xAI Grok Imagine Image Quality Text to Image is a fast AI image generation model that creates high-quality images from text prompts using a dedicated RunPod workflow. Ready-to-use REST inference API for creative design, concept art, marketing visuals, product mockups, social media content, and professional AI image generation workflows with simple integration, no coldstarts, and affordable pricing.
Chờ

$0.06cho mỗi lần chạy·~16 / $1

A cinematic drama still of an adult man standing outside an old family house at dusk, one warm window light glowing inside, overgrown garden, unopened gate, tired expression, long journey behind him, emotional homecoming, realistic film photography, soft natural light, atmospheric composition, ultra-detailed

A nostalgic cinematic still of an adult man sitting alone in an old empty cinema, red velvet seats, dusty projector beam, blank movie screen, half-lit face, quiet regret, warm muted tones, realistic film photography, atmospheric composition, ultra-detailed
xAI Grok Imagine Image Quality Text-to-Image generates high-quality images from text prompts with support for multiple aspect ratios, two resolution tiers, selectable output formats, and multi-image generation in a single request. It is suitable for concept art, marketing creatives, social media visuals, product imagery, and other prompt-driven image generation workflows.
High-quality text-to-image generation Turn natural-language prompts into polished visual outputs.
Flexible aspect ratios Choose from common preset aspect ratios for social, editorial, and widescreen content.
Two resolution tiers
Select 1k or 2k depending on your quality and budget needs.
Multiple image generation
Generate up to 4 images 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.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text prompt describing the image you want to generate. |
| aspect_ratio | No | Output aspect ratio. Supported values: 1:1, 16:9, 9:16, 4:3, 3:4, 3:2, 2:3. Default: 1:1. |
| resolution | No | Output resolution tier: 1k or 2k. Default: 1k. |
| num_images | No | Number of images to generate. Range: 1–4. Default: 1. |
| output_format | No | Output image format: jpeg, png, or webp. Default: jpeg. |
1k for lower cost or 2k for higher-quality output.1 to 4.jpeg, png, or webp.A futuristic city street at night, neon reflections on wet pavement, cinematic lighting, ultra-detailed, high contrast, atmospheric sci-fi mood
Pricing is based on resolution and num_images.
| Resolution | Cost per Image |
|---|---|
| 1k | $0.06 |
| 2k | $0.08 |
| Resolution | 1 Image | 2 Images | 3 Images | 4 Images |
|---|---|---|---|---|
| 1k | $0.06 | $0.12 | $0.18 | $0.24 |
| 2k | $0.08 | $0.16 | $0.24 | $0.32 |
1k costs $0.06 per image2k costs $0.08 per imagenum_imagesaspect_ratio and output_format do not affect pricingnum_images to explore multiple variations in a single run.1k for quick iteration and 2k for higher-quality final outputs.num_images when you want multiple variations from the same prompt.png when image quality matters more than file size.prompt is the only required field.num_images supports values from 1 to 4.aspect_ratio defaults to 1:1.resolution defaults to 1k.output_format defaults to jpeg.resolution and num_images.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/x-ai/grok-imagine-image-quality/text-to-image with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Grok Imagine Image Quality Text To Image below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "1:1",
"resolution": "1k",
"num_images": 1,
"output_format": "jpeg"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/x-ai/grok-imagine-image-quality/text-to-image" \
-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
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/x-ai/grok-imagine-image-quality/text-to-image";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "1:1",
"resolution": "1k",
"num_images": 1,
"output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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));
}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",
"aspect_ratio": "1:1",
"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/text-to-image", 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 Text To Image is a xAI model for image generation, exposed as a REST API on WaveSpeedAI. xAI Grok Imagine Image Quality Text to Image is a fast AI image generation model that creates high-quality images from text prompts using a dedicated RunPod workflow. Ready-to-use REST inference API for creative design, concept art, marketing visuals, product mockups, social media content, and professional AI image generation 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 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-text-to-image.
Grok Imagine Image Quality Text To Image starts at $0.060 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`, `aspect_ratio`, `resolution`, `enable_base64_output`, `num_images`, `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/x-ai/x-ai-grok-imagine-image-quality-text-to-image.
Median end-to-end generation time on WaveSpeedAI is around 18 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.
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.