Kissing Pro | Trending Video Effects API

Professional Kissing Effect

Requirements

Number of Images

Required
Only a single image upload is supported

Number of People

Only supports dual-person group photos and dual-person collages

Image Requirements

Better results are achieved when the input image is clear and features half-body frontal shots

Prompt

Required
Preserve the original format as much as possible (see the "prompt" parameter in the request example for details)
Minor prompt modifications are allowed for personalization, provided the core structure and majority of content remain unchanged

Effect Boundaries

Potential edge cases:
Third person appearing during kissing scenes
Incomplete kissing gestures

Best Practices

Use clear, front-facing photos
Ensure both subjects are clearly visible
Use half-body shots for optimal results
Keep the default prompt structure
Ensure subjects' faces are clearly visible

Kissing Pro API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed/kissing-pro with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Kissing Pro below.

HTTP example

# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed/kissing-pro" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d '{
    "image": "https://example.com/your-input.jpg"
}'

# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY"

# When status is "completed", read the output from data.outputs[0].

Node.js example

// npm install wavespeed
const WaveSpeed = require('wavespeed');

const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env

const result = await client.run("video-effects/kissing-pro", {
        "image": "https://example.com/your-input.jpg"
});

console.log(result.outputs[0]); // → URL of the generated output

Python example

# pip install wavespeed
import wavespeed

output = wavespeed.run(
    "video-effects/kissing-pro",
    {
    "image": "https://example.com/your-input.jpg"
}
)

print(output["outputs"][0])  # → URL of the generated output

Kissing Pro API — Frequently asked questions

What is the Kissing Pro API?

Kissing Pro is a Video Effects model for AI inference, exposed as a REST API on WaveSpeedAI. Convert two-person photos into natural, synchronized kissing videos with lifelike motion using Photo-To-Video AI. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Kissing Pro API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID; poll the prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/video-effects/video-effects-kissing-pro.

How much does Kissing Pro cost per run?

Kissing Pro starts at $0.20 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Kissing Pro accept?

Key inputs: `image`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/video-effects/video-effects-kissing-pro.

How long does Kissing Pro take to generate?

Average end-to-end generation time on WaveSpeedAI is around 870 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.

Can I use Kissing Pro outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Video Effects). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

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