
Keine Treffer. Versuche „video“, „FLUX“ oder „webhooks“.
Modelle werden geladen…
flux/dev
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/flux/dev?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"prompt": "A lighthouse keeper'\''s cottage on a windswept cliff at blue hour, warm light glowing from small square windows, waves breaking far below, long-exposure sea mist, moody cinematic landscape photograph, 35mm, rich detail.",
"num_outputs": 1
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/flux/dev?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A lighthouse keeper's cottage on a windswept cliff at blue hour, warm light glowing from small square windows, waves breaking far below, long-exposure sea mist, moody cinematic landscape photograph, 35mm, rich detail.",
"num_outputs": 1
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"prompt": "A lighthouse keeper's cottage on a windswept cliff at blue hour, warm light glowing from small square windows, waves breaking far below, long-exposure sea mist, moody cinematic landscape photograph, 35mm, rich detail.",
"num_outputs": 1,
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/flux/dev", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
# 1. Connect once (Claude Code; Cursor, VS Code, Claude Desktop: https://doitong.com/de/developers/docs/mcp)
claude mcp add --transport http doitong-models https://api.doitong.com/v1/mcp \
--header "Authorization: Bearer $DOITONG_API_KEY"
# 2. Your agent calls the "run" tool with:
{
"model_id": "flux/dev",
"input": {
"prompt": "A lighthouse keeper's cottage on a windswept cliff at blue hour, warm light glowing from small square windows, waves breaking far below, long-exposure sea mist, moody cinematic landscape photograph, 35mm, rich detail.",
"num_outputs": 1
}
}
Rufe das Modell FLUX.1 [dev] (flux/dev) über die Doitong API auf. Dokumentation für Agenten: https://doitong.com/de/developers/models/flux/dev.md
POST https://api.doitong.com/v1/run/flux/dev?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/json
{
"prompt": "A lighthouse keeper's cottage on a windswept cliff at blue hour, warm light glowing from small square windows, waves breaking far below, long-exposure sea mist, moody cinematic landscape photograph, 35mm, rich detail.",
"num_outputs": 1
}
Wenn der Antwortstatus "queued" oder "running" lautet, frage GET https://api.doitong.com/v1/requests/REQUEST_ID?wait=20 ab, bis er "succeeded" lautet; die Dateien befinden sich in outputs[].url. POST https://api.doitong.com/v1/quote/flux/dev mit demselben Body gibt den exakten Preis vor der Ausführung zurück.
Diese Anfrage: 0,025 $ 25 API-Credits Auf dieser Seite ausführen
Dein neuer API-Schlüssel. Sie wird nur jetzt angezeigt und befindet sich bereits im Code und im untenstehenden API-Schlüssel-Feld. Halten Sie sie geheim: Fügen Sie sie niemals in Code ein, der in einem Browser ausgeführt wird.
Die Steuerelemente werden aus dem Modellschema generiert. Code und Preis werden aktualisiert, während du sie änderst.
/run/flux/dev?wait=20
Preis → 0,025 $
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/flux/dev?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"prompt": "A lighthouse keeper'\''s cottage on a windswept cliff at blue hour, warm light glowing from small square windows, waves breaking far below, long-exposure sea mist, moody cinematic landscape photograph, 35mm, rich detail.",
"num_outputs": 1
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/flux/dev?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A lighthouse keeper's cottage on a windswept cliff at blue hour, warm light glowing from small square windows, waves breaking far below, long-exposure sea mist, moody cinematic landscape photograph, 35mm, rich detail.",
"num_outputs": 1
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"prompt": "A lighthouse keeper's cottage on a windswept cliff at blue hour, warm light glowing from small square windows, waves breaking far below, long-exposure sea mist, moody cinematic landscape photograph, 35mm, rich detail.",
"num_outputs": 1,
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/flux/dev", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "run",
"arguments": {
"model_id": "flux/dev",
"input": {
"prompt": "A lighthouse keeper's cottage on a windswept cliff at blue hour, warm light glowing from small square windows, waves breaking far below, long-exposure sea mist, moody cinematic landscape photograph, 35mm, rich detail.",
"num_outputs": 1
},
"wait_seconds": 20
}
}
}
Dein Schlüssel bleibt in diesem Browser: Er wird nur an die Doitong API gesendet und nur gespeichert (localStorage), wenn du „Merken“ aktivierst. API-Schlüssel abrufen
Öffentliche Werke der Doitong-Community mit FLUX.1. Verwende einen Prompt mit einem Klick erneut.
Close-up portrait seorang wanita berkulit pucat berdiri setengah tubuh di dalam air berwarna lembut kebiruan, dikelilingi bunga teratai berkelopak penuh yang bermekaran indah di permukaan air. Rambutnya pirang pucat tersanggul longgar dengan beberapa helaian tipis jatuh alami membingkai sisi...
Close-up portrait seorang wanita berkulit pucat berdiri setengah tubuh di dalam air berwarna lembut kebiruan, dikelilingi bunga teratai berkelopak penuh yang bermekaran indah di permukaan air. Rambutnya pirang pucat tersanggul longgar dengan beberapa helaian tipis jatuh alami membingkai sisi...
Prompt Details Seorang wanita berkulit pucat berdiri setengah tubuh di dalam air berwarna lembut kebiruan, dikelilingi bunga teratai berkelopak penuh yang bermekaran indah di permukaan air. Rambutnya pirang pucat tersanggul longgar dengan beberapa helaian tipis jatuh alami membingkai sisi...
Seorang wanita berkulit pucat berdiri setengah tubuh di dalam air berwarna lembut kebiruan, dikelilingi bunga teratai berkelopak penuh yang bermekaran indah di permukaan air. Rambutnya pirang pucat tersanggul longgar dengan beberapa helaian tipis jatuh alami membingkai sisi wajahnya. Ia memeluk...
"Potret sinematik ultra-realistis close-up seorang anak laki-laki kecil (balita) yang sangat menggemaskan dengan mata cokelat besar yang ekspresif, rambut hitam berantakan yang tebal, mengenakan kaus putih kerah henley. Dia berpose dengan kedua tangan terkatup di depan dada, menunjukkan senyum...
"Potret sinematik ultra-realistis close-up seorang anak laki-laki kecil (balita) yang sangat menggemaskan dengan mata cokelat besar yang ekspresif, rambut hitam berantakan yang tebal, mengenakan kaus putih kerah henley. Dia berpose dengan kedua tangan terkatup di depan dada, menunjukkan senyum...
"Potret sinematik ultra-realistis close-up seorang anak laki-laki kecil (balita) yang sangat menggemaskan dengan mata cokelat besar yang ekspresif, rambut hitam berantakan yang tebal, mengenakan kaus putih kerah henley. Dia berpose dengan kedua tangan terkatup di depan dada, menunjukkan senyum...
Foto sinematik hiper-realistis resolusi tinggi menampilkan seorang pria muda Indonesia dengan tubuh ramping dan proporsional, rambut hitam pendek agak acak, mengenakan jaket kulit hitam, celana riding fit, dan sepatu motor. Ia sedang melakukan aksi freestyle ekstrem dengan motor sport 500cc...
Foto sinematik hiper-realistis resolusi tinggi menampilkan seorang pria muda Indonesia dengan tubuh ramping dan proporsional, rambut hitam pendek agak acak, mengenakan jaket kulit hitam dan celana riding fit. Ia sedang bersiap melakukan freestyle menggunakan motor sport 500cc berwarna merah,...
Das guidance-distillierte FLUX-Modell mit 12 Milliarden Parametern und einem vollständigen Sampling-Zeitplan (standardmäßig 28 Schritte, anpassbar von 1 bis 50) sowie einer anpassbaren Guidance-Skala. Es folgt detaillierten Prompts und rendert Hände, Gesichter und kurzen Text deutlich besser als die schnell-Variante, allerdings zu einem höheren Preis. Voreingestellte oder benutzerdefinierte Größen, bis zu vier Bilder pro Anfrage, JPEG- oder PNG-Ausgabe.
Sende entweder den kanonischen Namen oder den nativen Namen, nicht beide. Kanonische Namen
prompterforderlich
image_sizeoptional
num_inference_stepsoptional
seedoptional
guidance_scaleoptional
num_imagesoptional
num_outputs
output_formatoptional
accelerationoptional
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "flux/dev",
"status": "succeeded",
"created_at": "2026-10-20T12:00:00Z",
"started_at": "2026-10-20T12:00:02Z",
"completed_at": "2026-10-20T12:00:09Z",
"queue_position": null,
"cost": {
"credits": 25,
"usd": "0.025",
"charged": 25,
"refunded": 0
},
"input": {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field, telephoto lens",
"image_size": "landscape_4_3",
"num_outputs": 1
},
"output": {
"images": [
{
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"width": 1024,
"height": 768,
"content_type": "image/jpeg"
}
],
"timings": {},
"seed": 42,
"has_nsfw_concepts": [
false
],
"prompt": "string"
},
"outputs": [
{
"type": "image",
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_size": 1843200,
"width": 1024,
"height": 768,
"stored": true
}
],
"error": null,
"metrics": {
"inference_seconds": 6.8
},
"urls": {
"self": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
"cancel": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
}
}
Für KI-Agenten: Diese Seite ist als Markdown verfügbar unter https://doitong.com/de/developers/models/flux/dev.md. Alle Modelle und Anleitungen: https://doitong.com/developers/llms.txt