Datasets:

ZhengmingYu
/

DMAD-Wan-data

---
license: other
license_name: ultravideo-dataset-license
license_link: LICENSE
pretty_name: DMAD Wan2.1 training data
task_categories:
- text-to-video
tags:
- video
- latents
- distillation
- webdataset
- wan2.1
viewer: false
extra_gated_heading: "Access to the DMAD Wan2.1 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. Permitted Use**  
  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. Restrictions**  
  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. Redistribution Requirements**  
  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. Disclaimers & Liability**  
  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.  

  ---

  ### **5. Definitions**  
  - **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.  

  ---

  ### **6. Termination**  
  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

  --- 

  ### **Key Notes for Users**:  
  - **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 Wan2.1 training data

The training data of the DMAD 4-step students of [Wan2.1-T2V](https://github.com/Wan-Video/Wan2.1) (1.3B and 14B), 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/wan`](https://github.com/Yzmblog/DMAD/tree/main/train/wan) of the [DMAD repository](https://github.com/Yzmblog/DMAD);
the released students are at [ZhengmingYu/DMAD](https://huggingface.co/ZhengmingYu/DMAD) (`wan2.1/`).

This is exactly the data both students were trained on, i.e. what `train/wan/experiments/dmad/prepare_data.sh` produces
from [UltraVideo](https://huggingface.co/datasets/APRIL-AIGC/UltraVideo), so training can start without generating the
teacher samples (Wan2.1-T2V-14B, 100 steps with classifier-free guidance, for 42,158 captions). No video is stored:
the teacher samples and the real clips are kept only as Wan2.1 VAE latents.

## Contents

531 GB in 5,270 WebDataset shards `shards/shard_000000.tar` ... `shards/shard_005269.tar`, 8 rows per shard
(the last one 6): 42,158 rows, one per de-duplicated UltraVideo caption, row index = caption index. Each row has five
members:

| Member | Shape / type | What |
| --- | --- | --- |
| `<index>.latent.pt` | `[16, 21, 60, 104]` bfloat16 | Wan2.1-T2V-14B teacher sample for the caption: 100-step Euler, CFG 5, shift 3, seed = index, 81 frames at 832x480 |
| `<index>.embed.pt` | `[512, 4096]` bfloat16 | umT5-XXL embedding of the caption |
| `<index>.prompt.txt` | text | the caption (UltraVideo "Detailed Description") |
| `<index>.real_latent.pt` | `[16, 21, 60, 104]` bfloat16 | the UltraVideo clip that the caption describes, VAE-encoded into the same latent space |
| `<index>.real_meta.json` | json | the clip id and its preprocessing (`mode`, `speed_factor`, `src_frames`, `src_fps`, `hdr_tonemapped`) |

The real clips are preprocessed as in [`rcm/datasets/build_real_latents.py`](https://github.com/Yzmblog/DMAD/blob/main/train/wan/rcm/datasets/build_real_latents.py):
81 frames sampled uniformly over the whole clip (the teacher puts the whole caption into one clip, so the real clip is
time-normalized the same way; the speed factor is recorded), resized to cover 832x480 and center-cropped, and HDR
clips (about 4%) tone-mapped to SDR bt709 before encoding. Training crops both latents to the model's temporal length.

## Use

```bash
hf download ZhengmingYu/DMAD-Wan-data --repo-type dataset --local-dir /path/to/data
```

then, in `train/wan`:

```bash
DATA=/path/to/data bash experiments/dmad/train_1pt3B.sh                    # 1 node x 8 GPUs
DATA=/path/to/data NODE_RANK=<i> MASTER_ADDR=<host> bash experiments/dmad/train_14B.sh   # on each of 8 nodes
```

The training scripts read `$DATA/shards/shard*.tar` with `rcm/datasets/webdataset_dmad.py`. To download only part of
the data (e.g. for a quick test), use `--include "shards/shard_0000*.tar"`. The latents are tied to the Wan2.1 VAE and
umT5 text encoder; to rebuild or extend the data, see "Data" in the `train/wan` README.

## License

* `real_latent.pt`, `real_meta.json` and `prompt.txt` / `embed.pt` 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/)).
* `latent.pt` are AI-generated outputs of Wan2.1-T2V-14B (Apache License 2.0, see [`LICENSE_Wan2.1`](LICENSE_Wan2.1)),
  conditioned on the UltraVideo captions.

## 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}
}
@article{wan2025,
  title={Wan: Open and Advanced Large-Scale Video Generative Models},
  author={Team Wan and others},
  journal={arXiv preprint arXiv:2503.20314},
  year={2025}
}
```

	---
	license: other
	license_name: ultravideo-dataset-license
	license_link: LICENSE
	pretty_name: DMAD Wan2.1 training data
	task_categories:
	- text-to-video
	tags:
	- video
	- latents
	- distillation
	- webdataset
	- wan2.1
	viewer: false
	extra_gated_heading: "Access to the DMAD Wan2.1 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. Permitted Use
	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. Restrictions
	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. Redistribution Requirements
	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. Disclaimers & Liability
	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.

	---

	### 5. Definitions
	- 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.

	---

	### 6. Termination
	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

	---

	### Key Notes for Users:
	- 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 Wan2.1 training data

	The training data of the DMAD 4-step students of [Wan2.1-T2V](https://github.com/Wan-Video/Wan2.1) (1.3B and 14B), 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/wan`](https://github.com/Yzmblog/DMAD/tree/main/train/wan) of the [DMAD repository](https://github.com/Yzmblog/DMAD);
	the released students are at [ZhengmingYu/DMAD](https://huggingface.co/ZhengmingYu/DMAD) (`wan2.1/`).

	This is exactly the data both students were trained on, i.e. what `train/wan/experiments/dmad/prepare_data.sh` produces
	from [UltraVideo](https://huggingface.co/datasets/APRIL-AIGC/UltraVideo), so training can start without generating the
	teacher samples (Wan2.1-T2V-14B, 100 steps with classifier-free guidance, for 42,158 captions). No video is stored:
	the teacher samples and the real clips are kept only as Wan2.1 VAE latents.

	## Contents

	531 GB in 5,270 WebDataset shards `shards/shard_000000.tar` ... `shards/shard_005269.tar`, 8 rows per shard
	(the last one 6): 42,158 rows, one per de-duplicated UltraVideo caption, row index = caption index. Each row has five
	members:

	\| Member \| Shape / type \| What \|
	\| --- \| --- \| --- \|
	\| `<index>.latent.pt` \| `[16, 21, 60, 104]` bfloat16 \| Wan2.1-T2V-14B teacher sample for the caption: 100-step Euler, CFG 5, shift 3, seed = index, 81 frames at 832x480 \|
	\| `<index>.embed.pt` \| `[512, 4096]` bfloat16 \| umT5-XXL embedding of the caption \|
	\| `<index>.prompt.txt` \| text \| the caption (UltraVideo "Detailed Description") \|
	\| `<index>.real_latent.pt` \| `[16, 21, 60, 104]` bfloat16 \| the UltraVideo clip that the caption describes, VAE-encoded into the same latent space \|
	\| `<index>.real_meta.json` \| json \| the clip id and its preprocessing (`mode`, `speed_factor`, `src_frames`, `src_fps`, `hdr_tonemapped`) \|

	The real clips are preprocessed as in [`rcm/datasets/build_real_latents.py`](https://github.com/Yzmblog/DMAD/blob/main/train/wan/rcm/datasets/build_real_latents.py):
	81 frames sampled uniformly over the whole clip (the teacher puts the whole caption into one clip, so the real clip is
	time-normalized the same way; the speed factor is recorded), resized to cover 832x480 and center-cropped, and HDR
	clips (about 4%) tone-mapped to SDR bt709 before encoding. Training crops both latents to the model's temporal length.

	## Use

	```bash
	hf download ZhengmingYu/DMAD-Wan-data --repo-type dataset --local-dir /path/to/data
	```

	then, in `train/wan`:

	```bash
	DATA=/path/to/data bash experiments/dmad/train_1pt3B.sh # 1 node x 8 GPUs
	DATA=/path/to/data NODE_RANK=<i> MASTER_ADDR=<host> bash experiments/dmad/train_14B.sh # on each of 8 nodes
	```

	The training scripts read `$DATA/shards/shard*.tar` with `rcm/datasets/webdataset_dmad.py`. To download only part of
	the data (e.g. for a quick test), use `--include "shards/shard_0000*.tar"`. The latents are tied to the Wan2.1 VAE and
	umT5 text encoder; to rebuild or extend the data, see "Data" in the `train/wan` README.

	## License

	* `real_latent.pt`, `real_meta.json` and `prompt.txt` / `embed.pt` 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/)).
	* `latent.pt` are AI-generated outputs of Wan2.1-T2V-14B (Apache License 2.0, see [`LICENSE_Wan2.1`](LICENSE_Wan2.1)),
	conditioned on the UltraVideo captions.

	## 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}
	}

	@article{wan2025,
	title={Wan: Open and Advanced Large-Scale Video Generative Models},
	author={Team Wan and others},
	journal={arXiv preprint arXiv:2503.20314},
	year={2025}
	}
	```