Flux Dev Ultra Fast | High-Quality Text-to-Image API

FLUX Dev Ultra Fast — wavespeed-ai/flux-dev-ultra-fast

FLUX Dev Ultra Fast is a lightweight, high-speed image generation and editing model built for rapid iteration. It supports both text-to-image generation and image-guided editing (image-to-image), with optional mask-based localized edits for targeted changes. This model is ideal when you need quick creative exploration, bulk variants, or fast prompt testing while maintaining strong visual quality.

Key capabilities

Text-to-image generation for fast concepting
Image-to-image editing for guided variations
Masked editing (inpainting-style) for localized changes
Adjustable strength to control how closely results follow the input image
Custom output size, seed control, and standard image formats
Efficient for high-volume runs and rapid experimentation

Use cases

Rapid creative iterations for ads, thumbnails, and social content
Style and composition exploration with quick turnaround
Background swaps and wardrobe edits using a mask
Product mockups and marketing variations (colorways, materials, packaging)
Portrait enhancement workflows where speed matters (non-destructive variations)

Pricing

Output	Price
Per image	$0.005

Inputs

prompt (required): what to generate or how to edit
image (optional): source image for image-to-image guidance
mask_image (optional): region mask for localized edits

Parameters

strength: how strongly the output is allowed to deviate from the input image (only for image-to-image)
width / height: output size (e.g., 1024×1024)
num_inference_steps: sampling steps
guidance_scale: prompt adherence strength
num_images: number of images to generate per run
seed: random seed (-1 for random; set for reproducible results)
output_format: jpeg / png / webp, etc.
enable_base64_output: return BASE64 instead of URL (API only)
enable_sync_mode: wait for upload and return directly (API only)

Prompting tips

For photoreal shots, specify lens/lighting and keep subject details explicit.
For edits, describe the change clearly and rely on the input image to preserve identity and layout.
If using a mask, focus the prompt on what should change inside the masked region.

Example prompts

A young couple standing side by side at a traditional wedding ceremony, warm smiles, intricate embroidered attire, realistic lighting, shallow depth of field, ultra-detailed, natural skin texture.
Edit: keep the same pose and background, change the clothing to a modern black suit and white dress, clean studio lighting, photorealistic.

Flux Dev Ultra Fast API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-dev-ultra-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 Flux Dev Ultra 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",
    "strength": 0.8,
    "size": "1024*1024",
    "num_inference_steps": 28,
    "seed": -1,
    "guidance_scale": 3.5,
    "num_images": 1,
    "output_format": "jpeg"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-dev-ultra-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/wavespeed-ai/flux-dev-ultra-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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "strength": 0.8,
        "size": "1024*1024",
        "num_inference_steps": 28,
        "seed": -1,
        "guidance_scale": 3.5,
        "num_images": 1,
        "output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "strength": 0.8,
    "size": "1024*1024",
    "num_inference_steps": 28,
    "seed": -1,
    "guidance_scale": 3.5,
    "num_images": 1,
    "output_format": "jpeg"
}

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/wavespeed-ai/flux-dev-ultra-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)

Flux Dev Ultra Fast API — Frequently asked questions

What is the Flux Dev Ultra Fast API?

Flux Dev Ultra Fast is a WaveSpeedAI model for image generation, exposed as a REST API on WaveSpeedAI. FLUX.1 [dev] is a 12B rectified-flow transformer for high-quality text-to-image generation, optimized for ultra-fast inference. 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 Flux Dev Ultra 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/wavespeed-ai/flux-dev-ultra-fast.

How much does Flux Dev Ultra Fast cost per run?

Flux Dev Ultra Fast starts at $0.005 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 Flux Dev Ultra Fast accept?

Key inputs: `prompt`, `image`, `size`, `seed`, `guidance_scale`, `num_inference_steps`. 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/wavespeed-ai/flux-dev-ultra-fast.

How long does Flux Dev Ultra Fast take to generate?

Median end-to-end generation time on WaveSpeedAI is around 9 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 Flux Dev Ultra Fast outputs commercially?

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

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