# Image Generation

Image Generation

## Endpoint and authentication

`POST https://api.tu-zi.com/v1/images/generations`

Transport: json

Use an inference API key. Examples contain placeholders; read credentials from environment variables.

```http
Authorization: Bearer YOUR_API_KEY
Content-Type: application/json
```

## Body parameters

| Field | Type | Required | Description / constraints |
| --- | --- | --- | --- |
| model | string | Yes | Image model identifier. Example: gpt-image-1 |
| prompt | string | Yes | Image generation prompt. Example: 极简产品摄影，白色陶瓷咖啡杯放在浅色木桌上，柔和自然窗光，干净背景，细节清晰 |
| size | string | No | Output dimensions, for example 1024x1024. Form initial value (not an upstream default): "1024x1024" Values: "1024x1024", "1536x1024", "1024x1536", "512x512", "256x256" Example: 1024x1024 |
| n | number | No | Number of images to generate. Form initial value (not an upstream default): 1 Range in this definition: 1 ~ 10 Example: 1 |
| quality | string | No | Image quality setting. Form initial value (not an upstream default): "standard" Values: "standard", "hd", "auto" Example: standard |
| response_format | string | No | Image response format, typically url or b64_json. Form initial value (not an upstream default): "url" Values: "url", "b64_json" Example: url |
| style | string | No | Image style: vivid or natural, where supported. Values: "vivid", "natural" Example: vivid |
| background | string | No | Background mode; transparent requests transparency where supported. Values: "auto", "transparent", "opaque" Example: auto |

## Minimal request example

```json
{
  "model": "YOUR_MODEL",
  "prompt": "A white ceramic cup"
}
```

## Before running

Replace https://api.tu-zi.com with this site origin (do not append /v1 twice), and YOUR_MODEL with the exact marketplace model ID. Install requests for Python; use Node.js 20+ and save JavaScript as .mjs. WebSocket samples list their own dependencies.

Create an inference key on the site and set API_KEY in the environment; replace YOUR_API_KEY in cURL. Prepare local input.png/input.wav files for upload examples.

Start with a minimal request, then read the result using the response fields below. For async APIs, a returned task ID requires polling; acceptance does not mean generation has finished.

## Code examples

### curl

```bash
curl --request POST 'https://api.tu-zi.com/v1/images/generations' \
  --header 'Authorization: Bearer YOUR_API_KEY' \
  --header 'Content-Type: application/json' \
  --data '{
  "model": "YOUR_MODEL",
  "prompt": "A white ceramic cup"
}'
```

### python

```python
import json
import os
import requests

url = "https://api.tu-zi.com/v1/images/generations"
headers = json.loads("{\"Authorization\":\"Bearer YOUR_API_KEY\",\"Content-Type\":\"application/json\"}")
headers["Authorization"] = "Bearer " + os.environ["API_KEY"]
payload = json.loads("{\"model\":\"YOUR_MODEL\",\"prompt\":\"A white ceramic cup\"}")
response = requests.request("POST", url, headers=headers, json=payload, timeout=120)
response.raise_for_status()
result = response.json()
if isinstance(result, dict) and (result.get("success") is False or result.get("error")):
    raise RuntimeError(json.dumps(result, ensure_ascii=False))
print(result)  # Also inspect protocol-specific status/code fields.
```

### javascript

```javascript
// Node.js 20+
const url = "https://api.tu-zi.com/v1/images/generations";
const headers = {
  "Authorization": "Bearer YOUR_API_KEY",
  "Content-Type": "application/json"
};
headers.Authorization = 'Bearer ' + process.env.API_KEY;
const response = await fetch(url, { method: "POST", headers, body: JSON.stringify({
  "model": "YOUR_MODEL",
  "prompt": "A white ceramic cup"
}) });
if (!response.ok) throw new Error(await response.text());
const result = await response.json();
if (result?.success === false || result?.error) throw new Error(JSON.stringify(result));
console.log(result); // Also inspect protocol-specific status/code fields.
```

## Response fields

| Field | Type | Description / constraints |
| --- | --- | --- |
| data[].url | string | Image URL in URL output mode. |
| data[].b64_json | string | Base64 image when that output mode is supported. |

Illustrative response; some endpoints show essential fields only. Optional fields may be absent.

```json
{
  "created": 1793491200,
  "data": [
    {
      "url": "https://example.com/result.png"
    }
  ]
}
```

## Errors and retries

Check HTTP status and protocol-specific errors; do not mix error formats across protocols. For 401/403 check credentials and permissions, for 429 back off, and for 5xx check gateway/upstream status. Error bodies may contain error/message/code depending on the protocol.

Use bounded exponential backoff for reads. Do not blindly retry task creation, generation, or key mutations, to avoid duplicate billing or execution.

## Constraints

This page describes the base protocol. Use the selected model quick start for supported fields, enums, limits and asynchronous behavior. Replace YOUR_MODEL. Form initial values are not upstream defaults. Inspect every item in data[]: url is an image download URL; b64_json is Base64-encoded image bytes to decode and save, not a URL. Handle either form. HTTP 200, empty data[] or text alone does not prove image generation succeeded. Do not send the site API key when downloading result URLs. Save and validate the actual image format/MIME; renaming an extension does not convert an image.

## Documentation source

Definition version: 1

Reviewed: 2026-11-02

Source: https://api.tu-zi.com/en/docs/api/reference-relay-image-generation

Related protocol guide: https://api.tu-zi.com/docs/api/images

This export contains documentation and examples only, without real credentials, account data or user requests.