# LTX Video-0.9.7 13B Distilled · Multiconditioning API

> Video-to-video yenye masharti ya picha na video, usaidizi wa LoRA na uboreshaji wa awamu mbili wenye awamu ya hiari ya maelezo

Hutengeneza video kutoka kwa `prompt` inayohitajika, ikiongozwa na ingizo la masharti katika `images` na `videos`. LoRA maalum zinaweza kupitishwa katika `loras`. Matokeo ni 480p au 720p, yakiwa na `num_frames` kati ya 9 na 1441 na `frame_rate` kati ya 1 na 60. Idadi tofauti ya hatua kwa awamu ya kwanza na ya pili, awamu ya hiari ya maelezo na vidhibiti vya rangi kama `temporal_adain_factor` huruhusu kurekebisha matokeo.

- Model id: `ltx-video-13b-distilled/multiconditioning`
- Made by: Lightricks
- Category: Video to Video
- Output: video
- Price per second of your output video: from $0.04 / second of output video (cheapest run $0.04 (40 API credits))
- Run: `POST https://api.doitong.ru/v1/run/ltx-video-13b-distilled/multiconditioning`
- Quote: `POST https://api.doitong.ru/v1/quote/ltx-video-13b-distilled/multiconditioning`
- Docs page: https://doitong.com/sw/developers/models/ltx-video-13b-distilled/multiconditioning

## Pricing

Price: **from $0.04 / second of output video** (the lowest tier; the quote prices your exact input). Price per second of your output video: the input files are measured before the run (length, resolution, frame rate). The quote measures the same files and is exactly what the run charges.

The full example input below costs $0.202 = 202 API credits (5.0417 s output video × $0.04).

Prices are the upstream price in USD, paid from the API balance in API credits (1000 API credits = 1 USD). `POST https://api.doitong.ru/v1/quote/ltx-video-13b-distilled/multiconditioning` with the same JSON body returns the exact price for that input; the quote is exactly what a run charges. Invalid input is never charged and failed or canceled requests are refunded automatically.

## Input parameters

| Parameter | Canonical name | Type | Required | Default | Allowed values / range | Description |
|---|---|---|---|---|---|---|
| `prompt` |  | string | yes |  |  | Maagizo ya maandishi yanayoongoza uzalishaji. |
| `negative_prompt` |  | string | no | `worst quality, inconsistent motion, blurry, jittery, distorted` |  | Maagizo hasi ya maandishi kwa uzalishaji. |
| `loras` |  | array of object {path, weight_name, scale} | no | `[]` |  | Uzito wa LoRA unaotumika wakati wa uzalishaji. |
| `loras[].path` |  | string | yes |  |  | Mahali pa uzito wa LoRA, panayotolewa kama URL au njia. |
| `loras[].weight_name` |  | string \| null | no |  |  | Jina la uzani wa LoRA kwa kuchagua kati ya uzani wengi katika hazina ya HuggingFace; ni hazina zilizo na uzani wengi pekee zinahitaji hii. Mpangilio huu hauna athari isipokuwa `path` itambue hazina ya HuggingFace. |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | Kizidishi kinachopima uzito wa LoRA unapopakiwa. |
| `resolution` |  | enum (string) | no | `720p` | `480p`, `720p` | Mwonekano wa video ya tokeo. |
| `aspect_ratio` |  | enum (string) | no | `auto` | `9:16`, `1:1`, `16:9`, `auto` | Uwiano wa picha wa video. |
| `seed` |  | integer \| null | no |  |  | Mbegu inayodhibiti nasibu wakati wa uzalishaji. |
| `num_frames` |  | integer | no | `121` | 9 – 1,441 | Jumla ya idadi ya fremu za video. |
| `first_pass_num_inference_steps` |  | integer | no | `8` | 2 – 12 | Idadi ya hatua za uelekezaji kwa upitishaji wa kwanza. |
| `second_pass_num_inference_steps` |  | integer | no | `8` | 2 – 12 | Idadi ya hatua za uelekezaji kwa upitishaji wa pili. |
| `second_pass_skip_initial_steps` |  | integer | no | `5` | 1 – 11 | Idadi ya awali ya hatua za uelekezaji za kuacha wakati wa upitishaji wa pili, ikielekeza upitishaji huo kwenye maelezo madogo badala ya mabadiliko makubwa. |
| `frame_rate` |  | integer | no | `24` | 1 – 60 | Kasi ya fremu ya video. |
| `expand_prompt` |  | boolean | no | `false` |  | Upanuzi wa kidokezo kupitia modeli ya lugha. |
| `reverse_video` |  | boolean | no | `false` |  | Kubadilisha mpangilio wa uchezaji wa video. |
| `enable_safety_checker` |  | boolean | no | `true` |  | Swichi ya ukaguzi wa usalama ambayo mpangilio wake wa kuzimwa huheshimiwa tu kwa akaunti zilizoidhinishwa. Maombi yote bila uidhinishaji huo hukaguliwa usalama. |
| `enable_detail_pass` |  | boolean | no | `false` |  | Upitishaji wa pili wa hiari wa uboreshaji unaoboresha maelezo ya video unapowashwa. |
| `temporal_adain_factor` |  | number | no | `0.5` | 0 – 1 | Nguvu ya urekebishaji wa kawaida wa kisaidizi (AdaIN), inayosawazisha uthabiti wa rangi kwenye mipangilio ya juu dhidi ya utofauti mkubwa wa rangi kwenye mipangilio ya chini. Hufanya kazi kwenye vipande vilivyozalishwa isipokuwa cha kwanza, ikirekebisha mgawanyo wa rangi kote kwenye video ili kusaidia kukabili ongezeko la taratibu la mjazo au utofautishaji. |
| `tone_map_compression_ratio` |  | number | no | `0` | 0 – 1 | Kiwango cha mgandamizo wa ramani ya toni, kutoka bila mgandamizo kwenye 0.0 hadi mgandamizo wa juu kwenye 1.0. Kugandamiza masafa badilifu ya video kwa njia hii huboresha ubora wa kuona. |
| `constant_rate_factor` |  | integer | no | `29` | 0 – 51 | Kipengele cha kiwango thabiti (CRF) kinachotumiwa kwa mgandamizo wa ingizo, ambacho kinaweza kuboresha ubora wa mwendo kwa kuleta media iliyotolewa karibu na data ya mafunzo ya modeli. |
| `images` |  | array of object {image_url, start_frame_num, strength} | no | `[]` |  | Picha za urekebishaji zilizotolewa kupitia URL. |
| `images[].image_url` |  | string (image URL) | yes |  |  | Picha ya urekebishaji iliyotolewa kupitia URL. |
| `images[].start_frame_num` |  | integer | no | `0` | 0 – 1,440 | Nafasi ya kuanzia ya urekebishaji wa picha, iliyobainishwa kwa kutumia nambari ya fremu inayogawanyika kwa 8. |
| `images[].strength` |  | number | no | `1` | 0 – 1 | Nguvu ya urekebishaji: 0.0 huzima urekebishaji, huku 1.0 ikiutumia kikamilifu. |
| `videos` |  | array of object {video_url, conditioning_type, preprocess, start_frame_num, strength, limit_num_frames, max_num_frames, resample_fps, target_fps, reverse_video} | no | `[]` |  | Video zinazotolewa kwa urekebishaji. |
| `videos[].video_url` |  | string (video URL) | yes |  |  | Video ya urekebishaji iliyotolewa kupitia URL. |
| `videos[].conditioning_type` |  | enum (string) \| null | no | `rgb` | `rgb`, `depth`, `pose`, `canny` | Aina ya urekebishaji wa video inayotumiwa kuchagua mtiririko wa uchakataji awali wakati uchakataji huo umewezeshwa, na kutumia uzito wa LoRA wa ndani ya muktadha kwa usahihi. |
| `videos[].preprocess` |  | boolean | no | `false` |  | Huwezesha uchakataji awali ili kuendana na aina ya urekebishaji wa video. Kwa urekebishaji wa RGB, kuiwasha hakufanyi mabadiliko. |
| `videos[].start_frame_num` |  | integer | no | `0` | 0 – 1,440 | Nafasi ya kuanzia ya urekebishaji kwenye video, iliyobainishwa kama nambari ya fremu inayogawanyika kwa 8. |
| `videos[].strength` |  | number | no | `1` | 0 – 1 | Nguvu ya urekebishaji: 0.0 huzima urekebishaji, huku 1.0 ikiutumia kikamilifu. |
| `videos[].limit_num_frames` |  | boolean | no | `false` |  | Kigeuzi cha kikomo cha fremu za video: kukiwasha hufanya `max_num_frames` kuwa kikomo cha fremu zinazochukuliwa kwenye video. |
| `videos[].max_num_frames` |  | integer | no | `1441` | 1 – 1,441 | Kikomo cha fremu za video za kutumia, ambapo None huchagua video nzima badala ya idadi yenye kikomo ya fremu. |
| `videos[].resample_fps` |  | boolean | no | `false` |  | Kigeuzi cha ubadilishaji wa kasi ya fremu za video: kukiwasha huchukua sampuli upya za video kwa kasi iliyobainishwa na `target_fps`. |
| `videos[].target_fps` |  | integer | no | `24` | 1 – 60 | Kasi ya fremu ya kuchukua sampuli upya katika FPS, inayotumika tu `resample_fps` ikiwa imewashwa. |
| `videos[].reverse_video` |  | boolean | no | `false` |  | Kubadilisha mpangilio wa video kwa kazi zinazohitaji urekebishaji ufuate fremu kwa kurudi nyuma. |

## Example input

Required parameters only:

```json
{
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
```

A fuller example:

```json
{
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
}
```

## Run it

Set `DOITONG_API_KEY` to a key from https://doitong.com/developers/dashboard#keys. `?wait=20` returns the finished request in the same call when it is ready within 20 seconds; otherwise poll `GET /requests/{id}` (it also accepts `?wait=`), or pass `webhook_url`.

### curl

```bash
# 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-video-13b-distilled/multiconditioning?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"
  }'

# 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"
```

### JavaScript

```javascript
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-video-13b-distilled/multiconditioning?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"
  }),
});
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 + ")");
```

### Python

```python
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",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/ltx-video-13b-distilled/multiconditioning", 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"])
```

### MCP

The MCP server at `https://api.doitong.ru/v1/mcp` exposes the tools `search_models`, `get_model`, `quote`, `run`, `get_request`, `cancel_request`, `list_requests`, `create_upload` and `get_account`. A `run` call for this model:

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "run",
    "arguments": {
      "model_id": "ltx-video-13b-distilled/multiconditioning",
      "input": {
        "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
      },
      "wait_seconds": 20
    }
  }
}
```

## Response

Every call returns a request object. `status` is one of `queued`, `running`, `succeeded`, `failed`, `canceled`. `outputs` lists the generated files with URLs on our storage (kept forever, or 7 days with `?storage=temporary`; `storage` says which); `output` is the model's own result object with the same URLs.

```json
{
  "id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
  "object": "request",
  "model": "ltx-video-13b-distilled/multiconditioning",
  "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": 202,
    "usd": "0.202",
    "charged": 202,
    "refunded": 0
  },
  "input": {
    "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
    },
    "prompt": "A vibrant, abstract composition featuring a person with outstretched arms, rendered in a kaleidoscope of colors against a deep, dark background. The figure is composed of intricate, swirling patterns reminiscent of a mosaic, with hues of orange, yellow, blue, and green that evoke the style of artists such as Wassily Kandinsky or Bridget Riley. The camera zooms into the face striking portrait of a man, reimagined through the lens of old-school video-game graphics. The subject's face is rendered in a kaleidoscope of colors, with bold blues and reds set against a vibrant yellow backdrop. His dark hair is pulled back, framing his profile in a dramatic pose.",
    "seed": 42
  },
  "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"
  }
}
```

### Output fields (`output`)

| Field | Type | Description |
|---|---|---|
| `video` | object {url, content_type, file_name, file_size} | Faili ya video iliyozalishwa. |
| `prompt` | string | Kidokezo kilichotumika kutengeneza tokeo. |
| `seed` | integer | Thamani ya seed inayotumika wakati wa uzalishaji. |

## Related models

- [LTX Video-0.9.7 13B Distilled](https://doitong.com/developers/models/ltx-video-13b-distilled.md) (`ltx-video-13b-distilled`): from $0.04 / second of output video
- [LTX Video-0.9.7 13B Distilled · Image to Video](https://doitong.com/developers/models/ltx-video-13b-distilled/image-to-video.md) (`ltx-video-13b-distilled/image-to-video`): from $0.04 / second of output video
- [LTX Video-0.9.7 13B Distilled · Extend](https://doitong.com/developers/models/ltx-video-13b-distilled/extend.md) (`ltx-video-13b-distilled/extend`): $0.04 / video
- [Ltx 2.3](https://doitong.com/developers/models/ltx-2.3/reframe.md) (`ltx-2.3/reframe`): from $0.10 / second of input video
- [LTX 2.3 22B](https://doitong.com/developers/models/ltx-2.3-22b/reference-video-to-video/lora.md) (`ltx-2.3-22b/reference-video-to-video/lora`): $0.00181 / megapixel of output video
- [Ltx 2.3 Quality · Clean Plate](https://doitong.com/developers/models/ltx-2.3-quality/clean-plate.md) (`ltx-2.3-quality/clean-plate`): $0.00241 / megapixel of output video
- [Ltx 2.3 Quality · Colorization](https://doitong.com/developers/models/ltx-2.3-quality/colorization.md) (`ltx-2.3-quality/colorization`): $0.00241 / megapixel of output video
- [Ltx 2.3 Quality · Cross Eyed](https://doitong.com/developers/models/ltx-2.3-quality/cross-eyed.md) (`ltx-2.3-quality/cross-eyed`): $0.00241 / megapixel of output video

## Links

- HTML page: https://doitong.com/developers/models/ltx-video-13b-distilled/multiconditioning
- All models and guides for AI agents: https://doitong.com/developers/llms.txt
- OpenAPI 3.1 with this model's typed request body: https://doitong.com/developers/openapi.json?model=ltx-video-13b-distilled/multiconditioning
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.ru/v1/models/ltx-video-13b-distilled/multiconditioning