# Kolors API

> فوٹوریئلسٹک نتائج کے لیے بنایا گیا متن-سے-تصویر ماڈل، شیڈیولر کے انتخاب اور فی درخواست 8 تصاویر تک کے ساتھ

متنی `prompt` سے تصاویر بناتا ہے، اختیاری `negative_prompt` کے ساتھ۔ `guidance_scale` (1 سے 10)، `num_inference_steps` (1 سے 150) کو ایڈجسٹ کریں اور چھ اختیارات میں سے `scheduler` منتخب کریں۔ چھ پیش سیٹ سائزز میں فی کال 1 سے 8 تصاویر png یا jpeg کے طور پر بناتا ہے، جبکہ `seed` تکرار پذیری فراہم کرتا ہے۔

- Model id: `kolors`
- Made by: Kuaishou
- Category: Text to Image
- Output: image
- Estimated price per run: $0.035 / image (cheapest run $0.035 (35 API credits))
- Run: `POST https://api.doitong.ru/v1/run/kolors`
- Quote: `POST https://api.doitong.ru/v1/quote/kolors`
- Docs page: https://doitong.com/ur/developers/models/kolors

## Pricing

Price: **$0.035 / 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 $0.035 = 35 API credits (1 image × 35 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/kolors` 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 |  |  | جتنی ممکن ہو تفصیل کے ساتھ مطلوبہ تصویر بیان کریں تاکہ بہترین نتائج حاصل ہوں۔ |
| `negative_prompt` |  | string | no | `` |  | خارج کی جانے والی تصویر کی تفصیلات بتانے والا منفی پرامپٹ، مثلاً رنگ، اشیا، مناظر یا باریک خصوصیات۔ مثالوں میں مونچھ، دھندلا پن یا کم ریزولوشن شامل ہیں۔ |
| `guidance_scale` |  | number | no | `5` | 1 – 10 | تصویر کے انتخاب کے لیے پرامپٹ کی پابندی کی طاقت، CFG (Classifier Free Guidance) کے طور پر ظاہر کی جاتی ہے۔ |
| `num_inference_steps` |  | integer | no | `50` | 1 – 150 | چلائے جانے والے inference steps کی تعداد۔ |
| `seed` |  | integer \| null | no |  |  | بے ترتیب generator seed۔ |
| `enable_safety_checker` |  | boolean | no | `true` |  | حفاظتی جانچ کا ٹوگل؛ جہاں جانچ لاگو ہو وہاں غیر محفوظ تصاویر کی جگہ سیاہ تصاویر رکھی جاتی ہیں۔ غیر مجاز درخواستوں کے لیے جانچ لازمی ہے؛ صرف مجاز اکاؤنٹس اسے غیر فعال کر سکتے ہیں۔ |
| `num_images` | `num_outputs` | integer | no | `1` | 1 – 8 | تخلیق کے لیے مطلوبہ تصاویر کی تعداد۔ |
| `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` | تخلیق شدہ تصویر کی dimensions۔ |
| `scheduler` |  | enum (string) | no | `EulerDiscreteScheduler` | `EulerDiscreteScheduler`, `EulerAncestralDiscreteScheduler`, `DPMSolverMultistepScheduler`, `DPMSolverMultistepScheduler_SDE_karras`, `UniPCMultistepScheduler`, `DEISMultistepScheduler` | ماڈل کے execution کے لیے منتخب کردہ scheduler۔ |
| `output_format` |  | enum (string) | no | `png` | `jpeg`, `png` | نتیجہ خیز تصویر کا فائل فارمیٹ۔ |

## 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.ru/v1/run/kolors?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.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/kolors?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.ru/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/kolors", 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": "kolors",
      "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": "kolors",
  "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": 35,
    "usd": "0.035",
    "charged": 35,
    "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.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
    "cancel": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
  }
}
```

### Output fields (`output`)

| Field | Type | Description |
|---|---|---|
| `images` | array of object {url, width, height, content_type} | تیار کردہ تصاویر کی فائل کی معلومات۔ |
| `timings` | object |  |
| `seed` | integer | تصویر کی تخلیق کا سیڈ، جس میں ان پٹ میں سیڈ نہ دینے پر تصادفی طور پر منتخب قدر شامل ہوتی ہے؛ بصورتِ دیگر فراہم کردہ قدر دہرائی جاتی ہے۔ |
| `has_nsfw_concepts` | array of boolean | بتاتا ہے کہ تخلیق کردہ تصاویر میں NSFW تصورات موجود ہیں یا نہیں۔ |
| `prompt` | string | تصویر کی تخلیق کے دوران استعمال ہونے والا پرامپٹ۔ |

## Related models

- [Kolors Image to Image](https://doitong.com/developers/models/kolors/image-to-image.md) (`kolors/image-to-image`): $0.009 / 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
- [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

## Links

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