Meta Muse Image Text-to-Image generates high-quality images from text prompts for creative visuals, concept art, marketing assets, social content, and production workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle

$0.011per run·~90 / $1

A cool young foreign woman crouching among enormous tropical plants inside a futuristic glass greenhouse, short copper-red hair, wearing dark green work overalls with subtle mechanical attachments, one mechanical gardening arm unfolding from a backpack while she carefully holds a glowing flower in her bare hand, condensation on glass walls, sunlight refracting through mist, botanical science-fiction aesthetic, cinematic naturalism, highly detailed, unusual but believable.

A striking young European woman walking through a luxurious retro-futuristic train carriage, captured mid-stride while fastening one white glove with her teeth, wearing a sharply tailored midnight-blue uniform with silver geometric details and knee-high boots, short platinum hair swept to one side, panoramic windows revealing a glowing alien landscape outside, polished brass and dark velvet interior, dramatic cinematic perspective, retro-futurism inspired by 1960s luxury travel, sophisticated editorial photography, rich textures, full body visible.
Meta Muse Image Text-to-Image generates high-quality images from text prompts. Describe the subject, composition, lighting, style, and visual details, then choose an aspect ratio and output format to create the final image.
Text-to-image generation
Generate images directly from natural-language prompts.
Simple creative workflow
Provide a prompt, select layout settings, and generate an image in one request.
Flexible aspect ratio
Choose the output aspect ratio based on your target format.
Multiple output formats
Select the image format that fits your workflow.
Low-cost image generation
Generate images at a fixed price per image.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text prompt describing the image to generate. Include subject, scene, composition, lighting, mood, and style. |
| aspect_ratio | No | Aspect ratio of the generated image. Default: 1:1. |
| output_format | No | Output image format. For example: webp. |
Pricing is fixed at $0.011 per image.
| Output | Cost |
|---|---|
| One generated image | $0.011 |
1:1 for square images and other aspect ratios for platform-specific layouts.webp when you want compact web-friendly image output.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/meta/muse-image/text-to-image with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Muse Image Text To Image below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "1:1",
"output_format": "webp"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/meta/muse-image/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="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/meta/muse-image/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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "1:1",
"output_format": "webp"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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",
"aspect_ratio": "1:1",
"output_format": "webp"
}
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/meta/muse-image/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 = 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)Muse Image Text To Image is a Meta model for image generation, exposed as a REST API on WaveSpeedAI. Meta Muse Image Text-to-Image generates high-quality images from text prompts for creative visuals, concept art, marketing assets, social content, and production workflows. Ready-to-use REST inference API, best performance, no coldstarts, 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/meta/meta-muse-image-text-to-image.
Muse Image Text To Image starts at $0.011 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`, `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/meta/meta-muse-image-text-to-image.
Sign up for a free WaveSpeedAI account to claim starter credits, copy your API key from /accesskey, then call the endpoint shown in the API tab of the playground. The playground also auto-generates a code sample in Python, JavaScript, or cURL for the parameters you've set.
Commercial usage rights depend on the model's license, set by its provider (Meta). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.