# MiniMax H3 Image to Video Audio LoRA Trainer API

> LoRA trainer na nagtuturo ng first-frame image-to-video na may audio mula sa mga video clip

Nagsasanay ng LoRA mula sa zip ng mga video clip sa `training_data_url`, gamit ang first-frame conditioning upang ma-animate ang still larawan tungo sa video. Mga video lamang ang dapat laman ng dataset. Kasama sa mga setting ang `rank` na 8 hanggang 128, `number_of_steps` hanggang 15000, `number_of_frames` mula 22 hanggang 124, `frame_rate` mula 8 hanggang 60 at `trigger_phrase`. Naglalabas ng LoRA weights at config file.

- 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.ru/v1/run/minimax/h3/i2v/trainer`
- Quote: `POST https://api.doitong.ru/v1/quote/minimax/h3/i2v/trainer`
- Docs page: https://doitong.com/fil/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.ru/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 para sa ZIP na naglalaman ng mga training video sa .mp4, .mov, .avi, o .mkv format; inirerekomenda ang hindi bababa sa 10 clip, at mas mainam ang mas malalaking koleksiyon. Hindi maaaring training media ang mga larawan: tinatanggihan ang larawan-only dataset at archive na naghahalo ng training larawan at video, bagama't pinapayagan ng ilang endpoint ang mga larawan bilang conditioning sidecar. Maaaring sumama sa mga clip ang opsyonal na caption file, kung saan kapareho ng base name ng video nito ang bawat text file. |
| `rank` |  | enum (integer) | no | `32` | `8`, `16`, `32`, `64`, `128` | LoRA adaptation rank, na ipinagpapalit ang mas mataas na memory consumption para sa mas malaking capacity habang tumataas ang setting. |
| `number_of_steps` |  | integer | no | `2000` | 1 – 15,000 | Kabuuang bilang ng training step. |
| `learning_rate` |  | number | no | `0.0002` | 0.000001 – 1 | Optimization learning rate, na nagbabalanse sa posibleng pagbilis ng training at panganib ng overfitting kapag tinaasan. |
| `number_of_frames` |  | integer | no | `73` | 22 – 124 | Bilang ng frame para sa bawat training sample; kapag hinati sa 17, dapat may remainder na 5. Kabilang sa mga halimbawa ang 22, 39, 56, 73, 90, 107, at 124. |
| `frame_rate` |  | integer | no | `24` | 8 – 60 | Target frame rate ng video, sinusukat sa frames per segundo. |
| `resolution` |  | enum (string) | no | `medium` | `low`, `medium`, `high` | Training resolution. Kapag tinaasan ang resolution, tumataas ang memory requirements. |
| `aspect_ratio` |  | enum (string) | no | `16:9` | `21:9`, `16:9`, `4:3`, `1:1`, `3:4`, `9:16` | Piliin ang training aspect ratio mula sa parehong set na available sa MiniMax H3 API. |
| `trigger_phrase` |  | string | no | `` |  | LoRA style activation phrase, na ipinapasok sa simula ng mga caption habang nagta-training. |
| `first_frame_conditioning_p` |  | number | no | `0.5` | 0 – 1 | Pagbutihin ang image-to-video performance sa pamamagitan ng pagtaas ng dalas ng paggamit ng training sa first-frame conditioning; itinatakda ng value na ito ang probability na iyon. |
| `auto_scale_input` |  | boolean | no | `false` |  | Kung nagtutugma ang target fps at frame count sa pamamagitan ng awtomatikong video scaling; ino-enable ng true ang adjustment na ito. |
| `split_input_into_scenes` |  | boolean | no | `true` |  | Paghahati ng eksena para sa mga video na lumalagpas sa duration threshold, pinapagana kapag true. |
| `split_input_duration_threshold` |  | number | no | `30` | 1 – 60 | Trigger ng paghahati ng eksena na ipinapahayag bilang duration sa segundo: ang mga video lamang na mas mahaba sa threshold ang hinahati. |
| `debug_dataset` |  | boolean | no | `false` |  | Preprocessing inspection mode para suriin ang mga video, larawan, at caption bago ang buong training run. Kapag pinagana, ibinabalik ng trainer ang na-process na training dataset sa isang downloadable archive para sa manu-manong pagsusuri. |
| `strict_dataset` |  | boolean | no | `false` |  | Mahigpit na paghawak sa mga preprocessing problem na kung hindi ay ila-log at lulutasin gamit ang dokumentadong fallback. Kapag enabled, tinatanggihan ang caption na nangangailangan ng blank prompt, nawawalang sidecar keyframe, at video na hindi mabasa ang audio sa halip na palitan ang mga ito ng blank prompt, clip frame, o katahimikan. |

## 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.ru/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.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/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.ru/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.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": "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.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
  }
}
```

### Output fields (`output`)

| Field | Type | Description |
|---|---|---|
| `lora_file` | object {url, content_type, file_name, file_size} | URL para sa trained LoRA weights sa .safetensors format. |
| `config_file` | object {url, content_type, file_name, file_size} | Configuration para sa setup ng inference endpoint. |
| `debug_dataset` | object {url, content_type, file_name, file_size} \| null | Available lamang para sa mga kahilingang may naka-enable na `debug_dataset`; naglalaman ang mada-download na archive na ito ng training data pagkatapos ng 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.ru/v1/models/minimax/h3/i2v/trainer