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Luma Uni v1 Edit API

luma /

Luma Uni v1 Edit is a fast AI image editing model that reworks source images from text instructions while preserving composition and supporting optional reference images. Ready-to-use REST inference API for photo editing, product image updates, creative retouching, marketing assets, brand visuals, style changes, and professional image editing workflows with simple integration, no coldstarts, and affordable pricing.

image-to-image
Wejście

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Change the time to the midnight.

$0.042za uruchomienie·~23 / $1

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Change the time to the midnight.

Change the time to the midnight.

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README

Luma UNI V1 Edit

Luma UNI V1 Edit edits an input image using a natural-language prompt, with optional reference-image guidance and flexible output format support. It is suitable for scene changes, style edits, mood adjustments, lighting transformations, and other prompt-driven image editing workflows.

Why Choose This?

  • Prompt-based image editing
    Edit an existing image by describing the change you want in natural language.

  • Reference-guided visual control
    Add one or more reference images when you want stronger style or subject guidance.

  • Simple editing workflow
    Upload an image, write a prompt, optionally add references, and generate the edited result.

  • Flexible output format
    Export the edited image in a supported format such as jpeg.

  • Production-ready API
    Suitable for visual retouching, scene transformation, content iteration, and creative image editing workflows.

Parameters

ParameterRequiredDescription
promptYesText instruction describing the desired edit.
imageYesInput image to edit.
output_formatNoOutput image format, such as jpeg.
referenceNoOptional reference images for visual guidance.

How to Use

  1. Upload your image — provide the source image you want to edit.
  2. Write your prompt — describe what should change in the image.
  3. Choose output format (optional) — select the file format that best fits your workflow.
  4. Add references (optional) — upload one or more reference images if you want stronger style or visual guidance.
  5. Submit — run the model and download the edited image.

Example Prompt

Change the time to the midnight.

Pricing

Pricing includes a fixed base image charge plus a reference-related surcharge.

ModeCost
Edit with no extra references$0.045
Edit with 1 reference image$0.048
Edit with 2 reference images$0.051
Edit with 3 reference images$0.054

Billing Rules

  • Editing adds a built-in $0.003 surcharge for the required source image workflow
  • Each additional reference image adds $0.003
  • output_format does not affect pricing

Best Use Cases

  • Scene transformation — Change time of day, environment mood, or background feel.
  • Lighting adjustment — Shift an image into nighttime, sunset, studio, or other lighting conditions.
  • Style adaptation — Move an image toward a different visual tone with optional references.
  • Creative retouching — Refine a shot for storytelling, ads, or social content.
  • Prompt-driven variations — Explore multiple edited versions from the same base image.

Pro Tips

  • Be clear and direct about what should change in the source image.
  • Add references only when you need stronger style guidance, since they increase the price slightly.
  • Keep prompts focused on the main transformation rather than restating unchanged details.
  • Use jpeg for lightweight delivery unless your workflow requires another format.
  • Try the edit once without references first, then add them if the result needs more control.

Notes

  • prompt and image are required.
  • Pricing depends on the number of additional reference images.
  • The required input image is already accounted for in the edit pricing.
  • Better prompts usually improve edit accuracy and scene consistency.

Related Models

  • Luma UNI V1 Text-to-Image — Generate images directly from text prompts.
  • Luma image generation workflows — Useful when you need different image generation or editing styles.
Uwaga:Ta strona korzysta z modeli AI udostępnianych przez podmioty trzecie.

Uni v1 Edit API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/luma/uni-v1/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 Uni v1 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": "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/luma/uni-v1/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=$(printf '%s' "$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/luma/uni-v1/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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "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/luma/uni-v1/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 = 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)

Uni v1 Edit API — Frequently asked questions

What is the Uni v1 Edit API?

Uni v1 Edit is a Luma model for image editing, exposed as a REST API on WaveSpeedAI. Luma Uni v1 Edit is a fast AI image editing model that reworks source images from text instructions while preserving composition and supporting optional reference images. Ready-to-use REST inference API for photo editing, product image updates, creative retouching, marketing assets, brand visuals, style changes, and professional image editing workflows with simple integration, no coldstarts, and affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Uni v1 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/luma/luma-uni-v1-edit.

How much does Uni v1 Edit cost per run?

Uni v1 Edit starts at $0.042 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 Uni v1 Edit accept?

Key inputs: `prompt`, `image`, `output_format`, `reference`. 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/luma/luma-uni-v1-edit.

How long does Uni v1 Edit take to generate?

Median end-to-end generation time on WaveSpeedAI is around 73 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 Uni v1 Edit outputs commercially?

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

Luma Uni v1 Edit API | WaveSpeedAI