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Flux 2 Klein 4B Edit LoRA

wavespeed-ai /

FLUX.2 [klein] 4B Edit with LoRA support enables precise image-to-image editing with natural language instructions, multi-reference support, and LoRA customization. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.

lora-support
Wejście

Bezczynny

make it a real picture

$0.017za uruchomienie·~58 / $1

PrzykładyZobacz wszystkie

make it a real picture

make it a real picture

make it a real picture

make it a real picture

turn the pink into green

turn the pink into green

change the male to a female

change the male to a female

Add a female lying on the hammock

Add a female lying on the hammock

Powiązane modele

README

FLUX.2 Klein 4B Edit LoRA

FLUX.2 Klein 4B Edit LoRA is a lightweight yet powerful image editing model with full LoRA support. Upload images, describe your edits, and optionally apply up to 3 custom LoRA adapters for personalized styles — all with fast generation and affordable pricing.

Why Choose This?

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

  • LoRA support Apply up to 3 custom LoRA adapters for personalized styles, characters, or visual aesthetics.

  • Multi-image input Upload up to 4 reference images for context-aware editing.

  • 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.

  • Lightweight and fast 4B parameter model optimized for quick turnaround at affordable pricing.

Parameters

ParameterRequiredDescription
promptYesText description of the desired edit
imagesYesSource images to edit (up to 4 images)
lorasNoList of LoRA adapters to apply (up to 3)
sizeNoOutput dimensions (empty = same as input image)
seedNoRandom seed for reproducibility (-1 for random)

LoRA Format

Each LoRA in the loras array has:

  • path (required) — URL to the LoRA weights file
  • scale (optional) — Weight multiplier, default 1

How to Use

  1. Write your prompt — describe the edit you want (e.g., "make it a real picture", "add sunset lighting").
  2. Upload images — add up to 4 source images using "+ Add Item" button.
  3. Add LoRAs (optional) — click "+ Add Item" to include custom LoRA adapters.
  4. Set size (optional) — specify output dimensions or leave empty to match input.
  5. Set seed — use -1 for random, or specify a number for reproducibility.
  6. Run — submit and download the edited image.

Pricing

ItemCost
Per image$0.017

Simple flat-rate pricing regardless of image size or LoRA count.

Best Use Cases

  • Style Transfer — Transform images with custom LoRA styles.
  • Reality Enhancement — Convert illustrations or renders to photorealistic images.
  • Character Consistency — Apply character LoRAs while editing images.
  • Creative Retouching — Apply artistic edits with natural language and LoRA combinations.
  • Batch Editing — Affordable pricing enables large-scale image editing.

Pro Tips

  • Be specific in your prompt — clearly describe what should change.
  • Use LoRAs to add consistent styles across multiple edits.
  • Leave size empty to preserve original image dimensions.
  • Start with LoRA scale 1.0 and adjust based on results.
  • Use the same seed to compare different prompts or LoRA combinations.

Notes

  • Maximum 4 images can be uploaded per generation.
  • Up to 3 LoRAs can be applied simultaneously.
  • If size is not specified, output matches input image dimensions.
  • For best results, use high-quality source images.

Related Models

Uwaga:Ta strona korzysta z modeli AI udostępnianych przez podmioty trzecie.

Flux 2 Klein 4b Edit Lora API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-2-klein-4b/edit-lora 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 4b Edit Lora 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-4b/edit-lora" \
  -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-4b/edit-lora";
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-4b/edit-lora", 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 4b Edit Lora API — Frequently asked questions

What is the Flux 2 Klein 4b Edit Lora API?

Flux 2 Klein 4b Edit Lora is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. FLUX.2 [klein] 4B Edit with LoRA support enables precise image-to-image editing with natural language instructions, multi-reference support, and LoRA customization. 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 4b Edit Lora 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-4b-edit-lora.

How much does Flux 2 Klein 4b Edit Lora cost per run?

Flux 2 Klein 4b Edit Lora starts at $0.017 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 4b Edit Lora accept?

Key inputs: `prompt`, `images`, `seed`, `enable_base64_output`, `enable_sync_mode`, `loras`. 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-4b-edit-lora.

How long does Flux 2 Klein 4b Edit Lora take to generate?

Median end-to-end generation time on WaveSpeedAI is around 19 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 4b Edit Lora 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 4B Edit LoRA | Custom LoRA Image API | WaveSpeedAI