# LTX Video-0.9.7 13B Distilled · Multiconditioning API

> Video-to-video with image and video conditioning, LoRA support and a two-pass refinement with optional detail pass

Generates a video from a required `prompt`, guided by conditioning inputs in `images` and `videos`. Custom LoRAs can be passed in `loras`. Output is 480p or 720p, with `num_frames` from 9 to 1441 and `frame_rate` from 1 to 60. Separate step counts for the first and second pass, an optional detail pass and color controls such as `temporal_adain_factor` allow tuning of the result.

- 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.com/v1/run/ltx-video-13b-distilled/multiconditioning`
- Quote: `POST https://api.doitong.com/v1/quote/ltx-video-13b-distilled/multiconditioning`
- Docs page: https://doitong.com/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.com/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 |  |  | Text instructions that guide generation. |
| `negative_prompt` |  | string | no | `worst quality, inconsistent motion, blurry, jittery, distorted` |  | Negative text instructions for generation. |
| `loras` |  | array of object {path, weight_name, scale} | no | `[]` |  | LoRA weights applied during generation. |
| `loras[].path` |  | string | yes |  |  | Location of the LoRA weights, given as a URL or path. |
| `loras[].weight_name` |  | string \| null | no |  |  | LoRA weight name for selecting among multiple weights in a HuggingFace repository; only repositories containing multiple weights require it. This setting has no effect unless `path` identifies a HuggingFace repository. |
| `loras[].scale` |  | number | no | `1` | 0 – 4 | Multiplier that scales the LoRA weight as it is loaded. |
| `resolution` |  | enum (string) | no | `720p` | `480p`, `720p` | Resolution for the output video. |
| `aspect_ratio` |  | enum (string) | no | `auto` | `9:16`, `1:1`, `16:9`, `auto` | Video aspect ratio. |
| `seed` |  | integer \| null | no |  |  | Seed controlling randomness during generation. |
| `num_frames` |  | integer | no | `121` | 9 – 1,441 | Total frame count for the video. |
| `first_pass_num_inference_steps` |  | integer | no | `8` | 2 – 12 | Inference step count for the first pass. |
| `second_pass_num_inference_steps` |  | integer | no | `8` | 2 – 12 | Inference step count for the second pass. |
| `second_pass_skip_initial_steps` |  | integer | no | `5` | 1 – 11 | Initial inference-step count to omit during the second pass, shifting that pass toward fine-detail work rather than large-scale changes. |
| `frame_rate` |  | integer | no | `24` | 1 – 60 | Frame rate for the video. |
| `expand_prompt` |  | boolean | no | `false` |  | Prompt expansion through a language model. |
| `reverse_video` |  | boolean | no | `false` |  | Reversal of the video's playback order. |
| `enable_safety_checker` |  | boolean | no | `true` |  | Safety-checking switch whose off setting is honored only for authorized accounts. All requests without that authorization undergo safety checks. |
| `enable_detail_pass` |  | boolean | no | `false` |  | Optional second refinement pass that improves video detail when enabled. |
| `temporal_adain_factor` |  | number | no | `0.5` | 0 – 1 | Adaptive instance normalization (AdaIN) strength, balancing color consistency at higher settings against greater color variation at lower settings. It operates on generated chunks except the first, normalizing color distributions throughout the video to help counter a progressive rise in saturation or contrast. |
| `tone_map_compression_ratio` |  | number | no | `0` | 0 – 1 | Tone-mapping compression level, ranging from no compression at 0.0 to maximum compression at 1.0. Compressing the video's dynamic range this way improves visual quality. |
| `constant_rate_factor` |  | integer | no | `29` | 0 – 51 | Constant rate factor (CRF) used for input compression, which may improve motion quality by bringing the supplied media closer to the model's training data. |
| `images` |  | array of object {image_url, start_frame_num, strength} | no | `[]` |  | Conditioning images supplied by URL. |
| `images[].image_url` |  | string (image URL) | yes |  |  | Conditioning image supplied by URL. |
| `images[].start_frame_num` |  | integer | no | `0` | 0 – 1,440 | Image-conditioning start position, specified using a frame number divisible by 8. |
| `images[].strength` |  | number | no | `1` | 0 – 1 | Conditioning intensity: 0.0 disables conditioning, while 1.0 applies it fully. |
| `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 | `[]` |  | Videos supplied for conditioning. |
| `videos[].video_url` |  | string (video URL) | yes |  |  | Conditioning video supplied by URL. |
| `videos[].conditioning_type` |  | enum (string) \| null | no | `rgb` | `rgb`, `depth`, `pose`, `canny` | Video conditioning category used to choose the preprocessing pipeline when preprocessing is enabled, and to apply in-context LoRA weights correctly. |
| `videos[].preprocess` |  | boolean | no | `false` |  | Enables preprocessing to match the video's conditioning type. For RGB conditioning, enabling it makes no changes. |
| `videos[].start_frame_num` |  | integer | no | `0` | 0 – 1,440 | Conditioning start position in the video, specified as a frame number divisible by 8. |
| `videos[].strength` |  | number | no | `1` | 0 – 1 | Conditioning intensity: 0.0 disables conditioning, while 1.0 applies it fully. |
| `videos[].limit_num_frames` |  | boolean | no | `false` |  | Video frame-limit switch: enabling it makes `max_num_frames` the cap on frames taken from the video. |
| `videos[].max_num_frames` |  | integer | no | `1441` | 1 – 1,441 | Video frame-use cap, with None selecting the entire video rather than a limited frame count. |
| `videos[].resample_fps` |  | boolean | no | `false` |  | Video frame-rate conversion switch: enabling it resamples the video at the rate specified by `target_fps`. |
| `videos[].target_fps` |  | integer | no | `24` | 1 – 60 | Resampling frame rate in FPS, applicable only with `resample_fps` enabled. |
| `videos[].reverse_video` |  | boolean | no | `false` |  | Video-order reversal for tasks that need conditioning to follow the frames backward. |

## 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.com/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.com/v1/requests/REQUEST_ID?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY"
```

### JavaScript

```javascript
const API = "https://api.doitong.com/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.com/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.com/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.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
  }
}
```

### Output fields (`output`)

| Field | Type | Description |
|---|---|---|
| `video` | object {url, content_type, file_name, file_size} | Video file produced by generation. |
| `prompt` | string | Prompt that was used to generate the output. |
| `seed` | integer | Seed value applied during generation. |

## 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.com/v1/models/ltx-video-13b-distilled/multiconditioning