The arrival of Wan2.2 marks a breakthrough in open-source video generation, combining state-of-the-art diffusion techniques with a powerful Mixture-of-Experts (MoE) architecture to deliver cinematic-quality results at large scale. Unlike earlier versions, Wan2.2 separates denoising across timesteps using specialized expert models, dramatically increasing model capacity without raising computational costs. This architectural leap, paired with training on a huge dataset (+65.6% more images and +83.2% more videos than Wan2.1), enables it to excel at complex motion, semantic understanding, and customizable aesthetics. If you want to generate visually rich cinematic sequences, control lighting and composition with fine detail, or explore advanced hybrid text-to-video (T2V) and image-to-video (I2V) generation, Wan2.2 offers unmatched creative flexibility. With its efficient VAE compression and support for 720P@24fps video generation on consumer GPUs like the RTX 4090, it empowers both researchers and creators to experiment with high-definition video synthesis at scale. And with the latest Wan2.2-S2V-14B, the model extends into audio-driven cinematic video creation, opening the door to storytelling where sound, image, and motion are generated in perfect sync.
In this guide, we’ll see how to install, setup and run Wan2.2 S2V in GPU accelerated environment.
Prerequisites
The minimum system requirements for running this model are:
- GPU: 1x H100 or 1x H200
- Storage: 50 GB (preferable)
- VRAM: at least 85 GB
- Anaconda installed
Step-by-step process to install and run Wan2.2 S2V
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 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install einops timm pillow
pip install git+https://github.com/huggingface/transformers
pip install git+https://github.com/huggingface/accelerate
pip install git+https://github.com/huggingface/diffusers
pip install huggingface_hub
pip install sentencepiece bitsandbytes protobuf decord numpy
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 librosa
pip install "huggingface_hub[cli]"
apt install ffmpeg
Output:
5. Login to HuggingFace CLI with your HF READ access token.
(Run the command, once prompted, enter your token)
hf auth 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-S2V-14B --local-dir ./Wan2.2-S2V-14B
Output:
2. Run the model for inference with a prompt.
(If this command throws OOM (Out Of Memory) error, try to decrease the size of the video significantly or set a --num_clip limit to limit the clips in the video. And if still doesn’t work, then you may need to switch to a GPU with more VRAM)
python generate.py --task s2v-14B --size 1024*704 --ckpt_dir ./Wan2.2-S2V-14B/ --offload_model True --convert_model_dtype --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard." --image "examples/i2v_input.JPG" --audio "examples/talk.wav"
Output:
The output generation can take longer as this is a heavy model.
Here’s the final generated video:
https://drive.google.com/file/d/1mnNpcthMX53NJ24lbQ8HpE2_4W7G9wjD/view?usp=sharing
Conclusion
Wan2.2 is an open-source video generation model, blending an MoE-based diffusion architecture, vast training data, and advanced aesthetic controls to achieve cinematic-level results across text-to-video, image-to-video, and now audio-to-video tasks. Its ability to produce high-definition, complex, and stylistically rich motion makes it a powerful tool for both research and creative storytelling. Complementing these innovations, NodeShift provides the GPU-accelerated infrastructure needed to install, run, and scale Wan2.2 seamlessly, removing the barriers of heavy local setup and ensuring smooth execution even for large models like S2V-14B.
