WAN-V2.2
Bring your static images to life with the advanced physics engine of wan-2-2-i2v; create fluid videos with high motion consistency while preserving object integrity.
Avg Run Time: 85.000s
Model Slug: wan-2-2-i2v
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Output
Example Result
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API & SDK
Create a Prediction
Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.
Get Prediction Result
Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.
Readme
Overview
Wan-2-2-i2v is an advanced AI video generation model developed by Wan-AI, designed specifically for high-quality image-to-video synthesis. It leverages a Mixture-of-Experts (MoE) architecture, a technique proven effective in large language models, to optimize both the quality and efficiency of video generation. The model is part of the Wan2.2 series, which introduces significant improvements over previous versions, particularly in terms of video realism, detail, and computational efficiency.
Key features of Wan-2-2-i2v include support for both 480p and 720p video generation, single-GPU inference capability, and a two-expert MoE system that dynamically switches between experts during the denoising process. This architecture allows the model to maintain high output quality while keeping computational and memory requirements manageable. The model is notable for its ability to generate visually coherent and detailed videos from static images, making it suitable for a wide range of creative and professional applications.
Technical Specifications
- Architecture: Mixture-of-Experts (MoE) diffusion model with two experts (high-noise and low-noise)
- Parameters: Approximately 27 billion total parameters (14B per expert, only 14B active per step)
- Resolution: Supports 480p and 720p video generation
- Input/Output formats: Input - static images (JPG, PNG); Output - video files (MP4, GIF, or similar)
- Performance metrics: Achieves lowest validation loss among tested variants, indicating high fidelity to ground-truth video distribution
Key Considerations
- The model requires a GPU with at least 80GB VRAM for optimal performance at higher resolutions
- For best results, ensure the aspect ratio of the input image matches the desired output video
- The MoE architecture automatically manages expert switching based on signal-to-noise ratio, so manual tuning is minimal
- Prompt engineering is important: detailed and context-rich prompts yield more accurate and visually appealing videos
- Quality and speed are balanced by the MoE design, but higher resolutions and longer videos will increase inference time
- Avoid using low-quality or ambiguous input images, as these can degrade output quality
Tips & Tricks
- Use high-resolution, well-lit images as input to maximize video detail and coherence
- Structure prompts with clear descriptions of scene, style, and desired motion to guide the model effectively
- Experiment with prompt variations to iteratively refine video outputs; small changes in wording can impact results
- For specific effects (e.g., slow motion, dramatic lighting), include explicit instructions in the prompt
- If resources allow, generate multiple samples and select the best output, as minor variations can occur between runs
Capabilities
- Generates high-quality, realistic videos from single static images
- Supports both 480p and 720p output resolutions
- Maintains temporal coherence and visual consistency across frames
- Adaptable to a wide range of visual styles and subject matter based on prompt input
- Efficient inference enabled by the MoE architecture, with only one expert active per step
- Capable of running on consumer-grade GPUs with sufficient VRAM
What Can I Use It For?
- Professional animation and video content creation for marketing, entertainment, and education
- Creative projects such as animated illustrations, concept art visualization, and short film prototyping
- Business use cases including product demos, explainer videos, and dynamic presentations
- Personal projects like animated avatars, social media content, and hobbyist video art
- Industry-specific applications in advertising, gaming, and digital storytelling, as reported in technical discussions and user showcases
Things to Be Aware Of
- Some experimental features may behave unpredictably, especially with highly abstract or unconventional prompts
- Users have noted occasional artifacts or inconsistencies in complex scenes with rapid motion or intricate backgrounds
- Performance is heavily dependent on GPU resources; lower VRAM may limit resolution or increase inference time
- Consistency across multiple runs is generally high, but minor variations can occur due to the stochastic nature of diffusion models
- Positive feedback highlights the model’s ability to generate detailed, visually appealing videos with minimal manual tuning
- Some users report that prompt specificity greatly influences output quality, emphasizing the importance of prompt engineering
- Negative feedback patterns include occasional frame flicker or loss of detail in challenging scenarios
Limitations
- Requires high-end GPU hardware (80GB VRAM recommended) for full-resolution video generation
- May struggle with highly complex scenes, rapid motion, or ambiguous input images
- Not optimal for real-time or low-latency applications due to computational demands
Pricing
Pricing Detail
This model runs at a cost of $0.41 per execution.
Pricing Type: Fixed
The cost remains the same regardless of which model you use or how long it runs. There are no variables affecting the price. It is a set, fixed amount per run, as the name suggests. This makes budgeting simple and predictable because you pay the same fee every time you execute the model.
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