# LTX 2.3 Trainer (V2) - Keyframe Interpolation API

> Sikavel LoRA savo generirinel o hareket maškar anglune thaj palune keyframe-ura, opcionale e maškarutnesa

Sikavel jekh LoRA vaš keyframe interpolacija: pe inference des o angluno thaj agorutno kadro thaj o modelo kerel o video maškar lende. Aktivir `include_middle_keyframe` te trenirines pe angluno, maškarutno thaj agorutno kadro; akharutno validacijako samplo musaj te del `middle_image_url`. O data avel sar zip katar `training_data_url`, dži 2 validacijake entryenca.

- Model id: `ltx23-trainer-v2/interpolate`
- Made by: Lightricks
- Category: Training
- Output: file
- Price: $0.0024 / step (cheapest run $4.80 (4800 API credits))
- Run: `POST https://api.doitong.com/v1/run/ltx23-trainer-v2/interpolate`
- Quote: `POST https://api.doitong.com/v1/quote/ltx23-trainer-v2/interpolate`
- Docs page: https://doitong.com/rom/developers/models/ltx23-trainer-v2/interpolate

## Pricing

Price: **$0.0024 / step**. Billed per training step (`number_of_steps`).

The full example input below costs $4.80 = 4800 API credits (2000 steps × 2.4 API credits).

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/ltx23-trainer-v2/interpolate` 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 |
|---|---|---|---|---|---|---|
| `training_data_url` |  | string | yes |  |  | URL vašo `.zip` savo si training-datenca, organizime palal o alosardo training modo. Sako media fajlo šaj te avel les accompanying `.txt` caption jekhetane base name-esa. |
| `rank` |  | enum (integer) | no | `32` | `8`, `16`, `32`, `64`, `128` | LoRA adaptation rank, savo lel maj-baro memory thaj del maj-bari capacity kana vazdel pes. |
| `number_of_steps` |  | integer | no | `2000` | 100 – 10,000 | Sa e training steps-enge gind. |
| `learning_rate` |  | number | no | `0.0002` | 0.000001 – 1 | Optimization learning rate. |
| `number_of_frames` |  | integer | no | `89` | 9 – 121 | Kadro-gind sako training sample-oske. Te del o gind pe 8, o remainder si 1; exempelura si 1, 9, 17, 25, 33, 41, 49, 57, 65, 73, 81, 89, 97. |
| `frame_rate` |  | integer | no | `24` | 8 – 60 | Training-videosko ciljuno kadro rate andro kadro pe sekunda. Native rate vašo LTX-2.3 si 24. |
| `resolution` |  | enum (string) | no | `medium` | `low`, `medium`, `high` | Training rezolucija. Maj-bari rezolucija mangel maj-baro memory. |
| `aspect_ratio` |  | enum (string) | no | `1:1` | `16:9`, `1:1`, `9:16` | Training aspect ratio. |
| `trigger_phrase` |  | string | no | `` |  | LoRA stiloski activation fraza, thovi pes andro captionsko starto andro training. |
| `auto_scale_input` |  | boolean | no | `false` |  | Automatikano video adjustment te jakharel e manglini FPS thaj kadro-gind kana si aktivno; kipo datasets ačhen bi paruvipen. |
| `split_input_into_scenes` |  | boolean | no | `true` |  | Scene splitting videoske save našen durata threshold, aktivno kana si true. |
| `split_input_duration_threshold` |  | number | no | `30` | 1 – 60 | Scene-splitting trigger, sikavdo durata andro sekunda: numa videona maj-bare sar threshold si split-ime. |
| `debug_dataset` |  | boolean | no | `false` |  | Preprocessing inspection modo vašo video, kipo thaj caption proveripen angleder full training run. Kana si aktivno, trainer del processed training dataset andro download-ime archive vašo manušikano dikhipen. |
| `validation` |  | array of object {prompt, start_image_url, end_image_url, middle_image_url} | no | `[]` | ≤ 2 items | Validation data dine sar lista; jek entry phandela keyframe kipo promptoha. |
| `validation[].prompt` |  | string | yes |  |  | Prompto savo si lavdo andro validation. |
| `validation[].start_image_url` |  | string (image URL) | yes |  | ≥ 1 characters | Keyframe kiposko URL vaj angluno kadro. |
| `validation[].end_image_url` |  | string (image URL) | yes |  | ≥ 1 characters | Keyframe kiposko URL vaj paluno kadro. |
| `validation[].middle_image_url` |  | string (image URL) \| null | no |  |  | Middle-keyframe kiposko URL vaj first+middle+last interpolation. Runs save `include_middle_keyframe=true` mangena les; averfjel optionalno si. |
| `validation_negative_prompt` |  | string | no | `blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts.` |  | Negativno prompto lavdo andro validation, savo lel single-stage preview process. Kaj kadava process približuil distilled production inference duje stage-enca, leski guidance thaj preview kvaliteta si aver finalno rezultatosar. |
| `validation_number_of_frames` |  | integer | no | `89` | 9 – 121 | Kadro-gind sako validation videoske. |
| `validation_frame_rate` |  | integer | no | `24` | 8 – 60 | Ciljuno kadro rate validation videoske, andro kadro pe sekunda; LTX-2.3 si native 24-esa. |
| `validation_resolution` |  | enum (string) | no | `high` | `low`, `medium`, `high` | Rezolucija savi si lavdi andro validation. |
| `validation_aspect_ratio` |  | enum (string) | no | `1:1` | `16:9`, `1:1`, `9:16` | Aspect ratio savo si lavdo andro validation. |
| `stg_scale` |  | number | no | `1` | 0 – 3 | Spatio-Temporal Guidance (STG) skala, rekomendime 1.0; 0.0 zaačharel STG. |
| `include_middle_keyframe` |  | boolean | no | `false` |  | Middle-keyframe inclusion vaj video keripe: true mangel `middle_image_url` ande sako validation sample thaj astarel o middle kadro khetane anglune thaj palune, na numa anglune thaj palune. |

## Example input

Required parameters only:

```json
{
  "training_data_url": "https://example.com/your-file.zip"
}
```

A fuller example:

```json
{
  "training_data_url": "https://example.com/your-file.zip"
}
```

## 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/ltx23-trainer-v2/interpolate?wait=20" \
  -H "Authorization: Bearer $DOITONG_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "training_data_url": "https://example.com/your-file.zip"
  }'

# 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/ltx23-trainer-v2/interpolate?wait=20", {
  method: "POST",
  headers,
  body: JSON.stringify({
    "training_data_url": "https://example.com/your-file.zip"
  }),
});
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 = {
    "training_data_url": "https://example.com/your-file.zip",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/ltx23-trainer-v2/interpolate", 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": "ltx23-trainer-v2/interpolate",
      "input": {
        "training_data_url": "https://example.com/your-file.zip"
      },
      "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": "ltx23-trainer-v2/interpolate",
  "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": 4800,
    "usd": "4.80",
    "charged": 4800,
    "refunded": 0
  },
  "input": {
    "training_data_url": "https://example.com/your-file.zip"
  },
  "output": {
    "video": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_name": "0-output.jpeg",
      "file_size": 1843200
    },
    "audio": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_name": "0-output.jpeg",
      "file_size": 1843200
    },
    "lora_file": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_name": "0-output.jpeg",
      "file_size": 1843200
    },
    "config_file": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_name": "0-output.jpeg",
      "file_size": 1843200
    },
    "debug_dataset": {
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_name": "0-output.jpeg",
      "file_size": 1843200
    }
  },
  "outputs": [
    {
      "type": "file",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_size": 1843200,
      "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} \| null | Jekhetano video preview validationostar, kana si dostupno vašo endpoint save keren video. |
| `audio` | object {url, content_type, file_name, file_size} \| null | Jekhetano audio validationostar, preview-sar kana si dostupno kadale audio-only routes-enge: /audio-inpaint, /audio-extend-suffix, /audio-extend-prefix, /t2a, /a2a, thaj /v2a. |
| `lora_file` | object {url, content_type, file_name, file_size} | URL trained LoRA weights-enge andro .safetensors formato. |
| `config_file` | object {url, content_type, file_name, file_size} | Konfiguracija vašo inference endpoint setup. |
| `debug_dataset` | object {url, content_type, file_name, file_size} \| null | Rezultato vaš o rodipe grešken, savo si dostupno kana `debug_dataset` si putardo: jekh arhiva savi šaj huljaves thaj savi inkrel e sikavipaskere podatke pala o anglalprocesiripe. |

## Related models

- [LTX 2.3 Trainer (V2) - Audio Inpainting](https://doitong.com/developers/models/ltx23-trainer-v2/audio-inpaint.md) (`ltx23-trainer-v2/audio-inpaint`): $0.0023 / step
- [LTX 2.3 Trainer (V2) - Audio+Video Reference IC-LoRA](https://doitong.com/developers/models/ltx23-trainer-v2/ic-lora/av2av.md) (`ltx23-trainer-v2/ic-lora/av2av`): $0.0068 / step
- [LTX 2.3 Trainer (V2) - Audio+Video Reference Transformation](https://doitong.com/developers/models/ltx23-trainer-v2/av2av.md) (`ltx23-trainer-v2/av2av`): $0.0068 / step
- [LTX 2.3 Trainer (V2) - Audio-to-Audio](https://doitong.com/developers/models/ltx23-trainer-v2/a2a.md) (`ltx23-trainer-v2/a2a`): $0.0023 / step
- [LTX 2.3 Trainer (V2) - Audio-to-Audio IC-LoRA](https://doitong.com/developers/models/ltx23-trainer-v2/ic-lora/a2a.md) (`ltx23-trainer-v2/ic-lora/a2a`): $0.0023 / step
- [LTX 2.3 Trainer (V2) - Audio-to-Video](https://doitong.com/developers/models/ltx23-trainer-v2/a2v.md) (`ltx23-trainer-v2/a2v`): $0.006 / step
- [LTX 2.3 Trainer (V2) - Backward Audio Extension](https://doitong.com/developers/models/ltx23-trainer-v2/audio-extend-suffix.md) (`ltx23-trainer-v2/audio-extend-suffix`): $0.0023 / step
- [LTX 2.3 Trainer (V2) - Backward Video Extension](https://doitong.com/developers/models/ltx23-trainer-v2/extend-suffix.md) (`ltx23-trainer-v2/extend-suffix`): $0.0061 / step

## Links

- HTML page: https://doitong.com/developers/models/ltx23-trainer-v2/interpolate
- 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=ltx23-trainer-v2/interpolate
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/ltx23-trainer-v2/interpolate