# Wan 2.5 Text to Image API

> 중국어 및 영어 프롬프트와 호출당 최대 4개 이미지를 지원하는 Wan 2.5 텍스트-투-이미지

중국어 또는 영어로 작성된 최대 2000자의 텍스트 `prompt`에서 이미지를 생성합니다. `num_images`는 1~4이며, `image_size`는 프리셋 또는 명시적 크기를 받고 전체 픽셀 수는 768x768~1440x1440입니다. LLM으로 프롬프트를 다시 작성하는 프롬프트 확장은 기본적으로 켜져 있습니다. `negative_prompt`와 `seed`도 사용할 수 있습니다.

- Model id: `wan-25-preview/text-to-image`
- Made by: Alibaba
- Category: Text to Image
- Output: image
- Price: $0.05 / image (cheapest run $0.05 (50 API credits))
- Run: `POST https://api.doitong.com/v1/run/wan-25-preview/text-to-image`
- Quote: `POST https://api.doitong.com/v1/quote/wan-25-preview/text-to-image`
- Docs page: https://doitong.com/ko/developers/models/wan-25-preview/text-to-image

## Pricing

Price: **$0.05 / image**. Billed per generated image (`num_images`, default 1).

The full example input below costs $0.05 = 50 API credits (1 image × 50 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/wan-25-preview/text-to-image` 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

Send either the canonical name or the native field name (not both). Canonical names are the same across models.

| Parameter | Canonical name | Type | Required | Default | Allowed values / range | Description |
|---|---|---|---|---|---|---|
| `prompt` |  | string | yes |  | ≥ 1 characters | 이미지 생성 지침입니다. 2000자로 제한되며 중국어 또는 영어로 작성해야 합니다. |
| `negative_prompt` |  | string \| null | no |  |  | 원하지 않는 콘텐츠를 지정하는 네거티브 프롬프트입니다. 500자로 제한됩니다. |
| `num_images` | `num_outputs` | integer | no | `1` | 1 – 4 | 1~4 범위의 이미지 생성 개수입니다. |
| `image_size` |  | object {width, height} \| enum (string) | no | `square` | `square_hd`, `square`, `portrait_4_3`, `portrait_16_9`, `landscape_4_3`, `landscape_16_9` | 생성된 이미지의 크기입니다. 총 픽셀 수는 768×768~1440×1440, 가로세로 비율은 1:4~4:1 범위여야 합니다. 크기를 직접 지정하거나 'square' 또는 'landscape_16_9'와 같은 이름이 지정된 프리셋을 선택합니다. |
| `enable_prompt_expansion` |  | boolean | no | `true` |  | LLM 기반 프롬프트 다시 쓰기입니다. 짧은 프롬프트에서 더 나은 결과를 얻는 대신 추가 처리 시간이 필요합니다. |
| `seed` |  | integer \| null | no |  |  | 생성을 재현 가능하게 하는 시드입니다. None으로 설정하면 무작위로 선택됩니다. |
| `enable_safety_checker` |  | boolean | no | `true` |  | 안전 검사 스위치입니다. 끔 설정에는 계정 인증이 필요합니다. 인증이 없으면 설정과 관계없이 모든 요청을 검사하며, true는 검사기를 활성화합니다. |

## 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/wan-25-preview/text-to-image?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/wan-25-preview/text-to-image?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/wan-25-preview/text-to-image", 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": "wan-25-preview/text-to-image",
      "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": "wan-25-preview/text-to-image",
  "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": 50,
    "usd": "0.05",
    "charged": 50,
    "refunded": 0
  },
  "input": {
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field"
  },
  "output": {
    "images": [
      {
        "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,
        "width": 1024,
        "height": 768
      }
    ],
    "seeds": [
      42
    ],
    "actual_prompt": "A lone samurai standing on the edge of a cliff at twilight, overlooking a vast valley shrouded in mist. The sky burns with deep orange and purple hues from the setting sun, casting long, dramatic shadows. The samurai’s silhouette glows against the horizon, with their sword reflecting a glint of fading light. The overall style is hyper-realistic, cinematic, and moody, with dramatic contrast and atmospheric depth."
  },
  "outputs": [
    {
      "type": "image",
      "url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
      "content_type": "image/jpeg",
      "file_size": 1843200,
      "width": 1024,
      "height": 768,
      "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 |
|---|---|---|
| `images` | array of object {url, content_type, file_name, file_size, width, height} | 모델이 생성한 이미지입니다. |
| `seeds` | array of integer | 개별 이미지를 생성하는 데 사용되는 시드 값입니다. |
| `actual_prompt` | string \| null | 다시 쓰기가 활성화되었을 때 실제로 사용되는 프롬프트입니다. |

## Related models

- [Wan 2.5 Image to Image](https://doitong.com/developers/models/wan-25-preview/image-to-image.md) (`wan-25-preview/image-to-image`): $0.05 / image
- [Wan 2.5 Text to Video](https://doitong.com/developers/models/wan-25-preview/text-to-video.md) (`wan-25-preview/text-to-video`): from $0.05 / second
- [Wan 2.5 Image to Video](https://doitong.com/developers/models/wan-25-preview/image-to-video.md) (`wan-25-preview/image-to-video`): from $0.05 / second
- [Qwen Image 3 Text to Image](https://doitong.com/developers/models/alibaba/qwen-image-3/text-to-image.md) (`alibaba/qwen-image-3/text-to-image`): from $0.04 / image
- [FLUX.1 \[dev\]](https://doitong.com/developers/models/flux/dev.md) (`flux/dev`): $0.025 / megapixel
- [FLUX.1 \[schnell\]](https://doitong.com/developers/models/flux/schnell.md) (`flux/schnell`): $0.003 / megapixel
- [FLUX.2 \[dev\]](https://doitong.com/developers/models/flux-2.md) (`flux-2`): $0.012 / megapixel
- [FLUX.2 \[pro\]](https://doitong.com/developers/models/flux-2-pro.md) (`flux-2-pro`): $0.015 / megapixel of output image

## Links

- HTML page: https://doitong.com/developers/models/wan-25-preview/text-to-image
- 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=wan-25-preview/text-to-image
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/wan-25-preview/text-to-image