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Flux 2 Klein 9B Edit

wavespeed-ai /

FLUX.2 [klein] 9B Edit is a high-quality image editing model with 9B parameters, offering precise modifications using natural language instructions. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.

image-to-image
輸入

就緒

Put the person in image1 into image2.

$0.016每次運行·~62 / $1

下一步:

示例查看全部

Put the person in image1 into image2.

Put the person in image1 into image2.

Put this character on the football field

Put this character on the football field

Change image1 to the oil painting style

Change image1 to the oil painting style

Change to a strawberry

Change to a strawberry

Change to a dog

Change to a dog

相關模型

README

FLUX.2 Klein 9B Edit

FLUX.2 Klein 9B Edit is a powerful image editing model with 9B parameters. Upload images and describe your edits in natural language — the model delivers higher quality transformations and better prompt understanding than the 4B variant.

Why Choose This?

  • Enhanced editing quality 9B parameter model delivers richer detail and better prompt interpretation than the 4B variant.

  • Text-guided editing Describe edits in natural language — transform styles, modify content, combine elements, and more.

  • Multi-image input Upload multiple reference images and combine elements across them (e.g., "Put the person in image1 into image2").

  • Flexible output sizing Optionally set output size, or leave empty to match input image dimensions.

  • Prompt Enhancer Built-in tool to automatically improve your prompts for better results.

Parameters

ParameterRequiredDescription
promptYesText description of the desired edit
imagesYesSource images to edit (can add multiple)
sizeNoOutput dimensions (empty = same as input image)
seedNoRandom seed for reproducibility (-1 for random)

How to Use

  1. Write your prompt — describe the edit you want (e.g., "Put the person in image1 into image2", "Change to watercolor style").
  2. Upload images — add source images using "+ Add Item" button.
  3. Set size (optional) — specify output dimensions or leave empty to match input.
  4. Set seed — use -1 for random, or specify a number for reproducibility.
  5. Run — submit and download the edited image.

Pricing

ItemCost
Per image$0.016

Simple flat-rate pricing regardless of image size.

Best Use Cases

  • Image Compositing — Combine elements from multiple images seamlessly.
  • Style Transfer — Transform images to different artistic styles with high fidelity.
  • Professional Editing — When 4B quality isn't enough for production work.
  • Content Transformation — Change moods, lighting, or visual themes with precision.
  • Complex Edits — Multi-image operations that require better understanding.

Pro Tips

  • Be specific in your prompt — clearly describe what should change.
  • Reference images by number in prompts (e.g., "Put the person in image1 into image2").
  • Leave size empty to preserve original image dimensions.
  • Use the same seed to compare different prompts on the same images.

Notes

  • If size is not specified, output matches input image dimensions.
  • 9B model offers better detail than 4B at slightly higher cost.
  • Need LoRA support? Try FLUX.2 Klein 9B Edit LoRA.

Related Models

提示:本網站部分功能由第三方 AI 模型提供支援。

Flux 2 Klein 9b Edit API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-2-klein-9b/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 Klein 9b 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-klein-9b/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-klein-9b/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-klein-9b/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 Klein 9b Edit API — Frequently asked questions

What is the Flux 2 Klein 9b Edit API?

Flux 2 Klein 9b Edit is a WaveSpeedAI model for image editing, exposed as a REST API on WaveSpeedAI. FLUX.2 [klein] 9B Edit is a high-quality image editing model with 9B parameters, offering precise modifications using natural language instructions. 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 Klein 9b 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-klein-9b-edit.

How much does Flux 2 Klein 9b Edit cost per run?

Flux 2 Klein 9b Edit starts at $0.016 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 Klein 9b Edit accept?

Key inputs: `prompt`, `images`, `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-klein-9b-edit.

How long does Flux 2 Klein 9b Edit 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 2 Klein 9b 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 Klein 9B Edit | Fast Image Editing API | WaveSpeedAI