metadata

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

Summary

This is the dataset proposed in our paper "VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models"

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.

Download

Automatical

Install the datasets 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

Users from China

For users from China, we cooperate with Wisemodel, and you can download them faster from here.

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 collected by DiscordChatExporter. You can do whatever you want with it under CC BY-NC 4.0 license.

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

Comparison with DiffusionDB

Click the WizMap (and wait for 5 seconds) for an interactive visualization of our 1.67 million prompts. Above is a thumbnail.

Please check our paper for a detailed comparison.

Curators

VidProM is created by Wenhao Wang and Professor Yi Yang.

License

The prompts and videos generated by Pika in our VidProM are licensed under the CC BY-NC 4.0 license. Additionally, similar to their original repositories, the videos from VideoCraft2, Text2Video-Zero, and ModelScope are released under the Apache license, the CreativeML Open RAIL-M license, and the CC BY-NC 4.0 license, respectively. Our code is released under the CC BY-NC 4.0 license.

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).

Datasets:

WenhaoWang
/

VidProM