Meta Muse Image Edit API Documentation

Meta Muse Image Edit API Documentation

Playground

Try it 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.

Features

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.

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result

set -euo pipefail

export WAVESPEED_API_KEY="your-api-key"

REQUEST_BODY=$(cat <<'JSON'
{
  "prompt": "A cinematic ocean wave at sunrise, highly detailed",
  "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 "Authorization: Bearer ${WAVESPEED_API_KEY}" \
  -H "Content-Type: application/json" \
  -d "${REQUEST_BODY}")

TASK=$(printf '%s' "${SUBMIT_RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "${TASK}" | jq -r '.id // empty')
if [ -z "${PREDICTION_ID}" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/${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' "${RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
  STATUS=$(printf '%s' "${RESULT}" | jq -r '.status // empty')

  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

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
promptstringYes-Describe the image to generate or the edit to apply.
image_urlsarray<string>Yes-1 ~ 10 itemsUpload 1 to 10 reference images.
aspect_ratiostringNo-21:9, 16:9, 4:3, 3:2, 1:1, 2:3, 3:4, 9:16, 9:21-
output_formatstringNowebpwebp, png, jpeg-

Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
data.idstringUnique identifier for the prediction, Task Id
data.modelstringModel ID used for the prediction
data.outputsarrayOutput values, usually URL strings; some models return text strings or structured result objects (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.statusstringTask status. completed is successful; failed, cancelled, timeout, and deleted are failure terminal statuses.
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds

Result Request Parameters

ParameterTypeRequiredDefaultDescription
idstringYes-Task ID

Result Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
dataobjectThe prediction data object containing all details
data.idstringUnique identifier for the prediction
data.modelstringModel ID used for the prediction
data.outputsarray<string | object>Array of generated outputs (empty when status is not completed). Items are usually URL strings, but may be text strings or structured result objects, depending on the model.
data.urlsobjectObject containing related API endpoints
data.statusstringStatus: completed is successful; failed, cancelled, timeout, and deleted are failure terminal statuses
data.created_atstringISO timestamp of when the request was created
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds
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