LTX-2.3 22B Video Trainer
Integrera
ltx23-video-trainer
# 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-video-trainer?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 $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx23-video-trainer?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"training_data_url": "https://example.com/your-file.zip"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + 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-video-trainer", 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-video-trainer",
"input": {
"training_data_url": "https://example.com/your-file.zip"
}
}
Anropa modellen LTX-2.3 22B Video Trainer (ltx23-video-trainer) via Doitong API. Dokumentation för agenter: https://doitong.com/sv/developers/models/ltx23-video-trainer.md
POST https://api.doitong.com/v1/run/ltx23-video-trainer?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-video-trainer med samma body returnerar det exakta priset före körningen.
Den här begäran: 9,60 $ 9 600 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-video-trainer?wait=20
pris → 9,60 $
# 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-video-trainer?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 $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx23-video-trainer?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"training_data_url": "https://example.com/your-file.zip"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + 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-video-trainer", 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-video-trainer",
"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 22B Video Trainer
Tränar en LoRA på LTX-2.3 22B-modellen för anpassade stilar och effekter från ett zip-arkiv med videor eller bilder, helst 10 eller fler filer. `number_of_steps` sträcker sig från 100 till 6000 (standardvärde 2000), `frame_rate` är som standard 25 och `first_frame_conditioning_p` är som standard 0.5. `with_audio` tvingar fram eller inaktiverar ljudträning, och `generate_audio_in_validation` styr ljud i förhandsvisningar.
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
with_audiovalfritt
audio_normalizevalfritt
audio_preserve_pitchvalfritt
first_frame_conditioning_pvalfritt
validationvalfritt
validation[].promptobligatoriskt
validation[].image_urlvalfritt
validation_negative_promptvalfritt
validation_number_of_framesvalfritt
validation_frame_ratevalfritt
validation_resolutionvalfritt
validation_aspect_ratiovalfritt
stg_scalevalfritt
generate_audio_in_validationvalfritt
Resultat
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "ltx23-video-trainer",
"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": 9600,
"usd": "9.60",
"charged": 9600,
"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
},
"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} | nullURL för tillgängliga valideringsvideor.
- 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} | nullNedladdningsbart felsökningsarkiv som endast returneras för indata där `debug_dataset` är aktiverat; det innehåller den förbehandlade träningsdatauppsättningen, inklusive avkodat ljud och video.
Fler modeller: Träning
För AI-agenter: den här sidan som Markdown finns på https://doitong.com/sv/developers/models/ltx23-video-trainer.md. Alla modeller och guider: https://doitong.com/developers/llms.txt