LTX 2.3 Trainer (V2) - Video-to-Audio
Integrera
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/sv/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"
}
}
Anropa modellen LTX 2.3 Trainer (V2) - Video-to-Audio (ltx23-trainer-v2/v2a) via Doitong API. Dokumentation för agenter: https://doitong.com/sv/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"
}
Om svarsstatusen är "queued" eller "running" ska du polla GET https://api.doitong.com/v1/requests/REQUEST_ID?wait=20 tills den är "succeeded"; filerna finns i outputs[].url. POST https://api.doitong.com/v1/quote/ltx23-trainer-v2/v2a med samma body returnerar det exakta priset före körningen.
Den här begäran: 9,80 $ 9 800 API-krediter Kör den på den här sidan
Din nya API-nyckel. Den visas bara nu och finns redan i koden och i API-nyckelfältet nedan. Håll den hemlig: lägg aldrig in den i kod som körs i en webbläsare.
Ditt första resultat visas här
Skapa en begäran nedan och kör den med din API-nyckel. Utdata kopieras till vår lagring och sparas för alltid eller 7 dagar kostnadsfritt.
Skapa en begäran
Kontrollerna genereras från modellschemat. Koden och priset uppdateras när du ändrar 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.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
}
}
}
Kör den här begäran
Din nyckel stannar i den här webbläsaren: den skickas endast till Doitong API och sparas endast i localStorage om du markerar Kom ihåg. Hämta en API-nyckel
Om LTX 2.3 Trainer (V2) - Video-to-Audio
Tränar en LoRA som skapar ett ljudspår som matchar det som händer på skärmen i en video. Varje träningsklipp måste innehålla ett ljudspår, så scenuppdelning är avstängd som standard för att undvika tysta avslutande segment. Valideringsproven (upp till 2) parar var och en en prompt med en stum video. Data skickas som ett zip-arkiv via `training_data_url`.
Parametrar
training_data_urlobligatoriskt
rankvalfritt
number_of_stepsvalfritt
learning_ratevalfritt
number_of_framesvalfritt
frame_ratevalfritt
resolutionvalfritt
aspect_ratiovalfritt
trigger_phrasevalfritt
auto_scale_inputvalfritt
split_input_into_scenesvalfritt
split_input_duration_thresholdvalfritt
debug_datasetvalfritt
validationvalfritt
validation[].promptobligatoriskt
validation[].video_urlobligatoriskt
validation_negative_promptvalfritt
validation_number_of_framesvalfritt
validation_frame_ratevalfritt
validation_resolutionvalfritt
validation_aspect_ratiovalfritt
stg_scalevalfritt
Resultat
{
"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"
}
}
Resultatets fält
- video
- object {url, content_type, file_name, file_size} | nullKombinerad videoförhandsgranskning från valideringen, när den är tillgänglig för ändpunkter som producerar video.
- audio
- object {url, content_type, file_name, file_size} | nullKombinerat ljud från valideringen, tillhandahållet som förhandsgranskning när det är tillgängligt för dessa ljudrutter: /audio-inpaint, /audio-extend-suffix, /audio-extend-prefix, /t2a, /a2a och /v2a.
- lora_file
- object {url, content_type, file_name, file_size}URL till tränade LoRA-vikter i formatet .safetensors.
- config_file
- object {url, content_type, file_name, file_size}Konfiguration för inställning av inferensändpunkten.
- debug_dataset
- object {url, content_type, file_name, file_size} | nullFelsökningsutdata som är tillgängliga när `debug_dataset` är aktiverat: ett arkiv som kan laddas ned och innehåller träningsdata efter förbehandling.
Andra LTX 2.3 Trainer-varianter
Fler modeller: Träning
För AI-agenter: den här sidan som Markdown finns på https://doitong.com/sv/developers/models/ltx23-trainer-v2/v2a.md. Alla modeller och guider: https://doitong.com/developers/llms.txt