Ltx 2.3 Quality · Text to Audio · LoRA
整合
模型 id
ltx-2.3-quality/text-to-audio/lora
# 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/ltx-2.3-quality/text-to-audio/lora?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"loras": [
{
"path": "https://example.com/your-file.safetensors"
}
]
}'
# 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 $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx-2.3-quality/text-to-audio/lora?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"loras": [
{
"path": "https://example.com/your-file.safetensors"
}
]
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"loras": [
{
"path": "https://example.com/your-file.safetensors",
},
],
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/ltx-2.3-quality/text-to-audio/lora", 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/zh-TW/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": "ltx-2.3-quality/text-to-audio/lora",
"input": {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"loras": [
{
"path": "https://example.com/your-file.safetensors"
}
]
}
}
透過 Doitong API 呼叫 Ltx 2.3 Quality · Text to Audio · LoRA 模型(ltx-2.3-quality/text-to-audio/lora)。代理程式文件:https://doitong.com/zh-TW/developers/models/ltx-2.3-quality/text-to-audio/lora.md
POST https://api.doitong.ru/v1/run/ltx-2.3-quality/text-to-audio/lora?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/json
{
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"loras": [
{
"path": "https://example.com/your-file.safetensors"
}
]
}
如果回應狀態為 "queued" 或 "running",請輪詢 GET https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20,直到狀態變為 "succeeded";檔案位於 outputs[].url。使用相同主體傳送 POST https://api.doitong.ru/v1/quote/ltx-2.3-quality/text-to-audio/lora,可在執行前取得確切價格。
此請求: ≈$0.70 700 API 點數 · ≈ ₽75.12 在此頁面執行
您的新 API 金鑰。 僅會在此時顯示,且已填入下方的程式碼與 API 金鑰欄位。請妥善保密:切勿將其放入在瀏覽器中執行的程式碼。
建立請求
控制項是根據模型結構描述生成的。變更設定時,程式碼和價格會同步更新。
POST
/run/ltx-2.3-quality/text-to-audio/lora?wait=20
報價 → $0.70
# 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/ltx-2.3-quality/text-to-audio/lora?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"loras": [
{
"path": "https://example.com/your-file.safetensors"
}
]
}'
# 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 $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx-2.3-quality/text-to-audio/lora?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"loras": [
{
"path": "https://example.com/your-file.safetensors"
}
]
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"loras": [
{
"path": "https://example.com/your-file.safetensors",
},
],
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/ltx-2.3-quality/text-to-audio/lora", 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": "ltx-2.3-quality/text-to-audio/lora",
"input": {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"loras": [
{
"path": "https://example.com/your-file.safetensors"
}
]
},
"wait_seconds": 20
}
}
}
關於 Ltx 2.3 Quality · Text to Audio · LoRA
從文字 `prompt` 生成音訊,並要求最多 3 個自訂 LoRA 的 `loras` 清單,每個最多 3 GB。時長約為 `num_frames` 除以 `frames_per_second`,`num_frames` 介於 9 和 481 之間。也接受 `negative_prompt`、`seed`,以及範圍為 1 到 20 的 `guidance_scale`。
參數
名稱類型
預設值允許值
說明
prompt必填
類型string
描述要生成之音訊的提示詞。
num_frames選填
類型integer
預設值121
9 – 481
以 LTX 影片影格數指定的音訊時長;將 `num_frames` 除以 `frames_per_second`,即可取得約略的秒數。
frames_per_second選填
類型number
預設值24
1 – 60
用於計算輸出音訊時長的影格率。
num_inference_steps選填
類型integer
預設值15
8 – 30
推論步驟總數。
guidance_scale選填
類型number
預設值1
1 – 20
無分類器引導的強度。
negative_prompt選填
類型string
預設值pc game, console game, video game, cartoon, childish, ugly
指定生成應避免內容的負面提示詞。
seed選填
類型integer | null
使生成結果可重現的種子。設為 None 會隨機選擇種子。
enable_prompt_expansion選填
類型boolean
預設值false
控制是否啟用提示詞擴展。
enable_safety_checker選填
類型boolean
預設值true
控制是否啟用安全性檢查。
loras必填
類型object[]
≤ 3 個項目
LTX-2.3 的 LoRA 附加項,每個上限為 3 GB,最多允許 3 個項目。只有在 registry 的 SSRF 安全下載器擷取各路徑後才會載入。
loras[].path必填
類型string
≤ 2048 characters
不超過 3 GB 的 LoRA .safetensors 檔案直接 HTTP(S) URL。載入前會使用 SSRF 安全下載器擷取。
loras[].scale選填
類型number
預設值1
0 – 2
在 0.0–2.0 範圍內,此強度越高,LoRA 對生成影片的影響越大。
loras[].transformer選填
類型enum
預設值both
highlowboth
Transformer 選擇旗標:使用 LTX-2.3 的幾乎所有情況都適合 'both',因為模型只有一個 transformer。此旗標用於支援未來包含多個 transformer 的架構。
必填
輸出
GET /requests/req_8k2m4q7x1z9c3v5b6n0p2r4t
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "ltx-2.3-quality/text-to-audio/lora",
"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": 700,
"usd": "0.70",
"charged": 700,
"refunded": 0
},
"input": {
"loras": [
{
"path": "https://example.com/your-file.safetensors"
}
],
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
},
"output": {
"audio": {
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp3",
"content_type": "audio/mpeg",
"file_name": "0-output.mp3",
"file_size": 1843200
},
"seed": 42,
"prompt": "string"
},
"outputs": [
{
"type": "audio",
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp3",
"content_type": "audio/mpeg",
"file_size": 1843200,
"duration": 12,
"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"
}
}
您的檔案,由您選擇每個 outputs[].url 都是儲存空間中的副本:可永久保留(按 GB-月計費),或免費保留 7 天;可透過帳戶設定或 ?storage= 設定。
所有模型使用相同結構圖片、影片和音訊皆包含 type、url、content_type、width、height 和 duration。output 是模型自己的結果物件。
輸出欄位
- audio
- object {url, content_type, file_name, file_size}模型產生的音訊。
- seed
- integer生成期間實際套用的種子。
- prompt
- string生成提示詞,包括使用前套用的任何擴寫內容。
其他 LTX 2.3 Quality 變體
更多模型:音訊
給 AI 代理程式:此頁面的 Markdown 位於 https://doitong.com/zh-TW/developers/models/ltx-2.3-quality/text-to-audio/lora.md. 所有模型與指南: https://doitong.com/developers/llms.txt