Seedream 5.0 Pro yayında | Görsel Üretici'de deneyin →

Flux Kontext Dev LoRA

wavespeed-ai /

Fast FLUX.1 Kontext [dev] endpoint with LoRA support for rapid image editing using pre-trained adapters for brand and style. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

lora-support
Giriş

Sürükleyip bırakın veya yüklemek için tıklayın

preview
width
height
1024 × 1024 px
Range: 256 - 1536

Boşta

Replace 'Feel POWER' with 'WaveSpeedAI'

$0.03çalıştırma başına·~33 / $1

ÖrneklerTümünü görüntüle

a dog frstingln illustration

a dog frstingln illustration

Replace 'Feel POWER' with 'WaveSpeedAI'

Replace 'Feel POWER' with 'WaveSpeedAI'

Transform the image into detailed anime style. Keep the original composition and layout, but render all characters with smooth outlines, large expressive eyes, vibrant hair colors, and soft cel-shading. Background should resemble high-quality anime scenes — painterly skies, stylized lighting, and subtle gradients. Make it feel like a frame from a cinematic anime.

Transform the image into detailed anime style. Keep the original composition and layout, but render all characters with smooth outlines, large expressive eyes, vibrant hair colors, and soft cel-shading. Background should resemble high-quality anime scenes — painterly skies, stylized lighting, and subtle gradients. Make it feel like a frame from a cinematic anime.

Turn it into a lego style

Turn it into a lego style

Turning clothes into bikinis

Turning clothes into bikinis

change the car color to pink

change the car color to pink

v3ct0r style, simple flat vector art, isolated on white bg, Mona Lisa

v3ct0r style, simple flat vector art, isolated on white bg, Mona Lisa

v3ct0r style, simple flat vector art, isolated on white bg, a girl

v3ct0r style, simple flat vector art, isolated on white bg, a girl

Mona Lisa, blockprint style

Mona Lisa, blockprint style

A young man with sunglasses, portrait, blockprint style

A young man with sunglasses, portrait, blockprint style

İlgili Modeller

README

FLUX Kontext Dev LoRA — wavespeed-ai/flux-kontext-dev-lora

FLUX Kontext Dev LoRA is an instruction-based image-to-image editing model with built-in LoRA support. Provide a source image plus a natural-language edit request, and optionally attach up to 3 LoRAs to steer style, subject consistency, or domain-specific aesthetics during the edit.

Key capabilities

  • Image-to-image editing from a source image + text instruction
  • LoRA-enabled inference: apply up to 3 LoRAs via input parameters
  • Works for both local edits (specific changes) and global transforms (overall look)
  • Ideal for batch consistency: reuse the same LoRA set for a stable visual identity

Pricing

$0.03 per image.

Cost per run = num_images × $0.03 Example: num_images = 4 → $0.12

Inputs and outputs

Input:

  • One source image (upload or public URL)
  • One edit instruction (prompt)
  • Optional: up to 3 LoRA items

Output:

  • One or more edited images (controlled by num_images)

Parameters

Core:

  • prompt: Edit instruction describing what to change and what to preserve
  • image: Source image
  • width / height: Output resolution
  • num_inference_steps: More steps can improve fidelity but increases latency
  • guidance_scale: Higher values follow the prompt more strongly; too high may over-edit
  • num_images: Number of variations generated per run
  • seed: Fixed value for reproducibility; -1 for random
  • output_format: jpeg or png

LoRA (up to 3 items):

  • loras: A list of LoRA entries (max 3)

  • path: Either owner/model-name or a direct.safetensors URL from the Internet

  • scale: LoRA strength (typically start around 0.6–1.0 and adjust)

Prompting guide

Use “preserve + edit + constraints” and let LoRAs drive the look:

Template: Keep [what must stay]. Change [what to edit]. Ensure [constraints]. Apply the LoRA style consistently without changing identity.

Example prompts

  • Keep the person’s face, hairstyle, and pose unchanged. Replace the background with a clean studio gradient. Match lighting and shadows.
  • Keep the product shape and label layout unchanged. Replace only the label text with “WaveSpeedAI”. Preserve typography perspective and print texture.
  • Remove small blemishes and reduce glare on the forehead while keeping natural skin texture and pores.

Best practices

  • Use fewer LoRAs when possible; add a second/third only if you need combined effects.
  • If results drift or over-style, reduce LoRA scale or guidance_scale and strengthen the preserve clause.
  • For consistent batches, keep the same LoRA set and scales, and fix seed when comparing prompt variants.
Not:Bu web sitesi, üçüncü taraflarca sağlanan yapay zeka modellerini kullanmaktadır.

Flux Kontext Dev Lora API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-kontext-dev-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 Kontext Dev Lora below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "num_inference_steps": 28,
    "guidance_scale": 2.5,
    "num_images": 1,
    "seed": -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-kontext-dev-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-kontext-dev-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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "num_inference_steps": 28,
        "guidance_scale": 2.5,
        "num_images": 1,
        "seed": -1,
        "output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "num_inference_steps": 28,
    "guidance_scale": 2.5,
    "num_images": 1,
    "seed": -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-kontext-dev-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 Kontext Dev Lora API — Frequently asked questions

What is the Flux Kontext Dev Lora API?

Flux Kontext Dev Lora is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Fast FLUX.1 Kontext [dev] endpoint with LoRA support for rapid image editing using pre-trained adapters for brand and style. 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 Kontext Dev 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-kontext-dev-lora.

How much does Flux Kontext Dev Lora cost per run?

Flux Kontext Dev Lora 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 Flux Kontext Dev Lora 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-kontext-dev-lora.

How long does Flux Kontext Dev Lora take to generate?

Average end-to-end generation time on WaveSpeedAI is around 13 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.

Can I use Flux Kontext Dev 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 Kontext Dev LoRA | Custom LoRA Image API | WaveSpeedAI