Gpt image 2
curl --request POST \
--url https://api.tensormaster.com/v1/tlimages/proimage \
--header 'Authorization: <api-key>' \
--header 'Content-Type: multipart/form-data' \
--form model=gpt-image-2 \
--form 'prompt=在简洁的影棚背景中生成一张高品质商品图' \
--form size=16:9 \
--form n=1 \
--form 'image=<string>' \
--form 'imageUrl=<string>' \
--form mask='@example-file' \
--form image.items='@example-file'import requests
url = "https://api.tensormaster.com/v1/tlimages/proimage"
files = {
"mask": ("example-file", open("example-file", "rb")),
"image.items": ("example-file", open("example-file", "rb"))
}
payload = {
"model": "gpt-image-2",
"prompt": "在简洁的影棚背景中生成一张高品质商品图",
"size": "16:9",
"n": "1",
"image": "<string>",
"imageUrl": "<string>"
}
headers = {"Authorization": "<api-key>"}
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', '在简洁的影棚背景中生成一张高品质商品图');
form.append('size', '16:9');
form.append('n', '1');
form.append('image', '<string>');
form.append('imageUrl', '<string>');
form.append('mask', '<string>');
form.append('image.items', '{
"fileName": "example-file"
}');
const options = {method: 'POST', headers: {Authorization: '<api-key>'}};
options.body = form;
fetch('https://api.tensormaster.com/v1/tlimages/proimage', 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://api.tensormaster.com/v1/tlimages/proimage",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => "-----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在简洁的影棚背景中生成一张高品质商品图\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"size\"\r\n\r\n16:9\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"n\"\r\n\r\n1\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"imageUrl\"\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<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image.items\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n{\r\n \"fileName\": \"example-file\"\r\n}\r\n-----011000010111000001101001--",
CURLOPT_HTTPHEADER => [
"Authorization: <api-key>",
"Content-Type: multipart/form-data"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.tensormaster.com/v1/tlimages/proimage"
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在简洁的影棚背景中生成一张高品质商品图\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"size\"\r\n\r\n16:9\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"n\"\r\n\r\n1\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"imageUrl\"\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<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image.items\"; 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", "<api-key>")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.tensormaster.com/v1/tlimages/proimage")
.header("Authorization", "<api-key>")
.body("-----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在简洁的影棚背景中生成一张高品质商品图\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"size\"\r\n\r\n16:9\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"n\"\r\n\r\n1\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"imageUrl\"\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<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image.items\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n{\r\n \"fileName\": \"example-file\"\r\n}\r\n-----011000010111000001101001--")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.tensormaster.com/v1/tlimages/proimage")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = '<api-key>'
request.body = "-----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在简洁的影棚背景中生成一张高品质商品图\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"size\"\r\n\r\n16:9\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"n\"\r\n\r\n1\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"imageUrl\"\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<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image.items\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n{\r\n \"fileName\": \"example-file\"\r\n}\r\n-----011000010111000001101001--"
response = http.request(request)
puts response.read_body{
"code": 1000,
"msg": "任务创建成功",
"data": {
"taskid": "abcd_1234567890abcdef"
}
}图片生成
Gpt image 2
使用 GPT Image 2 系列模型异步生成新图片,或编辑最多 16 张参考图。模型名决定 1K、2K 或 4K 分辨率档,输出质量固定为 high。请求返回任务 ID。
POST
/
v1
/
tlimages
/
proimage
Gpt image 2
curl --request POST \
--url https://api.tensormaster.com/v1/tlimages/proimage \
--header 'Authorization: <api-key>' \
--header 'Content-Type: multipart/form-data' \
--form model=gpt-image-2 \
--form 'prompt=在简洁的影棚背景中生成一张高品质商品图' \
--form size=16:9 \
--form n=1 \
--form 'image=<string>' \
--form 'imageUrl=<string>' \
--form mask='@example-file' \
--form image.items='@example-file'import requests
url = "https://api.tensormaster.com/v1/tlimages/proimage"
files = {
"mask": ("example-file", open("example-file", "rb")),
"image.items": ("example-file", open("example-file", "rb"))
}
payload = {
"model": "gpt-image-2",
"prompt": "在简洁的影棚背景中生成一张高品质商品图",
"size": "16:9",
"n": "1",
"image": "<string>",
"imageUrl": "<string>"
}
headers = {"Authorization": "<api-key>"}
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', '在简洁的影棚背景中生成一张高品质商品图');
form.append('size', '16:9');
form.append('n', '1');
form.append('image', '<string>');
form.append('imageUrl', '<string>');
form.append('mask', '<string>');
form.append('image.items', '{
"fileName": "example-file"
}');
const options = {method: 'POST', headers: {Authorization: '<api-key>'}};
options.body = form;
fetch('https://api.tensormaster.com/v1/tlimages/proimage', 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://api.tensormaster.com/v1/tlimages/proimage",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => "-----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在简洁的影棚背景中生成一张高品质商品图\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"size\"\r\n\r\n16:9\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"n\"\r\n\r\n1\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"imageUrl\"\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<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image.items\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n{\r\n \"fileName\": \"example-file\"\r\n}\r\n-----011000010111000001101001--",
CURLOPT_HTTPHEADER => [
"Authorization: <api-key>",
"Content-Type: multipart/form-data"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.tensormaster.com/v1/tlimages/proimage"
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在简洁的影棚背景中生成一张高品质商品图\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"size\"\r\n\r\n16:9\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"n\"\r\n\r\n1\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"imageUrl\"\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<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image.items\"; 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", "<api-key>")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.tensormaster.com/v1/tlimages/proimage")
.header("Authorization", "<api-key>")
.body("-----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在简洁的影棚背景中生成一张高品质商品图\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"size\"\r\n\r\n16:9\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"n\"\r\n\r\n1\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"imageUrl\"\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<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image.items\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n{\r\n \"fileName\": \"example-file\"\r\n}\r\n-----011000010111000001101001--")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.tensormaster.com/v1/tlimages/proimage")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = '<api-key>'
request.body = "-----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在简洁的影棚背景中生成一张高品质商品图\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"size\"\r\n\r\n16:9\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"n\"\r\n\r\n1\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image\"\r\n\r\n<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"imageUrl\"\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<string>\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"image.items\"; filename=\"example-file\"\r\nContent-Type: application/octet-stream\r\n\r\n{\r\n \"fileName\": \"example-file\"\r\n}\r\n-----011000010111000001101001--"
response = http.request(request)
puts response.read_body{
"code": 1000,
"msg": "任务创建成功",
"data": {
"taskid": "abcd_1234567890abcdef"
}
}授权
API密钥认证,请在请求头中包含Authorization字段
请求体
multipart/form-data
GPT Image 2 模型及输出分辨率档位
可用选项:
gpt-image-2, gpt-image-2-2k, gpt-image-2-4k 示例:
"gpt-image-2"
图片生成或编辑指令
Maximum string length:
32000示例:
"在简洁的影棚背景中生成一张高品质商品图"
文生图的输出画幅。支持 auto,以及 1:1、4:3、3:4、3:2、2:3、16:9、9:16、21:9、5:4、4:5,也可以传 1920x1080、512x512 等常见 WxH 像素尺寸。图生图请传 1024x1024 等 WxH 像素尺寸。交付尺寸由所选模型档位决定。
示例:
"16:9"
生成图片数量
必填范围:
1 <= x <= 10图生图的可选参考图。传多张图片时重复提交 image 字段;支持 PNG、WebP、JPG 和 JPEG。上传文件与 imageUrl 中的图片总数不能超过 16。
Maximum array length:
16图生图的可选参考图 URL。多个 URL 使用英文逗号分隔;上传文件与 URL 中的图片总数不能超过 16。
图生图的可选 PNG 蒙版。至少需要同时传一张参考图,尺寸须与第一张参考图一致;透明区域会被编辑,非透明区域会被保留。
⌘I
