curl --request POST \
  --url https://xxx.wengaocloud.com/v3/images/edits \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: multipart/form-data' \
  --form model=gpt-image-2 \
  --form 'prompt=<string>' \
  --form image='@example-file' \
  --form mask='@example-file'

import requests

url = "https://xxx.wengaocloud.com/v3/images/edits"

files = {
    "image": ("example-file", open("example-file", "rb")),
    "mask": ("example-file", open("example-file", "rb"))
}
payload = {
    "model": "gpt-image-2",
    "prompt": "<string>"
}
headers = {"Authorization": "Bearer <token>"}

response = requests.post(url, data=payload, files=files, headers=headers)

print(response.text)

const form = new FormData();
form.append('model', 'gpt-image-2');
form.append('prompt', '<string>');
form.append('image', '<string>');
form.append('mask', '{
  "fileName": "example-file"
}');

const options = {method: 'POST', headers: {Authorization: 'Bearer <token>'}};

options.body = form;

fetch('https://xxx.wengaocloud.com/v3/images/edits', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

package main

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

func main() {

	url := "https://xxx.wengaocloud.com/v3/images/edits"

	payload := strings.NewReader("-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"model\"\r\n\r\ngpt-image-2\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"prompt\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"mask\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n{\r\n  \"fileName\": \"example-file\"\r\n}\r\n-----011000010111000001101001--")

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

	req.Header.Add("Authorization", "Bearer <token>")

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

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

	fmt.Println(string(body))

}

{
  "created": 123,
  "background": "<string>",
  "output_format": "<string>",
  "quality": "low",
  "size": "<string>",
  "data": [
    {
      "url": "<string>",
      "revised_prompt": "<string>"
    }
  ],
  "usage": {
    "total_tokens": 123,
    "input_tokens": 123,
    "output_tokens": 123,
    "input_tokens_details": {
      "text_tokens": 123,
      "image_tokens": 123
    }
  }
}

{
  "error": {
    "type": "invalid_request_error",
    "code": "model_endpoint_mismatch",
    "message": "该模型不支持当前 endpoint。"
  }
}

{
  "error": {
    "type": "invalid_request_error",
    "code": "model_endpoint_mismatch",
    "message": "该模型不支持当前 endpoint。"
  }
}

{
  "error": {
    "type": "invalid_request_error",
    "code": "model_endpoint_mismatch",
    "message": "该模型不支持当前 endpoint。"
  }
}

{
  "error": {
    "type": "invalid_request_error",
    "code": "model_endpoint_mismatch",
    "message": "该模型不支持当前 endpoint。"
  }
}