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--- |
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license: cc-by-nc-4.0 |
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task_categories: |
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- text-to-video |
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- text-to-image |
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language: |
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- en |
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pretty_name: VidProM |
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size_categories: |
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- 1M<n<10M |
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source_datasets: |
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- original |
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tags: |
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- prompts |
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- text-to-video |
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- text-to-image |
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- Pika |
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- VideoCraft2 |
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- Text2Video-Zero |
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- ModelScope |
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- Video Generative Model Evaluation |
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- Text-to-Video Diffusion Model Development |
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- Text-to-Video Prompt Engineering |
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- Efficient Video Generation |
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- Fake Video Detection |
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- Video Copy Detection for Diffusion Models |
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configs: |
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- config_name: VidProM_unique |
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data_files: VidProM_unique.csv |
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--- |
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<p align="center"> |
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<img src="https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/teasor.png" width="800"> |
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</p> |
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# Summary |
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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)" |
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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. |
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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. |
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# Directory |
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``` |
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*DATA_PATH |
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*VidProM_unique.csv |
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*VidProM_semantic_unique.csv |
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*VidProM_embed.hdf5 |
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*original_files |
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*generate_1_ori.html |
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*generate_2_ori.html |
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... |
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*pika_videos |
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*pika_videos_1.tar |
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*pika_videos_2.tar |
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... |
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*vc2_videos |
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*vc2_videos_1.tar |
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*vc2_videos_2.tar |
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... |
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*t2vz_videos |
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*t2vz_videos_1.tar |
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*t2vz_videos_2.tar |
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... |
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*ms_videos |
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*ms_videos_1.tar |
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*ms_videos_2.tar |
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... |
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*example |
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``` |
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# Download |
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### Automatical |
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Install the [datasets](https://huggingface.co/docs/datasets/v1.15.1/installation.html) library first, by: |
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``` |
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pip install datasets |
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``` |
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Then it can be downloaded automatically with |
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``` |
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import numpy as np |
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from datasets import load_dataset |
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dataset = load_dataset('WenhaoWang/VidProM') |
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``` |
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### Manual |
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You can also download each file by ```wget```, for instance: |
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``` |
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wget https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/VidProM_unique.csv |
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``` |
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# Explanation |
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``VidProM_unique.csv`` contains the UUID, prompt, time, and 6 NSFW probabilities. |
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It can easily be read by |
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``` |
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import pandas |
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df = pd.read_csv("VidProM_unique.csv") |
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``` |
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Below are three rows from ``VidProM_unique.csv``: |
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| uuid | prompt | time | toxicity | obscene | identity_attack | insult | threat | sexual_explicit | |
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|--------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------|----------|---------|-----------------|---------|---------|-----------------| |
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| 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 | |
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| 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 | |
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| 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 | |
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``VidProM_semantic_unique.csv`` is a semantically unique version of ``VidProM_unique.csv``. |
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``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. |
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It can easily be read by |
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``` |
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import numpy as np |
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import h5py |
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def read_descriptors(filename): |
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hh = h5py.File(filename, "r") |
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descs = np.array(hh["embeddings"]) |
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names = np.array(hh["uuid"][:], dtype=object).astype(str).tolist() |
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return names, descs |
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uuid, features = read_descriptors('VidProM_embed.hdf5') |
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``` |
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``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). |
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``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. |
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``example`` is a subfolder which contains 10,000 datapoints. |
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# Datapoint |
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<p align="center"> |
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<img src="https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/datapoint.png" width="800"> |
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</p> |
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# Visualization |
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<p align="center"> |
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<img src="https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/WizMap.png" width="800"> |
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</p> |
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Click the [WizMap](https://poloclub.github.io/wizmap/?dataURL=https%3A%2F%2Fhuggingface.co%2Fdatasets%2FWenhaoWang%2FVidProM%2Fresolve%2Fmain%2Fdata_gpu13.ndjson&gridURL=https%3A%2F%2Fhuggingface.co%2Fdatasets%2FWenhaoWang%2FVidProM%2Fresolve%2Fmain%2Fgrid_gpu13.json) |
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(and wait for 5 seconds) for an interactive visualization of our 1.67 million prompts. Above is a thumbnail. |
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# Comparison with DiffusionDB |
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<p align="center"> |
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<img src="https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/compare_table.png" width="800"> |
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</p> |
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<p align="center"> |
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<img src="https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/compare_visual.png" width="800"> |
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</p> |
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Please check our paper for a detailed comparison. |
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# Curators |
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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/). |
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# License |
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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). |
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# Citation |
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``` |
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@article{wang2024vidprom, |
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title={VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models}, |
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author={Wang, Wenhao and Yang, Yi}, |
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journal={arXiv preprint arXiv:2403.06098}, |
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year={2024} |
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} |
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``` |
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# Contact |
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If you have any questions, feel free to contact Wenhao Wang (wangwenhao0716@gmail.com). |
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