LTX 2.3 Trainer (V2) - Audio-to-Video
Integrasikan
ltx23-trainer-v2/a2v
# 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/a2v?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 $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx23-trainer-v2/a2v?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"training_data_url": "https://example.com/your-file.zip"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + 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/a2v", 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/id/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/a2v",
"input": {
"training_data_url": "https://example.com/your-file.zip"
}
}
Panggil model LTX 2.3 Trainer (V2) - Audio-to-Video (ltx23-trainer-v2/a2v) melalui Doitong API. Dokumentasi untuk agen: https://doitong.com/id/developers/models/ltx23-trainer-v2/a2v.md
POST https://api.doitong.com/v1/run/ltx23-trainer-v2/a2v?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/json
{
"training_data_url": "https://example.com/your-file.zip"
}
Jika status respons adalah "queued" atau "running", lakukan polling GET https://api.doitong.com/v1/requests/REQUEST_ID?wait=20 hingga statusnya menjadi "succeeded"; file terdapat di outputs[].url. POST https://api.doitong.com/v1/quote/ltx23-trainer-v2/a2v dengan body yang sama mengembalikan harga pasti sebelum dijalankan.
Permintaan ini: $12,00 12.000 kredit API Jalankan di halaman ini
Kunci API baru Anda. Ini hanya ditampilkan sekarang dan sudah dimasukkan ke dalam kode serta kolom kunci API di bawah. Simpan secara rahasia: jangan pernah menaruhnya di kode yang berjalan di browser.
Hasil pertama Anda akan muncul di sini
Buat permintaan di bawah ini dan jalankan dengan kunci API Anda. Output disalin ke penyimpanan kami, disimpan selamanya atau 7 hari gratis.
Buat permintaan
Kontrol dibuat dari skema model. Kode dan harga diperbarui saat Anda mengubahnya.
/run/ltx23-trainer-v2/a2v?wait=20
estimasi harga → $12,00
# 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/a2v?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 $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx23-trainer-v2/a2v?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"training_data_url": "https://example.com/your-file.zip"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + 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/a2v", 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/a2v",
"input": {
"training_data_url": "https://example.com/your-file.zip"
},
"wait_seconds": 20
}
}
}
Jalankan permintaan ini
Key Anda tetap berada di browser ini: key hanya dikirim ke Doitong API dan disimpan (localStorage) hanya jika Anda mencentang Ingat. Dapatkan kunci API
Tentang LTX 2.3 Trainer (V2) - Audio-to-Video
Melatih LoRA untuk pembuatan audio-to-video dari `.zip` dalam `training_data_url`. `rank` adalah 8 hingga 128, `number_of_steps` 100 hingga 10000, dan `number_of_frames` 9 hingga 121. `validation` menerima hingga 2 entri, masing-masing dengan trek audio conditioning. Output mencakup bobot LoRA, file konfigurasi, dan pratinjau opsional.
Parameter
training_data_urlwajib
rankopsional
number_of_stepsopsional
learning_rateopsional
number_of_framesopsional
frame_rateopsional
resolutionopsional
aspect_ratioopsional
trigger_phraseopsional
auto_scale_inputopsional
split_input_into_scenesopsional
split_input_duration_thresholdopsional
debug_datasetopsional
audio_normalizeopsional
audio_preserve_pitchopsional
validationopsional
validation[].promptwajib
validation[].image_urlwajib
validation[].audio_urlwajib
validation_negative_promptopsional
validation_number_of_framesopsional
validation_frame_rateopsional
validation_resolutionopsional
validation_aspect_ratioopsional
stg_scaleopsional
Output
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "ltx23-trainer-v2/a2v",
"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": 12000,
"usd": "12.00",
"charged": 12000,
"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"
}
}
Field output
- video
- object {url, content_type, file_name, file_size} | nullPratinjau video gabungan dari validasi, jika tersedia untuk endpoint yang menghasilkan video.
- audio
- object {url, content_type, file_name, file_size} | nullAudio gabungan dari validasi, diberikan sebagai pratinjau jika tersedia untuk rute khusus audio berikut: /audio-inpaint, /audio-extend-suffix, /audio-extend-prefix, /t2a, /a2a, dan /v2a.
- lora_file
- object {url, content_type, file_name, file_size}URL bobot LoRA terlatih dalam format .safetensors.
- config_file
- object {url, content_type, file_name, file_size}Konfigurasi untuk penyiapan endpoint inferensi.
- debug_dataset
- object {url, content_type, file_name, file_size} | nullOutput debug yang tersedia saat `debug_dataset` diaktifkan: arsip yang dapat diunduh dan berisi data pelatihan setelah prapemrosesan.
Varian LTX 2.3 Trainer lainnya
Model lainnya: Pelatihan
Untuk agen AI: halaman ini dalam Markdown tersedia di https://doitong.com/id/developers/models/ltx23-trainer-v2/a2v.md. Semua model dan panduan: https://doitong.com/developers/llms.txt