *t2vz_videos_1.tar
		*t2vz_videos_2.tar
		...
    *ms_videos
        *ms_videos_1.tar
		*ms_videos_2.tar
		...
    *example

```


# Download 

### Automatical
Install the [datasets](https://huggingface.co/docs/datasets/v1.15.1/installation.html) library first, by:
```
pip install datasets
```
Then it can be downloaded automatically with
```
import numpy as np
from datasets import load_dataset
dataset = load_dataset('WenhaoWang/VidProM')
```

### Manual

You can also download each file by ```wget```, for instance:
```
wget https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/VidProM_unique.csv
```

# Explanation

``VidProM_unique.csv`` contains the UUID, prompt, time, and 6 NSFW probabilities.

It can easily be read by

```
import pandas
df = pd.read_csv("VidProM_unique.csv")
```

Below are three rows from ``VidProM_unique.csv``:
| uuid                                 | prompt                                                                                                                                                                 | time                     | toxicity | obscene | identity_attack | insult  | threat  | sexual_explicit |
|--------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------|----------|---------|-----------------|---------|---------|-----------------|
| 6a83eb92-faa0-572b-9e1f-67dec99b711d | Flying among clouds and stars, kitten Max discovered a world full of winged friends. Returning home, he shared his stories and everyone smiled as they imagined flying together in their dreams. | Sun Sep  3 12:27:44 2023 | 0.00129  | 0.00016 | 7e-05           | 0.00064 | 2e-05   | 2e-05           |
| 3ba1adf3-5254-59fb-a13e-57e6aa161626 | Use a clean and modern font for the text "Relate Reality 101." Add a small, stylized heart icon or a thought bubble above or beside the text to represent emotions and thoughts. Consider using a color scheme that includes warm, inviting colors like deep reds, soft blues, or soothing purples to evoke feelings of connection and intrigue. | Wed Sep 13 18:15:30 2023 | 0.00038  | 0.00013 | 8e-05           | 0.00018 | 3e-05   | 3e-05           |
| 62e5a2a0-4994-5c75-9976-2416420526f7 | zoomed out, sideview of an Grey Alien sitting at a computer desk                                                                                                       | Tue Oct 24 20:24:21 2023 | 0.01777  | 0.00029 | 0.00336         | 0.00256 | 0.00017 | 5e-05           |


``VidProM_semantic_unique.csv`` is a semantically unique version of ``VidProM_unique.csv``.

``VidProM_embed.hdf5`` is the 3072-dim embeddings of our prompts. They are embedded by text-embedding-3-large, which is the latest text embedding model of OpenAI.

It can easily be read by

```
import numpy as np
import h5py
def read_descriptors(filename):
    hh = h5py.File(filename, "r")
    descs = np.array(hh["embeddings"])
    names = np.array(hh["uuid"][:], dtype=object).astype(str).tolist()
    return names, descs

uuid, features = read_descriptors('VidProM_embed.hdf5')
```

``original_files`` are the HTML files from [official Pika Discord](https://discord.com/invite/pika) collected by [DiscordChatExporter](https://github.com/Tyrrrz/DiscordChatExporter). You can do whatever you want with it under [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/deed.en).

``pika_videos``, ``vc2_videos``, ``t2vz_videos``, and ``ms_videos`` are the generated videos by 4 state-of-the-art text-to-video diffusion models. Each contains 30 tar files.

``example`` is a subfolder which contains 10,000 datapoints.


# Datapoint
<p align="center">
  <img src="https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/datapoint.png" width="800">
</p>


# Comparison with DiffusionDB


<p align="center">
  <img src="https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/compare_table.png" width="800">
</p>

<p align="center">
  <img src="https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/compare_visual.png" width="800">
</p>

Please check our paper for a detailed comparison.

# Curators
VidProM is created by [Wenhao Wang](https://wangwenhao0716.github.io/) and Professor [Yi Yang](https://scholar.google.com/citations?user=RMSuNFwAAAAJ&hl=zh-CN) from [the ReLER Lab](https://reler.net/).

# License

The prompts and videos generated by [Pika](https://discord.com/invite/pika) in our VidProM are licensed under the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/deed.en). Additionally, similar to their original repositories, the videos from [VideoCraft2](https://github.com/AILab-CVC/VideoCrafter), [Text2Video-Zero](https://github.com/Picsart-AI-Research/Text2Video-Zero), and [ModelScope](https://huggingface.co/ali-vilab/modelscope-damo-text-to-video-synthesis) are released under the [Apache license](https://www.apache.org/licenses/LICENSE-2.0), the [CreativeML Open RAIL-M license](https://github.com/Picsart-AI-Research/Text2Video-Zero/blob/main/LICENSE), and the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/deed.en), respectively. Our code is released under the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/deed.en).


# Citation
```
@article{wang2024vidprom,
  title={VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models},
  author={Wang, Wenhao and Yang, Yi},
  journal={arXiv preprint arXiv:2403.06098},
  year={2024}
}
```

# Contact

If you have any questions, feel free to contact Wenhao Wang (wangwenhao0716@gmail.com).