A Deep Dive into Wan2.2
An interactive analysis of the first open-source Mixture-of-Experts (MoE) video generation model, translating a dense technical report into an explorable experience.
Executive Summary & Key Findings
Wan2.2 marks a pivotal moment for open-source AI, introducing a powerful MoE architecture that offers unprecedented quality and control. However, this power comes with significant trade-offs, creating a stark performance dichotomy between its flagship and consumer-grade models. This dashboard will guide you through these complexities.
- ✔State-of-the-Art Quality: The 14B MoE models deliver superior motion fidelity and aesthetic control, setting a new open-source benchmark.
- ✔Cinematic Control: Training on aesthetically-labeled data enables granular control over camera, light, and color via text prompts.
- ✖Performance Dichotomy: A massive gap exists between the slow, high-quality 14B models and the fast but critically flawed 5B model.
- ✖High Latency: The flagship 14B models are significantly slower than their predecessors, posing a major workflow challenge.
Model Suite Deep Dive
Wan2.2 offers specialized models for different tasks and hardware. Understanding their capabilities and trade-offs is the first step to effective use. Select a model to explore its details.
Wan2.2-T2V-A14B: The Premier Text-to-Video MoE Model
The flagship model, leveraging the full 27B MoE architecture for the highest quality text-to-video generation. It's the top performer on benchmarks, designed for creators who prioritize final visual fidelity and nuanced aesthetic control above all else.
Best For:
Final rendering in professional workflows, research, and high-quality artistic generation.
Target Hardware:
Professional/Cloud (>=80GB VRAM recommended)
Wan2.2-I2V-A14B: The Specialized Image-to-Video MoE Model
A dedicated MoE model fine-tuned for animating static images. It excels at producing stable video with fewer camera jitters and maintaining the artistic style of the source image with high integrity.
Best For:
Bringing concept art, character portraits, or illustrations to life with high consistency.
Target Hardware:
Professional/Cloud (>=80GB VRAM recommended)
Wan2.2-TI2V-5B: The Unified Model for Consumer Hardware
A traditional dense model (not MoE) that relies on a high-compression VAE to run on consumer GPUs. It unifies T2V and I2V tasks but suffers from widely reported quality issues, making it unsuitable for most high-fidelity applications.
Best For:
Rapid prototyping, casual experimentation, and for users who lack access to professional-grade GPUs.
Target Hardware:
Consumer (>=24GB VRAM recommended)
The Core Trade-Off: Performance vs. Quality
The choice between Wan2.2 models boils down to a classic dilemma. The following charts visualize the stark contrast in hardware requirements and the quality-for-latency trade-off you must make.
Hardware Requirements (VRAM)
Quality vs. Latency
Architecture Explained: The MoE Advantage
Wan2.2's core innovation is its two-expert MoE architecture. Instead of one giant model, it uses two specialized 14B models that hand-off the task, enabling higher quality without increasing per-step computational cost.
Expert 1: High-Noise
Active during initial, noisy timesteps ($t \ge t_{moe}$). Its job is to establish the video's global structure, composition, and basic motion from the prompt.
SNR Switch Point
Expert 2: Low-Noise
Takes over for later, cleaner timesteps ($t < t_{moe}$). Its job is to refine details, sharpen textures, and ensure temporal consistency, polishing the final output.
The Cinematic Control System
This "system" is the model's ability to understand the language of filmmaking. By using specific keywords in your prompt, you can direct the camera, lighting, and style. Click a keyword to see its effect and an example prompt.
Select a keyword from the grid below to learn more.
Competitive Landscape
Wan2.2 competes in a crowded field. Its value is defined by its open-source nature, offering ultimate control and quality as an alternative to closed-source or commercial platforms.
| Feature | Wan2.2 | OpenAI Sora | RunwayML | Stable Video Diffusion |
|---|---|---|---|---|
| Access Model | Open-Source | Closed-Source | Commercial (SaaS) | Open-Source (Non-Commercial) |
| Primary Strength | Quality & Control | Narrative Coherence | Speed & Workflow | Accessibility |
| Key Limitation | High Latency / Flawed 5B | No Public Access | Credit-Based Cost | Limited Control |
| Target User | Developers, Technical Artists | Enterprise Creatives | General Creatives | Hobbyists, Researchers |