Model reference · open weights

SkyReels-A2V

Video Skywork Image→video 1 build Its own licence terms 2k dl/mo

SkyReels-A2V is an open-weight video model from Skywork. SkyReels-V3-A2V-19B (BF16) weighs 54.4 GB; the smallest configuration that runs it is L40S 48 GB.

  • SkyReels-A2V is an image-to-video model developed by Skywork that generates talking avatars from a single portrait and an audio clip.
  • The model contains 18.9 billion parameters and supports audio inputs up to 200 seconds in length.
  • It is released under an "other" license.

Summary of the Skywork/SkyReels-V3-A2V-19B model card, 2026-10-01

What it is

Released bySkywork
Released2026-01-19
Parameters18.9B
VRAM54.4 GB for the weights

What it runs on

Memory and cards for SkyReels-V3-A2V-19B (BF16)

54.4 GBweights, file size
37.8 GBbiggest part
537 MBruntime overhead
CardWeightsMemory
RTX 3060 12 GB … RTX 5090 32 GBdoes not fit
L40S 48 GBtight (encoders offloaded)44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What Skywork says about SkyReels-A2V

Read the model card

👋 Playground . 🔧 API Platform · 🤗 Hugging Face · 🤖 ModelScope · 📑 Technical Report


Welcome to the SkyReels V3 repository! This is the official release of our flagship video generation model, built upon a unified multimodal in-context learning framework. SkyReels V3 natively supports three core generative capabilities: 1) multi-subject video generation from reference images, 2) video generation guided by audio, and 3) video-to-video generation.

🔥🔥🔥 News!!

🎥 Demos

The demos above showcase videos generated using our SkyReels-V3 unified multimodal in-context learning framework.

🚀 Quickstart

⚙️ Installation

# Clone the repository
git clone https://github.com/SkyworkAI/SkyReels-V3
cd SkyReels-V3

# Install dependencies (Recommended: Python 3.12+, CUDA 12.8+)
pip install -r requirements.txt

📥 Model Download

Models are available on Hugging Face and ModelScope:

Model TypeVariantLinks
Reference to Video14B-720P🤗 Hugging Face / 🤖 ModelScope
Video Extension14B-720P🤗 Hugging Face / 🤖 ModelScope
Talking Avatar19B-720P🤗 Hugging Face / 🤖 ModelScope

Note: By default, the script automatically downloads models from Hugging Face. To use a local path, specify it via the --model_id flag.


🎬 Inference Examples

1. Reference to Video

Reference-to-Video is a model that synthesizes coherent video sequences from 1 to 4 reference images and a text prompt. It excels at maintaining strong identity fidelity and narrative consistency for characters, objects, and backgrounds.

  • Single-GPU Inference:
    python3 generate_video.py --task_type reference_to_video --ref_imgs "https://skyreels-api.oss-accelerate.aliyuncs.com/examples/subject_reference/0_1.png,https://skyreels-api.oss-accelerate.aliyuncs.com/examples/subject_reference/0_2.png" --prompt "In a dimly lit, cluttered occult club room adorned with shelves full of books, skulls, and mysterious dolls, two young Asian girls are talking. One girl has vibrant teal pigtails with bangs, wearing a white collared polo shirt, while the other has a sleek black bob with bangs, also in a white polo shirt, conversing under the hum of fluorescent lights, a high-quality and detailed cinematic shot." --duration 5 --offload
    
  • Multi-GPU Inference (xDiT USP):
    torchrun --nproc_per_node=4 generate_video.py --task_type reference_to_video --ref_imgs "https://skyreels-api.oss-accelerate.aliyuncs.com/examples/subject_reference/0_1.png,https://skyreels-api.oss-accelerate.aliyuncs.com/examples/subject_reference/0_2.png" --prompt "In a dimly lit, cluttered occult club room adorned with shelves full of books, skulls, and mysterious dolls, two young Asian girls are talking. One girl has vibrant teal pigtails with bangs, wearing a white collared polo shirt, while the other has a sleek black bob with bangs, also in a white polo shirt, conversing under the hum of fluorescent lights, a high-quality and detailed cinematic shot." --duration 5 --offload --use_usp
    

💡 Notes:

  • The --task_type parameter must be set to reference_to_video.
  • The --ref_imgs parameter accepts 1 to 4 reference images. When providing multiple images, please separate their paths or URLs with commas.
  • The recommended output specification for this model is a 5-second video at 720p and 24 fps.
2. Video Extension

Extends existing videos while preserving motion continuity, scene coherence, and subject identity.

A. Single-shot Video Extension (5s to 30s)
  • Single-GPU Inference:
    python3 generate_video.py --task_type single_shot_extension --input_video https://skyreels-api.oss-accelerate.aliyuncs.com/examples/video_extension/test.mp4 --prompt "A man is making his way forward slowly, leaning on a white cane to prop himself up." --duration 5 --offload
    
  • **Mu

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

Running it yourself

Run it on a rented GPU

Rent a machine by the hour. ComfyUI is installed on it. Open ComfyUI through the tunnel and load the workflow from the model's card on Hugging Face; choose this model's file in its loader.

# on your rented machine (the ssh line is on its page in the console)
# get REPO FILE FOLDER: one file into /workspace/models/FOLDER, where ComfyUI loads it from
get() { hf download "$1" "$2" --local-dir /workspace/hf-files && mkdir -p "/workspace/models/$3" && mv "/workspace/hf-files/$2" "/workspace/models/$3/$4"; }

# the model (5.3 GB)
get Skywork/SkyReels-V3-A2V-19B diffusion_pytorch_model-00004-of-00004.safetensors diffusion_models

start-comfyui
Renting a GPU: connect, tunnels, ComfyUI
# on your computer, in a second terminal: ComfyUI in your browser at http://localhost:8188
# HOST and PORT are your machine's, from its page in the console
ssh -L 8188:localhost:8188 dev@HOST -p PORT
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