Stable Diffusion 3 delivers superior text-to-image outputs with better image quality, typography, and complex prompt understanding. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle

$0.03per run·~33 / $1

A photograph of Marina Bay at sunset. The sun has already set, but the afterglow has burned the sky into vivid shades of purple, pink, and orange. The lights of the Marina Bay Sands and surrounding skyscrapers have just turned on, reflecting on the perfectly still water. A long exposure shot, making the water and clouds appear silky and motion-blurred.

A photograph of East Coast Park at sunrise. The sun has just risen above the sea level, painting the sky in fiery shades of orange and deep red. The calm sea reflects the colors of the sky, with several distant cargo ships forming perfect silhouettes. Shot with a wide-angle lens to capture the magnificent expanse of the entire sky.

Professional photo, as the sun rises, soft golden morning light penetrates the thin mist over MacRitchie Reservoir. The empty TreeTop Walk bridge is faintly visible in the distant mist. The water's surface is like a mirror, reflecting the silhouettes of the lush rainforest on the shore. The image is filled with tranquility and the mysterious beauty of nature.

Looking out from a high-floor HDB window, the first rays of sunrise illuminate the Singapore city skyline. Low-hanging clouds are tinged with gold at the edges by the sun, while the building clusters below are still enveloped in pre-dawn blue tones. Shot with a telephoto lens, with a slight sense of atmospheric perspective.

At sunset, the sun is descending behind the wooden structure of the Henderson Waves Bridge. The warm light of the setting sun casts beautiful light and shadows on the bridge's unique wave-like form. The background is the green forest of the Southern Ridges. The composition highlights the integration of architecture and nature.

After a thunderstorm, a complete and vibrant double rainbow stretches across the Singapore city skyline. The sky is a deep blue, and the buildings, washed clean by the rain, appear exceptionally clear. The sun is shining brightly, filling the entire city with a sense of freshness and hope.
Create beautiful images from text with Stability AI's foundational Stable Diffusion 3 model. Featuring improved prompt understanding and visual quality, SD3 delivers reliable, high-quality results for both text-to-image and image-to-image generation at an accessible price point.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the image you want to generate. |
| image | No | Optional reference image for image-to-image generation (upload or URL). |
| aspect_ratio | No | Output aspect ratio: 1:1, 3:4, 4:3, 16:9, or 9:16. Default: 1:1. |
| seed | No | Random seed for reproducibility. Use -1 for random. |
| enable_base64_output | No | API only: Returns base64 string instead of URL. |
Flat rate per image generation.
| Output | Cost |
|---|---|
| Per image | $0.03 |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/stability-ai/stable-diffusion-3 with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Stable Diffusion 3 below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "1:1",
"seed": -1
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/stability-ai/stable-diffusion-3" \
-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/stability-ai/stable-diffusion-3";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "1:1",
"seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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",
"seed": -1
}
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/stability-ai/stable-diffusion-3", 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)Stable Diffusion 3 is a Stability AI model for image generation, exposed as a REST API on WaveSpeedAI. Stable Diffusion 3 delivers superior text-to-image outputs with better image quality, typography, and complex prompt understanding. 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/stability-ai/stability-ai-stable-diffusion-3.
Stable Diffusion 3 starts at $0.030 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`, `image`, `aspect_ratio`, `seed`, `enable_base64_output`. 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/stability-ai/stability-ai-stable-diffusion-3.
Median end-to-end generation time on WaveSpeedAI is around 1 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 (Stability AI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.