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Gen4 Image Turbo

runwayml /

RunwayML Gen-4 Image Turbo is cheaper and 2.5x faster than Gen-4 Image and supports up to 3 reference images to capture every angle. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

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$0.03par exécution·~33 / $1

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README

Runway Gen4 Image Turbo

Generate stunning images from text prompts with optional reference image guidance. Runway's Gen4 Image Turbo delivers fast, high-quality results with support for multiple aspect ratios and resolutions — perfect for rapid creative iteration.

Why It Looks Great

  • Reference-guided generation: Upload up to 3 reference images to guide style, composition, or subject appearance.
  • Flexible aspect ratios: Supports multiple formats including 9:16, 16:9, 1:1, and more for any use case.
  • High resolution output: Generate images up to 1080p for crisp, detailed results.
  • Fast turbo processing: Optimized for speed without sacrificing quality.
  • Reproducible results: Use the seed parameter to recreate exact outputs or explore variations.

Parameters

ParameterRequiredDescription
promptYesText description of the image you want to generate.
aspect_ratioNoOutput aspect ratio (e.g., 9:16, 16:9, 1:1). Default varies.
resolutionNoOutput resolution (e.g., 1080p).
reference_imagesNoUp to 3 reference images to guide generation (upload or URL).
seedNoRandom seed for reproducibility. Leave empty for random.
enable_base64_outputNoAPI only: Returns base64 string instead of URL.

How to Use

  1. Write your prompt — describe the image you want to create in detail.
  2. Choose aspect ratio — select the format that fits your needs (9:16 for portraits/stories, 16:9 for landscapes, etc.).
  3. Select resolution — pick your desired output quality.
  4. Add reference images (optional) — upload up to 3 images to guide style, subject, or composition.
  5. Set seed (optional) — use a specific number for reproducible results, or leave empty for random.
  6. Run — click the button to generate.
  7. Download — preview and save your generated image.

Pricing

Flat rate per image generation.

OutputCost
Per image$0.03

Examples

Images GeneratedTotal Cost
1$0.03
10$0.30
100$3.00

Best Use Cases

  • Social Media Content — Create scroll-stopping visuals for Instagram, TikTok, and stories with 9:16 aspect ratio.
  • Concept Art & Ideation — Rapidly explore visual ideas and creative directions.
  • Marketing & Advertising — Generate on-brand imagery for campaigns and promotions.
  • Character Design — Use reference images to maintain consistent character appearance across generations.
  • Style Transfer — Apply the aesthetic of reference images to new scenes and subjects.

Pro Tips for Best Results

  • Be specific in your prompts — include details about lighting, mood, style, and composition.
  • Use reference images strategically: one for style, one for subject, one for composition.
  • Keep the seed fixed when iterating on prompts to isolate the effect of text changes.
  • For consistent characters or styles across multiple images, always include the same reference image.
  • Match aspect ratio to your intended use case: 9:16 for mobile/stories, 16:9 for desktop/video thumbnails, 1:1 for profile pictures.

Notes

  • If using URLs for reference images, ensure they are publicly accessible. A preview thumbnail confirms successful loading.
  • The enable_base64_output option is only available through the API, not the web interface.
  • Reference images influence but do not guarantee exact replication — they guide style and composition.
Remarque :Ce site utilise des modèles d'IA fournis par des tiers.

Gen4 Image Turbo API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/runwayml/gen4-image-turbo 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 Gen4 Image Turbo 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": "4:3",
    "resolution": "1080p"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/runwayml/gen4-image-turbo" \
  -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/runwayml/gen4-image-turbo";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "aspect_ratio": "4:3",
        "resolution": "1080p"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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": "4:3",
    "resolution": "1080p"
}

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/runwayml/gen4-image-turbo", 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)

Gen4 Image Turbo API — Frequently asked questions

What is the Gen4 Image Turbo API?

Gen4 Image Turbo is a Runwayml model for image generation, exposed as a REST API on WaveSpeedAI. RunwayML Gen-4 Image Turbo is cheaper and 2.5x faster than Gen-4 Image and supports up to 3 reference images to capture every angle. 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 Gen4 Image Turbo 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/runwayml/runwayml-gen4-image-turbo.

How much does Gen4 Image Turbo cost per run?

Gen4 Image Turbo 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.

What inputs does Gen4 Image Turbo accept?

Key inputs: `prompt`, `aspect_ratio`, `resolution`, `seed`, `reference_images`, `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/runwayml/runwayml-gen4-image-turbo.

How long does Gen4 Image Turbo take to generate?

Median end-to-end generation time on WaveSpeedAI is around 14 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 Gen4 Image Turbo outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Runwayml). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.