Wan2.2 T2V A14B: MoE-Based Text-to-Video Generation

Wan2.2 T2V A14B is an open-source text-to-video generation model developed by Wan-AI that introduces a Mixture-of-Experts (MoE) architecture to video diffusion systems. Released in July 2025, the model generates 5-second videos at both 480P and 720P resolutions with cinematic aesthetics and complex motion capabilities that surpass previous open-source and commercial models.

Architecture: Dual-Expert MoE Design

The model employs a novel two-expert system that strategically separates the video generation process:

High-Noise Expert:

• Handles early denoising stages of generation
• Focuses on overall layout and composition
• Establishes fundamental scene structure and motion patterns

Low-Noise Expert:

• Manages later refinement stages
• Refines video details and aesthetic qualities
• Enhances cinematic elements including lighting, contrast, and color tone

Efficiency Through Specialization:

• Approximately 27B total parameters with only 14B active per inference step
• Automatic expert switching via signal-to-noise ratio (SNR) thresholds
• Computational efficiency comparable to smaller dense models
• Reduced computational waste through targeted expert deployment

Training and Data Scale

Wan2.2 T2V benefits from significantly expanded training data:

• 65.6% increase in training images compared to Wan2.1
• 83.2% increase in training videos
• Enhanced diversity in motion types and semantic content
• Improved generalization across cinematic styles and aesthetics

This expanded dataset enables superior handling of complex motion patterns and diverse aesthetic preferences.

Key Capabilities

The model demonstrates several distinguishing strengths:

• Cinematic Aesthetics: Granular control over lighting, composition, contrast, and color tone
• Complex Motion Generation: Superior performance on Wan-Bench 2.0 evaluations against commercial systems
• Multi-Resolution Support: Generates both 480P and 720P outputs
• Prompt Extension: Integration with Qwen models or DashScope API for enhanced prompt elaboration
• Consumer Hardware Compatibility: Runs efficiently on RTX 4090 through model offloading
• Parameter Optimization: Supports parameter-type conversion for improved inference speed

Performance and Benchmarks

According to proprietary benchmarks, Wan2.2 T2V demonstrates superior performance compared to leading commercial video generation systems. The model excels particularly in complex motion scenarios where traditional single-expert architectures struggle with temporal consistency and realistic movement patterns.

The dual-expert MoE design contributes to reduced artifacts and more natural motion dynamics through specialized processing at appropriate denoising stages.

Deployment Options

The model supports flexible deployment configurations:

• Single-GPU Inference: Model offloading enables deployment on consumer hardware
• Multi-GPU Inference: Advanced optimization for accelerated generation
• Framework Integration: Compatible with standard video generation workflows
• Resolution Flexibility: Adapts between 480P and 720P based on quality-speed requirements

Use Cases

Wan2.2 T2V excels in applications requiring text-driven video synthesis:

• Professional video content creation for marketing and advertising
• AI-assisted filmmaking and commercial production
• Cinematic previsualization and storyboarding
• Social media content generation
• Educational and tutorial video production
• Research applications in generative media
• Concept visualization for film and media industries
• Rapid prototyping of video concepts from text descriptions

Technical Considerations

The MoE architecture's separation of layout and refinement stages enables more stable generation compared to traditional single-model approaches. The SNR-based switching mechanism ensures appropriate processing intensity throughout the denoising pipeline, optimizing both quality and computational efficiency.

The model's focus on cinematic aesthetics makes it particularly suitable for professional content creation requiring granular control over visual characteristics. Users seeking stylized or artistic outputs will benefit from the expanded training dataset's diversity in aesthetic preferences.

Distinction from I2V Variant

While sharing identical MoE architecture principles, Wan2.2 T2V focuses exclusively on text-to-video generation from textual prompts. The complementary I2V variant (Wan2.2 I2V A14B) specializes in image-to-video synthesis, enabling conditional generation from static images. Both models leverage the same dual-expert design philosophy while optimizing for their respective input modalities.