Wan2.2 Animate 14B marks a transformative advancement in open and advanced large-scale video generation, offering creators unmatched control, realism, and cinematic results. Built on the groundbreaking Wan2.2 architecture, it introduces a Mixture-of-Experts (MoE) design that strategically divides the denoising process across specialized expert models, expanding model capacity without increasing computational load. With meticulously curated aesthetic data, it empowers users to generate videos with precise control over lighting, composition, color tone, and overall style, while training on +83% more videos than its predecessor ensures exceptional performance in complex motion, semantics, and visual coherence. The model’s high-definition 5B TI2V variant supports both text-to-video and image-to-video generation at 720P/24fps on consumer GPUs, making cinematic-level video creation accessible to both industrial and academic users alike. Recent additions, such as Wan2.2-Animate-14B, extend this capability to full character animation and replacement, faithfully replicating movement and expression with holistic realism.
In this guide, we’ll take you through a step-by-step walkthrough for installing and running this model locally or in GPU-accelerated environments to generate high quality realistic animated videos.
Prerequisites
The minimum system requirements for running this model are:
- GPU: 1x H100 or 1x A100
- Storage: 200 GB (preferable)
- VRAM: at least 80 GB
- Anaconda installed
Step-by-step process to install and run Wan2.2 Animate 14B
For the purpose of this tutorial, we’ll use a GPU-powered Virtual Machine by NodeShift since it provides high compute Virtual Machines at a very affordable cost on a scale that meets GDPR, SOC2, and ISO27001 requirements. Also, it offers an intuitive and user-friendly interface, making it easier for beginners to get started with Cloud deployments. However, feel free to use any cloud provider of your choice and follow the same steps for the rest of the tutorial.
Step 1: Setting up a NodeShift Account
Visit app.nodeshift.com and create an account by filling in basic details, or continue signing up with your Google/GitHub account.
If you already have an account, login straight to your dashboard.
Step 2: Create a GPU Node
After accessing your account, you should see a dashboard (see image), now:
- Navigate to the menu on the left side.
- Click on the GPU Nodes option.
- Click on Start to start creating your very first GPU node.
These GPU nodes are GPU-powered virtual machines by NodeShift. These nodes are highly customizable and let you control different environmental configurations for GPUs ranging from H100s to A100s, CPUs, RAM, and storage, according to your needs.
Step 3: Selecting configuration for GPU (model, region, storage)
- For this tutorial, we’ll be using 1x RTX A6000 GPU, however, you can choose any GPU as per the prerequisites.
- Similarly, we’ll opt for 200GB storage by sliding the bar. You can also select the region where you want your GPU to reside from the available ones.
Step 4: Choose GPU Configuration and Authentication method
- After selecting your required configuration options, you’ll see the available GPU nodes in your region and according to (or very close to) your configuration. In our case, we’ll choose a 1x RTX A6000 48GB GPU node with 64vCPUs/63GB RAM/200GB SSD.
2. Next, you’ll need to select an authentication method. Two methods are available: Password and SSH Key. We recommend using SSH keys, as they are a more secure option. To create one, head over to our official documentation.
Step 5: Choose an Image
The final step is to choose an image for the VM, which in our case is Nvidia Cuda.
That’s it! You are now ready to deploy the node. Finalize the configuration summary, and if it looks good, click Create to deploy the node.
Step 6: Connect to active Compute Node using SSH
- As soon as you create the node, it will be deployed in a few seconds or a minute. Once deployed, you will see a status Running in green, meaning that our Compute node is ready to use!
- Once your GPU shows this status, navigate to the three dots on the right, click on Connect with SSH, and copy the SSH details that appear.
As you copy the details, follow the below steps to connect to the running GPU VM via SSH:
- Open your terminal, paste the SSH command, and run it.
2. In some cases, your terminal may take your consent before connecting. Enter ‘yes’.
3. A prompt will request a password. Type the SSH password, and you should be connected.
Output:
Next, If you want to check the GPU details, run the following command in the terminal:
!nvidia-smi
Step 7: Set up the project environment with dependencies
- Create a virtual environment using Anaconda.
conda create -n wan python=3.11 -y && conda activate wan
Output:
2. Clone the official repository of the project.
git clone https://github.com/Wan-Video/Wan2.2.git && cd Wan2.2
Output:
3. Install required dependencies.
pip install torch>=2.4.0
apt-get update && apt-get install -y libgl1 libglib2.0-0
pip install moviepy
pip install onnxruntime-gpu==1.20.0 --extra-index-url https://download.pytorch.org/whl/cu121
Output:
4. Once inside the project directory, install project requirement from the root of the repo along with some other additional packages.
pip install -r requirements.txt
pip install -r requirements_animate.txt
pip install librosa
pip install "huggingface_hub[cli]"
Output:
5. Login to HuggingFace CLI with your HF READ access token.
(Run the command, once prompted, enter your token)
hugginface-cli login
Output:
6. If you’re on a remote machine (e.g., NodeShift GPU), you’ll need to do SSH port forwarding in order to access the model’s outputs on your local system.
If you’re using a GPU through a remote server (e.g., NodeShift), you can connect it to your visual studio code editor by following the steps below:
a) Install the “Remote-SSH” Extension by Microsoft on VS Code.
b) Type “Remote-SSH: Connect to Host” on the Command Palette.
c) Click on “Add a new host”.
d) Enter the host details, such as username and SSH password, and you should be connected.
Step 8: Download and Run the model
- Run the following command to download model weights.
huggingface-cli download Wan-AI/Wan2.2-Animate-14B --local-dir ./Wan2.2-Animate-14B
Output:
2. Download Flux.1 Kontext dev weights as a helper model for animation preprocessing task.
huggingface-cli download black-forest-labs/FLUX.1-Kontext-dev --local-dir ./Wan2.2-Animate-14B/process_checkpoint/FLUX.1-Kontext-dev
Output:
3. Run the preprocess pipeline to prepare the inputs in compatible format.
For animation task:
python ./wan/modules/animate/preprocess/preprocess_data.py \
--ckpt_path ./Wan2.2-Animate-14B/process_checkpoint \
--video_path ./examples/wan_animate/animate/video.mp4 \
--refer_path ./examples/wan_animate/animate/image.jpeg \
--save_path ./examples/wan_animate/animate/process_results \
--resolution_area 1280 720 \
--retarget_flag \
--use_flux
For replacement task:
python ./wan/modules/animate/preprocess/preprocess_data.py \
--ckpt_path ./Wan2.2-Animate-14B/process_checkpoint \
--video_path ./examples/wan_animate/replace/video.mp4 \
--refer_path ./examples/wan_animate/replace/image.jpeg \
--save_path ./examples/wan_animate/replace/process_results \
--resolution_area 1280 720 \
--iterations 3 \
--k 7 \
--w_len 1 \
--h_len 1 \
--replace_flag
Output:
4. Run the model for inference with a prompt given inside generate.py file.
For animation task:
python generate.py --task animate-14B --ckpt_dir ./Wan2.2-Animate-14B/ --src_root_path ./examples/wan_animate/animate/process_results/ --refert_num 1
For replacement task:
python generate.py --task animate-14B --ckpt_dir ./Wan2.2-Animate-14B/ --src_root_path ./examples/wan_animate/replace/process_results/ --refert_num 1 --replace_flag --use_relighting_lora
Output:
The output generation can take longer for a video with higher resolution.
Here’s the final generated video:
https://drive.google.com/file/d/1lt4EG4TToOCJ25dK1-4hwL_u_oUAClgj/view?usp=sharing
Conclusion
Wan2.2 Animate 14B combines cutting-edge video generation techniques, MoE architecture, curated cinematic aesthetics, and advanced high-definition TI2V support, to deliver unparalleled control and realism in animated video creation. With NodeShift Cloud, users can seamlessly run the model in GPU-accelerated environments without worrying about local hardware limitations, enabling fast, scalable, and efficient generation of cinematic-quality videos, character animations, and expressive content with minimal and affordable setup for teams of all scale.
