Instructions to use MCG-NJU/SteadyDancer-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MCG-NJU/SteadyDancer-14B with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MCG-NJU/SteadyDancer-14B", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -49,3 +49,19 @@ library_name: diffusers
|
|
| 49 |
This repository is the `checkpoint` of paper "SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation". SteadyDancer is a strong animation framework based on **Image-to-Video paradigm**, ensuring **robust first-frame preservation**. In contrast to prior *Reference-to-Video* approaches that often suffer from identity drift due to **spatio-temporal misalignments** common in real-world applications, SteadyDancer generates **high-fidelity and temporally coherent** human animations, outperforming existing methods in visual quality and control while **requiring significantly fewer training resources**.
|
| 50 |
|
| 51 |

|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 49 |
This repository is the `checkpoint` of paper "SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation". SteadyDancer is a strong animation framework based on **Image-to-Video paradigm**, ensuring **robust first-frame preservation**. In contrast to prior *Reference-to-Video* approaches that often suffer from identity drift due to **spatio-temporal misalignments** common in real-world applications, SteadyDancer generates **high-fidelity and temporally coherent** human animations, outperforming existing methods in visual quality and control while **requiring significantly fewer training resources**.
|
| 50 |
|
| 51 |

|
| 52 |
+
|
| 53 |
+
|
| 54 |
+
## 📚 Citation
|
| 55 |
+
|
| 56 |
+
If you find our paper or this codebase useful for your research, please cite us.
|
| 57 |
+
```BibTeX
|
| 58 |
+
@misc{zhang2025steadydancer,
|
| 59 |
+
title={SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation},
|
| 60 |
+
author={Jiaming Zhang and Shengming Cao and Rui Li and Xiaotong Zhao and Yutao Cui and Xinglin Hou and Gangshan Wu and Haolan Chen and Yu Xu and Limin Wang and Kai Ma},
|
| 61 |
+
year={2025},
|
| 62 |
+
eprint={2511.19320},
|
| 63 |
+
archivePrefix={arXiv},
|
| 64 |
+
primaryClass={cs.CV},
|
| 65 |
+
url={https://arxiv.org/abs/2511.19320},
|
| 66 |
+
}
|
| 67 |
+
```
|