How to Run Wan 14B I2V 720 Locally: A Step-by-Step Guide

So, you’ve heard about Wan 14B I2V 720, a powerful AI model that generates videos from text or images, and you want to run it on your own machine. Whether you’re a content creator, developer, or AI enthusiast, running this model locally gives you full control over privacy,

1000+ Pre-built AI Apps for Any Use Case

How to Run Wan 14B I2V 720 Locally: A Step-by-Step Guide

Start for free

So, you’ve heard about Wan 14B I2V 720, a powerful AI model that generates videos from text or images, and you want to run it on your own machine. Whether you’re a content creator, developer, or AI enthusiast, running this model locally gives you full control over privacy, customization, and experimentation. But where do you start?

This guide breaks down the process into simple, actionable steps. We’ll cover hardware requirements, software setup, model installation, and troubleshooting—no PhD required! Let’s dive in.

Want to Use Deepseek, ChatGPT Deep Research, Minimax Video, Wan Video Generator, FLUX Image Generator in ONE PLACE?

Create your first AI video now →

Wan 2.1 Text to Video AI Video Generator | Free AI tool | Anakin
Wan 2.1 Text to Video AI Video Generator is an innovative app that transforms written text into dynamic, high-quality videos using advanced AI, enabling users to create professional visual content in minutes with customizable templates, styles, and voiceovers.
Anakin.ai

What Is Wan 14B I2V 720?

(Assumptions based on naming conventions and similar models)

  • 14B Parameters: A massive model size (14 billion parameters) for high-quality video generation.
  • I2V 720: Likely an “Image-to-Video” model producing 720p resolution outputs.
  • Use Cases: Turn static images into dynamic videos, animate text prompts, or enhance existing footage.

Running this locally means you’ll need serious hardware, but the rewards include faster processing and offline access. Let’s get your machine ready.

Step 1: Check Your Hardware

Large AI models demand robust hardware. Here’s what you’ll need:

GPU Requirements

  • NVIDIA GPU: CUDA compatibility is essential.
  • Minimum: RTX 3080 (10GB VRAM).
  • Recommended: RTX 4090 (24GB VRAM) or A100/A6000 for smooth performance.
  • AMD GPUs: Less supported for AI workflows, but ROCm drivers might work.

CPU, RAM, and Storage

  • CPU: Modern multi-core processor (Intel i7/i9 or Ryzen 7/9).
  • RAM: 32GB+ to handle background tasks.
  • Storage: At least 50GB free space (for model weights and temporary files).

Verify Compatibility

For NVIDIA users:

nvidia-smi  # Check GPU driver and CUDA version

Ensure your GPU supports CUDA 11.8 or newer.

Step 2: Set Up Your Software Environment

Install Python and Package Managers

  1. Python 3.10+: Download from python.org.
  2. pip: Python’s package installer (comes with Python).
  3. Conda (optional): For managing virtual environments.

Create a Virtual Environment

Isolate dependencies to avoid conflicts:

conda create -n wan_env python=3.10
conda activate wan_env
# Or use venv:
python -m venv wan_env
source wan_env/bin/activate  # Linux/Mac
wan_env\\Scripts\\activate     # Windows

Install CUDA and PyTorch

CUDA Toolkit: Match your GPU driver version (e.g., CUDA 12.x).

PyTorch with CUDA Support:

pip3 install torch torchvision torchaudio --index-url <https://download.pytorch.org/whl/cu121>

Install Additional Dependencies

pip install transformers accelerate huggingface_hub ffmpeg-python opencv-python
  • transformers: For loading AI models.
  • accelerate: Optimizes distributed training/inference.
  • ffmpeg: Handles video encoding/decoding.

Step 3: Download the Model

Since Wan 14B I2V 720 isn’t widely documented, we’ll assume it’s hosted on Hugging Face or GitHub.

Option 1: Hugging Face Hub

Create an account at huggingface.co.

Find the model repository (e.g., Wan14B-I2V-720).

Use git-lfs to download large files:

sudo apt-get install git-lfs  # Linux
git lfs install
git clone <https://huggingface.co/username/Wan14B-I2V-720>

Option 2: Manual Download

  • Check the model’s official site for .bin or .safetensors files.
  • Store them in a dedicated folder (e.g., ./models/wan14b).

Step 4: Configure the Model

Create a Python script (e.g., run_wan.py) to load the model:

from transformers import AutoModelForVideoGeneration, AutoTokenizer
import torch

model_path = "./models/wan14b"  # Update this!
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForVideoGeneration.from_pretrained(
    model_path,
    torch_dtype=torch.float16,  # Save VRAM with mixed precision
    device_map="auto"           # Automatically uses GPU
)

# For image-to-video, load OpenCV to process inputs
import cv2
image = cv2.imread("input_image.jpg")

# Generate video (hypothetical API)
video_frames = model.generate(
    image=image,
    prompt="A spaceship flying through a nebula",
    num_frames=24,
    height=720,
    width=1280
)

# Save output
import ffmpeg
(video_frames
 .output("output.mp4", vcodec="libx264")
 .run())

Notes:

  • The actual API may vary. Check the model’s docs for correct methods.
  • Reduce num_frames or resolution if you encounter OOM (Out-of-Memory) errors.

Step 5: Run the Model

Execute your script:

python run_wan.py

Expected Output:

  • A video file (output.mp4) based on your input image and text prompt.

Step 6: Troubleshooting Common Issues

1. Out-of-Memory Errors

Fix: Lower video resolution, use fp16 precision, or enable gradient checkpointing:

model.gradient_checkpointing_enable()

2. Missing Dependencies

  • Fix: Install exact versions from the model’s requirements.txt.

3. CUDA Errors

Fix: Reinstall PyTorch with the correct CUDA version:

pip uninstall torch
pip install torch --extra-index-url <https://download.pytorch.org/whl/cu121>

4. Slow Performance

Enable accelerate’s optimizations:

accelerate config  # Follow prompts to optimize settings

Step 7: Optimize for Your Hardware

Quantization: Reduce model precision to 8-bit (if supported):

model = quantize_model(model)  # Hypothetical method

Model Parallelism: Split the model across multiple GPUs.

Use ONNX Runtime: Convert the model for faster inference.

Conclusion

Running Wan 14B I2V 720 locally is a challenging but rewarding project. With the right hardware and patience, you’ll unlock powerful video-generation capabilities. Remember to:

  • Monitor VRAM usage.
  • Experiment with prompts and parameters.
  • Join AI communities (e.g., Hugging Face forums, Reddit) for model-specific tips.

As AI models evolve, so do the tools. Keep learning, tweaking, and creating—your next viral video might be a terminal command away!

Further Resources:

Happy generating! 🚀

Stop