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("Skywork/SkyReels-A1", 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")

SkyReels-A1: Expressive Portrait Animation in Video Diffusion Transformers

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🌐 Github Β· πŸ‘‹ Playground Β· Discord

This repo contains Diffusers style model weights for Skyreels A1 models. You can find the inference code on SkyReels-A1 repository.

πŸ”₯πŸ”₯πŸ”₯ News!!

  • Mar 4, 2025: πŸŽ‰ We release audio-driven portrait image animation pipeline. SkyReels-A1
  • Feb 18, 2025: πŸ‘‹ We release the inference code and model weights of SkyReels-A1.
  • Feb 18, 2025: πŸ”₯ We have open-sourced I2V video generation model SkyReels-V1. This is the first and most advanced open-source human-centric video foundation model.

image/png Overview of SkyReels-A1 framework. Given an input video sequence and a reference portrait image, we extract facial expression-aware landmarks from the video, which serve as motion descriptors for transferring expressions onto the portrait. Utilizing a conditional video generation framework based on DiT, our approach directly integrates these facial expression-aware landmarks into the input latent space. In alignment with prior research, we employ a pose guidance mechanism constructed within a VAE architecture. This component encodes facial expression-aware landmarks as conditional input for the DiT framework, thereby enabling the model to capture essential low- dimensional visual attributes while preserving the semantic integrity of facial features.


Some generated results:

Citation

If you find SkyReels-A1 useful for your research, welcome to cite our work using the following BibTeX:

@article{qiu2025skyreels,
  title={SkyReels-A1: Expressive Portrait Animation in Video Diffusion Transformers},
  author={Qiu, Di and Fei, Zhengcong and Wang, Rui and Bai, Jialin and Yu, Changqian and Fan, Mingyuan and Chen, Guibin and Wen, Xiang},
  journal={arXiv preprint arXiv:2502.10841},
  year={2025}
}
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