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RefAlign-14B
gudaochangsheng/RefAlign-14B
RefAlign-14B is a image-text-to-video model from gudaochangsheng. Use it for the image-text-to-video task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
[](https://arxiv.org/abs/2603.25743) [](https://arxiv.org/pdf/2603.25743) [](https://huggingface.co/gudaochangsheng/RefAlign-1.3B)
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At a glance
- Task
- Image-Text-to-Video
- License
- mit
- Model type
- refalign
- Access
- Public
- Created
- Mar 26, 2026
- Updated
- May 13, 2026
- SHA
- a96024e9
Base models
Model card
From the Hugging Face model README
🚀 RefAlign: Representation Alignment for Reference-to-Video Generation
🏆 OpenS2V-Eval Leaderboard
RefAlign achieves state-of-the-art performance on OpenS2V-Eval across multiple metrics.
| Model | Venue | TotalScore ↑ | Aesthetic ↑ | MotionSmoothness ↑ | MotionAmplitude ↑ | FaceSim ↑ | GmeScore ↑ | NexusScore ↑ | NaturalScore ↑ |
|---|---|---|---|---|---|---|---|---|---|
| 🥇 RefAlign-14B (Ours) | Open-Source | 60.42% | 46.84% | 97.61% | 22.48% | 55.23% | 68.32% | 48.52% | 73.63% |
| 🥇 RefAlign-1.3B (Ours) | Open-Source | 56.30% | 42.96% | 94.74% | 20.74% | 53.06% | 66.85% | 43.97% | 66.25% |
| Saber | Closed-Source | 57.91% | 42.42% | 96.12% | 21.12% | 49.89% | 67.50% | 47.22% | 72.55% |
| VINO | Open-Source | 57.85% | 45.92% | 94.73% | 12.30% | 52.00% | 69.69% | 42.67% | 71.99% |
| BindWeave | Closed-Source | 57.61% | 45.55% | 95.90% | 13.91% | 53.71% | 67.79% | 46.84% | 66.85% |
| VACE-14B | Open-Source | 57.55% | 47.21% | 94.97% | 15.02% | 55.09% | 67.27% | 44.08% | 67.04% |
| Phantom-14B | Open-Source | 56.77% | 46.39% | 96.31% | 33.42% | 51.46% | 70.65% | 37.43% | 69.35% |
| Kling1.6 | Closed-Source | 56.23% | 44.59% | 86.93% | 41.60% | 40.10% | 66.20% | 45.89% | 74.59% |
| Phantom-1.3B | Open-Source | 54.89% | 46.67% | 93.30% | 14.29% | 48.56% | 69.43% | 42.48% | 62.50% |
| MAGREF-480P | Open-Source | 52.51% | 45.02% | 93.17% | 21.81% | 30.83% | 70.47% | 43.04% | 66.90% |
| SkyReels-A2-P14B | Open-Source | 52.25% | 39.41% | 87.93% | 25.60% | 45.95% | 64.54% | 43.75% | 60.32% |
| Vidu2.0 | Closed-Source | 51.95% | 41.48% | 90.45% | 13.52% | 35.11% | 67.57% | 43.37% | 65.88% |
📦 Model Weights
| Model | Params | Hugging Face | ModelScope |
|---|---|---|---|
| RefAlign-1.3B | 1.3B |  |  |
| RefAlign-14B | 14B | | |
⚠️ Note
The provided weights are DiT (Diffusion Transformer) checkpoints fine-tuned from Wan2.1.
To run RefAlign, please:
- Download the original Wan2.1 model (including VAE, text encoder, etc.).
- Replace the DiT weights in Wan2.1 with the RefAlign weights provided above.
No modification is required for other components.
🎬 Inference
# Inference RefAlign-1.3B
python examples/wanvideo/model_inference/Wan2.1-T2V-1.3B_subject.py
# Inference RefAlign-14B
python examples/wanvideo/model_inference/Wan2.1-T2V-14B_subject.py
Citation
If you find RefAlign useful, please consider giving our repository a star (⭐) and citing our paper.
@misc{wang2026refalign,
title={RefAlign: Representation Alignment for Reference-to-Video Generation},
author={Lei Wang and Yuxin Song and Ge Wu and Haocheng Feng and Hang Zhou and Jingdong Wang and Yaxing Wang and Jian Yang},
year={2026},
eprint={2603.25743},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Acknowledgement
This project is based on DiffSynth-Studio. Thanks for their awesome works. We sincerely acknowledge the excellent and inspiring prior work, Phantom, VINO, OpenS2V, Phantom-Data and Wan2.1.
Contact
If you have any questions, please feel free to reach out to me at [email protected].
- Task
- Image-Text-to-Video
- Type
- refalign
- License
- mit
- Created
- Mar 26, 2026
- Updated
- May 13, 2026