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xAI Grok Imagine Image Quality Edit is a fast AI image editing model that edits and enhances images with high-quality visual output using a dedicated RunPod workflow. Ready-to-use REST inference API for photo retouching, creative image edits, product image enhancement, marketing assets, social media visuals, and professional AI image editing workflows with simple integration, no coldstarts, and affordable pricing.

image-to-image
इनपुट

निष्क्रिय

Add a faint handwritten chalk message on the old blackboard: “You came back.” Preserve the woman, classroom layout, desks, broken windows, sunlight beams, and muted color palette. The message should look old, dusty, and naturally written in chalk.

$0.07प्रति रन·~14 / $1

आगे:

उदाहरणसभी देखें

Add a faint handwritten chalk message on the old blackboard: “You came back.” Preserve the woman, classroom layout, desks, broken windows, sunlight beams, and muted color palette. The message should look old, dusty, and naturally written in chalk.

Add a faint handwritten chalk message on the old blackboard: “You came back.” Preserve the woman, classroom layout, desks, broken windows, sunlight beams, and muted color palette. The message should look old, dusty, and naturally written in chalk.

संबंधित मॉडल

README

xAI Grok Imagine Image Quality Edit

xAI Grok Imagine Image Quality Edit edits an input image using natural-language instructions, with support for multiple aspect ratios, two resolution tiers, selectable output formats, and multi-image generation in a single request. It is suitable for image refinement, style changes, composition adjustments, product visuals, and other prompt-driven image editing workflows.

Why Choose This?

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

  • Quality-focused edit workflow Built for higher-quality image editing with support for resolution tiers and multiple output formats.

  • Flexible aspect ratios Choose auto to preserve the source framing, or select a preset aspect ratio for a new composition.

  • Multiple image generation Generate up to 4 edited variations in one request with num_images.

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

  • Simple pricing Pricing depends only on resolution and num_images.

Parameters

ParameterRequiredDescription
promptYesText prompt describing the desired edit.
imageYesInput image to edit. Quality edit supports one input image.
aspect_ratioNoOutput aspect ratio. Supported values: auto, 1:1, 16:9, 9:16, 4:3, 3:4, 3:2, 2:3. Default: auto.
resolutionNoOutput resolution tier: 1k or 2k. Default: 1k.
num_imagesNoNumber of edited images to generate. Range: 1–4. Default: 1.
output_formatNoOutput image format: jpeg, png, or webp. Default: jpeg.

How to Use

  1. Upload your image — provide the source image you want to edit.
  2. Write your prompt — describe what should change and what should remain consistent.
  3. Choose aspect ratio — keep auto to follow the source framing, or pick a preset ratio if you want a different composition.
  4. Choose resolution — use 1k for lower cost or 2k for higher-quality output.
  5. Set number of images — choose how many edited variations you want, from 1 to 4.
  6. Choose output format — select jpeg, png, or webp.
  7. Submit — run the model and download the edited images.

Example Prompt

Turn this product photo into a premium studio advertisement with soft cinematic lighting, a clean neutral background, realistic reflections, and polished commercial styling.

Pricing

Pricing is based on resolution and num_images.

Per Image

ResolutionCost per Image
1k$0.07
2k$0.09

Example Costs

Resolution1 Image2 Images3 Images4 Images
1k$0.07$0.14$0.21$0.28
2k$0.09$0.18$0.27$0.36

Billing Rules

  • 1k costs $0.07 per image
  • 2k costs $0.09 per image
  • Total price = per-image price × num_images
  • aspect_ratio and output_format do not affect pricing

Best Use Cases

  • Product photo refinement — Upgrade product shots for ads, listings, and brand materials.
  • Style changes — Rework an image into a new visual style or mood.
  • Composition adjustments — Use aspect ratio controls to adapt the edit for different formats.
  • Creative variations — Generate multiple edited options in one run with num_images.
  • Marketing creatives — Produce polished visuals for campaigns, social media, and presentations.

Pro Tips

  • Be specific about what should change and what should stay the same.
  • Use auto aspect ratio when you want to preserve the original image framing.
  • Use 1k for quick testing and 2k for higher-quality final outputs.
  • Increase num_images when you want multiple edit variations from the same prompt.
  • Choose png when image quality matters more than file size.

Notes

  • Both prompt and image are required.
  • This edit workflow supports one input image.
  • num_images supports values from 1 to 4.
  • aspect_ratio defaults to auto.
  • resolution defaults to 1k.
  • output_format defaults to jpeg.
  • Pricing depends only on resolution and num_images.

Related Models

नोट:यह वेबसाइट तृतीय पक्षों द्वारा प्रदान किए गए AI मॉडलों का उपयोग करती है। दस्तावेज़ की कीमतें केवल संदर्भ के लिए हैं और पुरानी हो सकती हैं। Generate बटन अनुमान दिखाता है; टास्क का अंतिम शुल्क ही मान्य होगा।

Grok Imagine Image Quality Edit API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/x-ai/grok-imagine-image-quality/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 Grok Imagine Image Quality 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",
    "aspect_ratio": "auto",
    "resolution": "1k",
    "num_images": 1,
    "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/x-ai/grok-imagine-image-quality/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/x-ai/grok-imagine-image-quality/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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "aspect_ratio": "auto",
        "resolution": "1k",
        "num_images": 1,
        "output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "aspect_ratio": "auto",
    "resolution": "1k",
    "num_images": 1,
    "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/x-ai/grok-imagine-image-quality/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)

Grok Imagine Image Quality Edit API — Frequently asked questions

What is the Grok Imagine Image Quality Edit API?

Grok Imagine Image Quality Edit is a xAI model for image editing, exposed as a REST API on WaveSpeedAI. xAI Grok Imagine Image Quality Edit is a fast AI image editing model that edits and enhances images with high-quality visual output using a dedicated RunPod workflow. Ready-to-use REST inference API for photo retouching, creative image edits, product image enhancement, marketing assets, social media visuals, and professional AI 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 Grok Imagine Image Quality 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/x-ai/x-ai-grok-imagine-image-quality-edit.

How much does Grok Imagine Image Quality Edit cost per run?

Grok Imagine Image Quality Edit starts at $0.070 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 Grok Imagine Image Quality Edit accept?

Key inputs: `prompt`, `image`, `aspect_ratio`, `resolution`, `enable_base64_output`, `num_images`. 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/x-ai/x-ai-grok-imagine-image-quality-edit.

How long does Grok Imagine Image Quality Edit take to generate?

Median end-to-end generation time on WaveSpeedAI is around 25 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 Grok Imagine Image Quality Edit outputs commercially?

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