DistillAlign: Coordinating Mode Covering and Mode Seeking in Autoregressive Video Distillation
Jiaxing Li1,2*,
Kai Zou1*,
Cindy Zhou1,3,
Kaichen Huang1,2,
Junyao Gao1,
Zile Wang1,
Yang Liu1,
Bin Liu1,
Bo An2,
Yangguang Li1â€
1Riemann Dynamics 2Nanyang Technological University 3Wellington College, UK
*Equal contribution †Corresponding author
Project Page ·
Paper ·
Code
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.
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 |
25K distillation samples generated by Wan2.1-T2V-14B |
| Wan2.1-1.3B data |
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):
See teacher_caches/README.md
for the exact protocol.
Citation
@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.