Create image generation tasks

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": "A ceramic teapot on a white studio background",
  "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": "A ceramic teapot on a white studio background",
  "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": "A ceramic teapot on a white studio background",
  "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": "A ceramic teapot on a white studio background",
  "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": "A ceramic teapot on a white studio background",
        "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: "A ceramic teapot on a white studio background",
    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: "A ceramic teapot on a white studio background",
    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\": \"A ceramic teapot on a white studio background\",\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":"A ceramic teapot on a white studio background","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: "A ceramic teapot on a white studio background",
  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" => "A ceramic teapot on a white studio background",
        "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\":\"A ceramic teapot on a white studio background\",\"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": "reference_too_large",
    "message": "Reference file exceeds the allowed size"
  }
}

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

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