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Akool Image Face Swap

akool /

Akool Image Face Swap swaps faces in photos using a source and target image, including multi-face replacement for group photos. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

portrait-transfer
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$0.16실행당·~62 / $10

관련 모델

README

Akool Image Face Swap

Akool Image Face Swap is a powerful AI-powered face swapping model that seamlessly replaces faces in images with remarkable precision and realism. Swap single or multiple faces in one image while preserving natural lighting, skin tone, and facial expressions.

Why It Stands Out

  • Multi-face swapping: Swap multiple faces in a single image simultaneously.
  • High-quality blending: Seamlessly integrates swapped faces with natural lighting and skin tone matching.
  • Face enhancement: Optional post-swap face quality enhancement for sharper, cleaner results.
  • Flexible input: Upload multiple source and target face images for precise control.
  • One-click operation: Simple workflow — upload images and get results instantly.

Parameters

ParameterRequiredDescription
imageYesThe main image where faces will be swapped.
source_imageYesFace(s) to use as replacement (can add multiple).
target_imageYesFace(s) in the main image to be replaced (can add multiple).
face_enhanceNoEnhance face quality after swapping (default: enabled).
enable_base64_outputNoReturn base64 string instead of URL (API only).

How to Use

  1. Upload the main image — the image where you want to swap faces.
  2. Add source image(s) — upload the face(s) you want to use as replacements.
  3. Add target image(s) — upload reference images of the face(s) to be replaced in the main image.
  4. Enable face enhance (recommended) — improves output quality.
  5. Click Run and wait for processing.
  6. Preview and download the result.

Best Use Cases

  • Creative Projects — Create fun, artistic face swaps for personal projects.
  • Content Creation — Generate engaging content for social media and entertainment.
  • Marketing & Advertising — Visualize models or personas in different scenarios.
  • Film & Video Production — Create concept images for pre-visualization.
  • E-commerce — Show products on different face types for diverse representation.

Pricing

OutputPrice
Per image$0.16

Pro Tips for Best Quality

  • Use high-resolution, well-lit face images for both source and target.
  • Ensure faces are clearly visible and not obscured by accessories or hair.
  • For best matching, use source faces with similar angles to the target faces.
  • Keep face_enhance enabled for sharper, more natural-looking results.
  • When swapping multiple faces, match source and target images in the same order.

Notes

  • Ensure uploaded image URLs are publicly accessible.
  • Processing time varies based on the number of faces and current queue load.
  • Please use responsibly and ensure your content complies with usage guidelines.
참고:이 웹사이트는 제3자가 제공하는 AI 모델을 사용합니다.

Image Face Swap API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/akool/image-face-swap with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Image Face Swap below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "source_image": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "target_image": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "face_enhance": false
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/akool/image-face-swap" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d "$REQUEST_BODY")

TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
  RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
    -H "Authorization: Bearer $WAVESPEED_API_KEY")
  RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
  STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
  case "$STATUS" in
    completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/akool/image-face-swap";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');

async function requestJson(url, options = {}) {
  const response = await fetch(url, options);
  if (!response.ok) throw new Error(await response.text());
  return response.json();
}

// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "source_image": [
                "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
        ],
        "target_image": [
                "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
        ],
        "face_enhance": false
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;

// 2. Poll until the prediction finishes.
while (true) {
  const resultBody = await requestJson(resultUrl, {
    headers: { "Authorization": `Bearer ${apiKey}` },
  });
  const result = resultBody.data ?? resultBody;
  if (result.status === "completed") {
    console.log(result.outputs);
    break;
  }
  if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
  if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
  await new Promise(resolve => setTimeout(resolve, 2000));
}
Python example
import json
import os
import time
from urllib.request import Request, urlopen

api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "source_image": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "target_image": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "face_enhance": False
}

def request_json(url, data=None):
    request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
    with urlopen(request) as response:
        return json.load(response)

# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/akool/image-face-swap", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
    raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"

# 2. Poll until the prediction finishes.
while True:
    result_body = request_json(result_url)
    result = result_body.get("data", result_body)
    status = result.get("status")
    if status == "completed":
        print(result.get("outputs", []))
        break
    if status in {"failed", "cancelled", "timeout"}:
        raise RuntimeError(result)
    if status not in {"created", "processing"}:
        raise RuntimeError(f"Unexpected status: {status}")
    time.sleep(2)

Image Face Swap API — Frequently asked questions

What is the Image Face Swap API?

Image Face Swap is a Akool model for AI inference, exposed as a REST API on WaveSpeedAI. Akool Image Face Swap swaps faces in photos using a source and target image, including multi-face replacement for group photos. 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 Image Face Swap 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 result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/akool/akool-image-face-swap.

How much does Image Face Swap cost per run?

Image Face Swap starts at $0.16 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 Image Face Swap accept?

Key inputs: `image`, `enable_base64_output`, `face_enhance`, `source_image`, `target_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/akool/akool-image-face-swap.

How long does Image Face Swap take to generate?

Median end-to-end generation time on WaveSpeedAI is around 6 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Image Face Swap outputs commercially?

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

Akool Image Face Swap | AI Portrait Transfer API | WaveSpeedAI