LTX 2.3 Trainer (V2) - Video-to-Audio
Integréieren
ltx23-trainer-v2/v2a
# 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/ltx23-trainer-v2/v2a?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"training_data_url": "https://example.com/your-file.zip"
}'
# 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 $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx23-trainer-v2/v2a?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"training_data_url": "https://example.com/your-file.zip"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"training_data_url": "https://example.com/your-file.zip",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/ltx23-trainer-v2/v2a", 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/lb/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": "ltx23-trainer-v2/v2a",
"input": {
"training_data_url": "https://example.com/your-file.zip"
}
}
Rufft de LTX 2.3 Trainer (V2) - Video-to-Audio-Modell (ltx23-trainer-v2/v2a) iwwer d'Doitong API op. Dokumentatioun fir Agenten: https://doitong.com/lb/developers/models/ltx23-trainer-v2/v2a.md
POST https://api.doitong.com/v1/run/ltx23-trainer-v2/v2a?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/json
{
"training_data_url": "https://example.com/your-file.zip"
}
Wann de Äntwertstatus "queued" oder "running" ass, frot GET https://api.doitong.com/v1/requests/REQUEST_ID?wait=20 of, bis en "succeeded" ass; d'Dateie stinn an outputs[].url. POST https://api.doitong.com/v1/quote/ltx23-trainer-v2/v2a mam selwechte Body gëtt de geneeë Präis virun der Ausféierung zréck.
Dës Ufro: 9,80 $ 9.800 API-Kreditter Op dëser Säit ausféieren
Ären neien API-Schlëssel. Si gëtt nëmmen elo ugewisen a steet schonn am Code an am API-Schlëssel-Feld hei drënner. Halt se geheim: Setz se ni an Code, deen an engem Browser leeft.
Äin éischt Resultat kënnt hei hin
Baut hei drënner eng Ufro a féiert se mat Ärem API-Schlëssel aus. Output gëtt an eis Späicherung kopéiert, fir ëmmer oder 7 Deeg gratis gespäichert.
Eng Ufro erstellen
D'Kontrolle ginn aus dem Modellschema generéiert. De Code an de Präis aktualiséiere sech, wann s du se ännerst.
/run/ltx23-trainer-v2/v2a?wait=20
Präis → 9,80 $
# 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/ltx23-trainer-v2/v2a?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"training_data_url": "https://example.com/your-file.zip"
}'
# 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 $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx23-trainer-v2/v2a?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"training_data_url": "https://example.com/your-file.zip"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"training_data_url": "https://example.com/your-file.zip",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/ltx23-trainer-v2/v2a", 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": "ltx23-trainer-v2/v2a",
"input": {
"training_data_url": "https://example.com/your-file.zip"
},
"wait_seconds": 20
}
}
}
Dës Ufro ausféieren
Äre Schlëssel bleift an dësem Browser: e gëtt nëmmen un d'Doitong API geschéckt an nëmme gespäichert (localStorage), wann s du „Erënneren“ aktivéiers. En API-Schlëssel kréien
Iwwer LTX 2.3 Trainer (V2) - Video-to-Audio
Trainéiert eng LoRA, déi e Soundtrack passend zur onscreen Aktioun vun engem Video erstellt. All Trainingsclip muss eng Audiostreck behalen, dofir ass d'Szenentrennung standardméisseg ausgeschalt, fir stomm Schlusssegmenter ze vermeiden. Validéierungsbeispiller (bis zu 2) verbannen all e Prompt mat engem stumme Video. D'Date sinn e zip iwwer `training_data_url`.
Parameteren
training_data_urlobligatoresch
rankoptional
number_of_stepsoptional
learning_rateoptional
number_of_framesoptional
frame_rateoptional
resolutionoptional
aspect_ratiooptional
trigger_phraseoptional
auto_scale_inputoptional
split_input_into_scenesoptional
split_input_duration_thresholdoptional
debug_datasetoptional
validationoptional
validation[].promptobligatoresch
validation[].video_urlobligatoresch
validation_negative_promptoptional
validation_number_of_framesoptional
validation_frame_rateoptional
validation_resolutionoptional
validation_aspect_ratiooptional
stg_scaleoptional
Output
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "ltx23-trainer-v2/v2a",
"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": 9800,
"usd": "9.80",
"charged": 9800,
"refunded": 0
},
"input": {
"training_data_url": "https://example.com/your-file.zip"
},
"output": {
"video": {
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_name": "0-output.jpeg",
"file_size": 1843200
},
"audio": {
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_name": "0-output.jpeg",
"file_size": 1843200
},
"lora_file": {
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_name": "0-output.jpeg",
"file_size": 1843200
},
"config_file": {
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_name": "0-output.jpeg",
"file_size": 1843200
},
"debug_dataset": {
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_name": "0-output.jpeg",
"file_size": 1843200
}
},
"outputs": [
{
"type": "file",
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_size": 1843200,
"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"
}
}
Output-Felder
- video
- object {url, content_type, file_name, file_size} | nullKombinéiert Video-Virschau aus der Validatioun, wa verfügbar fir Endpunkten, déi Video produzéieren.
- audio
- object {url, content_type, file_name, file_size} | nullKombinéierten Audio aus der Validatioun, als Virschau geliwwert, wa verfügbar fir dës Audio-only-Routen: /audio-inpaint, /audio-extend-suffix, /audio-extend-prefix, /t2a, /a2a an /v2a.
- lora_file
- object {url, content_type, file_name, file_size}URL fir trainéiert LoRA-Gewiichter am .safetensors-Format.
- config_file
- object {url, content_type, file_name, file_size}Konfiguratioun fir d'Ariichtung vum Inferenz-Endpunkts.
- debug_dataset
- object {url, content_type, file_name, file_size} | nullDebug-Output, verfügbar wann `debug_dataset` aktivéiert ass: en erofluedbart Archiv mat den Trainingsdaten no der Virveraarbechtung.
Aner LTX 2.3 Trainer-Varianten
Méi Modeller: Training
Fir AI-Agenten: dës Säit als Markdown ass ënner https://doitong.com/lb/developers/models/ltx23-trainer-v2/v2a.md. All Modeller a Guiden: https://doitong.com/developers/llms.txt