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+# Adaptive Video Distillation
+### Mitigating Oversaturation and Temporal Collapse in Few-Step Generation
+
+[Project Page](https://Adaptive-Video-Distillation.github.io/)  
+
+<video width="480" height="270" controls>
+  <source src="docs/sample.mp4" type="video/mp4">
+  Your browser does not support the video tag.
+</video>
+
+> **Adaptive Video Distillation**  
+> Yuyang You*, Yongzhi Li*, Jiahui Li, Yadong Mu, Quan Chen, Peng ...  
+> *CVPR 2026*  
+
 ---
-license: mit
-language:
-- en
-- zh
-base_model:
-- Wan-AI/Wan2.1-T2V-1.3B
----
-# {{ADV}}: {{Adaptive Video Distillation}}
 
-[![Paper](https://arxiv.org/abs/2603.21864)](https://arxiv.org/abs/{{arxiv_id}})
-[![Project Page](https://adaptive-video-distillation.github.io/)]({{project_page_url}})
-[![GitHub](https://github.com/yuyangyou/Adaptive-Video-Distillation)]({{github_url}})
+## Overview
+
+This is the official repository for ADV (Adaptive Video Distillation) — a video model distillation method based on DMD（Distribution Matching Distillation）. It addresses oversaturation and slow-motion issues in video generation model distillation, and is capable of learning from new data during distillation training.
+
+
+## Environment Setup
+
+```bash
+conda create -n AVD python=3.10 -y
+conda activate AVD
+pip install torch torchvision 
+pip install -r requirements.txt 
+python setup.py develop
+```
+
+Also download the Wan base models from [here](https://github.com/Wan-Video/Wan2.1) and save it to wan_models/Wan2.1-T2V-1.3B/
+
+## Inference Example 
+
+First download the checkpoints: [Autoregressive Model](https://huggingface.co/).
+
+
+### Inference Script
+
+```bash 
+python ./tests/wan/test_bidirectional_fewstep.py
+```
+
+## Training and Evaluation  
+
+### Dataset Preparation 
+
+We use the [MixKit Dataset](https://huggingface.co/datasets/LanguageBind/Open-Sora-Plan-v1.1.0/tree/main/all_mixkit) (6K videos) as a toy example for distillation. 
+
+To prepare the dataset, follow these steps. You can also download the final LMDB dataset from [here](https://huggingface.co/tianweiy/CausVid/tree/main/mixkit_latents_lmdb)
+
+```bash
+# download and extract video from the Mixkit dataset 
+python distillation_data/download_mixkit.py  --local_dir XXX 
+
+# convert the video to 480x832x81 
+python distillation_data/process_mixkit.py --input_dir XXX  --output_dir XXX --width 832   --height 480  --fps 16 
+
+# precompute the vae latent 
+torchrun --nproc_per_node 8 distillation_data/compute_vae_latent.py --input_video_folder XXX  --output_latent_folder XXX   --info_path sample_dataset/video_mixkit_6484_caption.json
+
+# combined everything into a lmdb dataset 
+python causvid/ode_data/create_lmdb_iterative.py   --data_path XXX  --lmdb_path XXX
+```
+
+## Training 
+
+Please first modify the wandb account information in the respective config.  
+
+Bidirectional DMD Training
+
+```bash
+torchrun --nnodes 1 --nproc_per_node=8 --master_port 29502 \
+    causvid/train_distillation_regression.py \
+    --config_path configs/wan_bidirectional_dmd.yaml
+
+```
+
+## Citation 
+
+Here is a arxiv version citation bib：
+
+```bib
+@misc{you2026adaptivevideodistillationmitigating,
+      title={Adaptive Video Distillation: Mitigating Oversaturation
+             and Temporal Collapse in Few-Step Generation},
+      author={Yuyang You and Yongzhi Li and Jiahui Li
+              and Yadong Mu and Quan Chen and Peng Jiang},
+      year={2026},
+      eprint={2603.21864},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2603.21864},
+}
+
+```
+
+## Acknowledgments
+
+Our implementation is largely based on the [Causvid](https://github.com/tianweiy/CausVid) and [Wan](https://github.com/Wan-Video/Wan2.1) model suite.