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wavespeed-ai/face-enhancer/image

WaveSpeedAI Image Face Enhancer restores and sharpens faces in photos, recovering natural facial detail from blurry, compressed, or low-quality portraits for photo restoration, portrait enhancement, and production workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

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$0.01per run·~100 / $1

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Image Face Enhancer

Image Face Enhancer restores and sharpens faces in photos while preserving the surrounding image. It automatically detects the most prominent faces, recovers details in areas such as eyes, skin, hair, and teeth, and blends the enhanced regions back into the original image.

The model is designed for portraits, group photos, compressed images, old photographs, and AI-generated images where facial detail needs improvement without applying a full-image restoration pass.

Why Choose This?

  • Automatic face detection
    Detect and enhance up to 3 of the largest faces in the image without masks or manual face selection.

  • Natural face restoration
    Recover facial detail lost to blur, compression, or low resolution while preserving the person's overall appearance and expression.

  • Occlusion-aware enhancement
    Preserve foreground elements such as hands, hair strands, and glasses when they overlap the face.

  • Localized processing
    Focus restoration on detected faces while keeping the surrounding background, clothing, and composition unchanged.

  • Original-resolution output
    Return the enhanced image at the same resolution as the source.

  • Multiple output formats
    Export the result as jpeg, png, or webp.

Parameters

ParameterRequiredDescription
imageYesInput image containing the faces to enhance. Supports upload or public URL.
output_formatNoOutput image format: jpeg, png, or webp. Default: jpeg.

How to Use

  1. Upload an image — Provide a portrait, group photo, or other image containing visible faces.
  2. Choose output format optional — Select jpeg, png, or webp.
  3. Submit — Run the face-enhancement process.
  4. Retrieve the result — Download the enhanced image at the original resolution.

Pricing

Pricing is fixed at $0.01 per image.

OutputCost
One enhanced image$0.01

output_format does not add a separate charge.

Best Use Cases

  • Old and low-quality photos — Restore facial detail in scanned, compressed, soft, or blurry portraits.
  • AI-generated images — Improve soft or distorted faces in generated artwork and photography.
  • Portrait enhancement — Sharpen facial details while preserving the surrounding image.
  • Social media and profile images — Improve face clarity for avatars, posts, and promotional imagery.
  • Group photos — Enhance up to three prominent faces in a single image.
  • E-commerce and marketing images — Improve facial detail in people-focused campaign and product imagery.

Pro Tips

  • Use images where the important faces are reasonably visible and large enough for restoration.
  • The model prioritizes the largest detected faces when more than three faces are present.
  • Very small, heavily blurred, or strongly occluded faces may show less improvement.
  • Choose png when you want to avoid additional lossy compression.
  • For group photos, make sure the faces you care about are among the most prominent in the frame.
  • Image Face Enhancer works best when facial structure is still recognizable in the source image.

Notes

  • image is required.
  • output_format defaults to jpeg.
  • Supported output formats are jpeg, png, and webp.
  • Up to 3 faces are enhanced per image, prioritizing the largest detected faces.
  • Output resolution matches the input image resolution.
  • The model primarily targets real human faces; illustrated or anime-style faces may show limited improvement.
  • Make sure you have permission to use uploaded images and likenesses.
Note:This website uses AI models provided by third parties. Documentation prices are for reference and may be outdated. The Generate button shows an estimate; the final task charge prevails.

Face Enhancer Image API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/face-enhancer/image 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 Face Enhancer Image 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",
    "output_format": "jpeg"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/face-enhancer/image" \
  -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="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"

# 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' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout|deleted) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    *) sleep 2 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/face-enhancer/image";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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", "deleted"].includes(result.status)) throw new Error(JSON.stringify(result));
  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",
    "output_format": "jpeg"
}

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/wavespeed-ai/face-enhancer/image", 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 = 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", "deleted"}:
        raise RuntimeError(result)
    time.sleep(2)

Face Enhancer Image API — Frequently asked questions

What is the Face Enhancer Image API?

Face Enhancer Image is a WaveSpeedAI model for image upscaling, exposed as a REST API on WaveSpeedAI. WaveSpeedAI Image Face Enhancer restores and sharpens faces in photos, recovering natural facial detail from blurry, compressed, or low-quality portraits for photo restoration, portrait enhancement, and production workflows. 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 Face Enhancer Image 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 Python, JavaScript, and cURL examples for submitting requests and polling results. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/face-enhancer-image.

How much does Face Enhancer Image cost per run?

Face Enhancer Image starts at $0.01 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 Face Enhancer Image accept?

Key inputs: `image`, `enable_base64_output`, `enable_sync_mode`, `output_format`. 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/wavespeed-ai/face-enhancer-image.

How do I get started with the Face Enhancer Image API?

Sign up for a free WaveSpeedAI account to claim starter credits, copy your API key from /accesskey, then call the endpoint shown in the API tab of the playground. The playground also auto-generates a code sample in Python, JavaScript, or cURL for the parameters you've set.

Can I use Face Enhancer Image outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). Check the provider's applicable terms and WaveSpeedAI's Terms of Service before commercial use.

llms.txt — wavespeed-ai/face-enhancer/image for AI agents and LLMs