创建图片任务 - AI Platform

POST

images

Create image task

curl --request POST \
  --url https://sigrunai.com/v1/images \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "gpt-image-2",
  "prompt": "极简产品静物，白色背景，柔光",
  "quality": "medium",
  "n": 1,
  "ratio": "1:1"
}
'

import requests

url = "https://sigrunai.com/v1/images"

payload = {
    "model": "gpt-image-2",
    "prompt": "极简产品静物，白色背景，柔光",
    "quality": "medium",
    "n": 1,
    "ratio": "1:1"
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    model: 'gpt-image-2',
    prompt: '极简产品静物，白色背景，柔光',
    quality: 'medium',
    n: 1,
    ratio: '1:1'
  })
};

fetch('https://sigrunai.com/v1/images', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://sigrunai.com/v1/images"

	payload := strings.NewReader("{\n  \"model\": \"gpt-image-2\",\n  \"prompt\": \"极简产品静物，白色背景，柔光\",\n  \"quality\": \"medium\",\n  \"n\": 1,\n  \"ratio\": \"1:1\"\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "Bearer <token>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

{
  "code": 20000,
  "msg": "<string>",
  "data": {
    "task_id": "<string>",
    "status": "<string>",
    "progress": 123,
    "model": "<string>",
    "created_at": 123,
    "completed_at": 123,
    "cost": 123,
    "error_message": "<string>",
    "result": {
      "image_url": "<string>",
      "video_url": "<string>"
    }
  }
}

创建 GPT Image 2 异步生图任务。成功后返回 task_id，请用查询图片任务轮询，完成后从 result.image_url 取图。

模型

`model`	说明	计费
`gpt-image-2`	推荐写法；用 `quality` 选择档位	见下
`gpt-image-2-low`	低价档	`per_task`
`gpt-image-2-medium`	中档	`per_task`
`gpt-image-2-high`	高档	`per_task`

当 model 为 gpt-image-2 且 quality 为 low / medium / high 时，按对应 gpt-image-2-* 计费模型扣费。quality 默认 auto。

请求参数

字段	类型	必填	说明
`model`	string	是	见上表
`prompt`	string	是	提示词，1–4000 字符
`quality`	string	否	`auto`（默认）、`low`、`medium`、`high`
`n`	int	否	生成张数，1–4，默认 1；费用按张数 × 单价
`ratio`	string	否	画面比例；也可用 `size`
`size`	string	否	`ratio` 别名
`resolution`	string	否	可选分辨率提示（如 `1k` / `2k`）
`images`	string[]	否	参考图公网 URL，最多 15 张

示例

curl -X POST 'https://sigrunai.com/v1/images' \
  -H 'Authorization: Bearer sk_xxx' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gpt-image-2",
    "prompt": "极简产品静物，白色背景，柔光",
    "quality": "medium",
    "n": 1,
    "ratio": "1:1"
  }'

成功响应示例：

{
  "code": 20000,
  "msg": "提交成功",
  "data": {
    "task_id": "img_xxxxxxxx",
    "status": "queued",
    "progress": 0,
    "model": "gpt-image-2",
    "cost": 0.2,
    "result": {}
  }
}

授权

Authorization

string

header

必填

API key from the console. Format: Bearer sk_xxx

请求体

application/json

model

string

必填

gpt-image-2 或 gpt-image-2-low / medium / high

示例:

"gpt-image-2"

prompt

string

必填

Required string length: 1 - 4000

quality

enum<string>

默认值:auto

可用选项:

auto,

low,

medium,

high

integer

默认值:1

必填范围: 1 <= x <= 4

ratio

string

size

string

ratio 别名

resolution

string

images

string<uri>[]

Maximum array length: 15

响应

200 - application/json

提交成功

code

integer

示例:

20000

msg

string

data

object

Show child attributes

最后修改于 2026年10月15日

⌘I

Create image task

curl --request POST \
  --url https://sigrunai.com/v1/images \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "gpt-image-2",
  "prompt": "极简产品静物，白色背景，柔光",
  "quality": "medium",
  "n": 1,
  "ratio": "1:1"
}
'

import requests

url = "https://sigrunai.com/v1/images"

payload = {
    "model": "gpt-image-2",
    "prompt": "极简产品静物，白色背景，柔光",
    "quality": "medium",
    "n": 1,
    "ratio": "1:1"
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    model: 'gpt-image-2',
    prompt: '极简产品静物，白色背景，柔光',
    quality: 'medium',
    n: 1,
    ratio: '1:1'
  })
};

fetch('https://sigrunai.com/v1/images', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://sigrunai.com/v1/images"

	payload := strings.NewReader("{\n  \"model\": \"gpt-image-2\",\n  \"prompt\": \"极简产品静物，白色背景，柔光\",\n  \"quality\": \"medium\",\n  \"n\": 1,\n  \"ratio\": \"1:1\"\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "Bearer <token>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

{
  "code": 20000,
  "msg": "<string>",
  "data": {
    "task_id": "<string>",
    "status": "<string>",
    "progress": 123,
    "model": "<string>",
    "created_at": 123,
    "completed_at": 123,
    "cost": 123,
    "error_message": "<string>",
    "result": {
      "image_url": "<string>",
      "video_url": "<string>"
    }
  }
}

​模型

​请求参数

​示例

授权

请求体

响应

模型

请求参数

示例