| --- |
| license: other |
| license_name: ultravideo-dataset-license |
| license_link: LICENSE |
| pretty_name: DMAD MiniMax-H3 training data |
| task_categories: |
| - text-to-video |
| tags: |
| - video |
| - audio |
| - latents |
| - distillation |
| extra_gated_heading: "Access to the DMAD MiniMax-H3 training data" |
| extra_gated_description: "This data is derived from the UltraVideo dataset and is released under the UltraVideo Dataset License Agreement (non-commercial research only). Requesting access means you accept the agreement below." |
| extra_gated_prompt: | |
| # **UltraVideo Dataset License Agreement (CC-BY-4.0 with Additional Restrictions)** |
| |
| |
| 1.1. You may **use, modify, and distribute** the **UltraVideo Dataset** (the "Data") for **non-commercial research purposes only**, provided you comply with: |
| - This Agreement, |
| - YouTube’s Terms of Service, and |
| - Any applicable copyright laws. |
|
|
| 1.2. **Computational Use** (e.g., training AI models, analysis) is permitted, but **Results** (defined in §5) must not include more than a **de minimis portion** of the original Data. |
|
|
| 1.3. The Data Provider waives claims against you or **Downstream Recipients** for compliant use, modification, or distribution. |
|
|
| --- |
|
|
| |
| 2.1. **Commercial use is prohibited**, including: |
| - Monetization, product integration, or service improvement. |
| - Redistribution for monetary or other consideration. |
|
|
| 2.2. **Ethical & Legal Compliance**: |
| - Do not use the Data for harmful, misleading, or unethical applications. |
| - Do not redistribute **raw videos, transcripts, or thumbnails** outside this dataset. |
| - Obtain **creator permission** for derivative works beyond research. |
|
|
| 2.3. **No Re-identification**: Handle any residual personal data in compliance with privacy laws (GDPR, CCPA, etc.). |
|
|
| --- |
|
|
| |
| 3.1. If redistributing the Data, you must: |
| - Include all original **credit/attribution** (e.g., Panda-70M/Koala-36M licenses). |
| - Bind recipients to **this Agreement**. |
|
|
| 3.2. **Derivative datasets** must retain the same license terms. |
|
|
| --- |
|
|
| |
| 4.1. **No Ownership Warranty**: The Data Provider does not claim rights to the Data. |
|
|
| 4.2. **"AS IS" Basis**: The Data may contain inaccuracies, biases, or sensitive content. Use at your own risk. |
|
|
| 4.3. **No Liability**: The Data Provider and **Upstream Data Providers** (e.g., YouTube) are not liable for: |
| - Damages from misuse (legal, privacy, or reputational). |
| - Violations of third-party terms or laws. |
|
|
| --- |
|
|
| |
| - **Data**: The UltraVideo Dataset (videos, metadata, etc.), modified or unmodified. |
| - **Results**: Outputs (e.g., AI models, research papers) containing ≤ de minimis Data. |
| - **Downstream Recipient**: Any entity receiving the Data through you. |
|
|
| --- |
|
|
| |
| The Data Provider may **revoke access** to the Data for legal, ethical, or security reasons. |
|
|
| --- |
|
|
| **By using the Data, you agree to these terms.** |
| *Last updated: 2025.05.22 |
|
|
| --- |
|
|
| |
| - **Non-commercial research only**. |
| - **Attribute properly** (CC-BY-4.0 + upstream licenses). |
| - **No redistribution of raw YouTube content**. |
| - **Comply with YouTube’s ToS and privacy laws**. |
| extra_gated_fields: |
| I will use this data for non-commercial research only and I accept the UltraVideo Dataset License Agreement: checkbox |
| extra_gated_button_content: "I accept the license and request access" |
| --- |
| |
| # DMAD MiniMax-H3 training data |
|
|
| The preprocessed training data of the DMAD 4-step students of [MiniMax-H3](https://huggingface.co/MiniMaxAI/MiniMax-H3) |
| (text-to-audio-video), from the paper [DMAD: Distribution Matching as Adversarial Distillation for Fast Visual |
| Generation](https://arxiv.org/abs/2610.02188). The training code is in |
| [`train/h3`](https://github.com/Yzmblog/DMAD/tree/main/train/h3) of the [DMAD repository](https://github.com/Yzmblog/DMAD); |
| the released students are at [ZhengmingYu/DMAD](https://huggingface.co/ZhengmingYu/DMAD). The training data of the |
| Wan2.1 students is at [ZhengmingYu/DMAD-Wan-data](https://huggingface.co/datasets/ZhengmingYu/DMAD-Wan-data). |
|
|
| Everything here is what `train/h3/scripts/prepare_data.sh` produces from [UltraVideo](https://huggingface.co/datasets/APRIL-AIGC/UltraVideo) |
| (`short.csv` and the `clips_short_1920` clips) and the MiniMax-H3 base model, so training can start without the |
| ~1 GPU-week of preprocessing. No video or audio is stored: the clips are kept only as MiniMax-H3 VAE latents, the |
| captions as text-encoder outputs (the captions themselves are included as text). |
|
|
| ## Contents |
|
|
| 303 GB, 59,816 data files, all tensors in bfloat16. `real_latents/latents/`, `prompt_cache/conditions/` and |
| `teacher_latents/` keep their files in sub-folders named by the last two characters of the file name |
| (`teacher_latents/23/00000123.pt`, `real_latents/latents/5b/<clip_id>.pt`), because the Hub allows at most 10,000 |
| files per directory; the trainer looks files up in both this and the flat layout, so nothing needs to be moved. |
|
|
| | Folder | Files | Size | What | |
| | --- | --- | --- | --- | |
| | `real_latents/` | 24,004 `.pt` + 8 manifests | 156 GB | UltraVideo clips encoded with the MiniMax-H3 video and audio VAEs (`data_process/encode_real_videos.py`) | |
| | `prompt_cache/` | 17,902 `.pt` + `metadata.jsonl` | 28 GB | MiniMax-H3 text-encoder outputs of the training captions (`data_process/encode_prompts.py`) | |
| | `teacher_latents/` | 17,898 `.pt` | 120 GB | one MiniMax-H3 sample per training caption, packed token rows (`data_process/gen_teacher_data.py`) | |
| | `ultravideo_prompts/` | 3 text files | 38 MB | the caption list, the caption-to-clip map, the training caption indices (`data_process/build_prompt_list.py`, `build_pool_indices.py`) | |
|
|
| **`real_latents/latents/<clip_id>.pt`** — `{"video_latent": [24, T', 48, 84], "audio_latent": [2, n, 32], "meta": {...}}` |
| for a 768x1344, 24 fps crop of the clip starting at `meta["t0"]`: 17,911 clips at the 124-frame tier (T' = 37, 5.2 s) |
| and 6,093 at the 107-frame tier (T' = 32); `meta` also records the source zip, duration and whether the HDR source |
| was tone-mapped. `manifest_rank*.jsonl` list every clip of the UltraVideo short set (the 18,179 clips shorter than |
| the 107-frame tier, and one clip rejected in a manual quality review, are recorded with `"skip"`). Training uses |
| the 124-frame tier (17,911 clips). |
| |
| **`prompt_cache/conditions/condition_<index>.pt`** — `{"conditioning": {"positive": {"prompt_embeds": [1, L, 5120], |
| "text_token_tags": [L]}}, "prompt": caption, "source_index": index}`: the MiniMax-H3 text encoder's output for |
| caption `index` (the line number in `ultravideo_prompts/prompts_dedup.txt`), as consumed by the H3 transformer. |
| `metadata.jsonl` maps `id` to the caption and the file. The 17,902 cached captions are those of the 124-frame clips |
| plus the four visualization prompts (indices 0-3). |
| |
| **`teacher_latents/<index>.pt`** — `{"video_rows": [1, 37296, 96], "audio_rows": [1, 414, 32], "meta": {...}}`: one |
| sample of the MiniMax-H3 base model for caption `index` with its official sampler (31 steps, video shift 12, audio |
| shift 3, seed = index), stored in the transformer's packed token layout at 768x1344 x 124 frames. |
|
|
| **`ultravideo_prompts/`** — `prompts_dedup.txt` (42,158 unique UltraVideo "Detailed Description" captions, one per |
| line, first-occurrence order), `ultravideo_index_to_clipid.jsonl` (`{"index", "clip_ids"}`, the `real_caption_map` |
| of the training config), `pool124_plus_viz_indices.txt` (the 17,902 caption indices used in training). |
| |
| ## Use |
| |
| ```bash |
| hf download ZhengmingYu/DMAD-H3-data --repo-type dataset --local-dir /path/to/dmad_h3_data |
| ``` |
| |
| then in `train/h3/configs/dmad_minimax_h3.yaml` (or `dmad_minimax_h3_full_critic.yaml`): |
| |
| ```yaml |
| dmad: |
| prompt_cache_dir: /path/to/dmad_h3_data/prompt_cache |
| real_latent_dir: /path/to/dmad_h3_data/real_latents |
| real_caption_map: /path/to/dmad_h3_data/ultravideo_prompts/ultravideo_index_to_clipid.jsonl |
| teacher_latent_dir: /path/to/dmad_h3_data/teacher_latents |
| ``` |
| |
| The latents are tied to the MiniMax-H3 VAEs and text encoder; they are not usable with other models. To rebuild |
| or extend the data, see "Data Preparation" in the `train/h3` README. |
| |
| ## License |
| |
| * `real_latents/`, `prompt_cache/` and `ultravideo_prompts/` are derived from the UltraVideo dataset and are |
| distributed under the [UltraVideo Dataset License Agreement](LICENSE) (CC-BY-4.0 with additional restrictions: |
| **non-commercial research use only**, no redistribution of the source videos, derivative datasets keep these |
| terms). By requesting access you accept that agreement; please also follow the attribution requirements of |
| UltraVideo and its upstream sources ([Panda-70M](https://snap-research.github.io/Panda-70M/), |
| [Koala-36M](https://koala36m.github.io/)). |
| * `teacher_latents/` are outputs of MiniMax-H3 and are AI-generated; see [`NOTICE_MiniMax-H3`](NOTICE_MiniMax-H3) and the |
| [MiniMax H3 Community License Agreement](LICENSE_MiniMax-H3). |
| |
| ## Citation |
| |
| ```bibtex |
| @article{yu2026dmad, |
| title={DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation}, |
| author={Yu, Zhengming and Yuan, Junkun and Yang, Haotian and Qian, Gordon Guocheng and Wang, Yizhi and Wang, Angtian and Yang, Yiding and Liu, Bo and Li, Xin and Wang, Wenping and Ma, Chongyang}, |
| journal={arXiv preprint arXiv:2610.02188}, |
| year={2026} |
| } |
| |
| @article{ultravideo, |
| title={UltraVideo: High-Quality UHD Video Dataset with Comprehensive Captions}, |
| author={Xue, Zhucun and Zhang, Jiangning and Hu, Teng and He, Haoyang and Chen, Yinan and Cai, Yuxuan and Wang, Yabiao and Wang, Chengjie and Liu, Yong and Li, Xiangtai and Tao, Dacheng}, |
| journal={arXiv preprint arXiv:2506.13691}, |
| year={2025} |
| } |
| ``` |
| |
