# Realistic Vision API

> 支持 LoRA 和嵌入以及负面提示词的照片级真实感文本生成图像扩散模型

根据文本 `prompt` 生成逼真图像，并通过 `negative_prompt` 排除不需要的细节。支持 `loras` 和 `embeddings`、6 种预设 `image_size` 选项，以及通过 `num_images` 每次请求生成 1 到 8 张图像。`num_inference_steps` 范围为 1 到 70，`guidance_scale` 范围为 0 到 20。输出格式可以是 jpeg 或 png，`seed` 可使结果可重复。

- Model id: `realistic-vision`
- Made by: Open model
- Category: Text to Image
- Output: image
- Estimated price per run: $0.009 / image (cheapest run $0.009 (9 API credits))
- Run: `POST https://api.doitong.com/v1/run/realistic-vision`
- Quote: `POST https://api.doitong.com/v1/quote/realistic-vision`
- Docs page: https://doitong.com/zh/developers/models/realistic-vision

## Pricing

Price: **$0.009 / image**. Estimated from the typical cost of one run, per output (`num_images`). The quote is exactly what the run charges.

The full example input below costs $0.009 = 9 API credits (1 image × 9 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/realistic-vision` 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 |  |  | 描述要生成的图像；请尽可能提供详细信息以获得最佳结果。 |
| `negative_prompt` |  | string | no | `(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)` |  | 指定要从图像中排除的细节的负面提示词。 |
| `image_size` |  | object {height, width} \| enum (string) | no | `{"height":1024,"width":1024}` | `square_hd`, `square`, `portrait_4_3`, `portrait_16_9`, `landscape_4_3`, `landscape_16_9` |  |
| `num_inference_steps` |  | integer | no | `35` | 1 – 70 | 要执行的推理步数。 |
| `guidance_scale` |  | number | no | `5` | 0 – 20 | 图像选择对提示词的遵循强度，以 CFG（Classifier Free Guidance）表示。 |
| `loras` |  | array of object {force, path, scale} | no | `[]` |  | 选择使用的 LoRA 权重，以列表形式提供。 |
| `loras[].force` |  | boolean | no | `false` |  | 是否必须使用嵌入：true 表示强制使用。 |
| `loras[].path` |  | string | yes |  |  | LoRA 权重来源，可提供解析为 safetensors 权重的 HF 模型标识符，或通过 URL 或路径定位的 safetensors 文件。 |
| `loras[].scale` |  | number | no | `1` | 0 – 1 | LoRA 权重合并到基础模型前应用的乘数。 |
| `embeddings` |  | array of object {path, tokens} | no | `[]` |  | 选择使用的嵌入，以列表形式提供。 |
| `embeddings[].path` |  | string | yes |  |  | 通过 URL 或文件路径指定的嵌入权重。 |
| `embeddings[].tokens` |  | array of string \| null | no |  |  | 嵌入 token 列表。省略时会自动命名：嵌入文件中的每个向量依次获得 <s0>、<s1>、... 中的一个 token。 |
| `expand_prompt` |  | boolean | no | `false` |  | 提示词扩展；为 true 时启用附加提示词。 |
| `num_images` | `num_outputs` | integer | no | `1` | 1 – 8 | 请求生成的图像数量。 |
| `seed` |  | integer \| null | no |  |  | 用于在 Stable Diffusion 中复现图像的种子。保持模型版本、提示词和种子不变，即可在每次运行中精确复现图像。 |
| `enable_safety_checker` |  | boolean | no | `true` |  | 安全检查器激活状态；true 启用检查。 |
| `format` |  | enum (string) | no | `jpeg` | `jpeg`, `png` | 结果图像的文件格式。 |
| `safety_checker_version` |  | enum (string) | no | `v1` | `v1`, `v2` | 在自定义 ViT 安全检查器（`v2`）和 CompVis 检查器（`v1`，默认值）之间进行选择。 |
| `request_id` |  | string | no | `` |  | 用于将响应与生成该响应的请求关联起来的请求标识符。 |

## 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/realistic-vision?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/realistic-vision?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/realistic-vision", 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": "realistic-vision",
      "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": "realistic-vision",
  "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": 9,
    "usd": "0.009",
    "charged": 9,
    "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",
        "width": 1024,
        "height": 768,
        "content_type": "image/jpeg"
      }
    ],
    "timings": {},
    "seed": 42,
    "has_nsfw_concepts": [
      false
    ],
    "prompt": "string"
  },
  "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, width, height, content_type} | 生成图像的文件信息。 |
| `timings` | object |  |
| `seed` | integer | 图像生成种子；如果输入未提供种子，则包含随机选择的值，否则重复使用所提供的值。 |
| `has_nsfw_concepts` | array of boolean | 指示生成的图像中是否出现 NSFW 概念。 |
| `prompt` | string | 图像生成期间使用的提示词。 |

## Related models

- [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
- [GPT Image 2.5 Text to Image](https://doitong.com/developers/models/openai/gpt-image-2.5/flare/text-to-image.md) (`openai/gpt-image-2.5/flare/text-to-image`): from $0.008 / image
- [Ideogram V4.5](https://doitong.com/developers/models/ideogram/v4.5.md) (`ideogram/v4.5`): from $0.03 / image
- [Nano Banana 2](https://doitong.com/developers/models/nano-banana-2.md) (`nano-banana-2`): from $0.06 / image
- [Nano Banana Pro](https://doitong.com/developers/models/nano-banana-pro.md) (`nano-banana-pro`): from $0.15 / image

## Links

- HTML page: https://doitong.com/developers/models/realistic-vision
- 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=realistic-vision
- Errors: https://doitong.com/developers/docs/errors
- Model details as JSON: https://api.doitong.com/v1/models/realistic-vision