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Flux 2 Max Edit | Fast Image Editing

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FLUX 2 Max Edit delivers production-grade image-to-image editing from Black Forest Labs—apply natural-language instructions and exact hex color control for consistent, studio-quality results. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.

image-to-image
Giriş

Boşta

Make it a couple.

$0.07çalıştırma başına·~14 / $1

Sonraki:

ÖrneklerTümünü görüntüle

Make it a couple.

Make it a couple.

Make it in year 2020.

Make it in year 2020.

Make him standing on Antarctica.

Make him standing on Antarctica.

İlgili Modeller

README

FLUX 2 Max — Edit

FLUX.2 [max] Edit is the top-tier image editing engine of the FLUX family, built for production teams who need reliable, high-fidelity edits from natural language instructions. It can take one or several reference images plus a prompt and turn them into clean, on-brand transformations suitable for final delivery—not just drafts.

Suitable for

  • Campaign key visuals and hero images
  • Brand-accurate product and packaging refreshes
  • E-commerce and marketing asset pipelines
  • Automated, high-value editing jobs where failure is expensive

Production-focused editing model

Rather than exposing a long list of inference knobs, FLUX.2 [pro] Edit is tuned to behave the same way every time: you pass in image(s) and a prompt, it returns a polished edit. Internal settings are fixed for production use, so non-experts and API-driven workflows both get stable behaviour without babysitting parameters.

Practical advantages

Multi-reference aware edits

Use several input images in the same request when you need to match outfits, backgrounds, or stylistic cues—pro understands how they relate and applies changes accordingly.

Plain-language control

Describe edits in normal English (“make the background a clean studio grey and match the logo colour to our brand red”) instead of painting masks, cutting layers, or writing complex configs. Hex colours for brand palettes are supported when you need exact matches.

Structure-preserving transformations

Refines lighting, texture, and local detail while keeping composition, perspective, and identity intact, so the result looks like a carefully shot original rather than an obviously patched image.

Minimal setup, maximum throughput

No guidance scales, schedulers, or step counts to tune—just prompt-to-edit. That makes it easy to plug into batch jobs, web backends, or no-code tools without a separate “parameter tuning” phase.

Consistent behaviour at scale

A fixed optimisation profile plus seed control mean that large edit batches behave predictably, which is crucial for A/B testing, QA, and evergreen production flows.

Outputs that drop into your pipeline

Exports standard PNG or JPEG so edited assets can go straight into design tools, websites, print workflows, or further post-maxduction with no extra conversion steps.

Parameters

  • Size: The size of generated media in pixels(width*Height).

Pricing

  • $0.07 per edited image

FLUX.2 family on WaveSpeedAI

Use FLUX.2 [pro] Edit alongside the rest of the FLUX.2 lineup for a complete generate-and-edit stack:

More Image Tools on WaveSpeedAI

  • Nano Banana Pro – Google’s Gemini-based text-to-image model for sharp, coherent, prompt-faithful visuals that work great for ads, keyframes, and product shots.
  • Seedream V4 – ’s style-consistent, multi-image generator ideal for posters, campaigns, and large batches of on-brand illustrations.
  • Qwen Edit Plus – an enhanced Qwen-based image editor for precise inpainting, cleanup, and local style changes while preserving overall composition.
Not:Bu web sitesi, üçüncü taraflarca sağlanan yapay zeka modellerini kullanmaktadır.

Flux 2 Max Edit API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-2-max/edit 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 2 Max Edit 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",
    "images": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "seed": -1
}
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-2-max/edit" \
  -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-2-max/edit";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "images": [
                "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
        ],
        "seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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));
}
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",
    "images": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "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/wavespeed-ai/flux-2-max/edit", 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 2 Max Edit API — Frequently asked questions

What is the Flux 2 Max Edit API?

Flux 2 Max Edit is a WaveSpeedAI model for image editing, exposed as a REST API on WaveSpeedAI. FLUX 2 Max Edit delivers production-grade image-to-image editing from Black Forest Labs—apply natural-language instructions and exact hex color control for consistent, studio-quality results. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Flux 2 Max Edit 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-2-max-edit.

How much does Flux 2 Max Edit cost per run?

Flux 2 Max Edit starts at $0.070 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 2 Max Edit accept?

Key inputs: `prompt`, `images`, `size`, `seed`, `enable_base64_output`, `enable_sync_mode`. 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-2-max-edit.

How long does Flux 2 Max Edit take to generate?

Median end-to-end generation time on WaveSpeedAI is around 32 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 2 Max Edit 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.

Flux 2 Max Edit | Fast Image Editing API on WaveSpeedAI