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Flux Kontext Dev

wavespeed-ai /

FLUX.1 Kontext Dev is an open-weight, open-code image-to-image model that edits images from text prompts for precise, text-guided retouching and style transfer. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

image-to-image
Eingabe

Bereit

Turn pictures into anime style

$0.025pro Durchlauf·~40 / $1

Weiter:

BeispieleAlle anzeigen

Turn pictures into anime style

Turn pictures into anime style

Change the car color to red.

Change the car color to red.

Change the background to a dark, eerie haunted castle with gothic architecture, cracked stone walls, flickering lanterns, and dense fog. Adjust the lighting and shadows on the girl so they match the ominous mood of the new background — cool tones, directional lighting from the castle, and subtle atmospheric glow. Keep the girl's pose and expression intact, but ensure overall harmony between subject and environment.

Change the background to a dark, eerie haunted castle with gothic architecture, cracked stone walls, flickering lanterns, and dense fog. Adjust the lighting and shadows on the girl so they match the ominous mood of the new background — cool tones, directional lighting from the castle, and subtle atmospheric glow. Keep the girl's pose and expression intact, but ensure overall harmony between subject and environment.

Turn it into a clay style, soft, handcrafted textures, rounded shapes, colorful modeling clay look

Turn it into a clay style, soft, handcrafted textures, rounded shapes, colorful modeling clay look

Put the Mona Lisa in sunglasses.

Put the Mona Lisa in sunglasses.

Into Ghibli style

Into Ghibli style

Add the text “COOL” to the image.

Add the text “COOL” to the image.

Change the background to sky

Change the background to sky

Turning skateboards into surfboards

Turning skateboards into surfboards

Turned into anime style

Turned into anime style

She is wearing a red bikini

She is wearing a red bikini

Turn pictures into card style.

Turn pictures into card style.

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README

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

FLUX.1 Kontext Dev is an open-weight, open-code image-to-image model built for instruction-based editing. Provide a source image plus a natural-language edit request, and the model rewrites the image while preserving the original context when asked—making it suitable for targeted retouching, object changes, background swaps, text edits, and controlled style transforms.

Key capabilities

  • Instruction-based image editing from a single input image
  • Strong subject and scene preservation when you explicitly request it
  • Local and global edits: change specific regions or the whole image
  • Iterative editing workflow: apply multiple edits step-by-step with minimal drift

Typical use cases

  • Retouching: lighting, exposure, cleanup, blemish removal
  • Object edits: add/remove/replace items, change colors/materials
  • Background replacement: swap environments while keeping the subject consistent
  • Text edits: add or replace words on signs, posters, packaging
  • Style transforms: convert to clay, illustration, cinematic, etc., while preserving composition

Pricing

$0.025 per image.

Cost per run = num_images × $0.025 Example: num_images = 4 → $0.10

Inputs and outputs

Input:

  • One source image (upload or public URL)
  • One edit instruction (prompt)

Output:

  • One or more edited images (controlled by num_images)

Parameters

  • prompt: Edit instruction describing what to change and what to keep
  • image: Source image
  • width / height: Output resolution
  • num_inference_steps: More steps usually improves quality 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
  • enable_base64_output: Return BASE64 instead of a URL (API only)
  • enable_sync_mode: Wait for generation and return results directly (API only)

Prompting guide

Write prompts like an editor’s brief:

  1. Preserve clause: what must stay the same
  2. Edit clause: what must change
  3. Constraints: realism level, lighting, placement, typography, materials
  4. Consistency: match shadows/highlights to the new scene

Template: Keep [what must stay]. Change [what to edit]. Ensure [constraints]. Match [lighting/shadows/style consistency].

Example prompts

  • Keep the person’s face, pose, and clothing unchanged. Change the background to a foggy gothic castle. Match lighting and shadows to the new environment.
  • Change the car color to red. Preserve reflections and keep the rest of the scene unchanged.
  • Add the text “COOL” on the sign in the same perspective, with realistic shadows, and do not alter anything else.
  • Turn the image into a clay style with handcrafted texture and soft studio lighting, while keeping the composition and subject identity.
  • Remove the background crowd and keep the main subject sharp and unchanged.

Best practices

  • Start simple, then iterate: do one change per run for maximum control.
  • If the edit is too aggressive, lower guidance_scale and strengthen the preserve clause.
  • For A/B comparisons, keep seed fixed and change only one parameter at a time.
  • Use aspect-matched width/height to avoid unintended stretching.
Hinweis:Diese Website nutzt KI-Modelle von Drittanbietern.

Flux Kontext Dev API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/flux-kontext-dev 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 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" \
  -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";
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", 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 API — Frequently asked questions

What is the Flux Kontext Dev API?

Flux Kontext Dev is a WaveSpeedAI model for image editing, exposed as a REST API on WaveSpeedAI. FLUX.1 Kontext Dev is an open-weight, open-code image-to-image model that edits images from text prompts for precise, text-guided retouching and style transfer. 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 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.

How much does Flux Kontext Dev cost per run?

Flux Kontext Dev starts at $0.025 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 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.

How long does Flux Kontext Dev take to generate?

Median end-to-end generation time on WaveSpeedAI is around 11 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 Kontext Dev 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.