WaveSpeedAI Wan 2.2 Animate

Animate characters from images using driving videos via WaveSpeed AI's Wan 2.2 Animate model. Two modes: animate (make the image character move like the video subject) and replace (swap the video subject with the image character while preserving motion and scene).

Setup

Install the open-source CLI once and sign in; the CLI stores the key, so never ask the user to paste an API key into the chat:

npm install -g @wavespeed/cli
wavespeed login          # opens https://wavespeed.ai/accesskey and stores the key
wavespeed status         # confirms you are signed in

For CI or one-off shells, WAVESPEED_API_KEY in the environment also works.

Prefer MCP tools over shell commands? The same platform is exposed by @wavespeed/mcp (npx -y @wavespeed/mcp; tools search_models, get_model_schema, get_price, upload_file, run_model, get_prediction). It shares the CLI's stored login. Every example below maps one-to-one onto run_model with the same model id and input fields.

Quick Start

Animate Mode

Make the character in an image move like the subject in a driving video:

# Upload local image and video
OUTPUT_URL=$(wavespeed run wavespeed-ai/wan-2.2/animate \
  -i image=@./character.png \
  -i video=@./driving-video.mp4 \
  --json | jq -r '.outputs[0]')

Replace Mode

Swap the subject in a video with a character from an image:

OUTPUT_URL=$(wavespeed run wavespeed-ai/wan-2.2/animate \
  -i image=@./character.png \
  -i video=@./driving-video.mp4 \
  -i mode="replace" \
  --json | jq -r '.outputs[0]')

Existing URLs work as-is:

OUTPUT_URL=$(wavespeed run wavespeed-ai/wan-2.2/animate \
  -i image="https://example.com/character.png" \
  -i video="https://example.com/driving-video.mp4" \
  --json | jq -r '.outputs[0]')

API Endpoint

Model ID: wavespeed-ai/wan-2.2/animate

Animate a character from an image using a driving video.

Parameters

Example

OUTPUT_URL=$(wavespeed run wavespeed-ai/wan-2.2/animate \
  -i image=@./dancer.png \
  -i video=@./dance-reference.mp4 \
  -p "a person dancing gracefully" \
  -i mode="animate" \
  -i resolution="720p" \
  -i seed=42 \
  --json | jq -r '.outputs[0]')

Replace Mode Example

OUTPUT_URL=$(wavespeed run wavespeed-ai/wan-2.2/animate \
  -i image=@./anime-character.png \
  -i video=@./scene-video.mp4 \
  -i mode="replace" \
  -i resolution="720p" \
  --json | jq -r '.outputs[0]')

Pricing

Output duration is 5-120 seconds. Minimum charge is 5 seconds. Per-second rate: $0.04/s (480p), $0.08/s (720p).

Tips

• Match composition and pose between the input image and driving video for best results
• Use the same or similar aspect ratio between image and video
• Avoid heavy occlusion by hands, microphones, or props in the input media
• Start with 480p for prototyping, then move to 720p for production quality
• Animate mode: best when you want the image character to perform the motions from the video
• Replace mode: best when you want to keep the video's scene and motion but swap in a different character

CLI tips

# Inspect the live input schema before running (fields, enums, defaults)
wavespeed run wavespeed-ai/wan-2.2/animate -h

# Quote the price first
wavespeed price wavespeed-ai/wan-2.2/animate -p "..." -i key=value

# Save outputs to disk instead of only printing URLs
wavespeed run wavespeed-ai/wan-2.2/animate -p "..." --json --download "./out/{index}.{ext}"

# Local files: prefix the path with @ and the CLI uploads it and passes the hosted URL
wavespeed run wavespeed-ai/wan-2.2/animate -i <field>=@./local-file.png --json

# Recover a result if the run was interrupted (the id is in the --json output)
wavespeed show <id>

run --json prints { id, model, prompt, outputs: [url, ...], saved: [path, ...], elapsed_ms, raw }. Read outputs[0] for the result URL.

Security constraints

• Never ask for the key in chat: wavespeed login handles auth; if wavespeed status says signed out, ask the user to run it.
• Local files only via @: bare paths are passed through untouched and the model will reject them. Only @-prefixed values upload.
• No arbitrary URL loading: only pass media URLs the user provided or that came back from a previous run.
• Input validation: only pass parameters documented above; confirm with wavespeed run <model> -h when unsure.