--- license: cc-by-nc-4.0 task_categories: - text-to-video language: - en size_categories: - 1M # 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. # 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 ... ``` # Download ### Automatically 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. ``VidProM_semantic_unique.csv`` is a semantically unique version of ``VidProM_unique.csv``. ``VidProM_embed.hdf5`` is the 3072-dim embeddings of each prompt. They are embedded by text-embedding-3-large, which is the latest text embedding model of OpenAI. ``original_files`` are the HTML files collected by DiscordChatExporter. ``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. # Datapoint # Comparison with DiffusionDB Please check our paper for a detailed comparison. # Citation # Contact If you have any questions, feel free to contact Wenhao Wang (wangwenhao0716@gmail.com).