Wan2.2-Animate-14B

Wan2.2-Animate-14B

Animates a character or swaps one into a video by copying a performer's movement and expressions, as one model from the Wan2.2 video family. Use only faces and likenesses you have the right to use.

Made by Wan-AI on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

About

What it is: Wan2.2-Animate-14B is a video model from Wan-AI for character animation and character replacement. It copies a performer's movement and expressions onto a character, and can also swap a character into an existing video.

It belongs to the Wan2.2 family of open video models. Hugging Face lists it at 17.3B parameters, built on Wan2.2-I2V-A14B. The weights and inference code are public, and the card also points to hosted pages where you can try it.

Who it suits: Video creators, animators and AI researchers who want an open model for character animation. Because it can place one character in another person's footage, use only faces and likenesses you have the right to use.

Industry | Who uses it | How they use it | Example
Media | Video creators | Animate a character from a reference performance | Make a mascot copy a presenter's movements
Animation | Animators | Prototype character motion without hand keyframing | Test a character's acting on a draft clip
Research | AI researchers | Study character animation and replacement models | Compare its outputs with those of other Wan2.2 models

People who only want text-to-video or image-to-video can skip it and look at the other Wan2.2 models.

Where to use it:

• Python, with the inference code from the Wan2.2 project's GitHub repository
• The hosted pages the card names: the Wan website, ModelScope Studio and a Hugging Face Space
• Windows, macOS or Linux with Python, for downloading the weights
• ComfyUI and Diffusers, which the card says carry other Wan2.2 models such as T2V, I2V and TI2V

Use case | Who | What they get back
Character animation | Creators and animators | A video of a character moving like the performer
Character replacement | Video editors | A clip with a different character in the scene
Trying it first | Anyone curious | A result from a hosted page before you download

How to set it up: Plan for a large download and a strong GPU, since the BF16 weights are about 34.5 GB.

• Python and pip, for the Hugging Face download tools
• A GPU with room for the weights, which Hugging Face puts at about 34.5 GB in BF16
• The Wan2.2 inference code from its GitHub repository

1. Install the huggingface_hub package.
2. Download the model files with the hf command.
3. Get the Wan2.2 inference code from its GitHub repository.
4. Follow its Wan-Animate section to run the model.

pip install -U huggingface_hub
hf download Wan-AI/Wan2.2-Animate-14B

How to use it: The card says the model and its inference code were released together and links a Wan-Animate run section in the code repository. That section is cut off here, so read it on the model page for the exact inputs and command.

Example: As the card describes it, you provide a character and a performance to copy, and the model returns a video of the character with the same movements and expressions. The exact input files depend on the run section of the inference code.

For newcomers and experts: Think of it as a puppet system for video. A reference performance supplies the motion and the face, and the model draws your character doing it. A good first try is one of the hosted pages the card lists, before you download anything.

Underneath, the Wan2.2 family puts a mixture-of-experts design into video diffusion, splitting the denoising work across timesteps between specialized experts. The card says this adds capacity at the same compute cost. Training used more images and videos than Wan2.1.

Give a character and a reference performance
The model copies movement and expression
Diffusion renders the new video
You get a character animation or swap

Good to know: The card compares Wan2.2's training data with Wan2.1, which is the maker's own report.

Title: Extra training data versus Wan2.1
Images: 65.6%
Videos: 83.2%

• Size: 17.3B parameters in Safetensors, with weights of about 34.5 GB in BF16 and about 8.6 GB at 4-bit as an estimate
• Variants: Hugging Face lists 4 quantized and 18 fine-tuned versions made by other people
• License: Apache 2.0
• Cost to run: no API key or paid service is named for local runs, though the hosted pages may have their own terms
• Data it reaches: the images and videos you give it, plus the files you download from Hugging Face
• Not for: the card names no restrictions beyond its license, but using someone's face or likeness needs their permission

On Hugging Face, 13 November 2026

• Task: video to video
• Library: diffusers
• Parameters: 17.3B
• Weights: about 34.5 GB in BF16; about 8.6 GB at 4-bit (estimate)
• Formats: Safetensors
• Downloads in the last 30 days: 235,243
• Likes: 1,311
• License: apache-2.0
• Based on: Wan-AI/Wan2.2-I2V-A14B
• Quantized versions: 4
• Fine-tunes: 18
• Tags: diffusers, onnx, safetensors, video-to-video

Links

• Model: https://huggingface.co/Wan-AI/Wan2.2-Animate-14B
• Files: https://huggingface.co/Wan-AI/Wan2.2-Animate-14B/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download Wan-AI/Wan2.2-Animate-14B

Then follow the usage in its model card. Nothing in this kit runs by itself.