Datasets:

vanzl
/

DiffusionART-Prompts

@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text

 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+geneval/train.jsonl filter=lfs diff=lfs merge=lfs -text

README.md ADDED Viewed

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+---
+license: other
+license_name: upstream-per-subset
+license_link: https://huggingface.co/datasets/vanzl/DiffusionART-Prompts#sources-and-terms
+task_categories:
+- text-to-image
+- text-to-video
+- image-to-video
+configs:
+- config_name: pickscore
+  data_files:
+  - split: train
+    path: pickscore/train.jsonl
+  - split: validation
+    path: pickscore/validation.jsonl
+- config_name: t2v
+  data_files:
+  - split: train
+    path: t2v/train.jsonl
+  - split: validation
+    path: t2v/validation.jsonl
+- config_name: t2v1000
+  data_files:
+  - split: validation
+    path: t2v1000/validation.jsonl
+- config_name: i2v
+  data_files:
+  - split: train
+    path: i2v/train.jsonl
+- config_name: geneval
+  data_files:
+  - split: train
+    path: geneval/train.jsonl
+  - split: validation
+    path: geneval/validation.jsonl
+- config_name: ocr
+  data_files:
+  - split: train
+    path: ocr/train.jsonl
+  - split: validation
+    path: ocr/validation.jsonl
+---
+# DiffusionART Prompts
+Public prompt files used by the DiffusionART training infrastructure. Download
+from this dataset; no upstream GitHub data download is needed at training time.
+The converted JSONL files preserve source order and text. GenEval also preserves
+the complete per-prompt scoring annotations. Existing PickScore/video manifests
+match the previously pinned SHA256 values. GenEval/OCR are the named public
+Flow-GRPO splits, not certified byte-identical copies of private historical runs.
+| Configuration | Content |
+| --- | --- |
+| pickscore | Flow-GRPO train/test prompts |
+| geneval | Flow-GRPO GenEval train/test prompts and annotations |
+| ocr | Flow-GRPO OCR train/test prompts |
+| t2v | DanceGRPO 50,000 training prompts and frozen VidProM validation100 |
+| t2v1000 | Frozen VidProM validation1000, with validation100 as its prefix |
+| i2v | DanceGRPO ConsisID 27,815 training prompts |
+For I2V evaluation, use the t2v validation prompts. **No reference images are
+included.** On-demand FLUX reference generation is a new evaluation protocol,
+not a reconstruction of the original fixed JPEGs. All methods in a comparison
+must share the reference images and evaluation settings.
+## Usage
+```python
+from datasets import load_dataset
+data = load_dataset("vanzl/DiffusionART-Prompts", "t2v", split="validation")
+```
+Training recipes pin an immutable dataset revision. `sources/` retains the
+unchanged upstream inputs for deterministic conversion; `provenance.json`
+records source URLs, hashes, row counts and converted-file hashes.
+## Sources and Terms
+- Flow-GRPO: https://github.com/yifan123/flow_grpo/tree/879042cf5707f8b90daa98d147d7deac2317c5da/dataset
+- DanceGRPO: https://github.com/XueZeyue/DanceGRPO/tree/15cc71d53cc2e6e18a68ee607d5fb6ba9a99e344/assets
+- VidProM: https://huggingface.co/datasets/WenhaoWang/VidProM (CC-BY-NC-4.0).
+- ConsisID source: https://huggingface.co/datasets/BestWishYsh/ConsisID-preview-Data.
+Upstream notices are retained in `attribution/`. This collection does not
+relicense third-party data under the code repository's license. Respect each
+source's terms, including VidProM's noncommercial restriction. FLUX-generated
+images, when generated separately, are subject to the generator's output terms.
+Paper: https://arxiv.org/abs/2610.12572
+Code: https://github.com/vanzll/DiffusionART

attribution/DanceGRPO-Apache-2.0.txt ADDED Viewed

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attribution/FlowGRPO-MIT.txt ADDED Viewed

	@@ -0,0 +1,21 @@

+MIT License
+Copyright (c) 2025 Jie Liu
+Permission is hereby granted, free of charge, to any person obtaining a copy
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+in the Software without restriction, including without limitation the rights
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+The above copyright notice and this permission notice shall be included in all
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+SOFTWARE.

attribution/VidProM-README.md ADDED Viewed

	@@ -0,0 +1,193 @@

+---
+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) (NeurIPS 2024).
+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
+```python
+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](https://wisemodel.cn/home), and you can download them faster from [here](https://wisemodel.cn/datasets/WenhaoWang/VidProM).
+# Explanation
+``VidProM_unique.csv`` contains the UUID, prompt, time, and 6 NSFW probabilities.
+It can easily be read by
+```python
+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
+```python
+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.jpg" width="800">
+</p>
+<p align="center">
+  <img src="https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/compare_visual.png" width="800">
+</p>
+<p align="center">
+  <img src="https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/WizMap_V_D.jpg" width="800">
+</p>
+Click the [WizMap](https://poloclub.github.io/wizmap/?dataURL=https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/data_vidprom_diffusiondb.ndjson&gridURL=https://huggingface.co/datasets/WenhaoWang/VidProM/resolve/main/grid_vidprom_diffusiondb.json)
+(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](https://wangwenhao0716.github.io/) and Professor [Yi Yang](https://scholar.google.com/citations?user=RMSuNFwAAAAJ&hl=zh-CN).
+# 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},
+  booktitle={Thirty-eighth Conference on Neural Information Processing Systems},
+  year={2024},
+  url={https://openreview.net/forum?id=pYNl76onJL}
+}
+```
+# Contact
+If you have any questions, feel free to contact Wenhao Wang (wangwenhao0716@gmail.com).

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