Image Editing: Edit an existing image with text instructions
curl --request POST \
--url https://api.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"contents": [
{
"parts": [
{
"text": "Combine the people from these two images into one office scene, making funny faces"
},
{
"inlineData": {
"mimeType": "image/png",
"data": "<BASE64_DATA_IMG_1>"
}
},
{
"inlineData": {
"mimeType": "image/png",
"data": "<BASE64_DATA_IMG_2>"
}
}
]
}
],
"generationConfig": {
"responseModalities": [
"IMAGE"
],
"imageConfig": {
"aspectRatio": "16:9",
"imageSize": "2K"
}
}
}
'import requests
url = "https://api.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent"
payload = {
"contents": [{ "parts": [{ "text": "Combine the people from these two images into one office scene, making funny faces" }, { "inlineData": {
"mimeType": "image/png",
"data": "<BASE64_DATA_IMG_1>"
} }, { "inlineData": {
"mimeType": "image/png",
"data": "<BASE64_DATA_IMG_2>"
} }] }],
"generationConfig": {
"responseModalities": ["IMAGE"],
"imageConfig": {
"aspectRatio": "16:9",
"imageSize": "2K"
}
}
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
contents: [
{
parts: [
{
text: 'Combine the people from these two images into one office scene, making funny faces'
},
{inlineData: {mimeType: 'image/png', data: '<BASE64_DATA_IMG_1>'}},
{inlineData: {mimeType: 'image/png', data: '<BASE64_DATA_IMG_2>'}}
]
}
],
generationConfig: {
responseModalities: ['IMAGE'],
imageConfig: {aspectRatio: '16:9', imageSize: '2K'}
}
})
};
fetch('https://api.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent', 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.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'contents' => [
[
'parts' => [
[
'text' => 'Combine the people from these two images into one office scene, making funny faces'
],
[
'inlineData' => [
'mimeType' => 'image/png',
'data' => '<BASE64_DATA_IMG_1>'
]
],
[
'inlineData' => [
'mimeType' => 'image/png',
'data' => '<BASE64_DATA_IMG_2>'
]
]
]
]
],
'generationConfig' => [
'responseModalities' => [
'IMAGE'
],
'imageConfig' => [
'aspectRatio' => '16:9',
'imageSize' => '2K'
]
]
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"Content-Type: application/json"
],
]);
$response = curl_exec($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.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent"
payload := strings.NewReader("{\n \"contents\": [\n {\n \"parts\": [\n {\n \"text\": \"Combine the people from these two images into one office scene, making funny faces\"\n },\n {\n \"inlineData\": {\n \"mimeType\": \"image/png\",\n \"data\": \"<BASE64_DATA_IMG_1>\"\n }\n },\n {\n \"inlineData\": {\n \"mimeType\": \"image/png\",\n \"data\": \"<BASE64_DATA_IMG_2>\"\n }\n }\n ]\n }\n ],\n \"generationConfig\": {\n \"responseModalities\": [\n \"IMAGE\"\n ],\n \"imageConfig\": {\n \"aspectRatio\": \"16:9\",\n \"imageSize\": \"2K\"\n }\n }\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "Bearer <token>")
req.Header.Add("Content-Type", "application/json")
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.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"contents\": [\n {\n \"parts\": [\n {\n \"text\": \"Combine the people from these two images into one office scene, making funny faces\"\n },\n {\n \"inlineData\": {\n \"mimeType\": \"image/png\",\n \"data\": \"<BASE64_DATA_IMG_1>\"\n }\n },\n {\n \"inlineData\": {\n \"mimeType\": \"image/png\",\n \"data\": \"<BASE64_DATA_IMG_2>\"\n }\n }\n ]\n }\n ],\n \"generationConfig\": {\n \"responseModalities\": [\n \"IMAGE\"\n ],\n \"imageConfig\": {\n \"aspectRatio\": \"16:9\",\n \"imageSize\": \"2K\"\n }\n }\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"contents\": [\n {\n \"parts\": [\n {\n \"text\": \"Combine the people from these two images into one office scene, making funny faces\"\n },\n {\n \"inlineData\": {\n \"mimeType\": \"image/png\",\n \"data\": \"<BASE64_DATA_IMG_1>\"\n }\n },\n {\n \"inlineData\": {\n \"mimeType\": \"image/png\",\n \"data\": \"<BASE64_DATA_IMG_2>\"\n }\n }\n ]\n }\n ],\n \"generationConfig\": {\n \"responseModalities\": [\n \"IMAGE\"\n ],\n \"imageConfig\": {\n \"aspectRatio\": \"16:9\",\n \"imageSize\": \"2K\"\n }\n }\n}"
response = http.request(request)
puts response.read_body{
"candidates": [
{
"content": {
"parts": [
{
"inlineData": {
"mimeType": "image/png",
"data": "<string>"
}
}
]
},
"finishReason": "STOP"
}
],
"usageMetadata": {
"promptTokenCount": 10,
"candidatesTokenCount": 258
}
}Nano Banana 2画像生成
画像編集 API リファレンス
Nano Banana 2 画像編集 API リファレンスとインタラクティブなプレイグラウンド — 画像 + 指示を与えて、編集結果を生成します
POST
/
v1beta
/
models
/
gemini-3.1-flash-image-preview:generateContent
Image Editing: Edit an existing image with text instructions
curl --request POST \
--url https://api.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"contents": [
{
"parts": [
{
"text": "Combine the people from these two images into one office scene, making funny faces"
},
{
"inlineData": {
"mimeType": "image/png",
"data": "<BASE64_DATA_IMG_1>"
}
},
{
"inlineData": {
"mimeType": "image/png",
"data": "<BASE64_DATA_IMG_2>"
}
}
]
}
],
"generationConfig": {
"responseModalities": [
"IMAGE"
],
"imageConfig": {
"aspectRatio": "16:9",
"imageSize": "2K"
}
}
}
'import requests
url = "https://api.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent"
payload = {
"contents": [{ "parts": [{ "text": "Combine the people from these two images into one office scene, making funny faces" }, { "inlineData": {
"mimeType": "image/png",
"data": "<BASE64_DATA_IMG_1>"
} }, { "inlineData": {
"mimeType": "image/png",
"data": "<BASE64_DATA_IMG_2>"
} }] }],
"generationConfig": {
"responseModalities": ["IMAGE"],
"imageConfig": {
"aspectRatio": "16:9",
"imageSize": "2K"
}
}
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
contents: [
{
parts: [
{
text: 'Combine the people from these two images into one office scene, making funny faces'
},
{inlineData: {mimeType: 'image/png', data: '<BASE64_DATA_IMG_1>'}},
{inlineData: {mimeType: 'image/png', data: '<BASE64_DATA_IMG_2>'}}
]
}
],
generationConfig: {
responseModalities: ['IMAGE'],
imageConfig: {aspectRatio: '16:9', imageSize: '2K'}
}
})
};
fetch('https://api.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent', 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.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'contents' => [
[
'parts' => [
[
'text' => 'Combine the people from these two images into one office scene, making funny faces'
],
[
'inlineData' => [
'mimeType' => 'image/png',
'data' => '<BASE64_DATA_IMG_1>'
]
],
[
'inlineData' => [
'mimeType' => 'image/png',
'data' => '<BASE64_DATA_IMG_2>'
]
]
]
]
],
'generationConfig' => [
'responseModalities' => [
'IMAGE'
],
'imageConfig' => [
'aspectRatio' => '16:9',
'imageSize' => '2K'
]
]
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"Content-Type: application/json"
],
]);
$response = curl_exec($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.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent"
payload := strings.NewReader("{\n \"contents\": [\n {\n \"parts\": [\n {\n \"text\": \"Combine the people from these two images into one office scene, making funny faces\"\n },\n {\n \"inlineData\": {\n \"mimeType\": \"image/png\",\n \"data\": \"<BASE64_DATA_IMG_1>\"\n }\n },\n {\n \"inlineData\": {\n \"mimeType\": \"image/png\",\n \"data\": \"<BASE64_DATA_IMG_2>\"\n }\n }\n ]\n }\n ],\n \"generationConfig\": {\n \"responseModalities\": [\n \"IMAGE\"\n ],\n \"imageConfig\": {\n \"aspectRatio\": \"16:9\",\n \"imageSize\": \"2K\"\n }\n }\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "Bearer <token>")
req.Header.Add("Content-Type", "application/json")
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.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"contents\": [\n {\n \"parts\": [\n {\n \"text\": \"Combine the people from these two images into one office scene, making funny faces\"\n },\n {\n \"inlineData\": {\n \"mimeType\": \"image/png\",\n \"data\": \"<BASE64_DATA_IMG_1>\"\n }\n },\n {\n \"inlineData\": {\n \"mimeType\": \"image/png\",\n \"data\": \"<BASE64_DATA_IMG_2>\"\n }\n }\n ]\n }\n ],\n \"generationConfig\": {\n \"responseModalities\": [\n \"IMAGE\"\n ],\n \"imageConfig\": {\n \"aspectRatio\": \"16:9\",\n \"imageSize\": \"2K\"\n }\n }\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"contents\": [\n {\n \"parts\": [\n {\n \"text\": \"Combine the people from these two images into one office scene, making funny faces\"\n },\n {\n \"inlineData\": {\n \"mimeType\": \"image/png\",\n \"data\": \"<BASE64_DATA_IMG_1>\"\n }\n },\n {\n \"inlineData\": {\n \"mimeType\": \"image/png\",\n \"data\": \"<BASE64_DATA_IMG_2>\"\n }\n }\n ]\n }\n ],\n \"generationConfig\": {\n \"responseModalities\": [\n \"IMAGE\"\n ],\n \"imageConfig\": {\n \"aspectRatio\": \"16:9\",\n \"imageSize\": \"2K\"\n }\n }\n}"
response = http.request(request)
puts response.read_body{
"candidates": [
{
"content": {
"parts": [
{
"inlineData": {
"mimeType": "image/png",
"data": "<string>"
}
}
]
},
"finishReason": "STOP"
}
],
"usageMetadata": {
"promptTokenCount": 10,
"candidatesTokenCount": 258
}
}右側のインタラクティブな Playground は、パラメータのドロップダウン選択をサポートしています。Authorization フィールドに API Key を入力し(形式:
Bearer sk-xxx)、ワンクリックでテストリクエストを送信できます。適用範囲: このページは画像編集用です。編集指示とともに、入力画像(base64 エンコード済み)を提供する必要があります。テキストのみから新しい画像を生成するには、Text-to-Image endpoint を使用してください。
🖥️ ブラウザ版 Playground の制限(重要)この endpoint は、レスポンス内で base64 エンコードされた画像(
inlineData.data、通常は数 MB)を返します。ブラウザのレンダリング制限により、右側の Playground ではレスポンス到着後に 请求时发生错误: unable to complete request と表示される場合があります — リクエスト自体は成功しています; ブラウザがそのような長い base64 文字列をレンダリングできないだけです。推奨ワークフロー(初心者向け):- 下の Python / Node.js / cURL のサンプルをコピーして、ローカルで実行してください。コードが自動的に
base64.b64decodes してレスポンスを処理し、画像をファイルに書き込みます。 - どうしてもブラウザ内の Playground を使う場合は、小さな参照画像(< 50KB)を使い、
imageSizeを最小のティア(例:512/1K)に設定してください。
⚠️ 誤り(各パートに
parts 配列構造(重要 — 複数画像編集の場合はこれを読んでください)各 part は、text または inlineData のどちらか一方でなければならず、両方を含めてはいけません。これは Google の公式 gemini-3.1-flash-image-preview 契約と一致しています。正しい: 1 つの text パート(指示)+ N 個の inlineData パート(画像ごとに 1 つずつ):"contents": [{
"parts": [
{"text": "Combine the people from these two images into one office scene"},
{"inlineData": {"mimeType": "image/png", "data": "<BASE64_DATA_IMG_1>"}},
{"inlineData": {"mimeType": "image/png", "data": "<BASE64_DATA_IMG_2>"}}
]
}]
text と inlineData の両方が含まれている — 未定義の動作になります):"contents": [{
"parts": [
{"inlineData": {...}, "text": "is this the prompt 1"},
{"inlineData": {...}, "text": "is this the prompt 2"}
]
}]
🖼️ 実行後は、Playground の
inlineData.data フィールドについてこの endpoint はJSON 形式(multipart ファイルアップロードではありません)を使用するため、Playground からローカルファイルを直接選択することはできません。まず画像をBase64 文字列に変換し、data 入力に貼り付ける必要があります。ワンラインコマンド: 変換 + クリップボードへコピー:# macOS
base64 -i your-image.jpg | tr -d '\n' | pbcopy
# Linux
base64 -w0 your-image.jpg | xclip -selection clipboard
# Windows PowerShell
[Convert]::ToBase64String([IO.File]::ReadAllBytes("your-image.jpg")) | Set-Clipboard
data フィールドに Cmd+V / Ctrl+V して貼り付けるだけです。また、mimeType を対応する image/jpeg または image/png に設定することも忘れないでください。推奨: 長い base64 文字列によるブラウザの遅延を避けるため、テストには小さな画像(< 200KB)を使ってください。頻繁に画像編集テストを行う場合は、代わりに下のコード例を使ってローカルで実行することをおすすめします。コード例
Python
import requests
import base64
API_KEY = "sk-your-api-key"
# Read the image to edit
with open("input.jpg", "rb") as f:
image_b64 = base64.b64encode(f.read()).decode()
response = requests.post(
"https://api.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent",
headers={"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"},
json={
"contents": [{
"parts": [
{"text": "Please blur the background to highlight the person in the foreground"},
{"inlineData": {"mimeType": "image/jpeg", "data": image_b64}}
]
}],
"generationConfig": {
"responseModalities": ["IMAGE"],
"imageConfig": {"aspectRatio": "16:9", "imageSize": "2K"}
}
},
timeout=300
).json()
img_data = response["candidates"][0]["content"]["parts"][0]["inlineData"]["data"]
with open("edited.png", 'wb') as f:
f.write(base64.b64decode(img_data))
print("Edited image saved to edited.png")
Node.js
import fs from "fs";
const API_KEY = "sk-your-api-key";
const imageB64 = fs.readFileSync("input.jpg").toString("base64");
const response = await fetch(
"https://api.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent",
{
method: "POST",
headers: {
"Authorization": `Bearer ${API_KEY}`,
"Content-Type": "application/json"
},
body: JSON.stringify({
contents: [{
parts: [
{ text: "Please blur the background to highlight the person in the foreground" },
{ inlineData: { mimeType: "image/jpeg", data: imageB64 } }
]
}],
generationConfig: {
responseModalities: ["IMAGE"],
imageConfig: { aspectRatio: "16:9", imageSize: "2K" }
}
})
}
);
const data = await response.json();
const imgBase64 = data.candidates[0].content.parts[0].inlineData.data;
fs.writeFileSync("edited.png", Buffer.from(imgBase64, "base64"));
cURL
# Note: convert image to base64 first
# IMAGE_B64=$(base64 -i input.jpg | tr -d '\n')
curl -X POST "https://api.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent" \
-H "Authorization: Bearer sk-your-api-key" \
-H "Content-Type: application/json" \
-d '{
"contents": [{
"parts": [
{"text": "Please blur the background to highlight the person in the foreground"},
{"inlineData": {"mimeType": "image/jpeg", "data": "'"$IMAGE_B64"'"}}
]
}],
"generationConfig": {
"responseModalities": ["IMAGE"],
"imageConfig": {"aspectRatio": "16:9", "imageSize": "2K"}
}
}'
マルチ画像編集
複数の入力画像を結合または比較する場合は、1つのtext パート(指示)に続けて、複数の inlineData パート(画像ごとに1つ)を使用してください。
Python(マルチ画像)
import requests
import base64
API_KEY = "sk-your-api-key"
def to_b64(path):
with open(path, "rb") as f:
return base64.b64encode(f.read()).decode()
# Prepare multiple images (2 here as an example)
images = ["person1.png", "person2.png"]
parts = [{"text": "Combine the people from these images into one office scene, making funny faces"}]
for path in images:
parts.append({"inlineData": {"mimeType": "image/png", "data": to_b64(path)}})
response = requests.post(
"https://api.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent",
headers={"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"},
json={
"contents": [{"parts": parts}],
"generationConfig": {
"responseModalities": ["TEXT", "IMAGE"],
"imageConfig": {"aspectRatio": "5:4", "imageSize": "2K"}
}
},
timeout=300
).json()
img_data = response["candidates"][0]["content"]["parts"][0]["inlineData"]["data"]
with open("merged.png", "wb") as f:
f.write(base64.b64decode(img_data))
cURL(マルチ画像、Google の公式形式に準拠)
curl -X POST "https://api.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent" \
-H "Authorization: Bearer sk-your-api-key" \
-H "Content-Type: application/json" \
-d '{
"contents": [{
"parts": [
{"text": "An office group photo of these people, they are making funny faces."},
{"inlineData": {"mimeType": "image/png", "data": "<BASE64_DATA_IMG_1>"}},
{"inlineData": {"mimeType": "image/png", "data": "<BASE64_DATA_IMG_2>"}},
{"inlineData": {"mimeType": "image/png", "data": "<BASE64_DATA_IMG_3>"}}
]
}],
"generationConfig": {
"responseModalities": ["TEXT", "IMAGE"],
"imageConfig": {"aspectRatio": "5:4", "imageSize": "2K"}
}
}'
パラメーター クイックリファレンス
| Parameter | Type | Required | Description |
|---|---|---|---|
contents[].parts | array | Yes | 1つのテキスト部分 + N個の inlineData 部分 で構成されます。各部分には text または inlineData のどちらか一方のみを含め、両方を含めることはありません |
contents[].parts[].text | string | Yes | 編集指示(最初の部分にのみ配置します) |
contents[].parts[].inlineData.mimeType | string | Yes | image/jpeg または image/png |
contents[].parts[].inlineData.data | string | Yes | Base64 エンコードされた画像(複数画像の編集では、画像ごとに 1 つの inlineData 部分を繰り返します) |
generationConfig.responseModalities | array | Yes | 通常は ["IMAGE"] |
generationConfig.imageConfig.aspectRatio | string | No | 14 種類の比率、デフォルトは 1:1 |
generationConfig.imageConfig.imageSize | string | No | 512 / 1K / 2K / 4K、デフォルトは 1K |
generationConfig.thinkingConfig.thinkingLevel | string | No | minimal(高速)/ High(深い推論) |
generationConfig.thinkingConfig.includeThoughts | boolean | No | thinking process テキストを返します |
マルチターンの会話型編集
Nano Banana 2 (gemini-3.1-flash-image-preview) は、真の会話型マルチターン編集をサポートします。各ターンで生成された画像を contents に role: "model" inlineData として再追加し、その後に次のユーザー指示を送ります。モデルは 会話履歴全体 に基づいて編集し、変更を積み重ねます(例: まずソファの色を変え、次にアクセサリーを追加する — 以前の変更は保持されます)。
これは逆画像モデルとは異なります。ネイティブの Gemini 形式は、
model-ロールの履歴ターンから画像を実際に読み取ります。ターンをまたぐ一貫性と段階的な洗練のため、以下の履歴バックフィルパターンを使用してください。import requests, base64
API_KEY = "sk-your-api-key"
URL = "https://api.apiyi.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent"
H = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}
CFG = {"responseModalities": ["IMAGE"], "imageConfig": {"aspectRatio": "1:1", "imageSize": "2K"}}
contents = [] # keep one running conversation history
def turn(instruction, save_to):
contents.append({"role": "user", "parts": [{"text": instruction}]})
data = requests.post(URL, headers=H,
json={"contents": contents, "generationConfig": CFG}, timeout=300).json()
part = next(p for p in data["candidates"][0]["content"]["parts"] if "inlineData" in p)
contents.append({"role": "model", "parts": [part]}) # key: backfill the output image into history
with open(save_to, "wb") as f:
f.write(base64.b64decode(part["inlineData"]["data"]))
return part
turn("Generate an orange cat sitting on a blue sofa, simple line-art style", "step1.png")
turn("Make the sofa red; keep the cat and composition unchanged", "step2.png") # edits the previous image
turn("Put a small yellow hat on the cat; keep everything else the same", "step3.png") # accumulates; red sofa kept
既存の画像からマルチターンを開始する: 最初のユーザーメッセージに、既存の写真を編集するための
inlineData(ご自身の画像)と指示を入れ、各ターンでモデル出力を contents にバックフィルし続けます。2つのマルチターン方式:
- 履歴バックフィル(上記、推奨):
contentsは、ユーザー/モデルの交互の履歴を維持し、より高い一貫性でターンをまたいで変更を積み重ねます。 - 再フィード(よりシンプル): 各ターンで 1 つのユーザーメッセージ(
text+ 直前の画像のinlineData)を送り、前のコンテキストを引き継がずに 1 ステップの編集を行います。
承認
API Key obtained from APIYI Console
ボディ
application/json
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