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.
Idle

$0.07per run·~14 / $1

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.
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grok-imagine-video-v1.5/image-to-video
image-to-video
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grok-imagine-video-v1.5/reference-to-video
image-to-video
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grok-imagine-video-v1.5/text-to-video
text-to-video
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grok-imagine-image-v2.0/edit
image-to-image
/filters:quality(82)/media/images/1786700032745264683_7hmyLV5d.webp)
grok-imagine-image-v2.0/text-to-image
text-to-image
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grok-imagine-image-quality/text-to-image
text-to-image
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.
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.
Multi-image reference editing
Provide up to 3 input images in one request.
Simple pricing
Pricing depends on resolution, num_images, and the number of input images.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text prompt describing the desired edit. |
| images | Yes | Input images to edit. Up to 3 reference images. |
| aspect_ratio | No | Output aspect ratio. Supported values: auto, 1:1, 16:9, 9:16, 4:3, 3:4, 3:2, 2:3. Default: auto. |
| resolution | No | Output resolution tier: 1k or 2k. Default: 1k. |
| num_images | No | Number of edited images to generate. Range: 1–4. Default: 1. |
| output_format | No | Output image format: jpeg, png, or webp. Default: jpeg. |
3 source images to edit from.auto to follow the source framing, or pick a preset ratio if you want a different composition.1k for lower cost or 2k for higher-quality output.1 to 4.jpeg, png, or webp.Turn this product photo into a premium studio advertisement with soft cinematic lighting, a clean neutral background, realistic reflections, and polished commercial styling.
Pricing is based on resolution, num_images, and the number of input images.
| Resolution | Cost per Image |
|---|---|
| 1k | $0.07 |
| 2k | $0.09 |
| Resolution | 1 Image | 2 Images | 3 Images | 4 Images |
|---|---|---|---|---|
| 1k | $0.07 | $0.14 | $0.21 | $0.28 |
| 2k | $0.09 | $0.18 | $0.27 | $0.36 |
| Input images | Surcharge |
|---|---|
| 1 | included |
| 2 | +$0.01 |
| 3 | +$0.02 |
1k costs $0.07 per generated image2k costs $0.09 per generated imagenum_images + $0.01 × (input images − 1)aspect_ratio and output_format do not affect pricingnum_images.auto aspect ratio when you want to preserve the original image framing.1k for quick testing and 2k for higher-quality final outputs.num_images when you want multiple edit variations from the same prompt.png when image quality matters more than file size.prompt and image are required.num_images supports values from 1 to 4.aspect_ratio defaults to auto.resolution defaults to 1k.output_format defaults to jpeg.resolution and num_images.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/x-ai/grok-imagine-image-quality/edit with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Grok Imagine Image Quality Edit below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"aspect_ratio": "auto",
"resolution": "1k",
"num_images": 1,
"output_format": "jpeg"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(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="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
doneconst 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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"aspect_ratio": "auto",
"resolution": "1k",
"num_images": 1,
"output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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));
}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",
"images": [
"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 = 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)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.
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/x-ai/x-ai-grok-imagine-image-quality-edit.
Grok Imagine Image Quality Edit starts at $0.07 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.
Key inputs: `prompt`, `images`, `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.
Reported generation time on WaveSpeedAI is around 22 seconds per request. This is an estimate, not a latency guarantee; queue time and input settings can change the total wait. live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (xAI). Check the provider's applicable terms and WaveSpeedAI's Terms of Service before commercial use.