# MiniMax H3 Reference to Video LoRA Trainer API

> LoRA-trener for referansestyrt video, med varm start fra en tidligere LoRA

Trener en LoRA på en zip-fil med videoklipp av motiver, slik at referanseinndata kan styre videogenereringen. `reference_conditioning_p` angir hvor ofte referanser brukes under treningen, og `resume_from_lora_url` fortsetter fra en tidligere trent LoRA. Følgende kan også konfigureres: `rank`, `number_of_steps`, `number_of_frames`, `frame_rate` og `aspect_ratio`. Returnerer vekter og en konfigurasjonsfil.

- Model id: `minimax/h3/ref2va/trainer`
- Made by: MiniMax
- Category: Training
- Output: file
- Price: $0.015 / step (cheapest run $30.00 (30000 API credits))
- Run: `POST https://api.doitong.com/v1/run/minimax/h3/ref2va/trainer`
- Quote: `POST https://api.doitong.com/v1/quote/minimax/h3/ref2va/trainer`
- Docs page: https://doitong.com/no/developers/models/minimax/h3/ref2va/trainer

## Pricing

Price: **$0.015 / step**. Billed per training step (`number_of_steps`).

The full example input below costs $30.00 = 30000 API credits (2000 steps × 15 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/minimax/h3/ref2va/trainer` 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 til en ZIP med treningsvideoer av motivet; datasett med bare bilder og blandede bilde-/videotreningsarkiver avvises, så bilder må i stedet være referansesidefiler. Valgfrie tillegg deler hvert klipps grunnnavn: en bildetekst som `clip01.txt` og ordnede referanser fra `ref_1` til `ref_4`, navngitt `clip01.ref_1.<ext>` til `clip01.ref_4.<ext>`; uten referansesidefiler leverer et bilde fra klippet sin egen referanse. `<ext>` bestemmer referansemodaliteten: bilder (.png/.jpg/.jpeg), lyd (.wav/.mp3/.flac/.m4a/.ogg/.aac) eller videoer (.mp4/.mov/.avi/.mkv/.webm), der lydsporene også blir lydreferanser. Treningsklipp støtter .mp4, .mov, .avi og .mkv; sikt mot minst 10 klipp, gjerne flere. |
| `rank` |  | enum (integer) | no | `32` | `8`, `16`, `32`, `64`, `128` | LoRA-tilpasningsrangering, som bytter økt minnebruk mot større kapasitet når innstillingen økes. |
| `number_of_steps` |  | integer | no | `2000` | 1 – 15,000 | Totalt antall treningstrinn. |
| `learning_rate` |  | number | no | `0.0002` | 0.000001 – 1 | Læringsrate for optimalisering, som balanserer en mulig hastighetsøkning i treningen mot risikoen for overtilpasning når den økes. |
| `number_of_frames` |  | integer | no | `73` | 22 – 124 | Antall bilder for hvert treningseksempel; når tallet deles på 17, må resten være 5. Eksempler er 22, 39, 56, 73, 90, 107 og 124. |
| `frame_rate` |  | integer | no | `24` | 8 – 60 | Målfrekvens for video, målt i bilder per sekund. |
| `resolution` |  | enum (string) | no | `medium` | `low`, `medium`, `high` | Treningsoppløsning. Høyere oppløsning øker minnekravene. |
| `aspect_ratio` |  | enum (string) | no | `16:9` | `21:9`, `16:9`, `4:3`, `1:1`, `3:4`, `9:16` | Velg treningsformatet fra samme sett som er tilgjengelig gjennom MiniMax H3 API. |
| `trigger_phrase` |  | string | no | `` |  | LoRA-stilens aktiveringsfrase, satt inn først i bildetekstene under trening. |
| `reference_conditioning_p` |  | number | no | `0.9` | 0 – 1 | Angi hvor ofte treningen bruker betinging med referansebilder i stedet for ubetinget tekst-til-video-lyd-trening. Den resterende sannsynligheten går til det ubetingede målet for å opprettholde stabil generering kun fra prompt. |
| `resume_from_lora_url` |  | string \| null | no |  |  | URL for oppvarmingsbasert trening med en .safetensors-LoRA som tidligere er opprettet av denne treneren, etterfulgt av antall ekstra treningstrinn i number_of_steps. Ingenting annet enn adaptervektene gjenopprettes: både optimalisering og læringsratestyring starter på nytt. |
| `auto_scale_input` |  | boolean | no | `false` |  | Om mål-fps og antall bilder samsvares gjennom automatisk videoskalering; true aktiverer denne justeringen. |
| `split_input_into_scenes` |  | boolean | no | `true` |  | Aktiver scenedeling for videoer som er lengre enn en varighetsterskel, og forkast referansesidefiler fra alle klipp som deles. |
| `split_input_duration_threshold` |  | number | no | `30` | 1 – 60 | Utløser for scenedeling uttrykt som en varighet i sekunder: bare videoer som er lengre enn terskelen, deles. |
| `debug_dataset` |  | boolean | no | `false` |  | Alternativ for å laste ned et arkiv for kontroll av forhåndsbehandlingen før full trening startes. Når dette aktiveres, pakker treneren det behandlede datasettet slik at du manuelt kan kontrollere bildetekster, referansebilder, videoer og bilder. |
| `strict_dataset` |  | boolean | no | `false` |  | Streng håndtering av forhåndsbehandlingsproblemer som ellers ville blitt logget og løst med den dokumenterte reserveverdien. Når dette aktiveres, avvises bildetekster som trenger tomme prompter, manglende referansesidefiler og videoer med uleselig lyd, i stedet for å tillate disse reserveverdiene. |

## 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/minimax/h3/ref2va/trainer?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/minimax/h3/ref2va/trainer?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/minimax/h3/ref2va/trainer", 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": "minimax/h3/ref2va/trainer",
      "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": "minimax/h3/ref2va/trainer",
  "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": 30000,
    "usd": "30.00",
    "charged": 30000,
    "refunded": 0
  },
  "input": {
    "training_data_url": "https://example.com/your-file.zip"
  },
  "output": {
    "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 |
|---|---|---|
| `lora_file` | object {url, content_type, file_name, file_size} | URL til trente LoRA-vekter i .safetensors-format. |
| `config_file` | object {url, content_type, file_name, file_size} | Konfigurasjon for oppsett av inferensendepunkt. |
| `debug_dataset` | object {url, content_type, file_name, file_size} \| null | Bare tilgjengelig for forespørsler med `debug_dataset` aktivert; dette nedlastbare arkivet inneholder treningsdataene etter forhåndsbehandling. |

## Related models

- [MiniMax H3 First Last Frame LoRA Trainer](https://doitong.com/developers/models/minimax/h3/flf2v/trainer.md) (`minimax/h3/flf2v/trainer`): $0.01 / step
- [MiniMax H3 Image to Video Audio LoRA Trainer](https://doitong.com/developers/models/minimax/h3/i2v/trainer.md) (`minimax/h3/i2v/trainer`): $0.01 / step
- [MiniMax H3 Text to Video LoRA Trainer](https://doitong.com/developers/models/minimax/h3/t2v/trainer.md) (`minimax/h3/t2v/trainer`): $0.005 / step
- [ERNIE-Image Trainer](https://doitong.com/developers/models/ernie-image-trainer.md) (`ernie-image-trainer`): $0.0012 / step
- [FLUX 2 \[klein\] 9b Base Trainer](https://doitong.com/developers/models/flux-2-klein-9b-base-trainer.md) (`flux-2-klein-9b-base-trainer`): $0.0043 / step
- [Flux 2 Klein 9B Base Trainer](https://doitong.com/developers/models/flux-2-klein-9b-base-trainer/edit.md) (`flux-2-klein-9b-base-trainer/edit`): $0.0139 / step
- [FLUX 2 Trainer](https://doitong.com/developers/models/flux-2-trainer.md) (`flux-2-trainer`): $0.0064 / step
- [FLUX 2 Trainer Edit](https://doitong.com/developers/models/flux-2-trainer/edit.md) (`flux-2-trainer/edit`): $0.0389 / step

## Links

- HTML page: https://doitong.com/developers/models/minimax/h3/ref2va/trainer
- 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=minimax/h3/ref2va/trainer
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/minimax/h3/ref2va/trainer