Instructions to use LiJiaxing/DistillAlign with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SelfForcing
How to use LiJiaxing/DistillAlign with SelfForcing:
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- Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| pipeline_tag: text-to-video | |
| tags: | |
| - video-generation | |
| - autoregressive-video | |
| - diffusion-distillation | |
| - wan2.1 | |
| - self-forcing | |
| <h1 align="center">DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation</h1> | |
| <p align="center"> | |
| <b>Jiaxing Li</b><sup>1,2*</sup>, | |
| <b>Kai Zou</b><sup>1*</sup>, | |
| <b>Cindy Zhou</b><sup>1,3</sup>, | |
| <b>Kaichen Huang</b><sup>1,2</sup>, | |
| <b>Junyao Gao</b><sup>1</sup>, | |
| <b>Zile Wang</b><sup>1</sup>, | |
| <br> | |
| <b>Yang Liu</b><sup>1</sup>, | |
| <b>Bin Liu</b><sup>1</sup>, | |
| <b>Bo An</b><sup>2</sup>, | |
| <b>Yangguang Li</b><sup>1†</sup> | |
| </p> | |
| <p align="center"> | |
| <sup>1</sup>Riemann Dynamics <sup>2</sup>Nanyang Technological University <sup>3</sup>Wellington College, UK | |
| </p> | |
| <p align="center"><sub><sup>*</sup>Equal contribution <sup>†</sup>Corresponding author</sub></p> | |
| <p align="center"> | |
| <a href="https://lijiaxing0213.github.io/DistillAlign">Project Page</a> · | |
| <a href="https://huggingface.co/papers/2607.26811">Paper</a> · | |
| <a href="https://github.com/LiJiaxing0213/DistillAlign">Code</a> | |
| </p> | |
| **DistillAlign** aligns and balances the **mode-covering** and **mode-seeking** | |
| objectives of the multi-stage video distillation pipeline — using only a | |
| **1.3B DMD teacher**, it already surpasses baselines refined with a **14B DMD | |
| teacher**. | |
| ## Checkpoints | |
| All released generators are **Wan2.1-1.3B students**; the size in each name | |
| refers to the **teacher** used during training. | |
| | Model | Checkpoint | Description | | |
| |---|---|---| | |
| | Initializer (1.3B teacher) | [distillalign_init_1p3b_teacher.pt](https://huggingface.co/LiJiaxing/DistillAlign/blob/main/checkpoints/distillalign_init_1p3b_teacher.pt) | Pre-DMD initializer, trained toward a Wan2.1-T2V-1.3B teacher | | |
| | Initializer (14B teacher) | [distillalign_init_14b_teacher.pt](https://huggingface.co/LiJiaxing/DistillAlign/blob/main/checkpoints/distillalign_init_14b_teacher.pt) | Pre-DMD initializer, trained toward a Wan2.1-T2V-14B teacher | | |
| | Distilled (1.3B teacher) | [distillalign_distill_1p3b_teacher.pt](https://huggingface.co/LiJiaxing/DistillAlign/blob/main/checkpoints/distillalign_distill_1p3b_teacher.pt) | Final joint-distilled generator, Wan2.1-T2V-1.3B DMD teacher | | |
| | Distilled (14B teacher) | [distillalign_distill_14b_teacher.pt](https://huggingface.co/LiJiaxing/DistillAlign/blob/main/checkpoints/distillalign_distill_14b_teacher.pt) | Final joint-distilled generator, Wan2.1-T2V-14B DMD teacher | | |
| Each checkpoint stores a plain `{"generator": state_dict}` and loads directly | |
| with the released inference and evaluation code. | |
| ## Distillation Data | |
| Prompts from VidProM, 25K clean-latent samples per teacher: | |
| | Data | Download | Description | | |
| |---|---|---| | |
| | Wan2.1-14B data | [DistillAlign_14B_25K](https://huggingface.co/datasets/LiJiaxing/DistillAlign_14B_25K) | 25K distillation samples generated by Wan2.1-T2V-14B | | |
| | Wan2.1-1.3B data | [DistillAlign_1p3b_25K](https://huggingface.co/datasets/LiJiaxing/DistillAlign_1p3b_25K) | 25K distillation samples generated by Wan2.1-T2V-1.3B | | |
| ## Evaluation Caches | |
| Teacher reference artifacts for the teacher-normalized distribution | |
| evaluation (16 prompts x 16 seeds, 25-step UniPC, timestep shift 8.0, | |
| CFG 5.0; V-JEPA2 ViT-H features, 2560-D): | |
| | Cache | Download | Description | | |
| |---|---|---| | |
| | Wan2.1-1.3B teacher reference features | [wan2.1_t2v_1.3b_reference_vjepa2.npz](https://huggingface.co/LiJiaxing/DistillAlign/blob/main/teacher_caches/wan2.1_t2v_1.3b_reference_vjepa2.npz) | 256 x 2560 features, ready for `--teacher-features` | | |
| | Wan2.1-14B teacher reference features | [wan2.1_t2v_14b_reference_vjepa2.npz](https://huggingface.co/LiJiaxing/DistillAlign/blob/main/teacher_caches/wan2.1_t2v_14b_reference_vjepa2.npz) | 256 x 2560 features, ready for `--teacher-features` | | |
| | Teacher reference videos | [teacher_caches/videos](https://huggingface.co/LiJiaxing/DistillAlign/tree/main/teacher_caches/videos) | 256 reference videos per teacher, for inspection and re-extraction | | |
| See [teacher_caches/README.md](https://huggingface.co/LiJiaxing/DistillAlign/blob/main/teacher_caches/README.md) | |
| for the exact protocol. | |
| ## Citation | |
| ```bibtex | |
| @misc{li2026distillalign, | |
| title = {DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation}, | |
| author = {Li, Jiaxing and Zou, Kai and Zhou, Cindy and Huang, Kaichen and Gao, Junyao and Wang, Zile and Liu, Yang and Liu, Bin and An, Bo and Li, Yangguang}, | |
| year = {2026}, | |
| eprint = {2607.26811}, | |
| archivePrefix = {arXiv}, | |
| primaryClass = {cs.CV}, | |
| url = {https://arxiv.org/abs/2607.26811} | |
| } | |
| ``` | |
| ## License | |
| The code and released weights follow the Apache License 2.0; upstream | |
| Wan2.1 model weights remain subject to their own licenses. | |