Link the Wan2.1 training data (DMAD-Wan-data)

README.md

@@ -49,6 +49,8 @@ students of the **EDM ImageNet-64** teacher. The inference and training code is
 
 ## Updates
 
+* **2026/10/15:** The [Wan2.1 training data](https://huggingface.co/datasets/ZhengmingYu/DMAD-Wan-data) (42,158
+  Wan2.1-14B teacher samples with their real-video latents), so `train/wan` can start training right away 🤗
 * **2026/10/10:** 8 GB GPUs: a 5 s [ComfyUI workflow](#ready-to-run-workflows-8-gb-gpu-and-up) for 8 GB GPUs, and
   `--low-vram` in the [code repository](https://github.com/Yzmblog/DMAD#on-consumer-gpus) now makes 5 s videos in under
   7 GiB of GPU memory and 15 s videos in 12 GiB 🪶
@@ -193,6 +195,10 @@ hf download ZhengmingYu/DMAD --include "wan2.1/*" --local-dir ckpt
 bash experiments/dmad/sample.sh 1.3B ckpt/wan2.1/dmad_wan2pt1_1pt3B.pth my_prompts.json outputs/samples   # in train/wan
 ```
 
+The training data of both students (42,158 rows: Wan2.1-14B teacher sample, umT5 embedding and the caption-paired
+UltraVideo clip latent, 531 GB of WebDataset shards) is at
+[ZhengmingYu/DMAD-Wan-data](https://huggingface.co/datasets/ZhengmingYu/DMAD-Wan-data).
+
 ## SDXL (text-to-image)
 
 UNet state dicts in fp16 (spectral norm folded in) that load into the standard SDXL pipeline; see