Model card

README.md

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+---
+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}
+}
+```