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GPT-Image-2 官方渠道 图像生成

  • OpenAI 官方 gpt-image-2 模型,基于 /v1/images/generations 兼容协议
  • 异步处理模式,返回 task_id 用于后续查询
  • 文生图 / 图生图 / 局部重绘(mask)三合一
  • 支持 PNG / WebP 透明背景(Alpha 通道)
  • 新增 resolution 档位字段,支持 1K / 2K / 4K 分辨率选择
  • 支持 15 种比例,1K / 2K / 4K 档均可用
  • 单次最多生成 4 张图片,参考图最多 16 张
  • 与 gpt-image-1.5-official 接口 95% 对齐,迁移只需改模型名
请求示例
curl --request POST \
  --url https://26j.cn/v1/images/generations \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "gpt-image-2-official",
    "prompt": "星空下的古老城堡",
    "size": "16:9",
    "resolution": "2k",
    "quality": "high",
    "n": 1
  }'
import requests

url = "https://26j.cn/v1/images/generations"

payload = {
    "model": "gpt-image-2-official",
    "prompt": "星空下的古老城堡",
    "size": "16:9",
    "resolution": "2k",
    "quality": "high",
    "n": 1
}

headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

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

print(response.json())
const url = "https://26j.cn/v1/images/generations";

const payload = {
  model: "gpt-image-2-official",
  prompt: "星空下的古老城堡",
  size: "16:9",
  resolution: "2k",
  quality: "high",
  n: 1,
};

const headers = {
  Authorization: "Bearer <token>",
  "Content-Type": "application/json",
};

fetch(url, {
  method: "POST",
  headers: headers,
  body: JSON.stringify(payload),
})
  .then((response) => response.json())
  .then((data) => console.log(data))
  .catch((error) => console.error("Error:", error));
package main

import (
    "bytes"
    "encoding/json"
    "fmt"
    "io/ioutil"
    "net/http"
)

func main() {
    url := "https://26j.cn/v1/images/generations"

    payload := map[string]interface{}{
        "model":      "gpt-image-2-official",
        "prompt":     "星空下的古老城堡",
        "size":       "16:9",
        "resolution": "2k",
        "quality":    "high",
        "n":          1,
    }

    jsonData, _ := json.Marshal(payload)

    req, _ := http.NewRequest("POST", url, bytes.NewBuffer(jsonData))
    req.Header.Set("Authorization", "Bearer <token>")
    req.Header.Set("Content-Type", "application/json")

    client := &http.Client{}
    resp, err := client.Do(req)
    if err != nil {
        panic(err)
    }
    defer resp.Body.Close()

    body, _ := ioutil.ReadAll(resp.Body)
    fmt.Println(string(body))
}
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.net.URI;

public class Main {
    public static void main(String[] args) throws Exception {
        String url = "https://26j.cn/v1/images/generations";

        String payload = """
        {
          "model": "gpt-image-2-official",
          "prompt": "星空下的古老城堡",
          "size": "16:9",
          "resolution": "2k",
          "quality": "high",
          "n": 1
        }
        """;

        HttpClient client = HttpClient.newHttpClient();
        HttpRequest request = HttpRequest.newBuilder()
            .uri(URI.create(url))
            .header("Authorization", "Bearer <token>")
            .header("Content-Type", "application/json")
            .POST(HttpRequest.BodyPublishers.ofString(payload))
            .build();

        HttpResponse<String> response = client.send(request,
            HttpResponse.BodyHandlers.ofString());

        System.out.println(response.body());
    }
}
<?php

$url = "https://26j.cn/v1/images/generations";

$payload = [
    "model" => "gpt-image-2-official",
    "prompt" => "星空下的古老城堡",
    "size" => "16:9",
    "resolution" => "2k",
    "quality" => "high",
    "n" => 1
];

$ch = curl_init($url);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($payload));
curl_setopt($ch, CURLOPT_HTTPHEADER, [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
]);

$response = curl_exec($ch);
curl_close($ch);

echo $response;
?>
require 'net/http'
require 'json'
require 'uri'

url = URI("https://26j.cn/v1/images/generations")

payload = {
  model: "gpt-image-2-official",
  prompt: "星空下的古老城堡",
  size: "16:9",
  resolution: "2k",
  quality: "high",
  n: 1
}

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = "Bearer <token>"
request["Content-Type"] = "application/json"
request.body = payload.to_json

response = http.request(request)
puts response.body
import Foundation

let url = URL(string: "https://26j.cn/v1/images/generations")!

let payload: [String: Any] = [
    "model": "gpt-image-2-official",
    "prompt": "星空下的古老城堡",
    "size": "16:9",
    "resolution": "2k",
    "quality": "high",
    "n": 1
]

var request = URLRequest(url: url)
request.httpMethod = "POST"
request.setValue("Bearer <token>", forHTTPHeaderField: "Authorization")
request.setValue("application/json", forHTTPHeaderField: "Content-Type")
request.httpBody = try? JSONSerialization.data(withJSONObject: payload)

let task = URLSession.shared.dataTask(with: request) { data, response, error in
    if let error = error {
        print("Error: \(error)")
        return
    }

    if let data = data, let responseString = String(data: data, encoding: .utf8) {
        print(responseString)
    }
}

task.resume()
using System;
using System.Net.Http;
using System.Text;
using System.Threading.Tasks;

class Program
{
    static async Task Main(string[] args)
    {
        var url = "https://26j.cn/v1/images/generations";

        var payload = @"{
            ""model"": ""gpt-image-2-official"",
            ""prompt"": ""星空下的古老城堡"",
            ""size"": ""16:9"",
            ""resolution"": ""2k"",
            ""quality"": ""high"",
            ""n"": 1
        }";

        using var client = new HttpClient();
        client.DefaultRequestHeaders.Add("Authorization", "Bearer <token>");

        var content = new StringContent(payload, Encoding.UTF8, "application/json");
        var response = await client.PostAsync(url, content);
        var result = await response.Content.ReadAsStringAsync();

        Console.WriteLine(result);
    }
}
import 'dart:convert';
import 'package:http/http.dart' as http;

void main() async {
  final url = Uri.parse('https://26j.cn/v1/images/generations');

  final payload = {
    'model': 'gpt-image-2-official',
    'prompt': '星空下的古老城堡',
    'size': '16:9',
    'resolution': '2k',
    'quality': 'high',
    'n': 1,
  };

  final response = await http.post(
    url,
    headers: {
      'Authorization': 'Bearer <token>',
      'Content-Type': 'application/json',
    },
    body: jsonEncode(payload),
  );

  print(response.body);
}
library(httr)
library(jsonlite)

url <- "https://26j.cn/v1/images/generations"

payload <- list(
  model = "gpt-image-2-official",
  prompt = "星空下的古老城堡",
  size = "16:9",
  resolution = "2k",
  quality = "high",
  n = 1
)

response <- POST(
  url,
  add_headers(
    Authorization = "Bearer <token>",
    `Content-Type` = "application/json"
  ),
  body = toJSON(payload, auto_unbox = TRUE),
  encode = "raw"
)

cat(content(response, "text"))
响应示例
{
  "code": 200,
  "data": [
    {
      "status": "submitted",
      "task_id": "task_01KPTXXXXXXXXXXXXXXX"
    }
  ]
}
{
  "error": {
    "code": 400,
    "message": "参数错误:size 不合法 / resolution 不支持 / 像素违规等",
    "type": "invalid_request_error"
  }
}
{
  "error": {
    "code": 401,
    "message": "身份验证失败,请检查您的API密钥",
    "type": "authentication_error"
  }
}
{
  "error": {
    "code": 402,
    "message": "账户余额不足,请充值后再试",
    "type": "payment_required"
  }
}
{
  "error": {
    "code": 403,
    "message": "访问被禁止,您没有权限访问此资源",
    "type": "permission_error"
  }
}
{
  "error": {
    "code": 429,
    "message": "请求过于频繁,请稍后再试",
    "type": "rate_limit_error"
  }
}
{
  "error": {
    "code": 500,
    "message": "服务器内部错误,请稍后重试",
    "type": "server_error"
  }
}
{
  "error": {
    "code": 502,
    "message": "网关错误,服务器暂时不可用",
    "type": "bad_gateway"
  }
}

Authorizations

Authorizationstring必填

所有接口均需要使用 Bearer Token 进行认证

获取 API Key:

访问 API Key 管理页面 获取您的 API Key

使用时在请求头中添加:

Authorization: Bearer YOUR_API_KEY

Body

modelstring必填默认 gpt-image-2-official

图像生成模型名称

固定填写 gpt-image-2-official(OpenAI 官方 gpt-image-2 模型)

nsfw_checkboolean默认 false

是否在提交图片任务前执行内容审核。

  • true:使用 omni-moderation-latest 审核提示词和输入图片
  • false 或不传:不发起审核请求,不增加审核成本与延迟(默认)
promptstring必填

图像生成的文本描述

  • 支持中英文,建议详细描述
  • 提交前会经过平台敏感词 / 安全审核,命中违规内容会直接返回错误
sizestring默认 1:1

画面比例

对外使用比例值,系统内部按 resolution 自动映射到具体像素。

支持以下比例,也可传入 auto 由服务端自动选择合适比例:

  • auto - 自动(由服务端根据 prompt / 参考图自动选择比例)
  • 1:1 - 正方形构图(默认,社交头像 / Logo)
  • 3:2 - 横构图(单反相机常见比例)
  • 2:3 - 竖构图(海报竖版)
  • 4:3 - 横构图(经典显示器 / PPT)
  • 3:4 - 竖构图
  • 5:4 - 横构图
  • 4:5 - 竖构图(Instagram 竖版帖子)
  • 16:9 - 横构图(宽屏视频封面)
  • 9:16 - 竖构图(手机全屏 / 短视频封面)
  • 2:1 - 横构图(网页 Banner)
  • 1:2 - 竖构图
  • 3:1 - 横构图(超宽 Banner)
  • 1:3 - 竖构图(超长海报)
  • 21:9 - 横构图(电影超宽屏)
  • 9:21 - 竖构图

也支持直接传入像素尺寸,例如 1881x836 / 887x1774。

当 size 传入 auto 时,默认比例为 1:1。

resolutionstring默认 1k

分辨率档位(新增字段)

控制实际出图清晰度。

  • 1k - 1024 基准,省钱日常够用(默认)
  • 2k - 2048 基准,适合海报 / 高清需求
  • 4k - 3840 基准,支持下方映射表中的 15 个比例

4K 支持下方映射表中的 15 个比例;也可以直接通过 size 传入表格中的像素尺寸。

qualitystring默认 auto

图片质量

  • auto - 自动(默认,通常等同 low)
  • low - 快速省钱,轮廓够用
  • medium - 平衡
  • high - 最高精度(4K + high 耗时 >120s)
moderationstring默认 auto

审核强度

  • auto - 默认审核强度
  • low - 更宽松的审核强度
output_formatstring默认 png

输出格式

  • png - 默认格式,支持透明背景
  • jpeg - 文件更小,不支持 Alpha 通道
  • webp - 支持透明背景,适合现代浏览器
output_compressioninteger

输出压缩强度,范围 0-100

  • 仅对 jpeg / webp 有效
ninteger默认 1

生成图片张数

取值范围:1 ~ 4

必须输入纯数字(如 1),不要加引号

image_urlsarray

参考图 URL 数组

mask_urlstring

遮罩图 URL,用于局部重绘(inpainting)

  • 需搭配 image_urls 一起使用

1、上传遮罩图前,请先确认图片 Alpha 通道为「是」。

2、遮罩图尺寸需与首张参考图一致。

尺寸 × 分辨率映射表

size × resolution → OpenAI 实际像素(15 比例 × 3 档位):

size 1k 2k 4k
1:1 1024×1024 2048×2048 2880×2880
3:2 1536×1024 2048×1360 3520×2336
2:3 1024×1536 1360×2048 2336×3520
4:3 1024×768 2048×1536 3312×2480
3:4 768×1024 1536×2048 2480×3312
5:4 1280×1024 2560×2048 3216×2576
4:5 1024×1280 2048×2560 2576×3216
16:9 1536×864 2048×1152 3840×2160
9:16 864×1536 1152×2048 2160×3840
2:1 2048×1024 2688×1344 3840×1920
1:2 1024×2048 1344×2688 1920×3840
3:1 1881×836 / 1536×512 3072×1024 3840×1280
1:3 887×1774 / 512×1536 1024×3072 1280×3840
21:9 2016×864 2688×1152 3840×1648
9:21 864×2016 1152×2688 1648×3840

说明:部分尺寸会按 16 倍数和像素上限做近似映射,例如 3:2 / 2:3 @ 2K 实际是 2048×1360,21:9 @ 4K 是 3840×1648;请以表格中的实际像素为准。

使用场景示例

文生图(最简请求)

{
  "model": "gpt-image-2-official",
  "prompt": "星空下的古老城堡"
}

文生图(透明贴纸)

{
  "model": "gpt-image-2-official",
  "prompt": "A cute cartoon orange cat sticker, full body, thick white outline, flat vector style, isolated on a fully transparent background",
  "size": "1:1",
  "resolution": "1k",
  "quality": "medium",
  "output_format": "png",
  "n": 1
}

图生图(移除背景)

{
  "model": "gpt-image-2-official",
  "prompt": "Remove the background, keep only the product, isolated on a fully transparent background",
  "image_urls": ["https://your-cdn.com/product.jpg"],
  "size": "1:1",
  "resolution": "1k",
  "output_format": "png"
}

2K 高清海报

{
  "model": "gpt-image-2-official",
  "prompt": "赛博朋克夜景",
  "size": "16:9",
  "resolution": "2k",
  "quality": "high",
  "output_format": "jpeg",
  "output_compression": 90
}

4K 壁纸

{
  "model": "gpt-image-2-official",
  "prompt": "雪山日出全景",
  "size": "16:9",
  "resolution": "4k",
  "quality": "high",
  "n": 1
}

图生图(多参考图融合)

{
  "model": "gpt-image-2-official",
  "prompt": "将两张参考图融合成一张插画海报,保留主体轮廓",
  "size": "1:1",
  "quality": "high",
  "image_urls": [
    "https://your-cdn.com/input-a.png",
    "https://your-cdn.com/input-b.png"
  ]
}

局部重绘(mask)

{
  "model": "gpt-image-2-official",
  "prompt": "把背景换成沙漠日落",
  "size": "1:1",
  "quality": "medium",
  "image_urls": ["https://your-cdn.com/photo.png"],
  "mask_url": "https://your-cdn.com/mask.png"
}

多张生成(n > 1)

{
  "model": "gpt-image-2-official",
  "prompt": "Four minimalist poster variations of a red fox",
  "size": "1:1",
  "quality": "low",
  "n": 4
}

直接传像素串(高级用法)

{
  "model": "gpt-image-2-official",
  "prompt": "wide cinematic shot",
  "size": "3840x2160",
  "quality": "high"
}

Response

codeinteger

响应状态码

dataarray

返回数据数组

查询任务结果

提交成功后返回 task_id,通过 GET /v1/tasks/{task_id} 轮询任务状态,详见 任务查询接口。

成功响应示例

{
  "code": 200,
  "data": {
    "actual_time": 14,
    "completed": 1784607890,
    "cost": 0.004792,
    "created": 1784607876,
    "credits_cost": 0.047920000000000004,
    "estimated_time": 60,
    "id": "task_01KPTXXXXXXXXXXXXXXX",
    "progress": 100,
    "result": {
      "images": [
        {
          "expires_at": 1784694290,
          "url": [
            "https://26j.cn/f/image/xxxxxxxx-gpt_image_2_official_task_xxx_0.png"
          ]
        }
      ]
    },
    "status": "completed",
    "usage": {
      "input_tokens": 22,
      "input_tokens_details": {
        "cached_tokens": 0,
        "image_tokens": 0,
        "text_tokens": 22
      },
      "output_tokens": 196,
      "output_tokens_details": {
        "image_tokens": 196,
        "text_tokens": 0
      },
      "total_tokens": 218
    }
  }
}

usage 字段说明本次请求的 token 计费用量:

字段 说明
input_tokens 输入消耗的 token 总数
input_tokens_details.cached_tokens 命中缓存的输入 token 数
input_tokens_details.image_tokens 输入图片占用的 token 数
input_tokens_details.text_tokens 输入文本(提示词)占用的 token 数
output_tokens 输出消耗的 token 总数
output_tokens_details.image_tokens 生成图片占用的 token 数
output_tokens_details.text_tokens 输出文本占用的 token 数
total_tokens 总 token 数,等于 input_tokens + output_tokens

图像生成任务的输出以图片 token 为主,因此 output_tokens_details.image_tokens 通常等于 output_tokens。上例中 total_tokens = 22 + 196 = 218。

任务状态流转:submitted → in_progress → completed / failed。

取图方式:data.result.images[0].url[0]。

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