LTX 2.3 Trainer (V2) - Audio+Video Reference Transformation
Integrer
ltx23-trainer-v2/av2av
# 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/av2av?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 $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx23-trainer-v2/av2av?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"training_data_url": "https://example.com/your-file.zip"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + 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/av2av", 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.com/v1/mcp \
--header "Authorization: Bearer $DOITONG_API_KEY"
# 2. Your agent calls the "run" tool with:
{
"model_id": "ltx23-trainer-v2/av2av",
"input": {
"training_data_url": "https://example.com/your-file.zip"
}
}
Kald LTX 2.3 Trainer (V2) - Audio+Video Reference Transformation-modellen (ltx23-trainer-v2/av2av) via Doitong API. Dokumentation til agenter: https://doitong.com/da/developers/models/ltx23-trainer-v2/av2av.md
POST https://api.doitong.com/v1/run/ltx23-trainer-v2/av2av?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.com/v1/requests/REQUEST_ID?wait=20 igen, indtil den er "succeeded"; filerne findes i outputs[].url. POST https://api.doitong.com/v1/quote/ltx23-trainer-v2/av2av med samme body returnerer den nøjagtige pris før kørslen.
Denne anmodning: 13,60 $ 13.600 API-kreditter 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/av2av?wait=20
pris → 13,60 $
# 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/av2av?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 $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx23-trainer-v2/av2av?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"training_data_url": "https://example.com/your-file.zip"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + 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/av2av", 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/av2av",
"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) - Audio+Video Reference Transformation
Tilpasser en LoRA på parrede klip, hvor hver referencevideo med lyd matches med et målklip. Angiv en zip via `training_data_url`, eventuelt med .txt-tekster. Scenesplitning er som standard deaktiveret, fordi den ville forskyde start/slut-parrene, så angiv klip, der allerede er skåret. Rank kan være 8 til 128, trin 100 til 10000, og op til 2 validation-elementer er tilladt.
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
audio_normalizevalgfri
audio_preserve_pitchvalgfri
validationvalgfri
validation[].promptpåkrævet
validation[].reference_video_urlpåkrævet
validation[].reference_audio_urlvalgfri
validation_negative_promptvalgfri
validation_number_of_framesvalgfri
validation_frame_ratevalgfri
validation_resolutionvalgfri
validation_aspect_ratiovalgfri
stg_scalevalgfri
reference_downscale_factorvalgfri
reference_temporal_scale_factorvalgfri
Output
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "ltx23-trainer-v2/av2av",
"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": 13600,
"usd": "13.60",
"charged": 13600,
"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"
}
}
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/av2av.md. Alle modeller og vejledninger: https://doitong.com/developers/llms.txt