How to Run Wan 14B txt2video 720p Locally: Your Step-by-Step Guide

(No PhD in AI Required!)

Imagine typing a text prompt like “a dolphin leaping over a rainbow” and watching an AI-generated 720p video materialize on your computer. That’s the magic of Wan 14B txt2video, an open-source model pushing the boundaries of text-to-video synthesis.

Wan 14B txt2video 720p test #AI #AIイラスト #Comfyui pic.twitter.com/q9cauU5Qlu
— toyxyz (@toyxyz3) February 26, 2025

But how do you run this futuristic tech on your own machine? In this guide, we’ll break it down into simple, jargon-free steps. Whether you’re a hobbyist, content creator, or just AI-curious, let’s turn your ideas into videos—no cloud subscription required.

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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 You’ll Need

Before diving in, let’s prep your setup. Here’s the checklist:

Hardware Requirements

GPU: At least an NVIDIA RTX 3060 (8GB+ VRAM).Why? Video generation is resource-heavy. Integrated graphics won’t cut it.
RAM: 16GB+ (32GB recommended for smoother runs).
Storage: 20GB+ free space (models and dependencies are chonky).

Software Stack

OS: Linux (Ubuntu 22.04 LTS preferred) or Windows 11 with WSL2.
Python 3.10+: The backbone of AI workflows.
CUDA Toolkit 11.8: For GPU acceleration.
Git: To clone the repository.

Patience:

First-time setup takes ~1 hour. Subsequent runs are faster.

Step 1: Install Prerequisites

Let’s lay the groundwork.

For Linux Users:

Open Terminal and run:

sudo apt update && sudo apt upgrade -y
sudo apt install python3.10 python3-pip git -y

For Windows Users:

Install Windows Subsystem for Linux (WSL2) Microsoft’s official guide.
Open Ubuntu Terminal via WSL2 and run the Linux commands above.

Install CUDA and PyTorch:

# Install CUDA 11.8
wget <https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run>
sudo sh cuda_11.8.0_520.61.05_linux.run

# Install PyTorch with CUDA support
pip3 install torch torchvision torchaudio --index-url <https://download.pytorch.org/whl/cu118>

Step 2: Clone the Wan 14B Repository

The model’s code lives on GitHub. Let’s grab it:

git clone <https://github.com/wan-org/Wan-14B-txt2video.git>
cd Wan-14B-txt2video

Pro Tip: Check the README.md for updates. The AI space evolves faster than TikTok trends!

Step 3: Set Up a Virtual Environment

Avoid dependency hell! Isolate your project:

python3 -m venv wan-env
source wan-env/bin/activate  # Linux/WSL
# For Windows CMD: .\\wan-env\\Scripts\\activate

Install requirements:

pip install -r requirements.txt

Step 4: Download Model Weights

The repository doesn’t include the actual AI model (it’s too large). Download the pre-trained weights:

Option 1 (Official):

Visit the model’s Hugging Face page (register if needed).

Use git lfs to download:

git lfs install
git clone <https://huggingface.co/wan-14b/txt2video-720p>

Move the txt2video-720p folder into the project directory.

Option 2 (Direct Download):

Some communities host mirrors. Check the project’s Discord for magnet links (but verify checksums!).

Step 5: Configure Your First Video

Time to create your masterpiece!

Craft Your Prompt:

Be specific. Instead of “a cityscape”, try:

“A futuristic neon-lit city at night, flying cars zooming between skyscrapers, cyberpunk style, 720p, 30fps.”

Adjust Settings in config.yaml:

Open the file and tweak:

output_resolution: [1280, 720]
num_frames: 90  # 3 seconds at 30fps
guidance_scale: 7.5  # Higher = more adherence to prompt
seed: 42  # Change for different results

Run the Script:

python generate.py --prompt "YOUR_PROMPT" --config config.yaml

Note: The first run will take longer (model initializes). Subsequent runs use cached weights.

Step 6: Monitor and Troubleshoot

Your terminal will look like a scene from The Matrix. Here’s what to watch for:

VRAM Usage: Run nvidia-smi (Linux/WSL) or Task Manager (Windows) to check GPU load.
Out of Memory? Reduce num_frames or output_resolution in config.yaml.
Stuck at 100% CPU? Ensure CUDA and PyTorch are properly installed.
Artifacts or Glitches? Increase guidance_scale or refine your prompt.

Step 7: Render and Post-Process

Once generated, your video (e.g., output_001.mp4) will be in the results folder.

Enhance It:

Upscale with FFmpeg:

ffmpeg -i output_001.mp4 -vf "scale=1280:720:flags=lanczos" upscaled.mp4

Add Sound: Use Audacity or royalty-free music from Epidemic Sound.

Optimization Tips

Batch Processing: Queue multiple prompts overnight.

Use xFormers: Install this library to speed up inference:

pip install xformers

Lower Precision: Use fp16 in config.yaml for faster (but slightly less crisp) videos.

FAQ: Your Burning Questions, Answered

Q: Can I run this on a Mac M2?

A: Sadly, no. Apple’s Metal API isn’t fully compatible with CUDA-dependent models.

Q: Why 720p and not 4K?

A: 720p requires ~8GB VRAM. 4K would need a $10,000 GPU (for now).

Q: My video is only 2 seconds long. Help!

A: Increase num_frames in config.yaml. Each frame = 1/30th of a second.

Q: Can I train my own version of Wan 14B?

A: Technically yes, but you’d need a dataset of labeled videos and a lot of compute.

Final Thoughts

Running Wan 14B txt2video locally is like having a Spielberg-tier director in your PC—it just needs clear instructions (and a decent GPU). While the tech isn’t perfect yet (expect occasional surreal glitches), it’s a thrilling glimpse into the future of content creation.

Go Forth and Create:

Make viral shorts for TikTok/YouTube.
Visualize dreams or storyboards.
Experiment with abstract art prompts (“melting clocks in a desert, Dali style”).

Remember, every AI-generated video today is a stepping stone to tomorrow’s holographic blockbusters. Happy rendering! 🎥✨

Got stuck? Drop a comment below or join the Wan community Discord for real-time help!