# LTX Video-0.9.7 13B Distilled · Multiconditioning API

> Video-til-video med billed- og videokonditionering, LoRA-understøttelse og to-pas-forfining med valgfri detaljepasning

Genererer en video fra en påkrævet `prompt`, styret af konditioneringsinput i `images` og `videos`. Brugerdefinerede LoRA'er kan angives i `loras`. Output er 480p eller 720p med `num_frames` fra 9 til 1441 og `frame_rate` fra 1 til 60. Separate trinantal for første og anden pasning, en valgfri detaljepasning og farvekontroller som `temporal_adain_factor` gør det muligt at finjustere resultatet.

- 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/da/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 |  |  | Tekstinstruktioner, der styrer genereringen. |
| `negative_prompt` |  | string | no | `worst quality, inconsistent motion, blurry, jittery, distorted` |  | Negative tekstinstruktioner til genereringen. |
| `loras` |  | array of object {path, weight_name, scale} | no | `[]` |  | LoRA-vægte, der anvendes under genereringen. |
| `loras[].path` |  | string | yes |  |  | Placering af LoRA-vægtene angivet som en URL eller sti. |
| `loras[].weight_name` |  | string \| null | no |  |  | LoRA-vægtnavn til valg mellem flere vægte i et HuggingFace-repository; kun repositories med flere vægte kræver det. Indstillingen har ingen effekt, medmindre `path` identificerer et HuggingFace-repository. |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | Multiplikator, der skalerer LoRA-vægten, når den indlæses. |
| `resolution` |  | enum (string) | no | `720p` | `480p`, `720p` | Opløsning for outputvideoen. |
| `aspect_ratio` |  | enum (string) | no | `auto` | `9:16`, `1:1`, `16:9`, `auto` | Videoens højde-bredde-forhold. |
| `seed` |  | integer \| null | no |  |  | Seed, der styrer tilfældigheden under genereringen. |
| `num_frames` |  | integer | no | `121` | 9 – 1,441 | Samlet antal frames i videoen. |
| `first_pass_num_inference_steps` |  | integer | no | `8` | 2 – 12 | Antal inferenstrin for første gennemløb. |
| `second_pass_num_inference_steps` |  | integer | no | `8` | 2 – 12 | Antal inferenstrin for andet gennemløb. |
| `second_pass_skip_initial_steps` |  | integer | no | `5` | 1 – 11 | Indledende antal inferenstrin, der udelades under andet gennemløb, så dette gennemløb fokuserer mere på fine detaljer end på store ændringer. |
| `frame_rate` |  | integer | no | `24` | 1 – 60 | Framerate for videoen. |
| `expand_prompt` |  | boolean | no | `false` |  | Promptudvidelse via en sprogmodel. |
| `reverse_video` |  | boolean | no | `false` |  | Vending af videoens afspilningsrækkefølge. |
| `enable_safety_checker` |  | boolean | no | `true` |  | Kontakt til sikkerhedskontrol, hvis deaktiverede indstilling kun respekteres for godkendte konti. Alle anmodninger uden denne godkendelse gennemgår sikkerhedskontrol. |
| `enable_detail_pass` |  | boolean | no | `false` |  | Valgfrit andet forbedringsgennemløb, der forbedrer videodetaljer, når det aktiveres. |
| `temporal_adain_factor` |  | number | no | `0.5` | 0 – 1 | Styrke for adaptiv instansnormalisering (AdaIN), der afvejer farvekonsistens ved højere indstillinger mod større farvevariation ved lavere indstillinger. Den anvendes på genererede bidder bortset fra den første og normaliserer farvefordelinger gennem hele videoen for at modvirke en gradvis stigning i mætning eller kontrast. |
| `tone_map_compression_ratio` |  | number | no | `0` | 0 – 1 | Komprimeringsniveau for tonemapping, fra ingen komprimering ved 0.0 til maksimal komprimering ved 1.0. Denne komprimering af videoens dynamiske område forbedrer den visuelle kvalitet. |
| `constant_rate_factor` |  | integer | no | `29` | 0 – 51 | Constant rate factor (CRF), der bruges til inputkomprimering og kan forbedre bevægelseskvaliteten ved at bringe de leverede medier tættere på modellens træningsdata. |
| `images` |  | array of object {image_url, start_frame_num, strength} | no | `[]` |  | Konditioneringsbilleder leveret via URL. |
| `images[].image_url` |  | string (image URL) | yes |  |  | Konditioneringsbillede leveret via URL. |
| `images[].start_frame_num` |  | integer | no | `0` | 0 – 1,440 | Startposition for billedkonditionering angivet med et framenummer, der kan divideres med 8. |
| `images[].strength` |  | number | no | `1` | 0 – 1 | Konditioneringsintensitet: 0.0 deaktiverer konditionering, mens 1.0 anvender den fuldt ud. |
| `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 | `[]` |  | Videoer leveret til konditionering. |
| `videos[].video_url` |  | string (video URL) | yes |  |  | Konditioneringsvideo leveret via URL. |
| `videos[].conditioning_type` |  | enum (string) \| null | no | `rgb` | `rgb`, `depth`, `pose`, `canny` | Kategori for videokonditionering, der bruges til at vælge præprocesseringspipeline, når præprocessering er aktiveret, og til at anvende in-context-LoRA-vægte korrekt. |
| `videos[].preprocess` |  | boolean | no | `false` |  | Aktiverer præprocessering, så den matcher videoens konditioneringstype. Ved RGB-konditionering medfører aktivering ingen ændringer. |
| `videos[].start_frame_num` |  | integer | no | `0` | 0 – 1,440 | Konditioneringens startposition i videoen angivet som et framenummer, der kan divideres med 8. |
| `videos[].strength` |  | number | no | `1` | 0 – 1 | Konditioneringsintensitet: 0.0 deaktiverer konditionering, mens 1.0 anvender den fuldt ud. |
| `videos[].limit_num_frames` |  | boolean | no | `false` |  | Kontakt for videoframegrænse: aktivering gør `max_num_frames` til grænsen for frames, der hentes fra videoen. |
| `videos[].max_num_frames` |  | integer | no | `1441` | 1 – 1,441 | Grænse for antal videoframes, der må bruges, hvor None vælger hele videoen i stedet for et begrænset frameantal. |
| `videos[].resample_fps` |  | boolean | no | `false` |  | Kontakt for konvertering af videoframerate: aktivering omsampler videoen med den rate, der er angivet af `target_fps`. |
| `videos[].target_fps` |  | integer | no | `24` | 1 – 60 | Framerate ved omsampling i FPS, kun gældende når `resample_fps` er aktiveret. |
| `videos[].reverse_video` |  | boolean | no | `false` |  | Vending af videoens rækkefølge til opgaver, hvor konditioneringen skal følge frames baglæns. |

## 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} | Videofil produceret ved generering. |
| `prompt` | string | Prompten, der blev brugt til at generere outputtet. |
| `seed` | integer | Seed-værdi anvendt under generering. |

## 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