| --- |
| license: apache-2.0 |
| tags: |
| - text-to-video |
| - video-generation |
| - autoregressive-video-generation |
| - self-distillation |
| - lora |
| pipeline_tag: text-to-video |
| --- |
| |
| # OPSD-V Checkpoints |
|
|
| This repository hosts checkpoints for **OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators**. |
|
|
| - Project page: https://kumapowerliu.github.io/opsd-v/ |
| - Paper: https://kumapowerliu.github.io/opsd-v/assets/opsdv.pdf |
| - Code: https://github.com/MeiGen-AI/opsd-v |
|
|
| ## Files |
|
|
| | Path | Description | |
| | --- | --- | |
| | `checkpoints/longlive_base.pt` | LongLive base few-step autoregressive generator. | |
| | `checkpoints/longlive_lora.pt` | Original LongLive LoRA adapter used as the starting point. | |
| | `checkpoints/opsdv_longlive_lora.pt` | OPSD-V LoRA checkpoint for the LongLive backbone. | |
| | `checkpoints/self_forcing_dmd_ema_as_generator.pt` | Self-Forcing DMD/EMA generator used as the base model. | |
| | `checkpoints/opsdv_self_forcing_lora.pt` | OPSD-V LoRA checkpoint for the Self-Forcing backbone. | |
|
|
| ## Usage |
|
|
| Please place these files under the `checkpoints/` directory of the OPSD-V code release and use the provided training or inference configs. |
|
|
| ```text |
| checkpoints/ |
| longlive_base.pt |
| longlive_lora.pt |
| opsdv_longlive_lora.pt |
| self_forcing_dmd_ema_as_generator.pt |
| opsdv_self_forcing_lora.pt |
| ``` |
|
|
| See the code repository README for detailed setup, inference, and training commands. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{liu2026opsdv, |
| title = {OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators}, |
| author = {Liu, Hongyu and Wang, Chun and Gao, Feng and He, Xuanhua and Ma, Yue and Wan, Ziyu and Zhang, Yong and Wei, Xiaoming and Chen, Qifeng}, |
| year = {2026}, |
| note = {Preprint} |
| } |
| ``` |
|
|
| ## License |
|
|
| The OPSD-V code release is Apache-2.0. Please also follow the licenses and usage terms of the corresponding upstream models and datasets. |
|
|
