Wan 2.2

Overview

Wan 2.2, released in July 2025, marks a significant advancement in Alibaba's open-source video generation ecosystem. The model transitions from diffusion transformer to Mixture-of-Experts (MoE) architecture, featuring 27B total parameters with 14B active during inference. This architectural evolution enables higher quality output while maintaining efficient computation through selective expert activation.

Training improvements are substantial: Wan 2.2 incorporates 65.6% more images and 83.2% more videos compared to Wan 2.1, resulting in significantly enhanced visual fidelity, motion coherence, and prompt adherence. The model now supports dual resolution output at 480P and 720P (1280x704 @ 24fps), with the higher resolution enabling professional-quality content creation suitable for broadcast and commercial applications.

Wan 2.2 introduces five specialized model variants optimized for distinct use cases: T2V-A14B for text-to-video generation, I2V-A14B for animating static images, TI2V-5B for combined text and image inputs, S2V-14B for speech-to-video synthesis, and Animate-14B for character animation. This modular approach allows developers to select the optimal variant for their specific application, balancing quality, speed, and resource requirements. Hardware requirements range from 24-80GB VRAM depending on variant and resolution, with consumer GPUs like RTX 4090 supported for 480P generation.

Key Features

• Mixture-of-Experts (MoE) architecture: 27B total parameters, 14B active
• Dual resolution support: 480P and 720P (1280x704 @ 24fps)
• 65.6% more training images and 83.2% more training videos than Wan 2.1
• Five specialized model variants for different use cases
• T2V-A14B: Advanced text-to-video generation with enhanced prompt understanding
• I2V-A14B: High-quality image-to-video animation and motion synthesis
• TI2V-5B: Combined text and image inputs for precise control
• S2V-14B: Speech-to-video generation synchronized with audio input
• Animate-14B: Character animation with motion and expression control
• 24-80GB VRAM requirements depending on variant and resolution
• Consumer GPU support (RTX 4090) for 480P generation
• Open-source Apache 2.0 license for commercial use

Use Cases

• Professional video production at 720P for broadcast quality
• Social media content creation with enhanced visual fidelity
• Character animation for games, films, and virtual productions
• Speech-synchronized video for virtual presenters and avatars
• Image animation for photo-to-video transformation
• Marketing and advertising with 720P resolution output
• Educational content with combined text and image inputs
• Virtual influencer and character content creation
• Storyboarding and pre-visualization at professional resolution
• Research in multimodal AI video generation
• Custom video generation pipelines with specialized variants
• Localized deployment for data privacy and control

Technical Specifications

Wan 2.2's Mixture-of-Experts architecture employs 27B total parameters with 14B active during inference, enabling sophisticated video generation while managing computational costs through selective expert activation. The MoE design allows different experts to specialize in various aspects of video generation such as motion dynamics, texture synthesis, temporal consistency, and semantic understanding.

The model supports dual resolution output: 480P for faster generation and lower VRAM requirements, and 720P (1280x704 @ 24fps) for professional-quality content. Training data expansion includes 65.6% more images and 83.2% more videos compared to Wan 2.1, resulting in improved visual quality, better motion coherence, reduced artifacts, and stronger prompt adherence. The enhanced training corpus enables more accurate physical simulation, better handling of complex scenes, and improved temporal consistency across longer sequences.

Model Variants

Wan 2.2 offers five specialized variants optimized for distinct applications. T2V-A14B is the flagship text-to-video model with 14B active parameters, optimized for natural language understanding and high-fidelity video synthesis. I2V-A14B specializes in image-to-video animation, transforming static images into dynamic video with controllable motion. TI2V-5B combines text and image inputs for precise creative control, ideal for iterative refinement and targeted modifications.

S2V-14B introduces speech-to-video capabilities, generating video content synchronized with audio input for virtual presenters, avatars, and spoken content visualization. Animate-14B focuses on character animation with advanced motion and expression control, supporting virtual influencer creation, game character animation, and film character pre-visualization. Each variant can be deployed independently or combined in production pipelines for comprehensive video generation workflows.

Hardware Requirements and Performance

Wan 2.2's hardware requirements vary by model variant and target resolution. 480P generation runs on consumer GPUs like RTX 4090 with 24GB VRAM, making the technology accessible to individual developers and small studios. 720P generation requires more substantial hardware, typically 40-80GB VRAM depending on the specific variant, aligning with workstation-class GPUs or multi-GPU configurations.

The Mixture-of-Experts architecture provides efficiency advantages through selective expert activation, reducing effective computation compared to dense models of similar capacity. Generation times scale with resolution and complexity, with 480P generation achieving practical speeds on consumer hardware while 720P generation benefits from professional workstation configurations. The model supports both Linux and Windows platforms with CUDA and PyTorch.

Training Improvements

Wan 2.2's training corpus represents a substantial expansion over Wan 2.1, incorporating 65.6% more images and 83.2% more videos. This expanded dataset enables the model to learn more diverse visual patterns, motion dynamics, object interactions, and scene compositions. The training improvements manifest as higher visual quality, reduced artifacts and inconsistencies, better prompt adherence and semantic understanding, improved physical realism, and enhanced temporal consistency.

The larger training dataset enables Wan 2.2 to handle more complex prompts, generate more diverse content styles, maintain consistency across challenging scenarios, and produce professional-quality output suitable for commercial applications. The training methodology incorporates advanced techniques for motion modeling, texture synthesis, and temporal coherence, resulting in videos that rival proprietary competitors in many scenarios.

Open Source and Commercial Use

Wan 2.2 maintains the Apache 2.0 license, providing complete freedom for commercial use, modification, and distribution. Organizations can self-host models for data privacy, fine-tune on proprietary datasets, optimize for specific hardware configurations, and integrate into commercial products without licensing fees. The open-source nature enables community contributions, custom variant development, and derivative tools.

This licensing model makes Wan 2.2 particularly attractive for enterprises requiring on-premises deployment, startups building video generation services, researchers developing novel techniques, and content creators seeking cost-effective solutions. The elimination of per-generation fees and usage restrictions enables economically viable deployment at scale.

Speech-to-Video and Character Animation

Wan 2.2's S2V-14B variant introduces speech-to-video capabilities, generating visual content synchronized with audio input. This enables creation of virtual presenters where video content responds to spoken narration, educational videos with automated visual accompaniment to lectures, and avatar systems where characters speak with synchronized lip movements and expressions. The speech-to-video pipeline understands semantic content of spoken audio, generating relevant visual representations rather than simply animating a static character.

The Animate-14B variant specializes in character animation with advanced control over motion, expression, and pose. This variant supports keyframe-based animation workflows, motion transfer from reference videos, expression control for emotional delivery, and pose guidance for specific character positions. Applications include virtual influencer content, game character animation, film character pre-visualization, and automated character-based storytelling. The model maintains temporal consistency across sequences while enabling precise creative control over character behavior.

Pricing and Availability

Wan 2.2 is completely free and open-source under the Apache 2.0 license. All five model variants are publicly available for download and self-hosting. There are no usage fees, API costs, or licensing restrictions. Users only need compatible hardware (NVIDIA GPUs with 24-80GB VRAM depending on variant and resolution) and standard deep learning infrastructure. This eliminates recurring costs and enables unlimited generation at zero marginal cost beyond electricity and hardware depreciation.

Code Example: Text-to-Video with Wan 2.2 (720P)

The following Python code demonstrates how to use Wan 2.2's T2V-A14B variant for high-quality 720P video generation using Hugging Face Diffusers:

from diffusers import DiffusionPipeline
import torch

# Load Wan 2.2 T2V-A14B model for 720P generation
# Note: Actual model repository path may vary
model_id = "alibaba-tongyi/wan-2.2-t2v-a14b"
pipe = DiffusionPipeline.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    variant="fp16"
)

# Move to GPU (requires 40GB+ VRAM for 720P)
pipe = pipe.to("cuda")

# Enable memory optimizations
pipe.enable_model_cpu_offload()
pipe.enable_vae_slicing()

# Define detailed text prompt
prompt = """A professional corporate video: modern glass office building exterior 
at golden hour, smooth drone camera ascending from ground level to reveal city 
skyline, cinematic lighting with warm sunset tones, high detail architecture"""

negative_prompt = "blurry, low quality, distorted, artifacts, watermark"

# Generate 720P video (1280x704 @ 24fps)
video = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_frames=120,  # 5 seconds at 24fps
    height=704,
    width=1280,  # 720P resolution
    num_inference_steps=50,
    guidance_scale=8.0,
    generator=torch.Generator("cuda").manual_seed(42)
).frames[0]

# Save high-quality video output
from diffusers.utils import export_to_video
export_to_video(video, "corporate_building_720p.mp4", fps=24)

print("720P video generated successfully")
print(f"Resolution: 1280x704, Frames: {len(video)}, Duration: 5s")

Code Example: Image-to-Video Animation (Local Inference)

The I2V-A14B variant enables animation of static images. This example demonstrates converting a photograph into a dynamic video using local GPU inference:

from diffusers import DiffusionPipeline
from PIL import Image
import torch

# Load Wan 2.2 I2V-A14B model for image-to-video
model_id = "alibaba-tongyi/wan-2.2-i2v-a14b"
pipe = DiffusionPipeline.from_pretrained(
    model_id,
    torch_dtype=torch.float16
)
pipe = pipe.to("cuda")
pipe.enable_model_cpu_offload()

# Load input image
input_image = Image.open("landscape_photo.jpg")

# Define motion prompt
motion_prompt = "Camera slowly pans right across the landscape, clouds moving gently, natural wind motion in trees"

# Generate video from image
video = pipe(
    image=input_image,
    prompt=motion_prompt,
    num_frames=120,
    height=480,
    width=854,
    num_inference_steps=50,
    guidance_scale=7.5
).frames[0]

# Export animated video
from diffusers.utils import export_to_video
export_to_video(video, "animated_landscape.mp4", fps=24)

print("Image successfully animated to video")

Code Example: Cloud API Inference

For production workloads without local GPU infrastructure, Wan 2.2 can be accessed via cloud API endpoints. This example shows API-based video generation:

import requests
import json
import base64
from PIL import Image
import io

# Wan API endpoint (example - check documentation for actual endpoint)
API_URL = "https://api.wan.video/v1/generate"
API_KEY = "your_api_key_here"

def generate_video_cloud(prompt, resolution="720p", duration=5):
    """
    Generate video using Wan 2.2 cloud API
    
    Args:
        prompt: Text description of the video
        resolution: '480p' or '720p'
        duration: Video duration in seconds (max varies by plan)
    """
    payload = {
        "model": "wan-2.2",
        "variant": "t2v-a14b",  # Use T2V variant
        "prompt": prompt,
        "resolution": resolution,
        "num_frames": duration * 24,  # 24fps
        "guidance_scale": 8.0,
        "num_inference_steps": 50
    }
    
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }
    
    # Submit generation request
    response = requests.post(API_URL, headers=headers, json=payload)
    
    if response.status_code == 200:
        result = response.json()
        video_id = result["video_id"]
        print(f"Video generation started: {video_id}")
        
        # Poll for completion
        status_url = f"{API_URL}/{video_id}/status"
        while True:
            status_response = requests.get(status_url, headers=headers)
            status = status_response.json()
            
            if status["status"] == "completed":
                video_url = status["video_url"]
                print(f"Video ready: {video_url}")
                return video_url
            elif status["status"] == "failed":
                print(f"Generation failed: {status['error']}")
                return None
            
            time.sleep(5)  # Wait 5 seconds before checking again
    else:
        print(f"API Error: {response.status_code} - {response.text}")
        return None

# Example usage
prompt = "Professional product showcase: smartphone rotating on white background with dramatic lighting, 4K quality, commercial style"
video_url = generate_video_cloud(prompt, resolution="720p", duration=5)

if video_url:
    # Download the video
    video_data = requests.get(video_url).content
    with open("product_showcase_720p.mp4", "wb") as f:
        f.write(video_data)
    print("Video downloaded successfully")

Professional Integration Services by 21medien

Wan 2.2's advanced capabilities including Mixture-of-Experts architecture, 720P output, and specialized model variants create significant opportunities for businesses, but also technical complexity in deployment and optimization. 21medien provides expert consulting and integration services to help organizations leverage Wan 2.2 for professional video production, marketing campaigns, virtual character content, and automated video workflows.

Our team specializes in infrastructure architecture for high-VRAM requirements (40-80GB), determining optimal GPU configurations and multi-GPU strategies, model variant selection based on use case and budget constraints, fine-tuning models on proprietary datasets for industry-specific content, prompt engineering strategies for consistent brand-aligned output, and production pipeline integration with existing creative tools. We help businesses navigate the tradeoffs between resolution quality, generation speed, hardware costs, and output requirements.

For enterprises considering Wan 2.2 deployment, we offer comprehensive services including technical feasibility assessment and ROI analysis, on-premises infrastructure planning vs. cloud GPU solutions, workflow automation for bulk video generation, quality assurance frameworks and output validation systems, integration with content management systems and creative software, and team training on prompt engineering and model operation. Whether you're building a video generation platform, automating marketing content creation, developing virtual character systems, or exploring AI video for your industry, we provide the expertise to successfully deploy and scale Wan 2.2.

The open-source nature of Wan 2.2 offers substantial cost advantages over proprietary solutions, but requires technical expertise to maximize value. Our consultation services help you determine if Wan 2.2 is the right solution for your use case, design optimal deployment architectures, and implement production-ready systems that deliver business results. Schedule a free consultation through our contact page to discuss how Wan 2.2's advanced capabilities can transform your video content strategy while maintaining full control over infrastructure and data.