# LTX-Video 13B 0.9.8 Distilled · Multiconditioning API

> 0.9.8 distilled 13B keripe kondicionirime jekhetane pe but kipo thaj video, plus jekh prompto

Kerel video kethane le kampel `prompt` thaj listenca e conditioning `images` thaj `videos`, kaj but referenci šaj te keren jekh rezultato. O rezultato si 480p vaj 720p, `aspect_ratio`-esa auto, 9:16, 1:1 vaj 16:9. `num_frames` džal 9 dži 1441, thaj `loras` thovel custom pharipen.

- Model id: `ltxv-13b-098-distilled/multiconditioning`
- Made by: Lightricks
- Category: Video to Video
- Output: video
- Price per second of your output video: from $0.02 / second of output video (cheapest run $0.02 (20 API credits))
- Run: `POST https://api.doitong.ru/v1/run/ltxv-13b-098-distilled/multiconditioning`
- Quote: `POST https://api.doitong.ru/v1/quote/ltxv-13b-098-distilled/multiconditioning`
- Docs page: https://doitong.com/rom/developers/models/ltxv-13b-098-distilled/multiconditioning

## Pricing

Price: **from $0.02 / 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 $0.101 = 101 API credits (5.0417 s output video × $0.02).

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/ltxv-13b-098-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 |  |  | Textoske instrukcije save guidin e keripe. |
| `negative_prompt` |  | string | no | `worst quality, inconsistent motion, blurry, jittery, distorted` |  | Negativno textoske instrukcije vašo keripe. |
| `loras` |  | array of object {path, weight_name, scale} | no | `[]` |  | LoRA weights save aplikuil pes andro keripe. |
| `loras[].path` |  | string | yes |  |  | Than e LoRA weights-enge, dino sar URL vaj path. |
| `loras[].weight_name` |  | string \| null | no |  |  | LoRA weight nav vaš alosaripen maškar buter weights ande HuggingFace repository; numa repository kaj si buter weights mangel les. Kadava settingo na kerel khanchi te `path` na sikavel HuggingFace repository. |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | Multiplier savo skalil e LoRA weight kana si load-ime. |
| `resolution` |  | enum (string) | no | `720p` | `480p`, `720p` | Rezolucija e rezultato-videoski. |
| `aspect_ratio` |  | enum (string) | no | `auto` | `9:16`, `1:1`, `16:9`, `auto` | Videosko aspect ratio. |
| `seed` |  | integer \| null | no |  |  | Seed savo kontroluil e randomnipen andro keripe. |
| `num_frames` |  | integer | no | `121` | 9 – 1,441 | Sa e videoske kadro-gind. |
| `first_pass_num_inference_steps` |  | integer | no | `8` | 2 – 12 | Inference step-gind vašo angluno pass. |
| `second_pass_num_inference_steps` |  | integer | no | `8` | 2 – 12 | Inference step-gind vašo dujto pass. |
| `second_pass_skip_initial_steps` |  | integer | no | `5` | 1 – 11 | Angluno inference-step-gind savo si te na lel andro dujto pass, savo kerel les maj-paše fine-detail keripasko na bare paruvipasko. |
| `frame_rate` |  | integer | no | `24` | 1 – 60 | Kadro rate vašo video. |
| `expand_prompt` |  | boolean | no | `false` |  | Promptosko bararipen language-modelosa. |
| `reverse_video` |  | boolean | no | `false` |  | Videoske playbackosko redosko vorbipen. |
| `enable_safety_checker` |  | boolean | no | `true` |  | Preklopnik vaš sigurnosno proveripen, savo off postavka si respektovano numa autorizirime kontenca. Sa mangipena bi kadi autorizacia džan ande sigurnosne provere. |
| `enable_detail_pass` |  | boolean | no | `false` |  | Opcionalno dujto refinement pass savo lačharel video-detaila kana si aktivno. |
| `temporal_adain_factor` |  | number | no | `0.5` | 0 – 1 | Adaptive instance normalization (AdaIN) zor, balancuil e rangi-jekhetaresipen baro settingurenca thaj maj-bari rangi-paruvipen tele settingurenca. Kerel pes pe generated chunks, numa na pe angluno; normalizuil e rangi-distribucije savore videosko, te lokharel progresivno saturation vaj contrast bararipen. |
| `tone_map_compression_ratio` |  | number | no | `0` | 0 – 1 | Tone-mapping compression level, 0.0-atar bi compression dži maximum compression 1.0-te. Kadava video dynamic-range compression lačharel e vizualno kvaliteta. |
| `constant_rate_factor` |  | integer | no | `29` | 0 – 51 | Constant rate factor (CRF) inputo-compressionoske, savo šaj te lačharel motion kvaliteta thaj te anel e dino media maj-paše modeloske training-datendar. |
| `images` |  | array of object {image_url, start_frame_num, strength} | no | `[]` |  | Conditioning kipo dino URL-esa. |
| `images[].image_url` |  | string (image URL) | yes |  |  | Conditioning kipo dino URL-esa. |
| `images[].start_frame_num` |  | integer | no | `0` | 0 – 1,440 | Kipo-conditioningosko start-point, sikavdo kadro-numera savo si divisible by 8. |
| `images[].strength` |  | number | no | `1` | 0 – 1 | Conditioning intensiteta: 0.0 leske na kerel conditioning, 1.0 aplikuil les pherdo. |
| `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 dino conditioningoske. |
| `videos[].video_url` |  | string (video URL) | yes |  |  | Conditioning video dino URL-esa. |
| `videos[].conditioning_type` |  | enum (string) \| null | no | `rgb` | `rgb`, `depth`, `pose`, `canny` | Video conditioning category savo lil e preprocessing pipeline kana preprocessing si aktivno, thaj te aplikuil pravilno e in-context LoRA weights. |
| `videos[].preprocess` |  | boolean | no | `false` |  | Aktivuil preprocessing te jakharel e videosko conditioning tipo. RGB conditioningoske, lesko aktivipen na kerel paruvipen. |
| `videos[].start_frame_num` |  | integer | no | `0` | 0 – 1,440 | Conditioningosko start-point andro video, sikavdo kadro-numera savo si divisible by 8. |
| `videos[].strength` |  | number | no | `1` | 0 – 1 | Conditioning intensiteta: 0.0 leske na kerel conditioning, 1.0 aplikuil les pherdo. |
| `videos[].limit_num_frames` |  | boolean | no | `false` |  | Video-kadro-limit switch: kana si aktivno, `max_num_frames` ovel limitosko kadro-gind savo lel pes videosar. |
| `videos[].max_num_frames` |  | integer | no | `1441` | 1 – 1,441 | Video-kadro-gindosko limitos, `None` lel sa o video na limitime gind. |
| `videos[].resample_fps` |  | boolean | no | `false` |  | Video-kadro-rate conversion switch: kana si aktivno, resampluil o video e `target_fps` rate-esa. |
| `videos[].target_fps` |  | integer | no | `24` | 1 – 60 | Resampling kadro rate andro FPS, numa kana si aktivno `resample_fps`. |
| `videos[].reverse_video` |  | boolean | no | `false` |  | Video-redosko vorbipen vaše tasks save manghen conditioning te džal palal e kadro. |

## 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/ltxv-13b-098-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/ltxv-13b-098-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/ltxv-13b-098-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": "ltxv-13b-098-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": "ltxv-13b-098-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": 101,
    "usd": "0.101",
    "charged": 101,
    "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} | Video-fajlo kerdino generiripeça. |
| `prompt` | string | Promptos savo si upotreblime vaš rezultatoski keripe. |
| `seed` | integer | Seed-valoro aplikovano ko generiripe. |

## Related models

- [LTX-Video 13B 0.9.8 Distilled](https://doitong.com/developers/models/ltxv-13b-098-distilled.md) (`ltxv-13b-098-distilled`): from $0.02 / second of output video
- [LTX-Video 13B 0.9.8 Distilled · Image to Video](https://doitong.com/developers/models/ltxv-13b-098-distilled/image-to-video.md) (`ltxv-13b-098-distilled/image-to-video`): from $0.02 / second of output video
- [LTX-Video 13B 0.9.8 Distilled · Extend](https://doitong.com/developers/models/ltxv-13b-098-distilled/extend.md) (`ltxv-13b-098-distilled/extend`): from $0.02 / second of output 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/ltxv-13b-098-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=ltxv-13b-098-distilled/multiconditioning
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.ru/v1/models/ltxv-13b-098-distilled/multiconditioning