Wan2.1-I2V-14B-720P is an advanced image-to-video model designed to create high-definition 720P videos from static images. Built on a cutting-edge diffusion transformer architecture, it enhances video quality while ensuring smooth motion and rich details.
Key Features:
✅ High-Resolution Video Generation – Produces crisp 720P videos with enhanced realism.
✅ Optimized for Consumer GPUs – Efficiently runs on high-end gaming and workstation GPUs.
✅ Multi-Task Capabilities – Supports image-to-video, text-to-video, video editing, and video-to-audio tasks.
✅ Strong Temporal Consistency – Ensures smooth motion transitions while preserving image details.
✅ Multilingual Text Support – Generates video content with English and Chinese text overlays.
With state-of-the-art video generation performance, Wan2.1-I2V-14B-720P outperforms many existing models in clarity, coherence, and motion stability. This model is ideal for creative content generation, digital media, and video animation projects.
| Model | Dimension | Input Dimension | Output Dimension | Feedforward Dimension | Frequency Dimension | Number of Heads | Number of Layers |
|---|
| 1.3B | 1536 | 16 | 16 | 8960 | 256 | 12 | 30 |
| 14B | 5120 | 16 | 16 | 13824 | 256 | 40 | 40 |
Model Resource
Hugging Face
Link: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P
Minimum GPU Configuration
Before proceeding, ensure your VM has a powerful GPU, such as:
- NVIDIA A100 (80GB)
- NVIDIA H100
- RTX 4090 (at least 24GB VRAM)
- A6000 (48GB VRAM)
- Multiple GPUs (8x A100 recommended for full 14B model)
Step-by-Step Process to Install Wan2.1 I2V 14B 720P Model Locally
For the purpose of this tutorial, we will use a GPU-powered Virtual Machine offered by NodeShift; however, you can replicate the same steps with any other cloud provider of your choice. NodeShift provides the most affordable Virtual Machines at a scale that meets GDPR, SOC2, and ISO27001 requirements.
Step 1: Sign Up and Set Up a NodeShift Cloud Account
Visit the NodeShift Platform and create an account. Once you’ve signed up, log into your account.
Follow the account setup process and provide the necessary details and information.
Step 2: Create a GPU Node (Virtual Machine)
GPU Nodes are NodeShift’s GPU Virtual Machines, on-demand resources equipped with diverse GPUs ranging from H100s to A100s. These GPU-powered VMs provide enhanced environmental control, allowing configuration adjustments for GPUs, CPUs, RAM, and Storage based on specific requirements.
Navigate to the menu on the left side. Select the GPU Nodes option, create a GPU Node in the Dashboard, click the Create GPU Node button, and create your first Virtual Machine deployment.
Step 3: Select a Model, Region, and Storage
In the “GPU Nodes” tab, select a GPU Model and Storage according to your needs and the geographical region where you want to launch your model.
We will use 1 x H100 GPU for this tutorial to achieve the fastest performance. However, you can choose a more affordable GPU with less VRAM if that better suits your requirements.
Step 4: Select Authentication Method
There are two authentication methods available: Password and SSH Key. SSH keys are a more secure option. To create them, please refer to our official documentation.
Step 5: Choose an Image
Next, you will need to choose an image for your Virtual Machine. We will deploy Wan2.1 I2V 14B 720P on an NVIDIA Cuda Virtual Machine. This proprietary, closed-source parallel computing platform will allow you to install Wan2.1 I2V 14B 720P on your GPU Node.
After choosing the image, click the ‘Create’ button, and your Virtual Machine will be deployed.
Step 6: Virtual Machine Successfully Deployed
You will get visual confirmation that your node is up and running.
Step 7: Connect to GPUs using SSH
NodeShift GPUs can be connected to and controlled through a terminal using the SSH key provided during GPU creation.
Once your GPU Node deployment is successfully created and has reached the ‘RUNNING’ status, you can navigate to the page of your GPU Deployment Instance. Then, click the ‘Connect’ button in the top right corner.
Now open your terminal and paste the proxy SSH IP or direct SSH IP.
Next, if you want to check the GPU details, run the command below:
nvidia-smi
Step 8: Check the Available Python version and Install the new version
Run the following commands to check the available Python version.
If you check the version of the python, system has Python 3.8.1 available by default. To install a higher version of Python, you’ll need to use the deadsnakes PPA.
Run the following commands to add the deadsnakes PPA:
sudo apt update
sudo apt install -y software-properties-common
sudo add-apt-repository -y ppa:deadsnakes/ppa
sudo apt update
Step 9: Install Python 3.11
Now, run the following command to install Python 3.11 or another desired version:
sudo apt install -y python3.11 python3.11-distutils python3.11-venv
Step 10: Update the Default Python3 Version
Now, run the following command to link the new Python version as the default python3:
sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.8 1
sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 2
sudo update-alternatives --config python3
Then, run the following command to verify that the new Python version is active:
python3 --version
Step 11: Install and Update Pip
Run the following command to install and update the pip:
python3 -m ensurepip --upgrade
python3 -m pip install --upgrade pip
Then, run the following command to check the version of pip:
pip --version
Step 12: Clone the Repository
Run the following command to clone the WAN2.1 repository:
git clone https://github.com/Wan-Video/Wan2.1.git
cd Wan2.1
Step 13: Install Pytorch
Run the following command to install the Pytorch:
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117
Step 14: Install Dependencies
Run the following command to install the dependencies:
pip install -r requirements.txt
Step 15: Install HuggingFace Hub
Run the following command to install the huggingface_hub:
pip install huggingface_hub
Step 16: Login Using Your Hugging Face API Token
Use the huggingface_hub cli to login directly in the terminal.
Run the following command to login in huggingface-cli:
huggingface-cli login
Then, enter the token and press the Enter key. Ensure you press Enter after entering the token so the input will not be visible.
After entering the token, you will see the following output:
Login Successful.
The current active token is (your_token_name).
Check the screenshot below for reference.
How to Generate a Hugging Face Token
- Create an Account: Go to the Hugging Face website and sign up for an account if you don’t already have one.
- Access Settings: After logging in, click on your profile photo in the top right corner and select “Settings.”
- Navigate to Access Tokens: In the settings menu, find and click on the “Access Tokens” tab.
- Generate a New Token: Click the “New token” button, provide a name for your token, and choose a role (either
read or write).
- Generate and Copy Token: Click the “Generate a token” button. Your new token will appear; click “Show” to view it and copy it for use in your applications.
- Secure Your Token: Ensure you keep your token secure and do not expose it in public code repositories.
Step 17: Download Model Using Huggingface-CLI
Run the following command to download model using huggingface-cli:
huggingface-cli download Wan-AI/Wan2.1-I2V-14B-720P --local-dir ./Wan2.1-I2V-14B-720P
Step 18: Run the Model with Prompt
Execute the following command to run the model with prompt:
python3 generate.py --task i2v-14B --size 1280*720 --ckpt_dir ./Wan2.1-I2V-14B-720P --image examples/i2v_input.JPG --prompt "A majestic dragon soaring through the sky with fire surrounding it, highly detailed, cinematic shot."
Step 19: Check the Output
Conclusion
Wan2.1-I2V-14B-720P is a powerful image-to-video model that delivers high-quality 720P video generation with smooth motion and detailed visuals. Built with an efficient diffusion transformer architecture, it ensures temporal consistency, making it ideal for tasks like image-to-video transformation, text-to-video, and video editing.
This guide provided a step-by-step process to install and run the model on a GPU-powered virtual machine, ensuring optimal performance across different hardware configurations. By following these instructions, users can seamlessly integrate Wan2.1-I2V-14B-720P into their creative workflows, whether for digital content production, animation, or media applications.
With its state-of-the-art video generation capabilities, Wan2.1-I2V-14B-720P stands as a versatile and scalable solution for anyone looking to create visually compelling and high-resolution animated content with ease.
