# MiniMax H3 Image to Video Audio LoRA Trainer API

> LoRA trainer kaj sikavel anglutne-freimosko image-to-video audioha andar video-klipura

Sikavel jekh LoRA andar zip video-kliponça ande `training_data_url`, le anglutne-freimoske conditioning-asa, te šaj jekh still imàgino avel animirime ande video. O dataset trubul te avel numa videonça. E settingura si `rank` 8 dži 128, `number_of_steps` dži 15000, `number_of_frames` 22 dži 124, `frame_rate` 8 dži 60 thaj jekh `trigger_phrase`. Del LoRA weightura thaj config fajlo.

- Model id: `minimax/h3/i2v/trainer`
- Made by: MiniMax
- Category: Training
- Output: file
- Price: $0.01 / step (cheapest run $20.00 (20000 API credits))
- Run: `POST https://api.doitong.com/v1/run/minimax/h3/i2v/trainer`
- Quote: `POST https://api.doitong.com/v1/quote/minimax/h3/i2v/trainer`
- Docs page: https://doitong.com/rom/developers/models/minimax/h3/i2v/trainer

## Pricing

Price: **$0.01 / step**. Billed per training step (`number_of_steps`).

The full example input below costs $20.00 = 20000 API credits (2000 steps × 10 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/i2v/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 vaš ZIP savo lel training videos ande .mp4, .mov, .avi vaj .mkv formato; najman 10 klipi rekomendime, taj bareder kolekcije si maj lačhe. Kipi na šaj te aven training media: image-only datasets taj archives save mixin training kipi videosa si rejectime, numa vajekh endpoint mukel kipi sar conditioning sidecars. Opcionalno caption fajlo šaj te avel e klipenca; sako text fajlo si sajekh base name-sa leske videosa. |
| `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` | 1 – 15,000 | Sa e training steps-enge gind. |
| `learning_rate` |  | number | no | `0.0002` | 0.000001 – 1 | Optimization learning rate, balancirinel jek possible speedup ande traininga riskoha overfittingesko kana bararel. |
| `number_of_frames` |  | integer | no | `73` | 22 – 124 | Kadro-count vaš sako training sample; kana delpe ko 17, trubul te avel remainder 5. Examples si 22, 39, 56, 73, 90, 107, taj 124. |
| `frame_rate` |  | integer | no | `24` | 8 – 60 | Ciljno frekvencija e videokadrenge, merime ande kadra pe sekunda. |
| `resolution` |  | enum (string) | no | `medium` | `low`, `medium`, `high` | Training rezolucija. Maj-bari rezolucija mangel maj-baro memory. |
| `aspect_ratio` |  | enum (string) | no | `16:9` | `21:9`, `16:9`, `4:3`, `1:1`, `3:4`, `9:16` | Avrirod e trainingosko aspect ratio katar sajekh set savo si dostupno ande MiniMax H3 API. |
| `trigger_phrase` |  | string | no | `` |  | LoRA stiloski activation fraza, thovi pes andro captionsko starto andro training. |
| `first_frame_conditioning_p` |  | number | no | `0.5` | 0 – 1 | Šukararel image-to-video performance te bararel sar barvales training astarel first-frame conditioning; kadava value si oda probability. |
| `auto_scale_input` |  | boolean | no | `false` |  | Te si target fps taj kadro-count matchime automatic video scalingesa; true aktivinel kava adjustment. |
| `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. |
| `strict_dataset` |  | boolean | no | `false` |  | Strogo arakhipen le problemengo anglal o proceso, save averčhane sas te registrisaren pes thaj te lačharen pes pe dokumentirime rezervne metodenca. Kana si aktivirimo, čhudel avri le tekstura save mangen čuče promptura, le video saven naj paše phandle ključno kadro-fajlura, thaj le video savengo audio našti te drabarel pes, umal te paruvdel len čuče promptosa, kadrosa andar o klipo vaj čutipesa. |

## 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/i2v/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/i2v/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/i2v/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/i2v/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/i2v/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": 20000,
    "usd": "20.00",
    "charged": 20000,
    "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 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 | Dostupno numa bašo mangipena `debug_dataset` aktivime, kava downloadable archive lel e training data palal preprocessing. |

## 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 Reference to Video LoRA Trainer](https://doitong.com/developers/models/minimax/h3/ref2va/trainer.md) (`minimax/h3/ref2va/trainer`): $0.015 / 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/i2v/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/i2v/trainer
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/minimax/h3/i2v/trainer