# PATINA · Material · Extract API

> بافت قابل‌تکرار را همراه با نقشه‌های PBR از تصویر منبع استخراج می‌کند و پرامپت سطح موردنظر را مشخص می‌کند

یک تصویر ورودی و پرامپتی درباره سطح موردنظر برای استخراج دریافت و بافتی قابل‌تکرار همراه با نقشه‌های PBR پیش‌بینی‌شده برمی‌گرداند. `maps` تعیین می‌کند کدام‌یک از basecolor، normal، roughness، metalness و height تولید شوند. گزینه‌ها شامل `tiling_mode`، `strength` از 0 تا 1، `upscale_factor` برابر 0، 2 یا 4 و 1 تا 4 خروجی هستند.

- Model id: `patina/material/extract`
- Made by: Open model
- Category: Image Editing
- Output: image
- Price per run: from $0.17 / run (cheapest run $0.17 (170 API credits))
- Run: `POST https://api.doitong.com/v1/run/patina/material/extract`
- Quote: `POST https://api.doitong.com/v1/quote/patina/material/extract`
- Docs page: https://doitong.com/fa/developers/models/patina/material/extract

## Pricing

Price: **from $0.17 / 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 $0.17 = 170 API credits (1 run × $0.17).

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/extract` 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 |  |  | توضیح بافت موردنظر برای استخراج از تصویر. |
| `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` | ابعاد پیکسلی بافت حاصل. |
| `num_inference_steps` |  | integer | no | `8` | 1 – 8 | تعداد گام‌های حذف نویز استفاده‌شده برای تولید بافت‌ها. |
| `seed` |  | integer \| null | no |  |  | seed تصادفی که تولید تکرارپذیر را ممکن می‌کند. |
| `num_images` | `num_outputs` | integer | no | `1` | 1 – 4 | تعداد تصاویر بافتی که باید ایجاد شوند. |
| `enable_prompt_expansion` |  | boolean | no | `true` |  | بسط پرامپت مبتنی بر LLM را برای افزودن جزئیات فعال می‌کند. |
| `enable_safety_checker` |  | boolean | no | `true` |  | بررسی ایمنی تصاویر تولیدشده را فعال می‌کند. |
| `tiling_mode` |  | enum (string) | no | `both` | `both`, `horizontal`, `vertical` | جهت کاشی‌کاری: 'both' برای کاشی‌کاری همه‌جهته، 'horizontal' یا 'vertical'. |
| `tile_size` |  | integer | no | `128` | 32 – 256 | اندازه کاشی در فضای نهفته؛ 64 نشان‌دهنده 512px و 128 نشان‌دهنده 1024px است. در هر محور کاشی‌شده، بُعد تصویر تولیدشده سقف خودکار را تعیین می‌کند. |
| `tile_stride` |  | integer | no | `64` | 16 – 128 | گام بین کاشی‌های فضای نهفته را تعیین کنید؛ سقف خودکار مانع از بیشترشدن آن از نصف اندازه مؤثر کاشی می‌شود. |
| `image_url` | `image` | string (image URL) | yes |  |  | URL تصویر منبع برای استخراج بافت. |
| `strength` |  | number | no | `0.6` | ≤ 1, > 0 | قدرت تبدیل که فقط روی تصویر ارائه‌شده از طریق image_url اعمال می‌شود؛ در غیر این صورت استفاده نمی‌شود. |
| `maps` |  | array of enum (string) | no | `["basecolor","normal","roughness","metalness","height"]` |  | نقشه‌های PBR انتخاب‌شده برای پیش‌بینی؛ هر پنج مورد به‌صورت پیش‌فرض انتخاب می‌شوند. انتخاب هیچ‌کدام، برآورد PBR را کاملاً رد می‌کند. |
| `upscale_factor` |  | enum (integer) | no | `0` | `0`, `2`, `4` | ضریب بزرگ‌نمایی بدون درز SeedVR که بر نقشه‌های PBR پیش‌بینی‌شده اثر می‌گذارد، اما بافت پایه را در وضوح اصلی نگه می‌دارد. برای وضوح دوبرابر 2، برای وضوح چهاربرابر 4 و برای غیرفعال‌کردن بزرگ‌نمایی 0 را انتخاب کنید. |
| `output_format` |  | enum (string) | no | `png` | `jpeg`, `png`, `webp` | قالب فایل تصویر برای بافت‌ها و نقشه‌های PBR خروجی. |

File parameters (`image`) 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",
  "image": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
```

A fuller example:

```json
{
  "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
  "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
```

## 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/extract?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",
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  }'

# 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/extract?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",
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  }),
});
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",
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
}

# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/patina/material/extract", 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/extract",
      "input": {
        "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
        "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
      },
      "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/extract",
  "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": 170,
    "usd": "0.17",
    "charged": 170,
    "refunded": 0
  },
  "input": {
    "prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
    "image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
  },
  "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 | تصاویر خروجی شامل نقشه‌های مواد PBR پیش‌بینی‌شده توسط PATINA در کنار بافت‌های کاشی‌پذیر تولیدشده توسط z-image هستند. |
| `seed` | integer | seed اعمال‌شده هنگام تولید بافت‌ها. |
| `prompt` | string | پرامپت تولید بافت، شامل هرگونه بسط اعمال‌شده. |
| `timings` | object | جزئیات زمان‌بندی کل فرایند، برحسب ثانیه. |

## Related models

- [PATINA · Material](https://doitong.com/developers/models/patina/material.md) (`patina/material`): from $0.08 / run
- [PATINA](https://doitong.com/developers/models/patina.md) (`patina`): from $0.0225 / run of input image
- [FLUX.1 Kontext \[pro\]](https://doitong.com/developers/models/flux-pro/kontext.md) (`flux-pro/kontext`): $0.04 / image
- [GPT Image 2.5 Edit](https://doitong.com/developers/models/openai/gpt-image-2.5/flare/edit.md) (`openai/gpt-image-2.5/flare/edit`): from $0.12 / image
- [Nano Banana 2 Edit](https://doitong.com/developers/models/nano-banana-2/edit.md) (`nano-banana-2/edit`): from $0.06 / image
- [Nano Banana Pro Edit](https://doitong.com/developers/models/nano-banana-pro/edit.md) (`nano-banana-pro/edit`): from $0.15 / image
- [Qwen Image 3 Edit](https://doitong.com/developers/models/alibaba/qwen-image-3/edit.md) (`alibaba/qwen-image-3/edit`): from $0.04 / image
- [Seedream 5.0 Pro Edit](https://doitong.com/developers/models/bytedance/seedream/v5/pro/edit.md) (`bytedance/seedream/v5/pro/edit`): from $0.0675 / run of output image

## Links

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