Imagen3 is Google's highest-quality text-to-image model, generating highly detailed, beautifully lit and photoreal images from text prompts. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Boşta

$0.038çalıştırma başına·~26 / $1

A young woman tying her hair in front of a foggy bathroom mirror, toothbrush in mouth, cozy pajamas, bathroom shelf cluttered with daily items

A group of friends eating hotpot at home, smiling, chatting, with realistic tabletop details

A woman drinking coffee alone in a corner café, reading a book, afternoon light through large windows

A grandmother knitting on an armchair, worn books stacked beside her, sleepy dog at her feet, old wooden clock on the wall, peaceful ambiance

Two roommates sitting on a cozy couch, laughing while watching a movie, popcorn bowl between them, string lights in the background

A man brewing coffee in a small kitchen, sunlight filtering through patterned curtains, steam rising, tiled countertop, houseplants on the windowsill

A serene mountain landscape during golden hour, with tall pine trees in the foreground, a calm lake reflecting the snowy peaks, and soft light casting long shadows. The sky is clear with a gentle orange glow, ultra-realistic, high-resolution DSLR style.

A whimsical, storybook-style illustration of a fox in a forest, standing upright wearing a green coat and reading a book under a mushroom. Soft watercolor textures, pastel colors, children’s storybook aesthetic.

A floating island in the sky with waterfalls cascading into the clouds, a small village with glowing lanterns, and a giant moon hanging close in the background. Magical atmosphere, detailed concept art, cinematic wide shot.

A claymation-style scene of a bear chef cooking pancakes in a cozy kitchen, with exaggerated textures and handmade imperfections. Soft lighting, warm tones, stop-motion animation aesthetic, very detailed.

A dreamy anime-style portrait of a girl with flowing silver hair, standing in a field of glowing fireflies under a starry sky. Soft lighting, detailed eyes, Studio Ghibli + Makoto Shinkai inspired mood, high-resolution.
Generate premium images with Google Imagen 3 — Google's most advanced text-to-image model. Renowned for exceptional photorealism, nuanced understanding of prompts, and superior detail rendering, Imagen 3 delivers the highest quality output for professional creative work.
Looking for faster generation? Try Google Imagen 3 Fast for speed-optimized output at a lower price.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the image you want to generate. |
| aspect_ratio | No | Output format: 16:9, 9:16, 1:1, 4:3, 3:4, etc. Default: 16:9. |
| num_images | No | Number of images to generate (1-4). Default: 1. |
| negative_prompt | No | Elements to avoid in the generated image. |
| seed | No | Random seed for reproducibility. Leave empty for random. |
Flat rate per image generated.
| Output | Cost |
|---|---|
| Per image | $0.038 |
| 4 images (max batch) | $0.152 |
| Model | Cost | Speed | Best For |
|---|---|---|---|
| Imagen 3 | $0.038 | Standard | Maximum quality, final deliverables |
| Imagen 3 Fast | $0.018 | Fast | Rapid iteration, testing, high-volume |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/google/imagen3 with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Imagen3 below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "1:1",
"num_images": 1
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/google/imagen3" \
-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/google/imagen3";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "1:1",
"num_images": 1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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",
"num_images": 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/google/imagen3", 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)Imagen3 is a Google model for image generation, exposed as a REST API on WaveSpeedAI. Imagen3 is Google's highest-quality text-to-image model, generating highly detailed, beautifully lit and photoreal images from text prompts. 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/google/google-imagen3.
Imagen3 starts at $0.038 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`, `seed`, `negative_prompt`, `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/google/google-imagen3.
Median end-to-end generation time on WaveSpeedAI is around 17 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 (Google). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.