LTX-2.3 22B · Text to Video · LoRA
Integrēt
ltx-2.3-22b/text-to-video/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-2.3-22b/text-to-video/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 $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx-2.3-22b/text-to-video/lora?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"loras": [
{
"path": "https://example.com/your-file.safetensors"
}
]
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + 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-2.3-22b/text-to-video/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/lv/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-2.3-22b/text-to-video/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"
}
]
}
}
Izsauciet LTX-2.3 22B · Text to Video · LoRA modeli (ltx-2.3-22b/text-to-video/lora), izmantojot Doitong API. Dokumentācija aģentiem: https://doitong.com/lv/developers/models/ltx-2.3-22b/text-to-video/lora.md
POST https://api.doitong.ru/v1/run/ltx-2.3-22b/text-to-video/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"
}
]
}
Ja atbildes statuss ir "queued" vai "running", periodiski izsauciet GET https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20, līdz tas kļūst par "succeeded"; faili atrodas outputs[].url. POST https://api.doitong.ru/v1/quote/ltx-2.3-22b/text-to-video/lora ar to pašu pamattekstu atgriež precīzu cenu pirms izpildes.
Faili, piemēram, paraugā: 0,203 $ 203 API kredīti · ≈ 21,78 ₽ jūsu faili tiek izmērīti pirms izpildes Izpildīt šajā lapā
Jūsu jaunā API atslēga. Tā tiek parādīta tikai tagad un jau ir ievietota kodā un tālāk esošajā API atslēgas laukā. Glabājiet to noslēpumā: nekad neievietojiet to kodā, kas darbojas pārlūkprogrammā.
Šeit būs jūsu pirmais rezultāts
Izveidojiet pieprasījumu tālāk un izpildiet to ar savu API atslēgu. Izvades tiek kopētas mūsu krātuvē un glabātas mūžīgi vai 7 dienas bez maksas.
Izveidot pieprasījumu
Vadīklas tiek ģenerētas no modeļa shēmas. Kods un cena tiek atjaunināti, kad tos maināt.
/run/ltx-2.3-22b/text-to-video/lora?wait=20
cena → 0,203 $
# 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-2.3-22b/text-to-video/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 $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx-2.3-22b/text-to-video/lora?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"loras": [
{
"path": "https://example.com/your-file.safetensors"
}
]
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + 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-2.3-22b/text-to-video/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-2.3-22b/text-to-video/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
}
}
}
Palaist šo pieprasījumu
Jūsu atslēga paliek šajā pārlūkprogrammā: tā tiek nosūtīta tikai uz Doitong API un saglabāta (localStorage) tikai tad, ja atzīmējat Atcerēties. Iegūt API atslēgu
Par LTX-2.3 22B · Text to Video · LoRA
Izmanto `prompt` un obligātu pielāgotu LoRA `loras` sarakstu. `video_size` piedāvā kvadrāta, portreta un ainavas iestatījumus, `num_frames` ir no 9 līdz 481, bet `fps` — no 1 līdz 60. Pieejama audio ģenerēšana, kameras LoRA un pirmā soļa destilācijas LoRA mērogs.
Parametri
promptobligāts
num_framesneobligāts
video_sizeneobligāts
generate_audioneobligāts
use_multiscaleneobligāts
fpsneobligāts
num_inference_stepsneobligāts
video_cfg_scaleneobligāts
video_stg_scaleneobligāts
video_rescaling_scaleneobligāts
video_modality_scaleneobligāts
audio_cfg_scaleneobligāts
audio_stg_scaleneobligāts
audio_rescaling_scaleneobligāts
audio_modality_scaleneobligāts
gradient_estimation_gammaneobligāts
use_restart_samplingneobligāts
schedulerneobligāts
accelerationneobligāts
camera_loraneobligāts
camera_lora_scaleneobligāts
negative_promptneobligāts
seedneobligāts
enable_prompt_expansionneobligāts
enable_safety_checkerneobligāts
video_output_typeneobligāts
video_qualityneobligāts
video_write_modeneobligāts
lorasobligāts
loras[].pathobligāts
loras[].weight_nameneobligāts
loras[].scaleneobligāts
distill_lora_first_pass_scaleneobligāts
distill_lora_second_pass_scaleneobligāts
Izvade
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "ltx-2.3-22b/text-to-video/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": 203,
"usd": "0.203",
"charged": 203,
"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": {
"video": {
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp4",
"content_type": "video/mp4",
"file_name": "0-output.mp4",
"file_size": 1843200,
"width": 1024,
"height": 768,
"fps": 1.0,
"duration": 5,
"num_frames": 1
},
"seed": 42,
"prompt": "A cowboy walking through a dusty town at high noon, camera following from behind, cinematic depth, realistic lighting, western mood, 4K film grain."
},
"outputs": [
{
"type": "video",
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp4",
"content_type": "video/mp4",
"file_size": 1843200,
"width": 1280,
"height": 720,
"duration": 5,
"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"
}
}
Izvades lauki
- video
- object {url, content_type, file_name, file_size, width, height, fps, duration, num_frames}Modeļa izveidotais video.
- seed
- integerSēklas vērtība, ko izmanto nejaušo skaitļu ģenerēšanas inicializēšanai.
- prompt
- stringUzvedne, kas vada ģenerēšanu.
Citi LTX 2.3 22b varianti
Vairāk modeļu: Teksts uz video
AI aģentiem: šī lapa Markdown formātā ir pieejama https://doitong.com/lv/developers/models/ltx-2.3-22b/text-to-video/lora.md. Visi modeļi un ceļveži: https://doitong.com/developers/llms.txt