Instructions to use MochunniaN1/One-to-All-14b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MochunniaN1/One-to-All-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("MochunniaN1/One-to-All-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
license: apache-2.0
pipeline_tag: image-to-video
library_name: diffusers
One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer
This repository contains the model and code for the paper One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer.
This project aims to provide a unified framework for high-fidelity character animation and image pose transfer for references with arbitrary layouts, addressing limitations in existing diffusion models regarding spatially misaligned reference-pose pairs.
- π Paper
- π Project Page
- π» Code on GitHub
π Showcase
Our model can adapt a single reference image to various motion patterns, demonstrating flexible motion control capabilities.
14B Model
| Reference | Motion 1 | Motion 2 | Motion 3 |
|---|---|---|---|
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
![]() |
1.3B Model
The 1.3 B model also delivers strong performance (from 1.3b_2 ckpt).
| Reference | Motion 1 | Motion 2 | Motion 3 |
|---|---|---|---|
![]() |
![]() |
![]() |
![]() |
Also support longer video & out-of-domain cases
![]() |
|
Acknowledgments
Our project is based on opensora. Some codes are brought from StableAnimator and Wan-Animate. Thanks for their awesome works.
π§ Contact
If you have any questions, please feel free to reach us at ssj180123@gmail.com
π Citation
If you find our work helpful or inspiring, please feel free to cite it.
@article{shi2025one,
title={One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer},
author={Shi, Shijun and Xu, Jing and Li, Zhihang and Peng, Chunli and Yang, Xiaoda and Lu, Lijing and Hu, Kai and Zhang, Jiangning},
journal={arXiv preprint arXiv:2511.22940},
year={2025}
}












