Hugging | Trending Video Effects API

Hugging Effect

Requirements

Number of Images

Mandatory
Only one image upload supported

Number of People

Only dual-person collage/group photo, or person-pet collage/group photo supported

Image Requirements

Over-half-body exposure with no props in hands for better results

Prompt

Mandatory
Format should remain unchanged as much as possible (details refer to the "prompt" parameter in the request example)
If custom requirements exist, modify the prompt without altering the structure and majority of the content

Effect Boundaries

When there is a size discrepancy between two subjects in the photo, there is a probability that the model may fail to recognize them, resulting in hugging videos that do not conform to physical laws

Best Practices

Use clear, front-facing photos
Ensure all subjects are clearly visible
Use over-half-body shots for optimal results
Keep the default prompt structure
Avoid having props in hands
Try to maintain similar sizes between subjects for better results

Hugging API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/video-effects/hugging 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 Hugging below.

HTTP example

# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/video-effects/hugging" \
  -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/hugging", {
        "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/hugging",
    {
    "image": "https://example.com/your-input.jpg"
}
)

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

Hugging API — Frequently asked questions

What is the Hugging API?

Hugging is a Video Effects model for AI inference, exposed as a REST API on WaveSpeedAI. Turn photos into natural, emotionally realistic hugging videos with Image-to-Video generation and lifelike motion. Free online generator. Ready-to-use REST API, no coldstarts, best performance, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Hugging 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-hugging.

How much does Hugging cost per run?

Hugging 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 Hugging 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-hugging.

How long does Hugging take to generate?

Average end-to-end generation time on WaveSpeedAI is around 130 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.

Can I use Hugging 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.

ExamplesView all

Related Models

README