Wan 2.2 Image to Video Fast

Transforms simple text prompts into breathtaking cinematic-quality videos in minutes.

Playground

Descriptive prompt for video content. Include vivid details for a richer video.

image

Click or Drag-n-Drop

You can drop your own file here

Input image URL for video base. Choose a detailed image for best results.

Sets randomness; leave blank for complete randomization. Use a specific seed for reproducibility.

Activates faster video generation. Disable for quality over speed.


Related Pixelflows

Discover Pixelflow templates that use this model.

Resources to get you started

Everything you need to know to get the most out of Wan 2.2 Image to Video Fast

Wan 2.2: A Mixture-of-Experts Model for Video Generation

Wan 2.2 is the latest and greatest in open-source AI video generation, developed by Alibaba's Tongyi Lab. This model introduces new architectural innovations that significantly advance the field of text-to-video and image-to-video generation while maintaining computational efficiency, making it affordable for many use cases. This is the A14B model that can output 480p and 720p videos. There is also a smaller 5B model that is consumer GPU friendly.

About the tech: Mixture-of-Experts (MoE) Architecture

The model leverages Mixture of Experts or MoE architecture and uses 2 expert models under the hood for the diffusion denoising process.

  • β€’High-noise expert: Processes early denoising stages, focusing on overall layout and structure
  • β€’Low-noise expert: Manages final stages, refining video details and reducing artifacts

This two model approach results in 14 billion active parameters per inference step and a total of 27 billion over all parameters combining both the models. The transition between the experts is decided based on the signal to noise ration (SNR) helping the pipeline intelligently transition between the two experts without sacrificing output quality

Wan 2.2 also brings substantial improvements over its predecessor through expanded training data, featuring 65% more images and 80% more videos. This enables advance motion generations helping generate complex body movements, dynamic scene transitions and fluid camera controls. It can also simulate realistic physics scenarios and object interactions. This makes it effective for character animations, sport scenes and other cimenatic sequences.

Another big leap due the added training data is tight control over lighting, composition, contrast and color tone. Wan 2.2 offers over 60 controllable parameters that enale control for camera aware prompting like "aerial orbit," "handheld tracking shot," or specific lighting requirements.

Use cases

Just like other Text to Video and Image to video models this model can be used for a range of use cases. Use to generate cinematic visuals for a project you are working on or generate short social media ads with a product in focus. You can also use the model to create simple animations that can be used as a website background or on a slide deck. The lower costs compared to a lot of open models out there makes it the first choice before trying other video generator model.

License details

Wan 2.2's open-source nature under the Apache 2.0 license makes it the best choice for a range of use cases including commercial and educational purposes.

Other Popular Models

Discover other models you might be interested in.

Take creative control today and thrive.

Start building with a free account or consult an expert for your Pro or Enterprise needs. Segmind's tools empower you to transform your creative visions into reality.

Pixelflow Banner

Cookie settings

We use cookies to enhance your browsing experience, analyze site traffic, and personalize content. By clicking "Accept all", you consent to our use of cookies.