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Meta Muse Image Edit transforms input images with text prompts, supporting prompt-guided image editing, visual refinements, creative variations, marketing assets, social content, and production workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

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Transform the unearthed artifact into an activated ancient machine while preserving the archaeologist’s identity, kneeling pose, clothing, and desert excavation scene. Add glowing lines spreading across the buried structure, floating dust pulled upward by unseen energy, and a faint circular hologram emerging above the object. Make the moment feel like the ruins are awakening.

$0.011per run·~90 / $1

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Transform the unearthed artifact into an activated ancient machine while preserving the archaeologist’s identity, kneeling pose, clothing, and desert excavation scene. Add glowing lines spreading across the buried structure, floating dust pulled upward by unseen energy, and a faint circular hologram emerging above the object. Make the moment feel like the ruins are awakening.

Transform the unearthed artifact into an activated ancient machine while preserving the archaeologist’s identity, kneeling pose, clothing, and desert excavation scene. Add glowing lines spreading across the buried structure, floating dust pulled upward by unseen energy, and a faint circular hologram emerging above the object. Make the moment feel like the ruins are awakening.

Related Models

README

Meta Muse Image Edit

Meta Muse Image Edit edits one or more input images using natural-language instructions. Upload reference images, describe the transformation you want, choose an aspect ratio and output format, and generate the edited result.

Why Choose This?

  • Prompt-guided image editing
    Edit images with simple natural-language instructions.

  • Multi-image input support
    Upload one or more reference images to guide the edit.

  • Flexible aspect ratio
    Choose the output aspect ratio based on your target layout.

  • Multiple output formats
    Select the image format that fits your workflow.

  • Low-cost editing workflow
    Generate edited images at a fixed price per run.

Parameters

ParameterRequiredDescription
promptYesText instruction describing the desired edit or transformation.
image_urlsYesInput image URLs to edit. Supports up to 10 images.
aspect_ratioNoAspect ratio of the generated image. Leave empty to use the default or closest supported layout.
output_formatNoOutput image format. For example: webp.

How to Use

  1. Upload images — Provide one or more input images to edit.
  2. Write your prompt — Describe what should change and what should remain consistent.
  3. Choose aspect ratio optional — Select the layout that matches your target format.
  4. Choose output format — Select the image format for the edited result.
  5. Submit — Generate the edited image and retrieve the output URL.

Pricing

Pricing is fixed at $0.011 per run.

OutputCost
One edit run$0.011

Best Use Cases

  • Prompt-guided image editing — Transform an existing image with text instructions.
  • Creative variations — Generate new versions of an image while keeping the main subject or composition.
  • Marketing visuals — Update campaign images, social assets, thumbnails, or product visuals.
  • Style changes — Adjust mood, lighting, scene style, or visual direction.
  • Multi-reference edits — Use multiple images to guide composition, subject, or style.

Pro Tips

  • Use clear prompts that describe both what should change and what should stay the same.
  • Upload clean, high-quality reference images for better edit results.
  • Use multiple images when identity, style, product details, or composition consistency matters.
  • Keep the prompt focused on the desired transformation.
  • Choose the aspect ratio based on the final use case.
  • Use webp when you want compact web-friendly output.

Related Models

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.

Muse Image Edit API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/meta/muse-image/edit 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 Muse Image Edit below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "image_urls": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "aspect_ratio": "21:9",
    "output_format": "webp"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/meta/muse-image/edit" \
  -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/meta/muse-image/edit";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "image_urls": [
                "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
        ],
        "aspect_ratio": "21:9",
        "output_format": "webp"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "image_urls": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "aspect_ratio": "21:9",
    "output_format": "webp"
}

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/meta/muse-image/edit", 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)

Muse Image Edit API — Frequently asked questions

What is the Muse Image Edit API?

Muse Image Edit is a Meta model for image editing, exposed as a REST API on WaveSpeedAI. Meta Muse Image Edit transforms input images with text prompts, supporting prompt-guided image editing, visual refinements, creative variations, marketing assets, social content, 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 Muse Image Edit 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/meta/meta-muse-image-edit.

How much does Muse Image Edit cost per run?

Muse Image Edit starts at $0.011 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 Muse Image Edit accept?

Key inputs: `prompt`, `aspect_ratio`, `image_urls`, `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/meta/meta-muse-image-edit.

How do I get started with the Muse Image Edit 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 Muse Image Edit outputs commercially?

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

Meta Muse Image Edit API on WaveSpeedAI