Image-to-Video
Diffusers
Safetensors
i2v
How to use from the
Use from the
Diffusers library
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")

SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation

Jiaming Zhang · Shengming Cao · Rui Li · Xiaotong Zhao · Yutao Cui
Xinglin Hou · Gangshan Wu · Haolan Chen · Yu Xu · Limin Wang · Kai Ma

Paper PDF Project Page
Multimedia Computing Group, Nanjing University   |   Platform and Content Group (PCG), Tencent

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.

teaser

📚 Citation

If you find our paper or this codebase useful for your research, please cite us.

@misc{zhang2025steadydancer,
      title={SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation}, 
      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},
      year={2025},
      eprint={2511.19320},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2511.19320}, 
}
Downloads last month
462
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 4 Ask for provider support

Model tree for MCG-NJU/SteadyDancer-14B

Finetuned
(21)
this model

Dataset used to train MCG-NJU/SteadyDancer-14B

Spaces using MCG-NJU/SteadyDancer-14B 3

Collection including MCG-NJU/SteadyDancer-14B

Paper for MCG-NJU/SteadyDancer-14B