创建图片生成任务

POST

openapi

images

generations

curl --request POST \
  --url https://api.neural4d.com/openapi/v1/images/generations \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "openai/gpt-image-2",
  "prompt": "白色摄影棚背景中的陶瓷茶壶",
  "input_references": [
    {
      "type": "image",
      "file_id": "550e8400-e29b-41d4-a716-446655440000"
    }
  ],
  "n": 1,
  "resolution": "1K",
  "aspect_ratio": "1:1"
}
'

curl --request POST \
  --url https://api.neural4d.com/openapi/v1/images/generations \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "model": "google/nano-banana-pro",
  "prompt": "白色摄影棚背景中的陶瓷茶壶",
  "input_references": [{
    "type": "image",
    "file_id": "550e8400-e29b-41d4-a716-446655440000"
  }],
  "n": 1,
  "resolution": "2K",
  "aspect_ratio": "16:9"
}'

curl --request POST \
  --url https://api.neural4d.com/openapi/v1/images/generations \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "model": "bytedance/seedream-4.5",
  "prompt": "白色摄影棚背景中的陶瓷茶壶",
  "input_references": [{
    "type": "image",
    "file_id": "550e8400-e29b-41d4-a716-446655440000"
  }],
  "n": 1,
  "resolution": "2K",
  "aspect_ratio": "1:1"
}'

curl --request POST \
  --url https://api.neural4d.com/openapi/v1/images/generations \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "model": "black-forest-labs/flux-2-pro",
  "prompt": "白色摄影棚背景中的陶瓷茶壶",
  "input_references": [{
    "type": "image",
    "file_id": "550e8400-e29b-41d4-a716-446655440000"
  }],
  "n": 1,
  "resolution": "1K",
  "aspect_ratio": "1:1"
}'

import requests

response = requests.post(
    "https://api.neural4d.com/openapi/v1/images/generations",
    headers={
        "Authorization": "Bearer <token>",
        "Content-Type": "application/json",
    },
    json={
        "model": "openai/gpt-image-2",
        "prompt": "白色摄影棚背景中的陶瓷茶壶",
        "input_references": [
            {
                "type": "image",
                "file_id": "550e8400-e29b-41d4-a716-446655440000",
            },
        ],
        "n": 1,
        "resolution": "1K",
        "aspect_ratio": "1:1",
    },
)
print(response.json())

const response = await fetch("https://api.neural4d.com/openapi/v1/images/generations", {
  method: "POST",
  headers: {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "openai/gpt-image-2",
    prompt: "白色摄影棚背景中的陶瓷茶壶",
    input_references: [
      { type: "image", file_id: "550e8400-e29b-41d4-a716-446655440000" }
    ],
    n: 1,
    resolution: "1K",
    aspect_ratio: "1:1",
  })
});
const data = await response.json();
console.log(data);

const response: Response = await fetch("https://api.neural4d.com/openapi/v1/images/generations", {
  method: "POST",
  headers: {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "openai/gpt-image-2",
    prompt: "白色摄影棚背景中的陶瓷茶壶",
    input_references: [{ type: "image", file_id: "550e8400-e29b-41d4-a716-446655440000" }],
    n: 1,
    resolution: "1K",
    aspect_ratio: "1:1"
  })
});
const data = await response.json();
console.log(data);

import java.net.URI;
import java.net.http.*;

HttpRequest request = HttpRequest.newBuilder()
    .uri(URI.create("https://api.neural4d.com/openapi/v1/images/generations"))
    .header("Authorization", "Bearer <token>")
    .header("Content-Type", "application/json")
    .POST(HttpRequest.BodyPublishers.ofString("{\n  \"model\": \"openai/gpt-image-2\",\n  \"prompt\": \"白色摄影棚背景中的陶瓷茶壶\",\n  \"input_references\": [{\"type\":\"image\",\"file_id\":\"550e8400-e29b-41d4-a716-446655440000\"}],\n  \"n\": 1,\n  \"resolution\": \"1K\",\n  \"aspect_ratio\": \"1:1\"\n}"))
    .build();
HttpResponse<String> response = HttpClient.newHttpClient().send(request, HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());

package main

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

payload := strings.NewReader(`{"model":"openai/gpt-image-2","prompt":"白色摄影棚背景中的陶瓷茶壶","input_references":[{"type":"image","file_id":"550e8400-e29b-41d4-a716-446655440000"}],"n":1,"resolution":"1K","aspect_ratio":"1:1"}`)
req, _ := http.NewRequest("POST", "https://api.neural4d.com/openapi/v1/images/generations", payload)
req.Header.Set("Authorization", "Bearer <token>")
req.Header.Set("Content-Type", "application/json")
resp, _ := http.DefaultClient.Do(req)
defer resp.Body.Close()
bodyBytes, _ := io.ReadAll(resp.Body)
fmt.Println(string(bodyBytes))

require "net/http"
require "json"

uri = URI("https://api.neural4d.com/openapi/v1/images/generations")
request = Net::HTTP::Post.new(uri)
request["Authorization"] = "Bearer <token>"
request["Content-Type"] = "application/json"
request.body = {
  model: "openai/gpt-image-2",
  prompt: "白色摄影棚背景中的陶瓷茶壶",
  input_references: [{ type: "image", file_id: "550e8400-e29b-41d4-a716-446655440000" }],
  n: 1,
  resolution: "1K",
  aspect_ratio: "1:1"
}.to_json

response = Net::HTTP.start(uri.hostname, uri.port, use_ssl: uri.scheme == "https") { |http| http.request(request) }
puts response.body

<?php

$ch = curl_init("https://api.neural4d.com/openapi/v1/images/generations");
curl_setopt_array($ch, [
    CURLOPT_CUSTOMREQUEST => "POST",
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_HTTPHEADER => ["Authorization: Bearer <token>", "Content-Type: application/json"],
    CURLOPT_POSTFIELDS => json_encode([
        "model" => "openai/gpt-image-2",
        "prompt" => "白色摄影棚背景中的陶瓷茶壶",
        "input_references" => [[
            "type" => "image",
            "file_id" => "550e8400-e29b-41d4-a716-446655440000",
        ]],
        "n" => 1,
        "resolution" => "1K",
        "aspect_ratio" => "1:1",
    ]),
]);
$response = curl_exec($ch);
curl_close($ch);
echo $response;

using System.Net.Http;
using System.Text;

using var client = new HttpClient();
using var request = new HttpRequestMessage(HttpMethod.Post, "https://api.neural4d.com/openapi/v1/images/generations");
request.Headers.TryAddWithoutValidation("Authorization", "Bearer <token>");
request.Content = new StringContent("{\"model\":\"openai/gpt-image-2\",\"prompt\":\"白色摄影棚背景中的陶瓷茶壶\",\"input_references\":[{\"type\":\"image\",\"file_id\":\"550e8400-e29b-41d4-a716-446655440000\"}],\"n\":1,\"resolution\":\"1K\",\"aspect_ratio\":\"1:1\"}", Encoding.UTF8, "application/json");
using var response = await client.SendAsync(request);
Console.WriteLine(await response.Content.ReadAsStringAsync());

{
  "id": "normal-image-c50fe63e-699d-4d61-93f5-2099ab159d6d",
  "status": "queued",
  "created_at": 1784044800,
  "mode": "image_to_image",
  "model": "openai/gpt-image-2",
  "data": [
    {
      "uuid": "c6f5dc4e-61ac-4694-9a8b-6a95f3ac4457",
      "status": "queued",
      "created_at": 1784044800
    }
  ],
  "usage": {
    "credits": 13
  }
}

{
  "error": {
    "code": "invalid_prompt",
    "message": "prompt is required and must be a non-empty string"
  }
}

{
  "error": {
    "code": "unsupported_model",
    "message": "Unsupported modelKey: missing-model"
  }
}

{
  "error": {
    "code": "model_unavailable",
    "message": "Model is unavailable: openai/gpt-image-2"
  }
}

{
  "error": {
    "code": "invalid_api_key",
    "message": "The API key is invalid or expired"
  }
}

{
  "error": {
    "code": "insufficient_credits",
    "message": "Insufficient credits for image generation"
  }
}

{
  "error": {
    "code": "rate_limit_exceeded",
    "message": "Too many requests. Retry after the indicated interval."
  }
}

{
  "error": {
    "code": "internal_error",
    "message": "An internal error occurred"
  }
}

授权

string

默认值:"Bearer ${YOUR_API_KEY}"

必填

使用 Bearer 方式发送的 Neural4D API 密钥。

请求体 application/json

Option 1
Option 2
Option 3
Option 4

enum<string>

必填

GPT Image 2 模型标识。可选值：openai/gpt-image-2。

string

必填

描述待生成或编辑图片的文本提示词。字符串长度：1 - 500。

object[]

通过 POST /openapi/v1/files 上传的可选参考图片。仅支持图片参考素材。支持 JPG、JPEG、PNG 和 WEBP 格式。数组长度：0 - 6 个元素。

隐藏子属性

input_references.

enum<string>

必填

参考素材类型。可选值：image。

input_references.

string<uuid>

必填

文件上传接口返回的 JPG、JPEG、PNG 或 WEBP 图片文件 ID。

integer

默认值:1

要创建的图片生成任务数量。取值范围：1 <= x <= 4。

enum<string>

默认值:"2K"

命名输出分辨率。可选值：1K、2K、4K。

enum<string>

默认值:"1:1"

目标图片宽高比。可选值：1:1、16:9、9:16、4:3、3:4。

enum<string>

必填

Nano Banana Pro 模型标识。可选值：google/nano-banana-pro。

string

必填

描述待生成或编辑图片的文本提示词。字符串长度：1 - 500。

object[]

通过 POST /openapi/v1/files 上传的可选参考图片。仅支持图片参考素材。支持 JPG、JPEG、PNG 和 WEBP 格式。数组长度：0 - 6 个元素。

隐藏子属性

input_references.

enum<string>

必填

参考素材类型。可选值：image。

input_references.

string<uuid>

必填

文件上传接口返回的 JPG、JPEG、PNG 或 WEBP 图片文件 ID。

integer

默认值:1

要创建的图片生成任务数量。取值范围：1 <= x <= 4。

enum<string>

默认值:"2K"

命名输出分辨率。可选值：1K、2K、4K。

enum<string>

默认值:"1:1"

目标图片宽高比。可选值：1:1、16:9、9:16、4:3、3:4。

enum<string>

必填

Seedream 4.5 模型标识。可选值：bytedance/seedream-4.5。

string

必填

描述待生成或编辑图片的文本提示词。字符串长度：1 - 500。

object[]

通过 POST /openapi/v1/files 上传的可选参考图片。仅支持图片参考素材。支持 JPG、JPEG、PNG 和 WEBP 格式。数组长度：0 - 6 个元素。

隐藏子属性

input_references.

enum<string>

必填

参考素材类型。可选值：image。

input_references.

string<uuid>

必填

文件上传接口返回的 JPG、JPEG、PNG 或 WEBP 图片文件 ID。

integer

默认值:1

要创建的图片生成任务数量。取值范围：1 <= x <= 4。

enum<string>

默认值:"2K"

命名输出分辨率。可选值：2K、4K。

enum<string>

默认值:"1:1"

目标图片宽高比。可选值：1:1、16:9、9:16、4:3、3:4。

enum<string>

必填

Flux 2 Pro 模型标识。可选值：black-forest-labs/flux-2-pro。

string

必填

描述待生成或编辑图片的文本提示词。字符串长度：1 - 500。

object[]

通过 POST /openapi/v1/files 上传的可选参考图片。仅支持图片参考素材。支持 JPG、JPEG、PNG 和 WEBP 格式。数组长度：0 - 6 个元素。

隐藏子属性

input_references.

enum<string>

必填

参考素材类型。可选值：image。

input_references.

string<uuid>

必填

文件上传接口返回的 JPG、JPEG、PNG 或 WEBP 图片文件 ID。

integer

默认值:1

要创建的图片生成任务数量。取值范围：1 <= x <= 4。

enum<string>

默认值:"2K"

命名输出分辨率。可选值：1K、2K。

enum<string>

默认值:"1:1"

目标图片宽高比。可选值：1:1、16:9、9:16、4:3、3:4。

curl --request POST \
  --url https://api.neural4d.com/openapi/v1/images/generations \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "openai/gpt-image-2",
  "prompt": "白色摄影棚背景中的陶瓷茶壶",
  "input_references": [
    {
      "type": "image",
      "file_id": "550e8400-e29b-41d4-a716-446655440000"
    }
  ],
  "n": 1,
  "resolution": "1K",
  "aspect_ratio": "1:1"
}
'

curl --request POST \
  --url https://api.neural4d.com/openapi/v1/images/generations \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "model": "google/nano-banana-pro",
  "prompt": "白色摄影棚背景中的陶瓷茶壶",
  "input_references": [{
    "type": "image",
    "file_id": "550e8400-e29b-41d4-a716-446655440000"
  }],
  "n": 1,
  "resolution": "2K",
  "aspect_ratio": "16:9"
}'

curl --request POST \
  --url https://api.neural4d.com/openapi/v1/images/generations \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "model": "bytedance/seedream-4.5",
  "prompt": "白色摄影棚背景中的陶瓷茶壶",
  "input_references": [{
    "type": "image",
    "file_id": "550e8400-e29b-41d4-a716-446655440000"
  }],
  "n": 1,
  "resolution": "2K",
  "aspect_ratio": "1:1"
}'

curl --request POST \
  --url https://api.neural4d.com/openapi/v1/images/generations \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "model": "black-forest-labs/flux-2-pro",
  "prompt": "白色摄影棚背景中的陶瓷茶壶",
  "input_references": [{
    "type": "image",
    "file_id": "550e8400-e29b-41d4-a716-446655440000"
  }],
  "n": 1,
  "resolution": "1K",
  "aspect_ratio": "1:1"
}'

import requests

response = requests.post(
    "https://api.neural4d.com/openapi/v1/images/generations",
    headers={
        "Authorization": "Bearer <token>",
        "Content-Type": "application/json",
    },
    json={
        "model": "openai/gpt-image-2",
        "prompt": "白色摄影棚背景中的陶瓷茶壶",
        "input_references": [
            {
                "type": "image",
                "file_id": "550e8400-e29b-41d4-a716-446655440000",
            },
        ],
        "n": 1,
        "resolution": "1K",
        "aspect_ratio": "1:1",
    },
)
print(response.json())

const response = await fetch("https://api.neural4d.com/openapi/v1/images/generations", {
  method: "POST",
  headers: {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "openai/gpt-image-2",
    prompt: "白色摄影棚背景中的陶瓷茶壶",
    input_references: [
      { type: "image", file_id: "550e8400-e29b-41d4-a716-446655440000" }
    ],
    n: 1,
    resolution: "1K",
    aspect_ratio: "1:1",
  })
});
const data = await response.json();
console.log(data);

const response: Response = await fetch("https://api.neural4d.com/openapi/v1/images/generations", {
  method: "POST",
  headers: {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "openai/gpt-image-2",
    prompt: "白色摄影棚背景中的陶瓷茶壶",
    input_references: [{ type: "image", file_id: "550e8400-e29b-41d4-a716-446655440000" }],
    n: 1,
    resolution: "1K",
    aspect_ratio: "1:1"
  })
});
const data = await response.json();
console.log(data);

import java.net.URI;
import java.net.http.*;

HttpRequest request = HttpRequest.newBuilder()
    .uri(URI.create("https://api.neural4d.com/openapi/v1/images/generations"))
    .header("Authorization", "Bearer <token>")
    .header("Content-Type", "application/json")
    .POST(HttpRequest.BodyPublishers.ofString("{\n  \"model\": \"openai/gpt-image-2\",\n  \"prompt\": \"白色摄影棚背景中的陶瓷茶壶\",\n  \"input_references\": [{\"type\":\"image\",\"file_id\":\"550e8400-e29b-41d4-a716-446655440000\"}],\n  \"n\": 1,\n  \"resolution\": \"1K\",\n  \"aspect_ratio\": \"1:1\"\n}"))
    .build();
HttpResponse<String> response = HttpClient.newHttpClient().send(request, HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());

package main

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

payload := strings.NewReader(`{"model":"openai/gpt-image-2","prompt":"白色摄影棚背景中的陶瓷茶壶","input_references":[{"type":"image","file_id":"550e8400-e29b-41d4-a716-446655440000"}],"n":1,"resolution":"1K","aspect_ratio":"1:1"}`)
req, _ := http.NewRequest("POST", "https://api.neural4d.com/openapi/v1/images/generations", payload)
req.Header.Set("Authorization", "Bearer <token>")
req.Header.Set("Content-Type", "application/json")
resp, _ := http.DefaultClient.Do(req)
defer resp.Body.Close()
bodyBytes, _ := io.ReadAll(resp.Body)
fmt.Println(string(bodyBytes))

require "net/http"
require "json"

uri = URI("https://api.neural4d.com/openapi/v1/images/generations")
request = Net::HTTP::Post.new(uri)
request["Authorization"] = "Bearer <token>"
request["Content-Type"] = "application/json"
request.body = {
  model: "openai/gpt-image-2",
  prompt: "白色摄影棚背景中的陶瓷茶壶",
  input_references: [{ type: "image", file_id: "550e8400-e29b-41d4-a716-446655440000" }],
  n: 1,
  resolution: "1K",
  aspect_ratio: "1:1"
}.to_json

response = Net::HTTP.start(uri.hostname, uri.port, use_ssl: uri.scheme == "https") { |http| http.request(request) }
puts response.body

<?php

$ch = curl_init("https://api.neural4d.com/openapi/v1/images/generations");
curl_setopt_array($ch, [
    CURLOPT_CUSTOMREQUEST => "POST",
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_HTTPHEADER => ["Authorization: Bearer <token>", "Content-Type: application/json"],
    CURLOPT_POSTFIELDS => json_encode([
        "model" => "openai/gpt-image-2",
        "prompt" => "白色摄影棚背景中的陶瓷茶壶",
        "input_references" => [[
            "type" => "image",
            "file_id" => "550e8400-e29b-41d4-a716-446655440000",
        ]],
        "n" => 1,
        "resolution" => "1K",
        "aspect_ratio" => "1:1",
    ]),
]);
$response = curl_exec($ch);
curl_close($ch);
echo $response;

using System.Net.Http;
using System.Text;

using var client = new HttpClient();
using var request = new HttpRequestMessage(HttpMethod.Post, "https://api.neural4d.com/openapi/v1/images/generations");
request.Headers.TryAddWithoutValidation("Authorization", "Bearer <token>");
request.Content = new StringContent("{\"model\":\"openai/gpt-image-2\",\"prompt\":\"白色摄影棚背景中的陶瓷茶壶\",\"input_references\":[{\"type\":\"image\",\"file_id\":\"550e8400-e29b-41d4-a716-446655440000\"}],\"n\":1,\"resolution\":\"1K\",\"aspect_ratio\":\"1:1\"}", Encoding.UTF8, "application/json");
using var response = await client.SendAsync(request);
Console.WriteLine(await response.Content.ReadAsStringAsync());

响应

已接受生成任务

string

必填

生成请求返回的批次任务 ID。示例："normal-image-c50fe63e-699d-4d61-93f5-2099ab159d6d"。

object[]

必填

本次请求创建的生成结果子项。数组长度：至少 1 个元素。

隐藏子属性

data[].

string<uuid>

必填

生成结果子项 UUID。

data[].

enum<string>

必填

标准化后的任务生命周期状态。可选值：queued、processing、succeeded、failed。

data[].

integer<int64>

必填

Unix 时间戳，单位为秒。

data[].

integer<int64>

任务更新时间的 Unix 时间戳，单位为秒。

data[].

object

可用时返回此任务扣除的点数。

显示子属性

data[].usage.

number<float>

必填

该任务或请求扣除的点数。取值范围：0 <= x <= infinity。示例：13。

data[].

object

异步处理失败时返回的错误详情。

显示子属性

data[].error.

string

必填

稳定且可供程序读取的任务失败代码。

data[].error.

string

必填

便于阅读的任务失败说明。

data[].error.

integer<int64>

任务失败时的 Unix 时间戳，单位为秒。

data[].error.

boolean

重试相同操作是否可能成功。

enum<string>

必填

本次提交的图片任务的当前状态。可选值：queued、processing、succeeded、failed。

integer<int64>

必填

批次任务创建时间的 Unix 时间戳，单位为秒。

enum<string>

必填

本次提交任务使用的图片生成模式。可选值：text_to_image、image_to_image。

enum<string>

必填

本次提交任务使用的图片生成模型。可选值：openai/gpt-image-2、google/nano-banana-pro、bytedance/seedream-4.5、black-forest-labs/flux-2-pro。

object

可用时返回本次提交任务扣除的总点数。

显示子属性

usage.

number<float>

必填

本次提交任务扣除的点数。示例：13。

请求无效

object

必填

结构化错误详情。

显示子属性

error.

string

必填

稳定且可供程序读取的错误码。示例："invalid_prompt"。

error.

string

必填

便于阅读的错误说明。示例："prompt is required and must be a non-empty string"。

error.

string<uri> | null

包含此错误更多信息的可选文档 URL。示例："https://api.neural4d.com/docs/errors#invalid_prompt"。

认证失败

object

必填

结构化错误详情。

显示子属性

error.

string

必填

稳定且可供程序读取的错误码。示例："invalid_api_key"。

error.

string

必填

便于阅读的错误说明。示例："The API key is invalid or expired"。

error.

string<uri> | null

包含此错误更多信息的可选文档 URL。示例："https://api.neural4d.com/docs/errors#invalid_api_key"。

当前已认证用户无权访问该资源或点数不足

object

必填

结构化错误详情。

显示子属性

error.

string

必填

稳定且可供程序读取的错误码。示例："insufficient_credits"。

error.

string

必填

便于阅读的错误说明。示例："Insufficient credits for image generation"。

error.

string<uri> | null

包含此错误更多信息的可选文档 URL。示例："https://api.neural4d.com/docs/errors#insufficient_credits"。

已超过请求或配额限制

object

必填

结构化错误详情。

显示子属性

error.

string

必填

稳定且可供程序读取的错误码。示例："rate_limit_exceeded"。

error.

string

必填

便于阅读的错误说明。示例："Too many requests. Retry after the indicated interval."。

error.

string<uri> | null

包含此错误更多信息的可选文档 URL。示例："https://api.neural4d.com/docs/errors#rate_limit_exceeded"。

内部或上游服务错误

object

必填

结构化错误详情。

显示子属性

error.

string

必填

稳定且可供程序读取的错误码。示例："internal_error"。

error.

string

必填

便于阅读的错误说明。示例："An internal error occurred"。

error.

string<uri> | null

包含此错误更多信息的可选文档 URL。示例："https://api.neural4d.com/docs/errors#internal_error"。

{
  "id": "normal-image-c50fe63e-699d-4d61-93f5-2099ab159d6d",
  "status": "queued",
  "created_at": 1784044800,
  "mode": "image_to_image",
  "model": "openai/gpt-image-2",
  "data": [
    {
      "uuid": "c6f5dc4e-61ac-4694-9a8b-6a95f3ac4457",
      "status": "queued",
      "created_at": 1784044800
    }
  ],
  "usage": {
    "credits": 13
  }
}

{
  "error": {
    "code": "invalid_prompt",
    "message": "prompt is required and must be a non-empty string"
  }
}

{
  "error": {
    "code": "unsupported_model",
    "message": "Unsupported modelKey: missing-model"
  }
}

{
  "error": {
    "code": "model_unavailable",
    "message": "Model is unavailable: openai/gpt-image-2"
  }
}

{
  "error": {
    "code": "invalid_api_key",
    "message": "The API key is invalid or expired"
  }
}

{
  "error": {
    "code": "insufficient_credits",
    "message": "Insufficient credits for image generation"
  }
}

{
  "error": {
    "code": "rate_limit_exceeded",
    "message": "Too many requests. Retry after the indicated interval."
  }
}

{
  "error": {
    "code": "internal_error",
    "message": "An internal error occurred"
  }
}

curl --request POST \
  --url https://api.neural4d.com/openapi/v1/images/generations \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "openai/gpt-image-2",
  "prompt": "白色摄影棚背景中的陶瓷茶壶",
  "input_references": [
    {
      "type": "image",
      "file_id": "550e8400-e29b-41d4-a716-446655440000"
    }
  ],
  "n": 1,
  "resolution": "1K",
  "aspect_ratio": "1:1"
}
'

curl --request POST \
  --url https://api.neural4d.com/openapi/v1/images/generations \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "model": "google/nano-banana-pro",
  "prompt": "白色摄影棚背景中的陶瓷茶壶",
  "input_references": [{
    "type": "image",
    "file_id": "550e8400-e29b-41d4-a716-446655440000"
  }],
  "n": 1,
  "resolution": "2K",
  "aspect_ratio": "16:9"
}'

curl --request POST \
  --url https://api.neural4d.com/openapi/v1/images/generations \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "model": "bytedance/seedream-4.5",
  "prompt": "白色摄影棚背景中的陶瓷茶壶",
  "input_references": [{
    "type": "image",
    "file_id": "550e8400-e29b-41d4-a716-446655440000"
  }],
  "n": 1,
  "resolution": "2K",
  "aspect_ratio": "1:1"
}'

curl --request POST \
  --url https://api.neural4d.com/openapi/v1/images/generations \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "model": "black-forest-labs/flux-2-pro",
  "prompt": "白色摄影棚背景中的陶瓷茶壶",
  "input_references": [{
    "type": "image",
    "file_id": "550e8400-e29b-41d4-a716-446655440000"
  }],
  "n": 1,
  "resolution": "1K",
  "aspect_ratio": "1:1"
}'

import requests

response = requests.post(
    "https://api.neural4d.com/openapi/v1/images/generations",
    headers={
        "Authorization": "Bearer <token>",
        "Content-Type": "application/json",
    },
    json={
        "model": "openai/gpt-image-2",
        "prompt": "白色摄影棚背景中的陶瓷茶壶",
        "input_references": [
            {
                "type": "image",
                "file_id": "550e8400-e29b-41d4-a716-446655440000",
            },
        ],
        "n": 1,
        "resolution": "1K",
        "aspect_ratio": "1:1",
    },
)
print(response.json())

const response = await fetch("https://api.neural4d.com/openapi/v1/images/generations", {
  method: "POST",
  headers: {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "openai/gpt-image-2",
    prompt: "白色摄影棚背景中的陶瓷茶壶",
    input_references: [
      { type: "image", file_id: "550e8400-e29b-41d4-a716-446655440000" }
    ],
    n: 1,
    resolution: "1K",
    aspect_ratio: "1:1",
  })
});
const data = await response.json();
console.log(data);

const response: Response = await fetch("https://api.neural4d.com/openapi/v1/images/generations", {
  method: "POST",
  headers: {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "openai/gpt-image-2",
    prompt: "白色摄影棚背景中的陶瓷茶壶",
    input_references: [{ type: "image", file_id: "550e8400-e29b-41d4-a716-446655440000" }],
    n: 1,
    resolution: "1K",
    aspect_ratio: "1:1"
  })
});
const data = await response.json();
console.log(data);

import java.net.URI;
import java.net.http.*;

HttpRequest request = HttpRequest.newBuilder()
    .uri(URI.create("https://api.neural4d.com/openapi/v1/images/generations"))
    .header("Authorization", "Bearer <token>")
    .header("Content-Type", "application/json")
    .POST(HttpRequest.BodyPublishers.ofString("{\n  \"model\": \"openai/gpt-image-2\",\n  \"prompt\": \"白色摄影棚背景中的陶瓷茶壶\",\n  \"input_references\": [{\"type\":\"image\",\"file_id\":\"550e8400-e29b-41d4-a716-446655440000\"}],\n  \"n\": 1,\n  \"resolution\": \"1K\",\n  \"aspect_ratio\": \"1:1\"\n}"))
    .build();
HttpResponse<String> response = HttpClient.newHttpClient().send(request, HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());

package main

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

payload := strings.NewReader(`{"model":"openai/gpt-image-2","prompt":"白色摄影棚背景中的陶瓷茶壶","input_references":[{"type":"image","file_id":"550e8400-e29b-41d4-a716-446655440000"}],"n":1,"resolution":"1K","aspect_ratio":"1:1"}`)
req, _ := http.NewRequest("POST", "https://api.neural4d.com/openapi/v1/images/generations", payload)
req.Header.Set("Authorization", "Bearer <token>")
req.Header.Set("Content-Type", "application/json")
resp, _ := http.DefaultClient.Do(req)
defer resp.Body.Close()
bodyBytes, _ := io.ReadAll(resp.Body)
fmt.Println(string(bodyBytes))

require "net/http"
require "json"

uri = URI("https://api.neural4d.com/openapi/v1/images/generations")
request = Net::HTTP::Post.new(uri)
request["Authorization"] = "Bearer <token>"
request["Content-Type"] = "application/json"
request.body = {
  model: "openai/gpt-image-2",
  prompt: "白色摄影棚背景中的陶瓷茶壶",
  input_references: [{ type: "image", file_id: "550e8400-e29b-41d4-a716-446655440000" }],
  n: 1,
  resolution: "1K",
  aspect_ratio: "1:1"
}.to_json

response = Net::HTTP.start(uri.hostname, uri.port, use_ssl: uri.scheme == "https") { |http| http.request(request) }
puts response.body

<?php

$ch = curl_init("https://api.neural4d.com/openapi/v1/images/generations");
curl_setopt_array($ch, [
    CURLOPT_CUSTOMREQUEST => "POST",
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_HTTPHEADER => ["Authorization: Bearer <token>", "Content-Type: application/json"],
    CURLOPT_POSTFIELDS => json_encode([
        "model" => "openai/gpt-image-2",
        "prompt" => "白色摄影棚背景中的陶瓷茶壶",
        "input_references" => [[
            "type" => "image",
            "file_id" => "550e8400-e29b-41d4-a716-446655440000",
        ]],
        "n" => 1,
        "resolution" => "1K",
        "aspect_ratio" => "1:1",
    ]),
]);
$response = curl_exec($ch);
curl_close($ch);
echo $response;

using System.Net.Http;
using System.Text;

using var client = new HttpClient();
using var request = new HttpRequestMessage(HttpMethod.Post, "https://api.neural4d.com/openapi/v1/images/generations");
request.Headers.TryAddWithoutValidation("Authorization", "Bearer <token>");
request.Content = new StringContent("{\"model\":\"openai/gpt-image-2\",\"prompt\":\"白色摄影棚背景中的陶瓷茶壶\",\"input_references\":[{\"type\":\"image\",\"file_id\":\"550e8400-e29b-41d4-a716-446655440000\"}],\"n\":1,\"resolution\":\"1K\",\"aspect_ratio\":\"1:1\"}", Encoding.UTF8, "application/json");
using var response = await client.SendAsync(request);
Console.WriteLine(await response.Content.ReadAsStringAsync());

{
  "id": "normal-image-c50fe63e-699d-4d61-93f5-2099ab159d6d",
  "status": "queued",
  "created_at": 1784044800,
  "mode": "image_to_image",
  "model": "openai/gpt-image-2",
  "data": [
    {
      "uuid": "c6f5dc4e-61ac-4694-9a8b-6a95f3ac4457",
      "status": "queued",
      "created_at": 1784044800
    }
  ],
  "usage": {
    "credits": 13
  }
}

{
  "error": {
    "code": "invalid_prompt",
    "message": "prompt is required and must be a non-empty string"
  }
}

{
  "error": {
    "code": "unsupported_model",
    "message": "Unsupported modelKey: missing-model"
  }
}

{
  "error": {
    "code": "model_unavailable",
    "message": "Model is unavailable: openai/gpt-image-2"
  }
}

{
  "error": {
    "code": "invalid_api_key",
    "message": "The API key is invalid or expired"
  }
}

{
  "error": {
    "code": "insufficient_credits",
    "message": "Insufficient credits for image generation"
  }
}

{
  "error": {
    "code": "rate_limit_exceeded",
    "message": "Too many requests. Retry after the indicated interval."
  }
}

{
  "error": {
    "code": "internal_error",
    "message": "An internal error occurred"
  }
}

开始使用

API 参考

授权

请求体 application/json

响应

​授权

​请求体 application/json

​响应

授权

请求体 application/json

响应