| --- |
| license: other |
| license_name: anyflow-wan-non-commercial |
| license_link: https://huggingface.co/nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers |
| base_model: nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers |
| pipeline_tag: text-to-video |
| library_name: diffusers |
| tags: |
| - lora |
| - reinforcement-learning |
| - few-step |
| - flow-map |
| - fewsteprl |
| --- |
| |
| # FewStepRL — Wan2.1-T2V-1.3B checkpoints |
|
|
| Released checkpoints of **FewStepRL: Fine-tuning Few-step Diffusion Models with Reinforcement Learning** |
| (video experiments, 4-step AnyFlow-Wan2.1-T2V-1.3B flow map, 480×832, 81 frames). |
|
|
| [Paper (arXiv:2611.08240)](https://arxiv.org/abs/2611.08240) · |
| [Code](https://github.com/EDAPINENUT/fewsteprl-wan21) · |
| [Project page](https://edapinenut.github.io/fewsteprl.page/) · |
| [Image checkpoints (SD3.5)](https://huggingface.co/Delcher/fewsteprl-sd35) |
|
|
| ## Contents |
|
|
| ``` |
| <trial>/ |
| config.yaml # the config the run saved |
| checkpoint-250/generator_ema.pt # EMA generator: LoRA A/B + condition_embedder.delta_embedder |
| SHA256SUMS |
| ``` |
|
|
| | trial | trainer | step | |
| |---|---|---| |
| | meanflownft | `meanflow_nft` | 250 | |
| | dmdnft | `dmdnft_rl` | 250 | |
| | dmdawm | `dmdawm_rl` | 250 | |
| | fewstepgrpo | `meanflowgrpo_rl` | 250 | |
| | meanflowawm | `meanflowawm_rl` | 250 | |
| | meanflowopsd | `meanflowopsd_rl` | 250 | |
| | reflmx | `meanflowrefl_rl` | 250 | |
|
|
| All trials share the reward HPSv3 general + HPSv3 percentile + VideoAlign MQ + VideoAlign TA. Scores (training |
| reward and VBench): [checkpoints/README.md](https://github.com/EDAPINENUT/fewsteprl-wan21/blob/main/checkpoints/README.md). |
|
|
| ## Usage |
|
|
| Download the weights into the `checkpoints/` directory of the code repository: |
|
|
| ```bash |
| git clone https://github.com/EDAPINENUT/fewsteprl-wan21 && cd fewsteprl-wan21 |
| bash download_scripts/download_assets.sh # AnyFlow-Wan, reward models, VBench weights |
| hf download Delcher/fewsteprl-wan21 --local-dir checkpoints --include "*.pt" |
| cd checkpoints && shasum -a 256 -c SHA256SUMS && cd .. |
| ``` |
|
|
| `generator_ema.pt` is applied on top of the full AnyFlow-Wan2.1-T2V-1.3B pipeline: |
|
|
| ```bash |
| python inference.py scripts/eval/configs/sample_wan.yaml --prompt "..." \ |
| --override seed=0 mode=meanflow_nft meanflow_nft_path=checkpoints/dmdnft/checkpoint-250/generator_ema.pt |
| bash scripts/eval/eval_ckpt.sh dmdnft 8 # training rewards on the 256 test prompts |
| ``` |
|
|
| ## License |
|
|
| The weights are derivatives of |
| [nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers](https://huggingface.co/nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers), |
| whose license restricts the model and derivative weights to **non-commercial use**. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{lin2026fewsteprl, |
| title = {FewStepRL: Fine-tuning Few-step Diffusion Models with Reinforcement Learning}, |
| author = {Lin, Haitao and Ren, Minsi and Li, Yang and Ge, Xingtong and |
| Hu, Zheyuan and Hu, Peiyan and Wu, Tailin}, |
| journal = {arXiv preprint arXiv:2611.08240}, |
| year = {2026} |
| } |
| ``` |
|
|
