Downloads · 30 days
0
wzhangek/CoDMD
CoDMD is a text-to-video model from wzhangek. Use it when you need video from a text prompt. The card lists the license as cc-by-nc-sa-4.0.
Downloads · 30 days
0
Access
Public
Updated Jul 15, 2026
Repo size
62.8 GB
Likes
1
Public
Click a slice to open those files.
.pt62.8 GB · 100%
From the Hugging Face model README
Paper | Project Page | Code
CoDMD distills Wan2.1 text-to-video diffusion models into 4-step generators while preserving joint dependency structures across video frames and samples through a copula-aware distillation loss.
This repository provides the official generator-only checkpoints for:
| Checkpoint | Base model | Sampling steps | File |
|---|---|---|---|
| CoDMD-1.3B | Wan2.1-T2V-1.3B | 4 | CoDMD_wan2.1_T2V_1.3B.pt |
| CoDMD-14B | Wan2.1-T2V-14B | 4 | CoDMD_wan2.1_T2V_14B.pt |
Each model.pt contains:
{"generator": state_dict}
The training-only critic and optimizer state are not included. The released checkpoints are intended for inference with the original CoDMD codebase, whose inference pipeline loads only the generator entry.
Clone and install CoDMD:
git clone https://github.com/JIA-Lab-research/CoDMD.git
cd CoDMD
pip install -r requirements.txt
pip install easydict
pip install -e .
Download the corresponding Wan2.1 base model, then set model_path in:
configs/wan_dmd_tar.yaml for CoDMD-1.3Bconfigs/wan_dmd_tar_14b.yaml for CoDMD-14BRun inference by pointing --checkpoint_folder to the folder containing the downloaded model.pt:
python inference.py \
--config_path configs/wan_dmd_tar_14b.yaml \
--checkpoint_folder <HF_REPO_DIR>/CoDMD_wan2.1_T2V_14B \
--output_folder ./results \
--prompt_file_path prompts.txt \
--num_seeds 5
For CoDMD-1.3B, use configs/wan_dmd_tar.yaml and the CoDMD_wan2.1_T2V_1.3B checkpoint folder.
The CoDMD release is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The Wan2.1 base models remain subject to their respective licenses and terms of use.
@misc{zhang2026codmdcopulaawaredistributionmatching,
title={CoDMD: Copula-aware Distribution Matching Distillation for Fast Video Generation},
author={Wenhu Zhang and Kun Cheng and Changyuan Wang and Shiyao Li and Yuechen Zhang and Wenbo Li and Jiajun Zha and Jingyi Zhang and Kang Zhao and Jiaya Jia},
year={2026},
eprint={2606.21982},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2606.21982}
}