TokenFlashTokenFlash
Api referenceImagesGpt image 2

GPT-Image-2 图像生成

  • 异步处理模式,返回任务ID用于后续查询
  • 基于 OpenAI Images 兼容协议,支持文生图 / 图生图
  • 支持 15 种图片比例,通过 size 字段传入
  • 通过 resolution1k / 2k / 4k)控制实际输出像素档位
  • 参考图最多 16 张,支持 URL 与 base64 混填
  • 按分辨率档位(1K / 2K / 4K)计费
curl --request POST \
  --url https://tokenflash.cn/v1/images/generations \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "gpt-image-2",
    "prompt": "一只橘猫坐在窗台上看夕阳,水彩画风格",
    "n": 1,
    "size": "16:9",
    "resolution": "2k"
  }'
import requests

url = "https://tokenflash.cn/v1/images/generations"

payload = {
    "model": "gpt-image-2",
    "prompt": "一只橘猫坐在窗台上看夕阳,水彩画风格",
    "n": 1,
    "size": "16:9",
    "resolution": "2k"
}

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

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

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

const payload = {
  model: "gpt-image-2",
  prompt: "一只橘猫坐在窗台上看夕阳,水彩画风格",
  n: 1,
  size: "16:9",
  resolution: "2k"
};

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://tokenflash.cn/v1/images/generations"

    payload := map[string]interface{}{
        "model":      "gpt-image-2",
        "prompt":     "一只橘猫坐在窗台上看夕阳,水彩画风格",
        "n":          1,
        "size":       "16:9",
        "resolution": "2k",
    }

    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://tokenflash.cn/v1/images/generations";

        String payload = """
        {
          "model": "gpt-image-2",
          "prompt": "一只橘猫坐在窗台上看夕阳,水彩画风格",
          "n": 1,
          "size": "16:9",
          "resolution": "2k"
        }
        """;

        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://tokenflash.cn/v1/images/generations";

$payload = [
    "model" => "gpt-image-2",
    "prompt" => "一只橘猫坐在窗台上看夕阳,水彩画风格",
    "n" => 1,
    "size" => "16:9",
    "resolution" => "2k"
];

$ch = curl_init($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://tokenflash.cn/v1/images/generations")

payload = {
  model: "gpt-image-2",
  prompt: "一只橘猫坐在窗台上看夕阳,水彩画风格",
  n: 1,
  size: "16:9",
  resolution: "2k"
}

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://tokenflash.cn/v1/images/generations")!

let payload: [String: Any] = [
    "model": "gpt-image-2",
    "prompt": "一只橘猫坐在窗台上看夕阳,水彩画风格",
    "n": 1,
    "size": "16:9",
    "resolution": "2k"
]

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://tokenflash.cn/v1/images/generations";

        var payload = @"{
            ""model"": ""gpt-image-2"",
            ""prompt"": ""一只橘猫坐在窗台上看夕阳,水彩画风格"",
            ""n"": 1,
            ""size"": ""16:9"",
            ""resolution"": ""2k""
        }";

        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://tokenflash.cn/v1/images/generations');

  final payload = {
    'model': 'gpt-image-2',
    'prompt': '一只橘猫坐在窗台上看夕阳,水彩画风格',
    'n': 1,
    'size': '16:9',
    'resolution': '2k'
  };

  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://tokenflash.cn/v1/images/generations"

payload <- list(
  model = "gpt-image-2",
  prompt = "一只橘猫坐在窗台上看夕阳,水彩画风格",
  n = 1,
  size = "16:9",
  resolution = "2k"
)

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

cat(content(response, "text"))
{
  "code": 200,
  "data": [
    {
      "status": "submitted",
      "task_id": "task_01KPQ7J7DWB7QZ3WCEK3YVPBRA"
    }
  ]
}
{
  "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": 429,
    "message": "请求过于频繁,请稍后再试",
    "type": "rate_limit_error"
  }
}
{
  "error": {
    "code": 500,
    "message": "服务器错误",
    "type": "server_error"
  }
}
{
  "error": {
    "code": 503,
    "message": "上游暂时不可用,请稍后再试",
    "type": "service_unavailable"
  }
}

Authorizations

string required

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

string required

图像生成模型名称

固定填写 gpt-image-2

string required

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
integer

生成图片张数

取值范围:1

警告

必须传入纯数字(如 1),不要加引号

string

图像生成的比例

支持以下比例,也可传入 auto 由服务端自动选择合适比例:

size类型
auto自动
1:1正方
3:2横图
2:3竖图
4:3横图
3:4竖图
5:4横图
4:5竖图
16:9横图
9:16竖图
2:1横图
1:2竖图
3:1横图
1:3竖图
21:9横图
9:21竖图

也支持直接传入像素尺寸,例如 1881x836 / 887x1774

string

输出分辨率档位

可选值:1k / 2k / 4k

size × resolution → 实际像素对应关系:

size1k2k4k
1:11024×1024 / 1254×12542048×20482880×2880
3:21536×10242048×13603520×2336
2:31024×15361360×20482336×3520
4:31024×7682048×15363312×2480
3:4768×10241536×20482480×3312
5:41280×1024 / 1448×10862560×20483216×2576
4:51024×1280 / 1122×14022048×25602576×3216
16:91536×864 / 1672×9412048×11523840×2160
9:16864×1536 / 941×16721152×20482160×3840
2:12048×1024 / 1774×8872688×13443840×1920
1:21024×2048 / 887×17741344×26881920×3840
3:11881×836 / 1536×5123072×10243840×1280
1:3887×1774 / 512×15361024×30721280×3840
21:92016×864 / 1915×8212688×11523840×1648
9:21864×2016 / 821×19151152×26881648×3840

警告

4K 支持上述 15 个比例;也可以直接通过 size 传入表格中的像素尺寸。

array

参考图数组(OpenAI 标准字段),传入后走图生图模式

boolean

是否使用官方渠道兜底

  • false:不使用(默认)
  • true:使用官方渠道

使用场景示例

文生图(最简请求)

{
  "model": "gpt-image-2",
  "prompt": "一只橘猫坐在窗台上看夕阳,水彩画风格"
}

文生图(指定比例 + 2K)

{
  "model": "gpt-image-2",
  "prompt": "a corgi astronaut on the moon, cinematic, 8k",
  "size": "16:9",
  "resolution": "2k"
}

文生图(4K 输出)

{
  "model": "gpt-image-2",
  "prompt": "星空下的古老城堡",
  "size": "16:9",
  "resolution": "4k"
}

图生图(参考图 = URL)

{
  "model": "gpt-image-2",
  "prompt": "把这张照片变成水彩画风格",
  "image_urls": [
    "https://example.com/photo.jpg"
  ]
}

图生图(参考图 = base64)

{
  "model": "gpt-image-2",
  "prompt": "把这张照片变成水彩画风格",
  "image_urls": [
    "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA..."
  ]
}

图生图(多参考图融合,URL + base64 混填)

{
  "model": "gpt-image-2",
  "prompt": "把这两张照片融合成一张海报",
  "size": "4:3",
  "resolution": "2k",
  "image_urls": [
    "https://example.com/photo-a.jpg",
    "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA..."
  ]
}

Response

code integer

响应状态码

data array

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": {
    "id": "task_01KPQ7J7DWB7QZ3WCEK3YVPBRA",
    "status": "completed",
    "progress": 100,
    "created": 1776748674,
    "completed": 1776748726,
    "actual_time": 52,
    "cost": 0.05279,
    "estimated_time": 100,
    "result": {
      "images": [
        {
          "url": [
            "https://upload.apimart.ai/f/image/xxxxxxxx-gpt_image_2_task_xxx_0.png"
          ],
          "expires_at": 1776835126
        }
      ]
    }
  }
}

取图方式:data.result.images[0].url[0]

任务状态说明

状态含义
submitted已提交
processing上游处理中
completed成功,result.images 可用
failed失败,查看 error.message

轮询建议

  • 首次查询延迟:提交后等待 10~20 秒再开始查询
  • 查询间隔:建议 3~5 秒一次,避免无脑毫秒级轮询
  • 超时参考:单张图一般 3060 秒完成(实测 actual_time 4453s)
  • 批量查询:若需同时查询多个任务,请使用 POST /v1/tasks/batch,请求体 {"task_ids": ["task_xxx", "task_yyy"]}

注意事项

  1. 异步处理:提交后返回 task_id,需轮询 /v1/tasks/{task_id} 获取最终图片 URL
  2. 内容审核prompt 会先经过平台敏感词 / 安全审核,命中违规内容会直接拒绝并不会计费
  3. 结果 URL:平台已将上游临时签名链接镜像到自家 R2 对象存储,返回的是稳定链接,客户端可直接访问
  4. URL 时效:响应中的 expires_at = completed + 24h 是业务层提示字段,建议尽快下载或转存到自己的 CDN
  5. 比例冲突:推荐只通过 size 字段传比例,不要在 prompt 里重复写比例,避免上游理解冲突
  6. 计费规则:按分辨率档位(1K / 2K / 4K)计费,失败不扣费,审核未通过不扣费
  7. 4K 支持比例:上述 15 个比例均支持 4K,也可以直接通过 size 传入对应像素尺寸
  8. 任务保留task_id 在数据库里默认保留若干天(由 TASK_RETENTION_DAYS 配置),过期后查询会返回"任务不存在或已过期"

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