Ltx 2.3 Quality · Decompression
集成
模型 id
ltx-2.3-quality/decompression
# 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/decompression?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
}'
# 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 $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx-2.3-quality/decompression?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
}
# 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/decompression", 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-CN/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/decompression",
"input": {
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
}
}
通过 Doitong API 调用 Ltx 2.3 Quality · Decompression 模型(ltx-2.3-quality/decompression)。智能体文档:https://doitong.com/zh-CN/developers/models/ltx-2.3-quality/decompression.md
POST https://api.doitong.ru/v1/run/ltx-2.3-quality/decompression?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/json
{
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
}
如果响应状态为 "queued" 或 "running",请轮询 GET https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20,直到状态变为 "succeeded";文件位于 outputs[].url。使用相同请求体 POST https://api.doitong.ru/v1/quote/ltx-2.3-quality/decompression,可在运行前返回确切价格。
与示例类似的文件: $0.27 270 API 积分 · ≈ ₽28.77 运行前会测量你的文件 在此页面运行
你的新 API 密钥。 它只会在此时显示,并且已填入下方的代码和 API 密钥字段中。请妥善保管:切勿将其放入在浏览器中运行的代码里。
构建请求
控件根据模型架构生成。修改控件时,代码和价格会同步更新。
POST
/run/ltx-2.3-quality/decompression?wait=20
报价 → $0.27
# 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/decompression?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
}'
# 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 $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ltx-2.3-quality/decompression?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4",
}
# 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/decompression", 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/decompression",
"input": {
"video": "https://content.doitong.com/video/showcase/v3/gen_fox_cartoon.mp4"
},
"wait_seconds": 20
}
}
}
关于 Ltx 2.3 Quality · Decompression
处理来自 `video_url` 的压缩且伪影严重的视频,生成更清晰的版本。可添加描述场景的简短 `prompt` 以指导结果。选择 480p 或 720p 输出;720p 可容纳约 6 秒,480p 约 15 秒。`num_frames` 接受 9 到 481,`generate_audio` 控制结果是否保留音轨。
参数
发送规范名称或原生名称之一,不要同时发送两者。 规范名称
名称类型
默认值允许值
描述
video_url必填
video
类型url · video
用于标识待转换源视频的 URL。
prompt可选
类型string
默认值
可选的简短场景或内容描述;为空时替换通用描述。为提升结果,提供的文本会加入该效果训练所用的标题中。
resolution可选
类型enum
默认值720p
480p720p
选择输出的短边分辨率,同时保留源宽高比:480p 对最长约 15 秒的片段会产生更柔和的结果,而 720p 对最长约 6 秒的片段会更清晰。较长片段请选择 480p,因为 IC-LoRA 控制路径限制分辨率与帧数的乘积;超出该预算的请求会返回明确错误。输出时长计算为 `num_frames` / `frames_per_second`。
num_frames可选
类型integer
默认值121
9 – 481
输出帧数,受 IC-LoRA 的分辨率与帧数乘积预算限制:720p 约 6 秒,480p 约 15 秒。超出预算时返回 422;请减少 `num_frames` 或选择 480p。片段时长(秒)等于 `num_frames` / `frames_per_second`。
frames_per_second可选
类型number
默认值24
1 – 60
输出视频帧率,以每秒帧数表示。
num_inference_steps可选
类型integer
默认值15
8 – 30
设置要运行的推理步数,最多 30;保持不变时使用 15。
guidance_scale可选
类型number
默认值1
1 – 20
使用此比例调整无分类器引导;默认调校兼顾高质量和快速生成。
generate_audio可选
类型boolean
默认值true
返回的 MP4 音轨开关:关闭时生成仅含视频的文件,开启时包含音频。
negative_prompt可选
类型string
默认值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
指定生成应避免内容的负面提示词。
seed可选
类型integer | null
使生成结果可复现的种子。将其设置为 None 会随机选择种子。
enable_safety_checker可选
类型boolean
默认值true
控制是否启用安全检查。
video_quality可选
类型enum
默认值high
lowmediumhighmaximum
应用于输出视频的质量预设。
video_write_mode可选
类型enum
默认值balanced
fastbalancedsmall
结果视频的写入模式。
必填
规范名称,也可接受
文件参数接受公开的 https:// URL、最大 10 MB 的 data: URI,或上传文件的 file_url。 文件
输出
GET /requests/req_8k2m4q7x1z9c3v5b6n0p2r4t
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "ltx-2.3-quality/decompression",
"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": 270,
"usd": "0.27",
"charged": 270,
"refunded": 0
},
"input": {
"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"
}
}
文件由你选择每个 outputs[].url 都是我们存储中的副本:可永久保存(按 GB-月付费),也可免费保存 7 天,由账户设置或 ?storage= 决定。
所有模型的结构一致图像、视频和音频的字段均为 type、url、content_type、width、height 和 duration。output 是模型自身的结果对象。
输出字段
- video
- object {url, content_type, file_name, file_size}模型生成的视频。
- seed
- integer生成期间实际使用的种子。
- prompt
- string生成提示词,包括使用前应用的任何扩展内容。
其他 LTX 2.3 Quality 变体
更多模型:视频生成视频
对于 AI 代理:此页面的 Markdown 版本位于 https://doitong.com/zh-CN/developers/models/ltx-2.3-quality/decompression.md. 所有模型和指南: https://doitong.com/developers/llms.txt