SkyReels-V1
Collection
SkyReels V1 open models collections • 2 items • Updated • 22
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-V1-Hunyuan-I2V", 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 V1: Human-Centric Video Foundation Model
🌐 Github · 👋 Playground · 💬 Discord
This repo contains Diffusers-format model weights for SkyReels V1 Image-to-Video models. You can find the inference code on our github repository SkyReels-V1.
Introduction
SkyReels V1 is the first and most advanced open-source human-centric video foundation model. By fine-tuning HunyuanVideo on O(10M) high-quality film and television clips, Skyreels V1 offers three key advantages:
- Open-Source Leadership: Our Text-to-Video model achieves state-of-the-art (SOTA) performance among open-source models, comparable to proprietary models like Kling and Hailuo.
- Advanced Facial Animation: Captures 33 distinct facial expressions with over 400 natural movement combinations, accurately reflecting human emotions.
- Cinematic Lighting and Aesthetics: Trained on high-quality Hollywood-level film and television data, each generated frame exhibits cinematic quality in composition, actor positioning, and camera angles.
🔑 Key Features
1. Self-Developed Data Cleaning and Annotation Pipeline
Our model is built on a self-developed data cleaning and annotation pipeline, creating a vast dataset of high-quality film, television, and documentary content.
- Expression Classification: Categorizes human facial expressions into 33 distinct types.
- Character Spatial Awareness: Utilizes 3D human reconstruction technology to understand spatial relationships between multiple people in a video, enabling film-level character positioning.
- Action Recognition: Constructs over 400 action semantic units to achieve a precise understanding of human actions.
- Scene Understanding: Conducts cross-modal correlation analysis of clothing, scenes, and plots.
2. Multi-Stage Image-to-Video Pretraining
Our multi-stage pretraining pipeline, inspired by the HunyuanVideo design, consists of the following stages:
- Stage 1: Model Domain Transfer Pretraining: We use a large dataset (O(10M) of film and television content) to adapt the text-to-video model to the human-centric video domain.
- Stage 2: Image-to-Video Model Pretraining: We convert the text-to-video model from Stage 1 into an image-to-video model by adjusting the conv-in parameters. This new model is then pretrained on the same dataset used in Stage 1.
- Stage 3: High-Quality Fine-Tuning: We fine-tune the image-to-video model on a high-quality subset of the original dataset, ensuring superior performance and quality.
Model Introduction
| Model Name | Resolution | Video Length | FPS | Download Link |
|---|---|---|---|---|
| SkyReels-V1-Hunyuan-I2V (Current) | 544px960p | 97 | 24 | 🤗 Download |
| SkyReels-V1-Hunyuan-T2V | 544px960p | 97 | 24 | 🤗 Download |
Usage
See the Guide for details.
Citation
@misc{SkyReelsV1,
author = {SkyReels-AI},
title = {Skyreels V1: Human-Centric Video Foundation Model},
year = {2025},
publisher = {Huggingface},
journal = {Huggingface repository},
howpublished = {\url{https://huggingface.co/Skywork/Skyreels-V1-Hunyuan-I2V}}
}
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