Seedream-4-5-251128 - Image Edit - gptproto

POST

api

bytedance

seedream-4-5-251128

image-edit

Seedream-4-5-251128 - Image Edit - gptproto

curl --request POST \
  --url https://gptproto.com/api/v3/bytedance/seedream-4-5-251128/image-edit

const options = {method: 'POST'};

fetch('https://gptproto.com/api/v3/bytedance/seedream-4-5-251128/image-edit', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://gptproto.com/api/v3/bytedance/seedream-4-5-251128/image-edit",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

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

func main() {

	url := "https://gptproto.com/api/v3/bytedance/seedream-4-5-251128/image-edit"

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

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

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

	fmt.Println(string(body))

}

require 'uri'
require 'net/http'

url = URI("https://gptproto.com/api/v3/bytedance/seedream-4-5-251128/image-edit")

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

request = Net::HTTP::Post.new(url)

response = http.request(request)
puts response.read_body

Authentication

Sign up for a GPTProto account at https://gptproto.com
Navigate to the API Keys section in your dashboard
Generate a new API key (sk-xxxxx)
Copy and securely store your API key For authentication details, please refer to the Authentication section.

Initiate Request

curl --location 'https://gptproto.com/api/v3/bytedance/seedream-4-5-251128/image-edit' \
--header 'Authorization: GPTPROTO_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
  "enable_base64_output": false,
  "enable_sync_mode": false,
  "images": [
    "https://d1q70pf5vjeyhc.cloudfront.net/media/92d2d4ca66f84793adcb20742b15d262/images/1757414555847323990_Si8cqCBF.jpeg"
  ],
  "prompt": "Turn this photo into a characterfigure. Behind it, place a box withthe character'\''s image printed on it,and a computer showing the Blendermodeling process on its screen. In frontof the box, add a round plastic basewith the character figure standing on it .set the scene indoors if possible.",
  "size": "2227*3183"
}'

const axios = require('axios');
let data = JSON.stringify({
  "enable_base64_output": false,
  "enable_sync_mode": false,
  "images": [
    "https://d1q70pf5vjeyhc.cloudfront.net/media/92d2d4ca66f84793adcb20742b15d262/images/1757414555847323990_Si8cqCBF.jpeg"
  ],
  "prompt": "Turn this photo into a characterfigure. Behind it, place a box withthe character's image printed on it,and a computer showing the Blendermodeling process on its screen. In frontof the box, add a round plastic basewith the character figure standing on it .set the scene indoors if possible.",
  "size": "2227*3183"
});

let config = {
  method: 'post',
  maxBodyLength: Infinity,
  url: 'https://gptproto.com/api/v3/bytedance/seedream-4-5-251128/image-edit',
  headers: { 
    'Authorization': 'GPTPROTO_API_KEY', 
    'Content-Type': 'application/json'
  },
  data : data
};

axios.request(config)
.then((response) => {
  console.log(JSON.stringify(response.data));
})
.catch((error) => {
  console.log(error);
});

import requests
import json

url = "https://gptproto.com/api/v3/bytedance/seedream-4-5-251128/image-edit"

payload = json.dumps({
  "enable_base64_output": False,
  "enable_sync_mode": False,
  "images": [
    "https://d1q70pf5vjeyhc.cloudfront.net/media/92d2d4ca66f84793adcb20742b15d262/images/1757414555847323990_Si8cqCBF.jpeg"
  ],
  "prompt": "Turn this photo into a characterfigure. Behind it, place a box withthe character's image printed on it,and a computer showing the Blendermodeling process on its screen. In frontof the box, add a round plastic basewith the character figure standing on it .set the scene indoors if possible.",
  "size": "2227*3183"
})
headers = {
  'Authorization': 'GPTPROTO_API_KEY',
  'Content-Type': 'application/json'
}

response = requests.request("POST", url, headers=headers, data=payload)

print(response.text)

package main

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

func main() {

  url := "https://gptproto.com/api/v3/bytedance/seedream-4-5-251128/image-edit"
  method := "POST"

  payload := strings.NewReader(`{
  "enable_base64_output": false,
  "enable_sync_mode": false,
  "images": [
    "https://d1q70pf5vjeyhc.cloudfront.net/media/92d2d4ca66f84793adcb20742b15d262/images/1757414555847323990_Si8cqCBF.jpeg"
  ],
  "prompt": "Turn this photo into a characterfigure. Behind it, place a box withthe character's image printed on it,and a computer showing the Blendermodeling process on its screen. In frontof the box, add a round plastic basewith the character figure standing on it .set the scene indoors if possible.",
  "size": "2227*3183"
}`)

  client := &http.Client {
  }
  req, err := http.NewRequest(method, url, payload)

  if err != nil {
    fmt.Println(err)
    return
  }
  req.Header.Add("Authorization", "GPTPROTO_API_KEY")
  req.Header.Add("Content-Type", "application/json")

  res, err := client.Do(req)
  if err != nil {
    fmt.Println(err)
    return
  }
  defer res.Body.Close()

  body, err := io.ReadAll(res.Body)
  if err != nil {
    fmt.Println(err)
    return
  }
  fmt.Println(string(body))
}

Query Result

If the request that generated your content includes the parameter enable_sync_mode set to true (some models do not support this parameter, but you still need to query the result by id), you must call the Query Result endpoint to retrieve the final output.

curl -X GET "https://gptproto.com/api/v3/predictions/{id}/result" \
  -H "Authorization: GPTPROTO_API_KEY" \
  -H "Content-Type: application/json"

import requests
import json

url = "https://gptproto.com/api/v3/predictions/{id}/result"
headers = {
    "Authorization: GPTPROTO_API_KEY",
    "Content-Type": "application/json"
}

response = requests.get(url, headers=headers)
result = response.json()
print(json.dumps(result, indent=2))

const url = "https://gptproto.com/api/v3/predictions/{id}/result";
const headers = {
  "Authorization: GPTPROTO_API_KEY",
  "Content-Type": "application/json"
};

fetch(url, {
  method: "GET",
  headers: headers
})
  .then(response => response.json())
  .then(data => console.log(data))
  .catch(error => console.error("Error:", error));

package main

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

func main() {
    url := "https://gptproto.com/api/v3/predictions/{id}/result"

    req, _ := http.NewRequest("GET", url, nil)
    req.Header.Set("Authorization", "GPTPROTO_API_KEY")
    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))
}

After submitting your prediction request, the response will contain the id you need.
You can find it in either:

data.id – the unique identifier of the prediction
data.urls[0].get – a ready-to-use GET URL that already embeds the id

Response Example

{
    "data": {
        "id": "abc",
        "model": "model_name",
        "outputs": [],
        "urls": {
            "get": "https://gptproto.com/api/v3/predictions/abc/result"
        },
        "status": "completed",
        "error": null,
        "executionTime": 0,
        "timings": {
            "inference": 0
        },
        "has_nsfw_contents": [],
        "created_at": "2026-01-01 00:00:00"
    },
    "message": "success",
    "code": 200
}

Parameters

Parameter	Type	Required	Default	Description
`enable_base64_output`	boolean	❌ No	`false`	Whether to output the result as base64 encoded string
`enable_sync_mode`	boolean	❌ No	`false`	Whether to enable synchronous mode (wait for result)
`prompt`	string	✅ Yes	`Turn this photo into a characterfigure. Behind it, place a box withthe character's image printed on it,and a computer showing the Blendermodeling process on its screen. In frontof the box, add a round plastic basewith the character figure standing on it .set the scene indoors if possible.`	Image generation prompt describing the desired image content
`size`	string	❌ No	`2227*3183`	Output image resolution (format: width*height)

Error Codes

Common Error Codes

Error Code	Error Name	Description
401	Unauthorized	API key is missing or invalid
403	Forbidden	Your API key doesn’t have permission to access this resource, or insufficient balance for the requested operation
429	Too Many Requests	You’ve exceeded your rate limit
500	Internal server error	An internal server error occurred
503	Content policy violation	Content blocked due to safety concerns (actual status code is 400)

​Authentication

​Initiate Request

​Query Result

​Parameters

​Error Codes

​Common Error Codes