Ltx 2.3 Quality · Reference Video to Video · LoRA
整合
模型 id
ltx-2.3-quality/reference-video-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-quality/reference-video-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",
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
"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 $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx-2.3-quality/reference-video-to-video/lora?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
"loras": [
{
"path": "https://example.com/your-file.safetensors"
}
]
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + 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",
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
"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/reference-video-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/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/reference-video-to-video/lora",
"input": {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
"loras": [
{
"path": "https://example.com/your-file.safetensors"
}
]
}
}
透過 Doitong API 呼叫 Ltx 2.3 Quality · Reference Video to Video · LoRA 模型(ltx-2.3-quality/reference-video-to-video/lora)。代理程式文件:https://doitong.com/zh-TW/developers/models/ltx-2.3-quality/reference-video-to-video/lora.md
POST https://api.doitong.ru/v1/run/ltx-2.3-quality/reference-video-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",
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
"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/reference-video-to-video/lora,可在執行前取得確切價格。
像範例一樣的檔案: $0.304 304 API 點數 · ≈ ₽32.62 您的檔案會在執行前計量 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/reference-video-to-video/lora?wait=20
報價 → $0.304
# 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/reference-video-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",
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
"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 $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx-2.3-quality/reference-video-to-video/lora?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
"loras": [
{
"path": "https://example.com/your-file.safetensors"
}
]
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + 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",
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
"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/reference-video-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-quality/reference-video-to-video/lora",
"input": {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
"loras": [
{
"path": "https://example.com/your-file.safetensors"
}
]
},
"wait_seconds": 20
}
}
}
關於 Ltx 2.3 Quality · Reference Video to Video · LoRA
根據必要的 `prompt` 與用於提供動態和結構的參考 `video_url` 生成影片,並另外需要 `loras`,最多 3 個 LoRA 檔案,每個最大 3 GB。可選的首幀、中間與末幀關鍵影格圖片(`image_url`、`mid_image_url`、`end_image_url`)會固定外觀。`video_strength` 與 `strength` 範圍為 0 至 1,`num_frames` 範圍為 9 至 481。
參數
請傳送標準名稱或原生名稱其中之一,不要同時傳送兩者。 標準名稱
名稱類型
預設值允許值
說明
prompt必填
類型string
用於引導生成的提示詞。
video_url必填
video
類型url · video
指向用於引導動態與結構之參考影片的 URL。
control_video_url選填
類型url · video
選用的預先計算控制影片;與 `skip_control_preprocess` 一起提供時,會取代內建控制估算。重新取樣以符合輸出尺寸與影格數後,會成為直接控制訊號。例如,影片可以包含已算好的深度、邊緣或姿勢合成結果。
skip_control_preprocess選填
類型boolean
預設值false
使用 `control_video_url` 的直接控制模式,不在內部估算深度、邊緣或姿勢;未提供該 URL 時會忽略此設定。此模式的輸出尺寸遵循明確指定的尺寸並以 64 對齊,以保持 RGB 與控制影片一致;`auto` 則在 LTX 限制內採用來源尺寸與長寬比。
preserve_original_video選填
類型boolean
預設值false
原始影片保留模式,僅適用於 `skip_control_preprocess`、`video_url` 與 `control_video_url`。預設停用時會維持標準控制處理;啟用後會從 `video_url` 開始生成,同時保留 `control_video_url` 的結構引導。透過 `strength` 設定去噪程度,以控制保留量;較低的值會保留更多原始像素。
image_url選填
image
類型url · image
作為第一個影格關鍵影格的選用圖像錨定風格或角色身分。
mid_image_url選填
類型url · image
片段中點附近的選用圖像錨定外觀,用於補充初始的 `image_url` 關鍵影格。可協助外觀跟隨鏡頭移動而非逐漸偏移;需要 `image_url`。
end_image_url選填
end_image
類型url · image
片段最終影格的選用圖像錨定外觀。當鏡頭移動結束於不同於起始圖像的位置時很有用;需要 `image_url`。
video_strength選填
類型number
預設值0.6
0 – 1
此條件強度控制可從參考影片改變動態與結構的自由度:較小的值允許更大的變化。
strength選填
類型number
預設值1
0 – 1
參考影片轉影片取樣的去噪控制,其效果取決於 `preserve_original_video`。模式關閉時只會縮短去噪排程;啟用時則控制來源像素的轉換,1.0 會全部重新生成,較低的值會保留更多內容(例如 0.5 約保留 50%)。
num_frames選填
類型integer
預設值121
9 – 481
輸出影格數,受以解析度乘以影格數計算的 IC-LoRA 穩定性預算限制。422 回應表示超出預算:請降低解析度或 `num_frames`。此預算約允許 720p 等級尺寸下的 150 個影格,較小尺寸則可更多。
resolution選填
類型object
預設值auto
autosquare_hdsquareportrait_4_3portrait_16_9landscape_4_3landscape_16_9
輸出影片尺寸,也會影響穩定時長:較小尺寸允許更多影格,較大尺寸則降低上限。內建控制估算會使尺寸接近官方控制流程;透過 `skip_control_preprocess` 與 `control_video_url` 的直接控制則遵循明確尺寸並以 64px 對齊。直接控制模式中,`auto` 會在 LTX 限制內沿用來源尺寸與長寬比。
frames_per_second選填
類型number
預設值24
1 – 60
輸出影片影格率,以每秒影格數表示。
num_inference_steps選填
類型integer
預設值15
8 – 30
設定要執行的推論步驟數,最多 30;維持不變時使用 15。
guidance_scale選填
類型number
預設值1
1 – 20
使用此尺度調整無分類器引導;預設調校同時以高品質和快速生成為目標。
generate_audio選填
類型boolean
預設值true
返回 MP4 的音訊軌切換:關閉會產生僅含影片的檔案,開啟則包含音訊。
negative_prompt選填
類型string
預設值color distortion, overexposure, static, blurry details, subtitles, style, artwork, painting, frame, still, dim overall tone, worst quality, low quality, JPEG compression artifacts, ugly, mutilated, extra fingers, poorly drawn hands, poorly drawn face, deformed, disfigured, malformed limbs, fused fingers, motionless frame, cluttered background, three legs, crowded background, walking backwards
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true
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。
video_quality選填
類型enum
預設值high
lowmediumhighmaximum
套用至輸出影片的品質預設。
video_write_mode選填
類型enum
預設值balanced
fastbalancedsmall
結果影片的寫入模式。
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
標準名稱,也可使用
檔案參數接受公開的 https:// URL、最大 10 MB 的 data: URI,或上傳檔案的 file_url。 檔案
輸出
GET /requests/req_8k2m4q7x1z9c3v5b6n0p2r4t
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "ltx-2.3-quality/reference-video-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": 304,
"usd": "0.304",
"charged": 304,
"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",
"video_url": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
},
"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
},
"seed": 42,
"prompt": "string"
},
"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"
}
}
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
- video
- object {url, content_type, file_name, file_size}模型產生的影片。
- 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:影片轉影片
給 AI 代理程式:此頁面的 Markdown 位於 https://doitong.com/zh-TW/developers/models/ltx-2.3-quality/reference-video-to-video/lora.md. 所有模型與指南: https://doitong.com/developers/llms.txt