AI video generation is rapidly evolving, and WAN 2.2 stands out as one of the latest breakthroughs. Developed by Wan AI (under Alibaba’s Tongyi / “Wan” initiative), WAN 2.2 builds on prior versions (like WAN 2.1) to deliver higher efficiency, better controllability, and open-source flexibility.
In this post, we’ll cover:
- What WAN 2.2 is
- Key innovations & architecture
- How to use WAN 2.2 (variants, setup)
- Strengths, limitations, and use cases
- SEO and content ideas around WAN 2.2
What Is WAN 2.2?
WAN 2.2 is a multimodal video generation model that supports:
- Text → Video (T2V)
- Image → Video (I2V)
- Hybrid / Text+Image → Video
- Speech / Audio → Video via specialized variants (e.g. S2V) .
It is open source (with code and model weights published) and is designed to be accessible even on powerful consumer GPUs.
WAN 2.2 improves over its predecessor by offering more precise control (over motion, lighting, framing, etc.), better temporal consistency, and more efficient compute.
Architecture & Key Innovations
Mixture-of-Experts (MoE) Design
WAN 2.2 uses a Mixture-of-Experts (MoE) architecture, specifically employing high-noise experts for early denoising steps (to compose global layout) and low-noise experts in later steps (to refine details). This division helps the model balance quality and efficiency.
VAE Compression & Efficient Design
WAN 2.2 employs an advanced VAE (variational autoencoder) structure to compress and handle latent representations efficiently. This helps reduce resource demands while preserving quality.
Cinematic Control & VACE Integration
WAN 2.2 integrates enhanced control systems (sometimes called VACE or similar modules) to allow users to influence:
- Camera motion & framing
- Lighting, color style
- Character consistency across frames
- Scene composition
This lets creators get more “cinematic” output rather than uncontrolled artifacted video.
Model Variants & Scalability
WAN 2.2 comes in multiple variants to suit different use cases:
| Variant | Use Case | Resolution / Frame Rate | Notes |
|---|---|---|---|
| T2V-A14B | Text → Video | 480p / 720p | MoE model specialized for text → video |
| I2V-A14B | Image → Video | 480p / 720p | MoE model for image → video |
| TI2V-5B | Hybrid (Text + Image) | 720p @ 24fps | Lightweight unified model, faster, less resource demand |
| S2V-14B | Speech / Audio → Video | 480p / 720p | Audio-driven video generation variant |
| Animate-14B | Character animation / replacement | 720p | For generating or replacing character movement . |
These variants allow flexibility: you can choose lighter models for faster runs or heavier ones for higher fidelity.
How to Use WAN 2.2
Setup & Installation
- Clone repository
git clone https://github.com/Wan-Video/Wan2.2.gitcd Wan2.2 - Install dependencies
pip install -r requirements.txt
And (if doing speech-to-video)pip install -r requirements_s2v.txt - Download model checkpoints
Models (T2V, I2V, TI2V, S2V, Animate) are available via Hugging Face or ModelScope.
For example, for T2V model:
bash : python generate.py –task t2v-A14B –size 1280*720 –ckpt_dir ./Wan2.2-T2V-A14B –prompt “A dragon flying over mountains at sunrise.”
- (Adjust GPU offloading, dtype conversion flags depending on your hardware)
The repository also includes integration with ComfyUI (a node-based UI) and diffusers frameworks, for easier workflows.
Generating Video: Tips & Best Practices
- Use more descriptive prompts (lighting, style, motion) to guide the model
- Use “prompt extension” (auto-expand prompt) features built into the WA N2.2 toolkit
- Use reference images for better consistency, especially for characters
- Adjust sampling steps, denoising schedules for quality vs speed tradeoffs
- Monitor GPU memory usage and batch sizes
Running on Cloud / High-end GPU
WAN 2.2 is heavy for some tasks. For longer videos or higher resolution, use cloud GPUs like H100. Generating a 720p video on a single H100 may take ~20–25 minutes.
Strengths, Limitations & Use Cases
Strengths
- Open source flexibility: Users/researchers can inspect, extend, fine-tune.
- Better motion & consistency: MoE architecture and cinematic controls reduce flicker and inconsistent frames.
- Supports multiple modalities: Text, image, audio inputs give flexibility in creative workflows
- Balance of efficiency & quality: Lighter models (e.g. 5B) make it more accessible for non-supercomputing environments
Limitations
- Hardware demand: Larger models (14B variants) require high VRAM or multiple GPUs
- Context length / duration constraints: Very long videos or complex stories may exceed model capabilities
- Artifacts & prompt sensitivity: For some scenes, the model may misinterpret or produce artifacts if prompt is ambiguous
- Motion complexity limits: Extremely intricate motions or fluid physics may still challenge it
Use Cases / Applications
- Content creation / social media: Quick visual content from script or image ideas
- Marketing & ads: Generate product animations, promotional videos
- Storyboarding / previsualization: Convert scripts or concepts into visual previews
- Art & creative experiments: Artists can explore motion, narrative, style
- Academic research: As a base to fine-tune or investigate video generation techniques