# PATINA · Material API

> 文生纹理生成器，可输出可平铺图像和 PBR 贴图，并支持可选的 img2img 和局部重绘

根据提示词创建可平铺纹理，并预测 PBR 贴图：basecolor、normal、roughness、metalness 和 height。使用 `tiling_mode` 设置双向、水平或垂直平铺，并使用 0、2 或 4 的 `upscale_factor` 放大贴图。传入 `image_url` 可启用图生图，添加 `mask_url` 可启用局部重绘。每次请求最多 4 张图像。

- Model id: `patina/material`
- Made by: Open model
- Category: Text to Image
- Output: image
- Price per run: from $0.08 / run (cheapest run $0.08 (80 API credits))
- Run: `POST https://api.doitong.com/v1/run/patina/material`
- Quote: `POST https://api.doitong.com/v1/quote/patina/material`
- Docs page: https://doitong.com/zh/developers/models/patina/material

## Pricing

Price: **from $0.08 / run** (the lowest tier; the quote prices your exact input). Price per run: the input files are measured before the run (length, resolution, frame rate). The quote measures the same files and is exactly what the run charges.

The full example input below costs $0.08 = 80 API credits (1 run × $0.08).

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/patina/material` 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 |  |  | 要创建的材质或纹理的文本描述。 |
| `image_size` |  | object {width, height} \| enum (string) | no | `square_hd` | `square_hd`, `square`, `portrait_4_3`, `portrait_16_9`, `landscape_4_3`, `landscape_16_9` | 生成纹理的像素尺寸。 |
| `num_inference_steps` |  | integer | no | `8` | 1 – 8 | 用于生成纹理的去噪步数。 |
| `seed` |  | integer \| null | no |  |  | 使生成结果可复现的随机种子。 |
| `num_images` | `num_outputs` | integer | no | `1` | 1 – 4 | 要创建的纹理图像数量。 |
| `enable_prompt_expansion` |  | boolean | no | `true` |  | 启用基于 LLM 的提示词扩展以添加细节。 |
| `enable_safety_checker` |  | boolean | no | `true` |  | 启用生成图像的安全检查。 |
| `tiling_mode` |  | enum (string) | no | `both` | `both`, `horizontal`, `vertical` | 平铺方向：'both' 表示全方向平铺，或选择 'horizontal'、'vertical'。 |
| `tile_size` |  | integer | no | `128` | 32 – 256 | 潜空间平铺尺寸，其中 64 表示 512px，128 表示 1024px。在每个平铺轴上，生成图像的尺寸会设置自动上限。 |
| `tile_stride` |  | integer | no | `64` | 16 – 128 | 设置潜空间平铺之间的步幅；自动上限会防止其超过有效平铺尺寸的一半。 |
| `image_url` | `image` | string (image URL) \| null | no |  |  | 源图像 URL；如果同时提供 mask_url，则用于图像修复，否则用于图像到图像处理。 |
| `mask_url` | `mask` | string (image URL) \| null | no |  |  | 与必需的 image_url 一起提供的图像修复遮罩 URL。遮罩会保留黑色区域不变，并重新生成白色区域。 |
| `strength` |  | number | no | `0.6` | ≤ 1, > 0 | 仅应用于通过 image_url 提供的图像的变换强度；其他情况下不使用。 |
| `maps` |  | array of enum (string) | no | `["basecolor","normal","roughness","metalness","height"]` |  | 用于预测的 PBR 贴图，默认选择全部五种。不选择任何贴图则完全跳过 PBR 估计。 |
| `upscale_factor` |  | enum (integer) | no | `0` | `0`, `2`, `4` | SeedVR 无缝放大倍数，影响预测的 PBR 贴图，但保持基础纹理的原始分辨率。选择 2 可将分辨率加倍，选择 4 可变为四倍，选择 0 可禁用放大。 |
| `output_format` |  | enum (string) | no | `png` | `jpeg`, `png`, `webp` | 输出纹理和 PBR 贴图的图像文件格式。 |

File parameters (`image`, `mask`) take a public `https://` URL, a `data:` URI up to 10 MB, or the `file_url` of an upload from `POST /files`.

## 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/patina/material?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/patina/material?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/patina/material", 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": "patina/material",
      "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": "patina/material",
  "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": 80,
    "usd": "0.08",
    "charged": 80,
    "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
      }
    ],
    "seed": 42,
    "prompt": "string",
    "timings": {}
  },
  "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 any | 输出图像包含 PATINA 预测的 PBR 材质贴图，以及 z-image 生成的可平铺纹理。 |
| `seed` | integer | 生成纹理时使用的种子。 |
| `prompt` | string | 纹理生成提示词，包括应用的任何扩展内容。 |
| `timings` | object | 覆盖完整处理过程的计时明细，单位为秒。 |

## Related models

- [PATINA](https://doitong.com/developers/models/patina.md) (`patina`): from $0.0225 / run of input image
- [PATINA · Material · Extract](https://doitong.com/developers/models/patina/material/extract.md) (`patina/material/extract`): from $0.17 / run
- [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

## Links

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