LTX 2.3 Trainer (V2) - Video-to-Audio
Integrer
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.ru/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.ru/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx23-trainer-v2/v2a?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"training_data_url": "https://example.com/your-file.zip"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.ru/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/da/developers/docs/mcp)
claude mcp add --transport http doitong-models https://api.doitong.ru/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"
}
}
Kald LTX 2.3 Trainer (V2) - Video-to-Audio-modellen (ltx23-trainer-v2/v2a) via Doitong API. Dokumentation til agenter: https://doitong.com/da/developers/models/ltx23-trainer-v2/v2a.md
POST https://api.doitong.ru/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"
}
Hvis svarstatus er "queued" eller "running", skal du forespørge GET https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20 igen, indtil den er "succeeded"; filerne findes i outputs[].url. POST https://api.doitong.ru/v1/quote/ltx23-trainer-v2/v2a med samme body returnerer den nøjagtige pris før kørslen.
Denne anmodning: 9,80 $ 9.800 API-kreditter · ≈ 1.026,06 ₽ Kør den på denne side
Din nye API-nøgle. Den vises kun nu og findes allerede i koden og i feltet til API-nøglen nedenfor. Hold den hemmelig: Anbring den aldrig i kode, der kører i en browser.
Dit første resultat kommer her
Opret en anmodning nedenfor, og kør den med din API-nøgle. Output kopieres til vores lagring, gemt for altid eller 7 dage gratis.
Opret en anmodning
Kontrollerne genereres fra modellens skema. Koden og prisen opdateres, når du ændrer dem.
/run/ltx23-trainer-v2/v2a?wait=20
pris → 9,80 $
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.ru/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.ru/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx23-trainer-v2/v2a?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"training_data_url": "https://example.com/your-file.zip"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.ru/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
}
}
}
Kør denne anmodning
Din nøgle forbliver i denne browser: Den sendes kun til Doitong API og gemmes kun i localStorage, hvis du markerer Husk. Hent en API-nøgle
Om LTX 2.3 Trainer (V2) - Video-to-Audio
Træner en LoRA, der producerer et lydspor, som matcher handlingen på skærmen i en video. Alle træningsklip skal bevare et lydspor, så scenedeling er som standard deaktiveret for at undgå lydløse afsluttende segmenter. Valideringseksempler (op til 2) parrer hver en prompt med en lydløs video. Data leveres som et zip-arkiv via `training_data_url`.
Parametre
training_data_urlpåkrævet
rankvalgfri
number_of_stepsvalgfri
learning_ratevalgfri
number_of_framesvalgfri
frame_ratevalgfri
resolutionvalgfri
aspect_ratiovalgfri
trigger_phrasevalgfri
auto_scale_inputvalgfri
split_input_into_scenesvalgfri
split_input_duration_thresholdvalgfri
debug_datasetvalgfri
validationvalgfri
validation[].promptpåkrævet
validation[].video_urlpåkrævet
validation_negative_promptvalgfri
validation_number_of_framesvalgfri
validation_frame_ratevalgfri
validation_resolutionvalgfri
validation_aspect_ratiovalgfri
stg_scalevalgfri
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.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
"cancel": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
}
}
Outputfelter
- video
- object {url, content_type, file_name, file_size} | nullSamlet videoforhåndsvisning fra valideringen, når den er tilgængelig for endpoints, der producerer video.
- audio
- object {url, content_type, file_name, file_size} | nullSamlet lyd fra valideringen, leveret som forhåndsvisning, når den er tilgængelig for disse lydruter: /audio-inpaint, /audio-extend-suffix, /audio-extend-prefix, /t2a, /a2a og /v2a.
- lora_file
- object {url, content_type, file_name, file_size}URL til trænede LoRA-vægte i .safetensors-format.
- config_file
- object {url, content_type, file_name, file_size}Konfiguration til opsætning af inferens-endpointet.
- debug_dataset
- object {url, content_type, file_name, file_size} | nullDebug-output, der er tilgængeligt, når `debug_dataset` er aktiveret: et arkiv, der kan downloades, og som indeholder træningsdataene efter præprocessering.
Andre LTX 2.3 Trainer-varianter
Flere modeller: Træning
Til AI-agenter: Denne side som Markdown findes på https://doitong.com/da/developers/models/ltx23-trainer-v2/v2a.md. Alle modeller og vejledninger: https://doitong.com/developers/llms.txt