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Model reference · open weights

Wan2.1-VACE

Video Wan-AI Image→video 2 builds Open weights 10k dl/mo

Wan2.1-VACE is an open-weight video model from Wan-AI. Wan2.1-VACE-14B (FP32) weighs 43.5 GB; the smallest configuration that runs it is L40S 48 GB.

  • Wan2.1-VACE is an image-to-video model developed by Wan-AI that supports video creation and editing.
  • It contains 2.2B parameters and is designed to generate videos in English and Chinese.
  • The model is released under the Apache-2.0 license.

Summary of the Wan-AI/Wan2.1-VACE-1.3B-diffusers model card, 2026-10-01. The estimate below is for another build of the family.

Hugging Face ↗GitHub ↗Paper ↗

What it is

Released byWan-AI
Released2025-06-04
Parameters2.2B
VRAM43.5 GB for the weights

What it runs on

Memory and cards for Wan2.1-VACE-14B (FP32)

43.5 GBweights, file size
31.6 GBbiggest part
537 MBruntime overhead
CardWeightsMemory
RTX 3060 12 GB … RTX 5090 32 GBdoes not fit
L40S 48 GBfits (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

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
Wan2.1-VACE-1.3B-diffusers ↗ 2.2BFP32 9.5 GBRTX 3060 12 GB
Wan2.1-VACE-14B (above) ↗ 17.3BFP32 43.5 GBL40S 48 GB

From the model card

What Wan-AI says about Wan2.1-VACE

Read the model card

💜 Wan &nbsp&nbsp ｜ &nbsp&nbsp 🖥️ GitHub &nbsp&nbsp | &nbsp&nbsp🤗 Hugging Face&nbsp&nbsp | &nbsp&nbsp🤖 ModelScope&nbsp&nbsp | &nbsp&nbsp 📑 Technical Report &nbsp&nbsp | &nbsp&nbsp 📑 Blog &nbsp&nbsp | &nbsp&nbsp💬 WeChat Group&nbsp&nbsp | &nbsp&nbsp 📖 Discord&nbsp&nbsp

Wan: Open and Advanced Large-Scale Video Generative Models

In this repository, we present Wan2.1, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. Wan2.1 offers these key features:

  • 👍 SOTA Performance: Wan2.1 consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks.
  • 👍 Supports Consumer-grade GPUs: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models.
  • 👍 Multiple Tasks: Wan2.1 excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation.
  • 👍 Visual Text Generation: Wan2.1 is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications.
  • 👍 Powerful Video VAE: Wan-VAE delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation.

Video Demos

    Your browser does not support the video tag.

🔥 Latest News!!

  • May 14, 2025: 👋 We introduce Wan2.1 VACE, an all-in-one model for video creation and editing, along with its inference code, weights, and technical report!
  • Apr 17, 2025: 👋 We introduce Wan2.1 FLF2V with its inference code and weights!
  • Mar 21, 2025: 👋 We are excited to announce the release of the Wan2.1 technical report. We welcome discussions and feedback!
  • Mar 3, 2025: 👋 Wan2.1's T2V and I2V have been integrated into Diffusers (T2V | I2V). Feel free to give it a try!
  • Feb 27, 2025: 👋 Wan2.1 has been integrated into ComfyUI. Enjoy!
  • Feb 25, 2025: 👋 We've released the inference code and weights of Wan2.1.

Community Works

If your work has improved Wan2.1 and you would like more people to see it, please inform us.

  • Phantom has developed a unified video generation framework for single and multi-subject references based on Wan2.1-T2V-1.3B. Please refer to their examples.
  • UniAnimate-DiT, based on Wan2.1-14B-I2V, has trained a Human image animation model and has open-sourced the inference and training code. Feel free to enjoy it!
  • CFG-Zero enhances Wan2.1 (covering both T2V and I2V models) from the perspective of CFG.
  • TeaCache now supports Wan2.1 acceleration, capable of increasing speed by approximately 2x. Feel free to give it a try!
  • DiffSynth-Studio provides more support for Wan2.1, including video-to-video, FP8 quantization, VRAM optimization, LoRA training, and more. Please refer to their examples.

📑 Todo List

  • Wan2.1 Text-to-Video
    • [x] Multi-GPU Inference code of the 14B and 1.3B models
    • [x] Checkpoints of the 14B and 1.3B models
    • [x] Gradio demo
    • [x] ComfyUI integration
    • [x] Diffusers integration
    • [ ] Diffusers + Multi-GPU Inference
  • Wan2.1 Image-to-Video
    • [x] Multi-GPU Inference code of the 14B model
    • [x] Checkpoints of the 14B model
    • [x] Gradio demo
    • [x] ComfyUI integration
    • [x] Diffusers integration
    • [ ] Diffusers + Multi-GPU Inference
  • Wan2.1 First-Last-Frame-to-Video
    • [x] Multi-GPU Inference code of the 14B model
    • [x] Checkpoints of the 14B model
    • [x] Gradio demo
    • [ ] ComfyUI integration
    • [x] Diffusers integration
    • [ ] Diffusers + Multi-GPU Inference
  • Wan2.1 VACE
    • [x] Multi-GPU Inference code of the 14B and 1.3B models
    • [x] Checkpoints of the 14B and 1.3B models
    • [x] Gradio demo
    • [x] ComfyUI integration
    • [x] Diffusers integration
    • [ ] Diffusers + Multi-GPU Inference

Quickstart

Installation

Clone the repo:

git clone https://github.com/Wan-Video/Wan2.1.git
cd Wan2.1

Install dependencies:

# Ensure torch >= 2.4.0
pip install -r requirements.txt
Model Download
ModelsDownload LinkNotes
T2V-14B🤗 Huggingface 🤖 ModelScopeSupports both 480P and 720P
I2V-14B-720P🤗 Huggingface

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. Runs as it is with diffusers, on the machine, in Python.

# on your rented machine: pip install diffusers transformers accelerate ftfy
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import export_to_video

pipe = DiffusionPipeline.from_pretrained("Wan-AI/Wan2.1-VACE-1.3B-diffusers", torch_dtype=torch.bfloat16).to("cuda")
frames = pipe(prompt="a drone shot over a forest at sunrise").frames[0]
export_to_video(frames, "/workspace/out.mp4", fps=16)
Renting a GPU: connect, tunnels, Python
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