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Google Imagen4 Fast is the fast variant of Google's Imagen 4 flagship text-to-image model for high-quality image generation. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

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Portrait of a 1920s flapper girl with pearl necklace and bob haircut, vintage film grain, muted colors20

$0.018每次運行·~55 / $1

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Portrait of a 1920s flapper girl with pearl necklace and bob haircut, vintage film grain, muted colors20

Portrait of a 1920s flapper girl with pearl necklace and bob haircut, vintage film grain, muted colors20

A floating castle above the clouds, glowing waterfalls falling into the sky, dreamy lighting, painterly fantasy style

A floating castle above the clouds, glowing waterfalls falling into the sky, dreamy lighting, painterly fantasy style

Still life of sunflowers in a ceramic vase, 19th-century oil painting style, thick brush strokes, warm tones

Still life of sunflowers in a ceramic vase, 19th-century oil painting style, thick brush strokes, warm tones

Elf warrior standing in a glowing forest, silver armor and long white hair, cinematic lighting, detailed environment

Elf warrior standing in a glowing forest, silver armor and long white hair, cinematic lighting, detailed environment

Portrait of an old man with a weathered face, wearing a fur coat, Rembrandt lighting, realistic oil painting

Portrait of an old man with a weathered face, wearing a fur coat, Rembrandt lighting, realistic oil painting

Children’s book style: a little fox baking cookies in a cozy woodland kitchen, soft lines, pastel palette

Children’s book style: a little fox baking cookies in a cozy woodland kitchen, soft lines, pastel palette

Human face made of clouds, eyes closed, blending into a starry sky, poetic surrealism

Human face made of clouds, eyes closed, blending into a starry sky, poetic surrealism

Pixel art city at dusk, small characters walking, animated clouds, 16-bit retro game aesthetic

Pixel art city at dusk, small characters walking, animated clouds, 16-bit retro game aesthetic

A futuristic robot cat on a neon-lit Tokyo street. Its body is made of shiny metal and transparent panels, with curious eyes. Cyberpunk style, close-up shot, cinematic quality.

A futuristic robot cat on a neon-lit Tokyo street. Its body is made of shiny metal and transparent panels, with curious eyes. Cyberpunk style, close-up shot, cinematic quality.

An abstract painting composed of liquid gold, deep blue ink, and white feathers. Flowing textures and shapes, with light radiating from the center, creating a calm and mysterious feeling. Surrealism, close-up shot.

An abstract painting composed of liquid gold, deep blue ink, and white feathers. Flowing textures and shapes, with light radiating from the center, creating a calm and mysterious feeling. Surrealism, close-up shot.

相關模型

README

Google Imagen 4 Fast

Imagen 4 Fast is the high-speed, cost-efficient variant of Google’s Imagen 4 image generation model. It delivers near-identical visual fidelity to the standard version but at significantly higher speed and lower cost — ideal for creative iteration, social content, and rapid prototyping.

⚡ Why it stands out

  • 10× Faster Generation Creates high-quality images in a fraction of the time, enabling instant visual feedback for creators.
  • Cost-Effective Performance Optimized for efficiency — generate more images on the same budget without compromising clarity or color accuracy.
  • Cinematic and Artistic Range Handles both photorealistic and stylized compositions with balanced lighting and rich texture detail.
  • Enhanced Text Rendering Maintains Imagen 4’s superior typography performance, producing crisp, legible text for posters and designs.
  • Resolution Flexibility Supports multiple aspect ratios up to 2 K resolution, suitable for digital media and print layouts.

⚙️ How to use

  • Input: text prompt

  • Resolution: up to 2048 × 2048 px

  • Compatible with descriptive prompts such as:

  • “A futuristic city at sunrise, glowing glass towers and flying cars in the distance.”

  • “A close-up portrait of a woman in soft studio lighting, ultra-realistic detail.”

💰 Pricing

  • $0.018 per image
  • Commercial use allowed

💡 Best Use Cases

  • Rapid Concepting — Quickly explore visual directions during creative brainstorming.
  • Social Media & Marketing — Generate eye-catching visuals for campaigns in seconds.
  • Design & Illustration — Produce high-resolution imagery for posters, banners, and print layouts.
  • Product Visualization — Render realistic textures, materials, and lighting for prototypes.

📝 Notes

Please ensure your prompts comply with Google’s Safety Guidelines. If an error occurs, review your prompt for restricted content, adjust it, and try again.

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Imagen4 Fast API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/google/imagen4-fast 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 Imagen4 Fast below.

HTTP example
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/imagen4-fast" \
  -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
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/google/imagen4-fast";
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));
}
Python example
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/imagen4-fast", 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)

Imagen4 Fast API — Frequently asked questions

What is the Imagen4 Fast API?

Imagen4 Fast is a Google model for image generation, exposed as a REST API on WaveSpeedAI. Google Imagen4 Fast is the fast variant of Google's Imagen 4 flagship text-to-image model for high-quality image generation. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Imagen4 Fast API?

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-imagen4-fast.

How much does Imagen4 Fast cost per run?

Imagen4 Fast starts at $0.018 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.

What inputs does Imagen4 Fast accept?

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-imagen4-fast.

How long does Imagen4 Fast take to generate?

Median end-to-end generation time on WaveSpeedAI is around 16 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Imagen4 Fast outputs commercially?

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.

Imagen4 Fast | High-Quality Text-to-Image API on WaveSpeedAI