Wan 2.2 Animate: MINDBLOWING FREE AI Model for Character Swaps With Full Control!
Wan 2.2 introduces powerful open-source character swap and animation capabilities, lowering the barrier for creators to produce deepfakes and custom character videos.
What this lesson covers
This video provides a first look at Alibaba's new open-source AI video model, Wan 2.2, focusing on its photo animate and character swap features. The creator demonstrates how to use the web platform to generate realistic character-swapped videos and discusses potential use cases and limitations.
Key takeaways from the creator
AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.
- 0:20 ↗
Wan 2.2 is an open-source model developed by Alibaba available on the web and runnable locally.
- 0:37 ↗
The model includes a 'photo animate' and 'character swap' feature requiring only a target video and a single image.
- 1:51 ↗
Users can choose between standard and pro settings within the platform, alongside speech-to-video options.
- 3:48 ↗
Photo animate preserves the background of the source photo, whereas character swaps replace the background with the target video's setting.
- 6:39 ↗
Pro memberships and credit packs are available, though standard generation times remain around 10 minutes.
Workflow outlined in the video
- Visit wan.video to test out the Wan 2.2 platform using either the free tier or credits.
- Select the avatar generation mode and choose between character swap or photo animate depending on your background preference.
- Upload a clean source image of a character and a driving video to execute a character swap.
- Experiment with pro settings to evaluate render quality improvements against generation times.
Before you use this workflow
These notes describe the source video at its publication date. Model access, pricing, connectors and interfaces may have changed. Check the original source and the provider’s current documentation before installing an add-on, connecting an account or spending credits.
ReelStack has not independently tested this workflow. Preview one representative shot and check motion, continuity and output quality before applying it to a full production. No result, cost saving or model capability is guaranteed.
How to interpret the numbers
30,278 views captured 24 October 2026. ReelStack recommendation score: 63/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.
Show the calculation
- 65% performance: 10.80× current views vs the channel's recent median; same-age history is not yet available. View-evidence factor 97% (views / (views + 1,000))
- 25% freshness: 0/100 with a 14-day half-life
- Momentum pending: collecting daily snapshots; its 20% weight goes to observed performance, not free points
- 10% engagement: 55/100; likes + 4× comments, smoothed with a 500-view neutral prior