Gemini может генерировать и обрабатывать изображения в диалоговом режиме. Вы можете запрашивать текст, изображения или их комбинацию как у быстрой модели Gemini 2.5 Flash (также известной как Nano Banana), так и у продвинутой модели Gemini 3 Pro Preview (также известной как Nano Banana Pro) , что позволяет создавать, редактировать и дорабатывать визуальные материалы с беспрецедентным контролем:
- Преобразование текста, изображений и нескольких изображений в изображения: создавайте высококачественные изображения на основе текстовых описаний, используйте текстовые подсказки для редактирования и настройки заданного изображения или используйте несколько входных изображений для создания новых сцен и переноса стилей.
- Итеративное совершенствование: в процессе общения вы будете улучшать свой образ на протяжении нескольких этапов, внося небольшие корректировки, пока он не станет идеальным.
- Высокоточная отрисовка текста: точное создание изображений с разборчивым и хорошо расположенным текстом, идеально подходящих для логотипов, диаграмм и плакатов.
Все сгенерированные изображения содержат водяной знак SynthID .
Генерация изображений (преобразование текста в изображение)
Python
from google import genai
from google.genai import types
from PIL import Image
client = genai.Client()
prompt = (
"Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme"
)
response = client.models.generate_content(
model="gemini-2.5-flash-image",
contents=[prompt],
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif part.inline_data is not None:
image = part.as_image()
image.save("generated_image.png")
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const prompt =
"Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme";
const response = await ai.models.generateContent({
model: "gemini-2.5-flash-image",
contents: prompt,
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("gemini-native-image.png", buffer);
console.log("Image saved as gemini-native-image.png");
}
}
}
main();
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
result, _ := client.Models.GenerateContent(
ctx,
"gemini-2.5-flash-image",
genai.Text("Create a picture of a nano banana dish in a " +
" fancy restaurant with a Gemini theme"),
)
for _, part := range result.Candidates[0].Content.Parts {
if part.Text != "" {
fmt.Println(part.Text)
} else if part.InlineData != nil {
imageBytes := part.InlineData.Data
outputFilename := "gemini_generated_image.png"
_ = os.WriteFile(outputFilename, imageBytes, 0644)
}
}
}
Java
import com.google.genai.Client;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
public class TextToImage {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-2.5-flash-image",
"Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme",
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("_01_generated_image.png"), blob.data().get());
}
}
}
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"contents": [{
"parts": [
{"text": "Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme"}
]
}]
}'
Редактирование изображений (преобразование текста и изображения в изображение)
Напоминание : Убедитесь, что у вас есть необходимые права на любые загружаемые вами изображения. Не создавайте контент, нарушающий права других лиц, включая видео или изображения, которые вводят в заблуждение, преследуют или причиняют вред. Использование вами этого сервиса генеративного ИИ регулируется нашей Политикой запрещенного использования .
Предоставьте изображение и используйте текстовые подсказки для добавления, удаления или изменения элементов, изменения стиля или корректировки цветовой гаммы.
В следующем примере показана загрузка изображений в кодировке base64. Для загрузки нескольких изображений, больших объемов данных и поддерживаемых типов MIME см. страницу "Понимание изображений" .
Python
from google import genai
from google.genai import types
from PIL import Image
client = genai.Client()
prompt = (
"Create a picture of my cat eating a nano-banana in a "
"fancy restaurant under the Gemini constellation",
)
image = Image.open("/path/to/cat_image.png")
response = client.models.generate_content(
model="gemini-2.5-flash-image",
contents=[prompt, image],
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif part.inline_data is not None:
image = part.as_image()
image.save("generated_image.png")
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const imagePath = "path/to/cat_image.png";
const imageData = fs.readFileSync(imagePath);
const base64Image = imageData.toString("base64");
const prompt = [
{ text: "Create a picture of my cat eating a nano-banana in a" +
"fancy restaurant under the Gemini constellation" },
{
inlineData: {
mimeType: "image/png",
data: base64Image,
},
},
];
const response = await ai.models.generateContent({
model: "gemini-2.5-flash-image",
contents: prompt,
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("gemini-native-image.png", buffer);
console.log("Image saved as gemini-native-image.png");
}
}
}
main();
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
imagePath := "/path/to/cat_image.png"
imgData, _ := os.ReadFile(imagePath)
parts := []*genai.Part{
genai.NewPartFromText("Create a picture of my cat eating a nano-banana in a fancy restaurant under the Gemini constellation"),
&genai.Part{
InlineData: &genai.Blob{
MIMEType: "image/png",
Data: imgData,
},
},
}
contents := []*genai.Content{
genai.NewContentFromParts(parts, genai.RoleUser),
}
result, _ := client.Models.GenerateContent(
ctx,
"gemini-2.5-flash-image",
contents,
)
for _, part := range result.Candidates[0].Content.Parts {
if part.Text != "" {
fmt.Println(part.Text)
} else if part.InlineData != nil {
imageBytes := part.InlineData.Data
outputFilename := "gemini_generated_image.png"
_ = os.WriteFile(outputFilename, imageBytes, 0644)
}
}
}
Java
import com.google.genai.Client;
import com.google.genai.types.Content;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
public class TextAndImageToImage {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-2.5-flash-image",
Content.fromParts(
Part.fromText("""
Create a picture of my cat eating a nano-banana in
a fancy restaurant under the Gemini constellation
"""),
Part.fromBytes(
Files.readAllBytes(
Path.of("src/main/resources/cat.jpg")),
"image/jpeg")),
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("gemini_generated_image.png"), blob.data().get());
}
}
}
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d "{
\"contents\": [{
\"parts\":[
{\"text\": \"'Create a picture of my cat eating a nano-banana in a fancy restaurant under the Gemini constellation\"},
{
\"inline_data\": {
\"mime_type\":\"image/jpeg\",
\"data\": \"<BASE64_IMAGE_DATA>\"
}
}
]
}]
}"
Многоэтапная обработка изображений
Продолжайте создавать и редактировать изображения в режиме диалога. Чат или многоэтапная переписка — рекомендуемый способ итерации работы над изображениями. В следующем примере показано задание на создание инфографики о фотосинтезе.
Python
from google import genai
from google.genai import types
client = genai.Client()
chat = client.chats.create(
model="gemini-3-pro-image-preview",
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE'],
tools=[{"google_search": {}}]
)
)
message = "Create a vibrant infographic that explains photosynthesis as if it were a recipe for a plant's favorite food. Show the \"ingredients\" (sunlight, water, CO2) and the \"finished dish\" (sugar/energy). The style should be like a page from a colorful kids' cookbook, suitable for a 4th grader."
response = chat.send_message(message)
for part in response.parts:
if part.text is not None:
print(part.text)
elif image:= part.as_image():
image.save("photosynthesis.png")
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({});
async function main() {
const chat = ai.chats.create({
model: "gemini-3-pro-image-preview",
config: {
responseModalities: ['TEXT', 'IMAGE'],
tools: [{googleSearch: {}}],
},
});
await main();
const message = "Create a vibrant infographic that explains photosynthesis as if it were a recipe for a plant's favorite food. Show the \"ingredients\" (sunlight, water, CO2) and the \"finished dish\" (sugar/energy). The style should be like a page from a colorful kids' cookbook, suitable for a 4th grader."
let response = await chat.sendMessage({message});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("photosynthesis.png", buffer);
console.log("Image saved as photosynthesis.png");
}
}
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
defer client.Close()
model := client.GenerativeModel("gemini-3-pro-image-preview")
model.GenerationConfig = &pb.GenerationConfig{
ResponseModalities: []pb.ResponseModality{genai.Text, genai.Image},
}
chat := model.StartChat()
message := "Create a vibrant infographic that explains photosynthesis as if it were a recipe for a plant's favorite food. Show the \"ingredients\" (sunlight, water, CO2) and the \"finished dish\" (sugar/energy). The style should be like a page from a colorful kids' cookbook, suitable for a 4th grader."
resp, err := chat.SendMessage(ctx, genai.Text(message))
if err != nil {
log.Fatal(err)
}
for _, part := range resp.Candidates[0].Content.Parts {
if txt, ok := part.(genai.Text); ok {
fmt.Printf("%s", string(txt))
} else if img, ok := part.(genai.ImageData); ok {
err := os.WriteFile("photosynthesis.png", img.Data, 0644)
if err != nil {
log.Fatal(err)
}
}
}
}
Java
import com.google.genai.Chat;
import com.google.genai.Client;
import com.google.genai.types.Content;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.GoogleSearch;
import com.google.genai.types.ImageConfig;
import com.google.genai.types.Part;
import com.google.genai.types.RetrievalConfig;
import com.google.genai.types.Tool;
import com.google.genai.types.ToolConfig;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
public class MultiturnImageEditing {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.tools(Tool.builder()
.googleSearch(GoogleSearch.builder().build())
.build())
.build();
Chat chat = client.chats.create("gemini-3-pro-image-preview", config);
GenerateContentResponse response = chat.sendMessage("""
Create a vibrant infographic that explains photosynthesis
as if it were a recipe for a plant's favorite food.
Show the "ingredients" (sunlight, water, CO2)
and the "finished dish" (sugar/energy).
The style should be like a page from a colorful
kids' cookbook, suitable for a 4th grader.
""");
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("photosynthesis.png"), blob.data().get());
}
}
}
// ...
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"contents": [{
"role": "user",
"parts": [
{"text": "Create a vibrant infographic that explains photosynthesis as if it were a recipe for a plants favorite food. Show the \"ingredients\" (sunlight, water, CO2) and the \"finished dish\" (sugar/energy). The style should be like a page from a colorful kids cookbook, suitable for a 4th grader."}
]
}],
"generationConfig": {
"responseModalities": ["TEXT", "IMAGE"]
}
}'
Затем вы можете использовать тот же чат, чтобы изменить язык изображения на испанский.
Python
message = "Update this infographic to be in Spanish. Do not change any other elements of the image."
aspect_ratio = "16:9" # "1:1","2:3","3:2","3:4","4:3","4:5","5:4","9:16","16:9","21:9"
resolution = "2K" # "1K", "2K", "4K"
response = chat.send_message(message,
config=types.GenerateContentConfig(
image_config=types.ImageConfig(
aspect_ratio=aspect_ratio,
image_size=resolution
),
))
for part in response.parts:
if part.text is not None:
print(part.text)
elif image:= part.as_image():
image.save("photosynthesis_spanish.png")
JavaScript
const message = 'Update this infographic to be in Spanish. Do not change any other elements of the image.';
const aspectRatio = '16:9';
const resolution = '2K';
let response = await chat.sendMessage({
message,
config: {
responseModalities: ['TEXT', 'IMAGE'],
imageConfig: {
aspectRatio: aspectRatio,
imageSize: resolution,
},
tools: [{googleSearch: {}}],
},
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("photosynthesis2.png", buffer);
console.log("Image saved as photosynthesis2.png");
}
}
Идти
message = "Update this infographic to be in Spanish. Do not change any other elements of the image."
aspect_ratio = "16:9" // "1:1","2:3","3:2","3:4","4:3","4:5","5:4","9:16","16:9","21:9"
resolution = "2K" // "1K", "2K", "4K"
model.GenerationConfig.ImageConfig = &pb.ImageConfig{
AspectRatio: aspect_ratio,
ImageSize: resolution,
}
resp, err = chat.SendMessage(ctx, genai.Text(message))
if err != nil {
log.Fatal(err)
}
for _, part := range resp.Candidates[0].Content.Parts {
if txt, ok := part.(genai.Text); ok {
fmt.Printf("%s", string(txt))
} else if img, ok := part.(genai.ImageData); ok {
err := os.WriteFile("photosynthesis_spanish.png", img.Data, 0644)
if err != nil {
log.Fatal(err)
}
}
}
Java
String aspectRatio = "16:9"; // "1:1","2:3","3:2","3:4","4:3","4:5","5:4","9:16","16:9","21:9"
String resolution = "2K"; // "1K", "2K", "4K"
config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.imageConfig(ImageConfig.builder()
.aspectRatio(aspectRatio)
.imageSize(resolution)
.build())
.build();
response = chat.sendMessage(
"Update this infographic to be in Spanish. " +
"Do not change any other elements of the image.",
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("photosynthesis_spanish.png"), blob.data().get());
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"contents": [
{
"role": "user",
"parts": [{"text": "Create a vibrant infographic that explains photosynthesis..."}]
},
{
"role": "model",
"parts": [{"inline_data": {"mime_type": "image/png", "data": "<PREVIOUS_IMAGE_DATA>"}}]
},
{
"role": "user",
"parts": [{"text": "Update this infographic to be in Spanish. Do not change any other elements of the image."}]
}
],
"tools": [{"google_search": {}}],
"generationConfig": {
"responseModalities": ["TEXT", "IMAGE"],
"imageConfig": {
"aspectRatio": "16:9",
"imageSize": "2K"
}
}
}'
Новое в изображении Gemini 3 Pro
Gemini 3 Pro Image ( gemini-3-pro-image-preview ) — это современная модель генерации и редактирования изображений, оптимизированная для профессионального создания ресурсов. Разработанная для решения самых сложных задач благодаря использованию передовых алгоритмов, она превосходно справляется со сложными многоэтапными задачами создания и модификации изображений.
- Вывод в высоком разрешении : встроенные возможности генерации изображений в разрешениях 1K, 2K и 4K.
- Расширенные возможности рендеринга текста : позволяет создавать разборчивый, стилизованный текст для инфографики, меню, диаграмм и маркетинговых материалов.
- Использование поиска Google : Модель может использовать поиск Google в качестве инструмента для проверки фактов и генерации изображений на основе данных в реальном времени (например, текущие карты погоды, биржевые графики, последние события).
- Режим мышления : Модель использует процесс «мышления» для анализа сложных задач. Она генерирует промежуточные «образы мыслей» (видимые в бэкэнде, но не оплачиваемые) для уточнения композиции перед созданием окончательного высококачественного результата.
- До 14 эталонных изображений : Теперь вы можете комбинировать до 14 эталонных изображений для получения окончательного изображения.
Используйте до 14 эталонных изображений.
В Gemini 3 Pro Preview можно комбинировать до 14 эталонных изображений. Эти 14 изображений могут включать в себя следующее:
- До 6 высококачественных изображений объектов для включения в итоговое изображение.
До 5 изображений людей для обеспечения единообразия характеров.
Python
from google import genai
from google.genai import types
from PIL import Image
prompt = "An office group photo of these people, they are making funny faces."
aspect_ratio = "5:4" # "1:1","2:3","3:2","3:4","4:3","4:5","5:4","9:16","16:9","21:9"
resolution = "2K" # "1K", "2K", "4K"
client = genai.Client()
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents=[
prompt,
Image.open('person1.png'),
Image.open('person2.png'),
Image.open('person3.png'),
Image.open('person4.png'),
Image.open('person5.png'),
],
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE'],
image_config=types.ImageConfig(
aspect_ratio=aspect_ratio,
image_size=resolution
),
)
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif image:= part.as_image():
image.save("office.png")
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const prompt =
'An office group photo of these people, they are making funny faces.';
const aspectRatio = '5:4';
const resolution = '2K';
const contents = [
{ text: prompt },
{
inlineData: {
mimeType: "image/jpeg",
data: base64ImageFile1,
},
},
{
inlineData: {
mimeType: "image/jpeg",
data: base64ImageFile2,
},
},
{
inlineData: {
mimeType: "image/jpeg",
data: base64ImageFile3,
},
},
{
inlineData: {
mimeType: "image/jpeg",
data: base64ImageFile4,
},
},
{
inlineData: {
mimeType: "image/jpeg",
data: base64ImageFile5,
},
}
];
const response = await ai.models.generateContent({
model: 'gemini-3-pro-image-preview',
contents: contents,
config: {
responseModalities: ['TEXT', 'IMAGE'],
imageConfig: {
aspectRatio: aspectRatio,
imageSize: resolution,
},
},
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("image.png", buffer);
console.log("Image saved as image.png");
}
}
}
main();
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
defer client.Close()
model := client.GenerativeModel("gemini-3-pro-image-preview")
model.GenerationConfig = &pb.GenerationConfig{
ResponseModalities: []pb.ResponseModality{genai.Text, genai.Image},
ImageConfig: &pb.ImageConfig{
AspectRatio: "5:4",
ImageSize: "2K",
},
}
img1, err := os.ReadFile("person1.png")
if err != nil { log.Fatal(err) }
img2, err := os.ReadFile("person2.png")
if err != nil { log.Fatal(err) }
img3, err := os.ReadFile("person3.png")
if err != nil { log.Fatal(err) }
img4, err := os.ReadFile("person4.png")
if err != nil { log.Fatal(err) }
img5, err := os.ReadFile("person5.png")
if err != nil { log.Fatal(err) }
parts := []genai.Part{
genai.Text("An office group photo of these people, they are making funny faces."),
genai.ImageData{MIMEType: "image/png", Data: img1},
genai.ImageData{MIMEType: "image/png", Data: img2},
genai.ImageData{MIMEType: "image/png", Data: img3},
genai.ImageData{MIMEType: "image/png", Data: img4},
genai.ImageData{MIMEType: "image/png", Data: img5},
}
resp, err := model.GenerateContent(ctx, parts...)
if err != nil {
log.Fatal(err)
}
for _, part := range resp.Candidates[0].Content.Parts {
if txt, ok := part.(genai.Text); ok {
fmt.Printf("%s", string(txt))
} else if img, ok := part.(genai.ImageData); ok {
err := os.WriteFile("office.png", img.Data, 0644)
if err != nil {
log.Fatal(err)
}
}
}
}
Java
import com.google.genai.Client;
import com.google.genai.types.Content;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.ImageConfig;
import com.google.genai.types.Part;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
public class GroupPhoto {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.imageConfig(ImageConfig.builder()
.aspectRatio("5:4")
.imageSize("2K")
.build())
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-3-pro-image-preview",
Content.fromParts(
Part.fromText("An office group photo of these people, they are making funny faces."),
Part.fromBytes(Files.readAllBytes(Path.of("person1.png")), "image/png"),
Part.fromBytes(Files.readAllBytes(Path.of("person2.png")), "image/png"),
Part.fromBytes(Files.readAllBytes(Path.of("person3.png")), "image/png"),
Part.fromBytes(Files.readAllBytes(Path.of("person4.png")), "image/png"),
Part.fromBytes(Files.readAllBytes(Path.of("person5.png")), "image/png")
), config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("office.png"), blob.data().get());
}
}
}
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d "{
\"contents\": [{
\"parts\":[
{\"text\": \"An office group photo of these people, they are making funny faces.\"},
{\"inline_data\": {\"mime_type\":\"image/png\", \"data\": \"<BASE64_DATA_IMG_1>\"}},
{\"inline_data\": {\"mime_type\":\"image/png\", \"data\": \"<BASE64_DATA_IMG_2>\"}},
{\"inline_data\": {\"mime_type\":\"image/png\", \"data\": \"<BASE64_DATA_IMG_3>\"}},
{\"inline_data\": {\"mime_type\":\"image/png\", \"data\": \"<BASE64_DATA_IMG_4>\"}},
{\"inline_data\": {\"mime_type\":\"image/png\", \"data\": \"<BASE64_DATA_IMG_5>\"}}
]
}],
\"generationConfig\": {
\"responseModalities\": [\"TEXT\", \"IMAGE\"],
\"imageConfig\": {
\"aspectRatio\": \"5:4\",
\"imageSize\": \"2K\"
}
}
}"
Освоение основ поиска Google
Используйте инструмент поиска Google для создания изображений на основе информации в реальном времени, такой как прогнозы погоды, графики фондового рынка или последние события.
Обратите внимание, что при использовании Grounding с поиском Google и генерацией изображений результаты поиска на основе изображений не передаются в модель генерации и исключаются из ответа.
Python
from google import genai
prompt = "Visualize the current weather forecast for the next 5 days in San Francisco as a clean, modern weather chart. Add a visual on what I should wear each day"
aspect_ratio = "16:9" # "1:1","2:3","3:2","3:4","4:3","4:5","5:4","9:16","16:9","21:9"
client = genai.Client()
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents=prompt,
config=types.GenerateContentConfig(
response_modalities=['Text', 'Image'],
image_config=types.ImageConfig(
aspect_ratio=aspect_ratio,
),
tools=[{"google_search": {}}]
)
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif image:= part.as_image():
image.save("weather.png")
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const prompt = 'Visualize the current weather forecast for the next 5 days in San Francisco as a clean, modern weather chart. Add a visual on what I should wear each day';
const aspectRatio = '16:9';
const resolution = '2K';
const response = await ai.models.generateContent({
model: 'gemini-3-pro-image-preview',
contents: prompt,
config: {
responseModalities: ['TEXT', 'IMAGE'],
imageConfig: {
aspectRatio: aspectRatio,
imageSize: resolution,
},
},
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("image.png", buffer);
console.log("Image saved as image.png");
}
}
}
main();
Java
import com.google.genai.Client;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.GoogleSearch;
import com.google.genai.types.ImageConfig;
import com.google.genai.types.Part;
import com.google.genai.types.Tool;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
public class SearchGrounding {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.imageConfig(ImageConfig.builder()
.aspectRatio("16:9")
.build())
.tools(Tool.builder()
.googleSearch(GoogleSearch.builder().build())
.build())
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-3-pro-image-preview", """
Visualize the current weather forecast for the next 5 days
in San Francisco as a clean, modern weather chart.
Add a visual on what I should wear each day
""",
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("weather.png"), blob.data().get());
}
}
}
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"contents": [{"parts": [{"text": "Visualize the current weather forecast for the next 5 days in San Francisco as a clean, modern weather chart. Add a visual on what I should wear each day"}]}],
"tools": [{"google_search": {}}],
"generationConfig": {
"responseModalities": ["TEXT", "IMAGE"],
"imageConfig": {"aspectRatio": "16:9"}
}
}'
В ответе содержится groundingMetadata , включающее следующие обязательные поля:
-
searchEntryPoint: Содержит HTML и CSS для отображения необходимых поисковых подсказок. -
groundingChunks: Возвращает 3 наиболее часто используемых веб-источника для привязки сгенерированного изображения к источнику.
Создавайте изображения с разрешением до 4K.
Gemini 3 Pro Image по умолчанию генерирует изображения с разрешением 1K, но также может выводить изображения с разрешением 2K и 4K. Для генерации изображений с более высоким разрешением укажите image_size в generation_config .
Необходимо использовать заглавную букву «K» (например, 1K, 2K, 4K). Параметры, написанные строчными буквами (например, 1k), будут отклонены.
Python
from google import genai
from google.genai import types
prompt = "Da Vinci style anatomical sketch of a dissected Monarch butterfly. Detailed drawings of the head, wings, and legs on textured parchment with notes in English."
aspect_ratio = "1:1" # "1:1","2:3","3:2","3:4","4:3","4:5","5:4","9:16","16:9","21:9"
resolution = "1K" # "1K", "2K", "4K"
client = genai.Client()
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents=prompt,
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE'],
image_config=types.ImageConfig(
aspect_ratio=aspect_ratio,
image_size=resolution
),
)
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif image:= part.as_image():
image.save("butterfly.png")
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const prompt =
'Da Vinci style anatomical sketch of a dissected Monarch butterfly. Detailed drawings of the head, wings, and legs on textured parchment with notes in English.';
const aspectRatio = '1:1';
const resolution = '1K';
const response = await ai.models.generateContent({
model: 'gemini-3-pro-image-preview',
contents: prompt,
config: {
responseModalities: ['TEXT', 'IMAGE'],
imageConfig: {
aspectRatio: aspectRatio,
imageSize: resolution,
},
},
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("image.png", buffer);
console.log("Image saved as image.png");
}
}
}
main();
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
defer client.Close()
model := client.GenerativeModel("gemini-3-pro-image-preview")
model.GenerationConfig = &pb.GenerationConfig{
ResponseModalities: []pb.ResponseModality{genai.Text, genai.Image},
ImageConfig: &pb.ImageConfig{
AspectRatio: "1:1",
ImageSize: "1K",
},
}
prompt := "Da Vinci style anatomical sketch of a dissected Monarch butterfly. Detailed drawings of the head, wings, and legs on textured parchment with notes in English."
resp, err := model.GenerateContent(ctx, genai.Text(prompt))
if err != nil {
log.Fatal(err)
}
for _, part := range resp.Candidates[0].Content.Parts {
if txt, ok := part.(genai.Text); ok {
fmt.Printf("%s", string(txt))
} else if img, ok := part.(genai.ImageData); ok {
err := os.WriteFile("butterfly.png", img.Data, 0644)
if err != nil {
log.Fatal(err)
}
}
}
}
Java
import com.google.genai.Client;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.GoogleSearch;
import com.google.genai.types.ImageConfig;
import com.google.genai.types.Part;
import com.google.genai.types.Tool;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
public class HiRes {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.imageConfig(ImageConfig.builder()
.aspectRatio("16:9")
.imageSize("4K")
.build())
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-3-pro-image-preview", """
Da Vinci style anatomical sketch of a dissected Monarch butterfly.
Detailed drawings of the head, wings, and legs on textured
parchment with notes in English.
""",
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("butterfly.png"), blob.data().get());
}
}
}
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"contents": [{"parts": [{"text": "Da Vinci style anatomical sketch of a dissected Monarch butterfly. Detailed drawings of the head, wings, and legs on textured parchment with notes in English."}]}],
"tools": [{"google_search": {}}],
"generationConfig": {
"responseModalities": ["TEXT", "IMAGE"],
"imageConfig": {"aspectRatio": "1:1", "imageSize": "1K"}
}
}'
Ниже представлен пример изображения, сгенерированного по этому запросу:
Процесс мышления
Модель предварительного просмотра изображений Gemini 3 Pro — это модель мышления, использующая процесс рассуждения («Мышление») для сложных запросов. Эта функция включена по умолчанию и не может быть отключена в API. Чтобы узнать больше о процессе мышления, см. руководство по Gemini Thinking .
Модель генерирует до двух промежуточных изображений для проверки композиции и логики. Последнее изображение в режиме «Мышление» также является окончательным отрендеренным изображением.
Вы можете проверить мысли, которые привели к созданию окончательного изображения.
Python
for part in response.parts:
if part.thought:
if part.text:
print(part.text)
elif image:= part.as_image():
image.show()
JavaScript
for (const part of response.candidates[0].content.parts) {
if (part.thought) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, 'base64');
fs.writeFileSync('image.png', buffer);
console.log('Image saved as image.png');
}
}
}
Мысленные подписи
«Сигнатуры мыслей» — это зашифрованные представления внутреннего мыслительного процесса модели, используемые для сохранения контекста рассуждений в многоходовых взаимодействиях. Все ответы содержат поле thought_signature . Как правило, если вы получили «сигнатуру мысли» в ответе модели, вы должны передать её обратно в точно таком же виде при отправке истории разговора в следующем ходу. Непередача «сигнатур мыслей» может привести к ошибке ответа. Для получения более подробной информации о сигнатурах мыслей в целом, обратитесь к документации.
Вот как работают сигнатуры мыслей:
- Все части
inline_dataсmimetypeизображения, входящие в ответ, должны иметь подпись. - Если в начале (перед любым изображением) сразу после мыслей есть текстовые фрагменты, то первый текстовый фрагмент также должен содержать подпись.
- Мысли не имеют подписей; если части
inline_dataсmimetypeизображения являются частью мыслей, они не будут иметь подписей.
Следующий код демонстрирует пример включения сигнатур мыслей:
[
{
"inline_data": {
"data": "<base64_image_data_0>",
"mime_type": "image/png"
},
"thought": true // Thoughts don't have signatures
},
{
"inline_data": {
"data": "<base64_image_data_1>",
"mime_type": "image/png"
},
"thought": true // Thoughts don't have signatures
},
{
"inline_data": {
"data": "<base64_image_data_2>",
"mime_type": "image/png"
},
"thought": true // Thoughts don't have signatures
},
{
"text": "Here is a step-by-step guide to baking macarons, presented in three separate images.\n\n### Step 1: Piping the Batter\n\nThe first step after making your macaron batter is to pipe it onto a baking sheet. This requires a steady hand to create uniform circles.\n\n",
"thought_signature": "<Signature_A>" // The first non-thought part always has a signature
},
{
"inline_data": {
"data": "<base64_image_data_3>",
"mime_type": "image/png"
},
"thought_signature": "<Signature_B>" // All image parts have a signatures
},
{
"text": "\n\n### Step 2: Baking and Developing Feet\n\nOnce piped, the macarons are baked in the oven. A key sign of a successful bake is the development of \"feet\"—the ruffled edge at the base of each macaron shell.\n\n"
// Follow-up text parts don't have signatures
},
{
"inline_data": {
"data": "<base64_image_data_4>",
"mime_type": "image/png"
},
"thought_signature": "<Signature_C>" // All image parts have a signatures
},
{
"text": "\n\n### Step 3: Assembling the Macaron\n\nThe final step is to pair the cooled macaron shells by size and sandwich them together with your desired filling, creating the classic macaron dessert.\n\n"
},
{
"inline_data": {
"data": "<base64_image_data_5>",
"mime_type": "image/png"
},
"thought_signature": "<Signature_D>" // All image parts have a signatures
}
]
Другие режимы генерации изображений
Gemini поддерживает и другие режимы взаимодействия с изображениями, основанные на структуре и контексте подсказки, в том числе:
- Преобразование текста в изображение(а) и текст (чередование): Выводит изображения с соответствующим текстом.
- Пример задания: «Составьте иллюстрированный рецепт паэльи».
- Изображение(я) и текст в изображение(я) и текст (чередующийся режим) : Использует входные изображения и текст для создания новых связанных изображений и текста.
- Пример задания: (С изображением обставленной комнаты) "Какие еще цвета диванов подойдут для моего помещения? Можете обновить изображение?"
Создание изображений в пакетном режиме.
Если вам нужно сгенерировать много изображений, вы можете использовать API пакетной обработки . Вы получаете более высокие лимиты запросов в обмен на обработку в течение до 24 часов.
Для получения примеров и кода по генерации изображений с помощью Batch API ознакомьтесь с документацией по Batch API и руководством пользователя .
Руководство по использованию подсказок и стратегии
Освоение искусства создания изображений начинается с одного фундаментального принципа:
Опишите сцену, а не просто перечислите ключевые слова. Главное преимущество модели — глубокое понимание языка. Повествовательный, описательный абзац почти всегда создаст более качественную и связную картину, чем список разрозненных слов.
Подсказки для генерации изображений
Следующие стратегии помогут вам создать эффективные подсказки для получения именно тех изображений, которые вы ищете.
1. Фотореалистичные сцены
Для получения реалистичных изображений используйте терминологию фотографии. Укажите ракурсы камеры, типы объективов, освещение и мелкие детали, чтобы помочь модели добиться фотореалистичного результата.
Шаблон
A photorealistic [shot type] of [subject], [action or expression], set in
[environment]. The scene is illuminated by [lighting description], creating
a [mood] atmosphere. Captured with a [camera/lens details], emphasizing
[key textures and details]. The image should be in a [aspect ratio] format.
Быстрый
A photorealistic close-up portrait of an elderly Japanese ceramicist with
deep, sun-etched wrinkles and a warm, knowing smile. He is carefully
inspecting a freshly glazed tea bowl. The setting is his rustic,
sun-drenched workshop. The scene is illuminated by soft, golden hour light
streaming through a window, highlighting the fine texture of the clay.
Captured with an 85mm portrait lens, resulting in a soft, blurred background
(bokeh). The overall mood is serene and masterful. Vertical portrait
orientation.
Python
from google import genai
from google.genai import types
client = genai.Client()
response = client.models.generate_content(
model="gemini-2.5-flash-image",
contents="A photorealistic close-up portrait of an elderly Japanese ceramicist with deep, sun-etched wrinkles and a warm, knowing smile. He is carefully inspecting a freshly glazed tea bowl. The setting is his rustic, sun-drenched workshop with pottery wheels and shelves of clay pots in the background. The scene is illuminated by soft, golden hour light streaming through a window, highlighting the fine texture of the clay and the fabric of his apron. Captured with an 85mm portrait lens, resulting in a soft, blurred background (bokeh). The overall mood is serene and masterful.",
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif part.inline_data is not None:
image = part.as_image()
image.save("photorealistic_example.png")
Java
import com.google.genai.Client;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
public class PhotorealisticScene {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-2.5-flash-image",
"""
A photorealistic close-up portrait of an elderly Japanese ceramicist
with deep, sun-etched wrinkles and a warm, knowing smile. He is
carefully inspecting a freshly glazed tea bowl. The setting is his
rustic, sun-drenched workshop with pottery wheels and shelves of
clay pots in the background. The scene is illuminated by soft,
golden hour light streaming through a window, highlighting the
fine texture of the clay and the fabric of his apron. Captured
with an 85mm portrait lens, resulting in a soft, blurred
background (bokeh). The overall mood is serene and masterful.
""",
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("photorealistic_example.png"), blob.data().get());
}
}
}
}
}
}
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const prompt =
"A photorealistic close-up portrait of an elderly Japanese ceramicist with deep, sun-etched wrinkles and a warm, knowing smile. He is carefully inspecting a freshly glazed tea bowl. The setting is his rustic, sun-drenched workshop with pottery wheels and shelves of clay pots in the background. The scene is illuminated by soft, golden hour light streaming through a window, highlighting the fine texture of the clay and the fabric of his apron. Captured with an 85mm portrait lens, resulting in a soft, blurred background (bokeh). The overall mood is serene and masterful.";
const response = await ai.models.generateContent({
model: "gemini-2.5-flash-image",
contents: prompt,
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("photorealistic_example.png", buffer);
console.log("Image saved as photorealistic_example.png");
}
}
}
main();
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
result, _ := client.Models.GenerateContent(
ctx,
"gemini-2.5-flash-image",
genai.Text("A photorealistic close-up portrait of an elderly Japanese ceramicist with deep, sun-etched wrinkles and a warm, knowing smile. He is carefully inspecting a freshly glazed tea bowl. The setting is his rustic, sun-drenched workshop with pottery wheels and shelves of clay pots in the background. The scene is illuminated by soft, golden hour light streaming through a window, highlighting the fine texture of the clay and the fabric of his apron. Captured with an 85mm portrait lens, resulting in a soft, blurred background (bokeh). The overall mood is serene and masterful."),
)
for _, part := range result.Candidates[0].Content.Parts {
if part.Text != "" {
fmt.Println(part.Text)
} else if part.InlineData != nil {
imageBytes := part.InlineData.Data
outputFilename := "photorealistic_example.png"
_ = os.WriteFile(outputFilename, imageBytes, 0644)
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"contents": [{
"parts": [
{"text": "A photorealistic close-up portrait of an elderly Japanese ceramicist with deep, sun-etched wrinkles and a warm, knowing smile. He is carefully inspecting a freshly glazed tea bowl. The setting is his rustic, sun-drenched workshop with pottery wheels and shelves of clay pots in the background. The scene is illuminated by soft, golden hour light streaming through a window, highlighting the fine texture of the clay and the fabric of his apron. Captured with an 85mm portrait lens, resulting in a soft, blurred background (bokeh). The overall mood is serene and masterful."}
]
}]
}'
2. Стилизованные иллюстрации и наклейки
Для создания стикеров, значков или других элементов необходимо четко указать стиль и запросить прозрачный фон.
Шаблон
A [style] sticker of a [subject], featuring [key characteristics] and a
[color palette]. The design should have [line style] and [shading style].
The background must be transparent.
Быстрый
A kawaii-style sticker of a happy red panda wearing a tiny bamboo hat. It's
munching on a green bamboo leaf. The design features bold, clean outlines,
simple cel-shading, and a vibrant color palette. The background must be white.
Python
from google import genai
from google.genai import types
client = genai.Client()
response = client.models.generate_content(
model="gemini-2.5-flash-image",
contents="A kawaii-style sticker of a happy red panda wearing a tiny bamboo hat. It's munching on a green bamboo leaf. The design features bold, clean outlines, simple cel-shading, and a vibrant color palette. The background must be white.",
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif part.inline_data is not None:
image = part.as_image()
image.save("red_panda_sticker.png")
Java
import com.google.genai.Client;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
public class StylizedIllustration {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-2.5-flash-image",
"""
A kawaii-style sticker of a happy red panda wearing a tiny bamboo
hat. It's munching on a green bamboo leaf. The design features
bold, clean outlines, simple cel-shading, and a vibrant color
palette. The background must be white.
""",
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("red_panda_sticker.png"), blob.data().get());
}
}
}
}
}
}
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const prompt =
"A kawaii-style sticker of a happy red panda wearing a tiny bamboo hat. It's munching on a green bamboo leaf. The design features bold, clean outlines, simple cel-shading, and a vibrant color palette. The background must be white.";
const response = await ai.models.generateContent({
model: "gemini-2.5-flash-image",
contents: prompt,
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("red_panda_sticker.png", buffer);
console.log("Image saved as red_panda_sticker.png");
}
}
}
main();
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
result, _ := client.Models.GenerateContent(
ctx,
"gemini-2.5-flash-image",
genai.Text("A kawaii-style sticker of a happy red panda wearing a tiny bamboo hat. It's munching on a green bamboo leaf. The design features bold, clean outlines, simple cel-shading, and a vibrant color palette. The background must be white."),
)
for _, part := range result.Candidates[0].Content.Parts {
if part.Text != "" {
fmt.Println(part.Text)
} else if part.InlineData != nil {
imageBytes := part.InlineData.Data
outputFilename := "red_panda_sticker.png"
_ = os.WriteFile(outputFilename, imageBytes, 0644)
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"contents": [{
"parts": [
{"text": "A kawaii-style sticker of a happy red panda wearing a tiny bamboo hat. It is munching on a green bamboo leaf. The design features bold, clean outlines, simple cel-shading, and a vibrant color palette. The background must be white."}
]
}]
}'
3. Точный текст в изображениях
Gemini отлично справляется с отображением текста. Четко указывайте текст, стиль шрифта (с описанием) и общий дизайн. Используйте Gemini 3 Pro Image Preview для профессионального создания графических материалов.
Шаблон
Create a [image type] for [brand/concept] with the text "[text to render]"
in a [font style]. The design should be [style description], with a
[color scheme].
Быстрый
Create a modern, minimalist logo for a coffee shop called 'The Daily Grind'. The text should be in a clean, bold, sans-serif font. The color scheme is black and white. Put the logo in a circle. Use a coffee bean in a clever way.
Python
from google import genai
from google.genai import types
client = genai.Client()
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents="Create a modern, minimalist logo for a coffee shop called 'The Daily Grind'. The text should be in a clean, bold, sans-serif font. The color scheme is black and white. Put the logo in a circle. Use a coffee bean in a clever way.",
config=types.GenerateContentConfig(
image_config=types.ImageConfig(
aspect_ratio="1:1",
)
)
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif part.inline_data is not None:
image = part.as_image()
image.save("logo_example.jpg")
Java
import com.google.genai.Client;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import com.google.genai.types.ImageConfig;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
public class AccurateTextInImages {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.imageConfig(ImageConfig.builder()
.aspectRatio("1:1")
.build())
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-3-pro-image-preview",
"""
Create a modern, minimalist logo for a coffee shop called 'The Daily Grind'. The text should be in a clean, bold, sans-serif font. The color scheme is black and white. Put the logo in a circle. Use a coffee bean in a clever way.
""",
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("logo_example.jpg"), blob.data().get());
}
}
}
}
}
}
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const prompt =
"Create a modern, minimalist logo for a coffee shop called 'The Daily Grind'. The text should be in a clean, bold, sans-serif font. The color scheme is black and white. Put the logo in a circle. Use a coffee bean in a clever way.";
const response = await ai.models.generateContent({
model: "gemini-3-pro-image-preview",
contents: prompt,
config: {
imageConfig: {
aspectRatio: "1:1",
},
},
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("logo_example.jpg", buffer);
console.log("Image saved as logo_example.jpg");
}
}
}
main();
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
result, _ := client.Models.GenerateContent(
ctx,
"gemini-3-pro-image-preview",
genai.Text("Create a modern, minimalist logo for a coffee shop called 'The Daily Grind'. The text should be in a clean, bold, sans-serif font. The color scheme is black and white. Put the logo in a circle. Use a coffee bean in a clever way."),
&genai.GenerateContentConfig{
ImageConfig: &genai.ImageConfig{
AspectRatio: "1:1",
},
},
)
for _, part := range result.Candidates[0].Content.Parts {
if part.Text != "" {
fmt.Println(part.Text)
} else if part.InlineData != nil {
imageBytes := part.InlineData.Data
outputFilename := "logo_example.jpg"
_ = os.WriteFile(outputFilename, imageBytes, 0644)
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"contents": [{
"parts": [
{"text": "Create a modern, minimalist logo for a coffee shop called The Daily Grind. The text should be in a clean, bold, sans-serif font. The color scheme is black and white. Put the logo in a circle. Use a coffee bean in a clever way."}
]
}],
"generationConfig": {
"imageConfig": {
"aspectRatio": "1:1"
}
}
}'
4. Макеты продукции и коммерческая фотосъемка.
Идеально подходит для создания качественных, профессиональных фотографий товаров для электронной коммерции, рекламы или брендинга.
Шаблон
A high-resolution, studio-lit product photograph of a [product description]
on a [background surface/description]. The lighting is a [lighting setup,
e.g., three-point softbox setup] to [lighting purpose]. The camera angle is
a [angle type] to showcase [specific feature]. Ultra-realistic, with sharp
focus on [key detail]. [Aspect ratio].
Быстрый
A high-resolution, studio-lit product photograph of a minimalist ceramic
coffee mug in matte black, presented on a polished concrete surface. The
lighting is a three-point softbox setup designed to create soft, diffused
highlights and eliminate harsh shadows. The camera angle is a slightly
elevated 45-degree shot to showcase its clean lines. Ultra-realistic, with
sharp focus on the steam rising from the coffee. Square image.
Python
from google import genai
from google.genai import types
client = genai.Client()
response = client.models.generate_content(
model="gemini-2.5-flash-image",
contents="A high-resolution, studio-lit product photograph of a minimalist ceramic coffee mug in matte black, presented on a polished concrete surface. The lighting is a three-point softbox setup designed to create soft, diffused highlights and eliminate harsh shadows. The camera angle is a slightly elevated 45-degree shot to showcase its clean lines. Ultra-realistic, with sharp focus on the steam rising from the coffee. Square image.",
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif part.inline_data is not None:
image = part.as_image()
image.save("product_mockup.png")
Java
import com.google.genai.Client;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
public class ProductMockup {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-2.5-flash-image",
"""
A high-resolution, studio-lit product photograph of a minimalist
ceramic coffee mug in matte black, presented on a polished
concrete surface. The lighting is a three-point softbox setup
designed to create soft, diffused highlights and eliminate harsh
shadows. The camera angle is a slightly elevated 45-degree shot
to showcase its clean lines. Ultra-realistic, with sharp focus
on the steam rising from the coffee. Square image.
""",
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("product_mockup.png"), blob.data().get());
}
}
}
}
}
}
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const prompt =
"A high-resolution, studio-lit product photograph of a minimalist ceramic coffee mug in matte black, presented on a polished concrete surface. The lighting is a three-point softbox setup designed to create soft, diffused highlights and eliminate harsh shadows. The camera angle is a slightly elevated 45-degree shot to showcase its clean lines. Ultra-realistic, with sharp focus on the steam rising from the coffee. Square image.";
const response = await ai.models.generateContent({
model: "gemini-2.5-flash-image",
contents: prompt,
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("product_mockup.png", buffer);
console.log("Image saved as product_mockup.png");
}
}
}
main();
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
result, _ := client.Models.GenerateContent(
ctx,
"gemini-2.5-flash-image",
genai.Text("A high-resolution, studio-lit product photograph of a minimalist ceramic coffee mug in matte black, presented on a polished concrete surface. The lighting is a three-point softbox setup designed to create soft, diffused highlights and eliminate harsh shadows. The camera angle is a slightly elevated 45-degree shot to showcase its clean lines. Ultra-realistic, with sharp focus on the steam rising from the coffee. Square image."),
)
for _, part := range result.Candidates[0].Content.Parts {
if part.Text != "" {
fmt.Println(part.Text)
} else if part.InlineData != nil {
imageBytes := part.InlineData.Data
outputFilename := "product_mockup.png"
_ = os.WriteFile(outputFilename, imageBytes, 0644)
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"contents": [{
"parts": [
{"text": "A high-resolution, studio-lit product photograph of a minimalist ceramic coffee mug in matte black, presented on a polished concrete surface. The lighting is a three-point softbox setup designed to create soft, diffused highlights and eliminate harsh shadows. The camera angle is a slightly elevated 45-degree shot to showcase its clean lines. Ultra-realistic, with sharp focus on the steam rising from the coffee. Square image."}
]
}]
}'
5. Минималистичный дизайн и использование негативного пространства.
Отлично подходит для создания фоновых изображений для веб-сайтов, презентаций или маркетинговых материалов, где будет накладываться текст.
Шаблон
A minimalist composition featuring a single [subject] positioned in the
[bottom-right/top-left/etc.] of the frame. The background is a vast, empty
[color] canvas, creating significant negative space. Soft, subtle lighting.
[Aspect ratio].
Быстрый
A minimalist composition featuring a single, delicate red maple leaf
positioned in the bottom-right of the frame. The background is a vast, empty
off-white canvas, creating significant negative space for text. Soft,
diffused lighting from the top left. Square image.
Python
from google import genai
from google.genai import types
client = genai.Client()
response = client.models.generate_content(
model="gemini-2.5-flash-image",
contents="A minimalist composition featuring a single, delicate red maple leaf positioned in the bottom-right of the frame. The background is a vast, empty off-white canvas, creating significant negative space for text. Soft, diffused lighting from the top left. Square image.",
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif part.inline_data is not None:
image = part.as_image()
image.save("minimalist_design.png")
Java
import com.google.genai.Client;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
public class MinimalistDesign {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-2.5-flash-image",
"""
A minimalist composition featuring a single, delicate red maple
leaf positioned in the bottom-right of the frame. The background
is a vast, empty off-white canvas, creating significant negative
space for text. Soft, diffused lighting from the top left.
Square image.
""",
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("minimalist_design.png"), blob.data().get());
}
}
}
}
}
}
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const prompt =
"A minimalist composition featuring a single, delicate red maple leaf positioned in the bottom-right of the frame. The background is a vast, empty off-white canvas, creating significant negative space for text. Soft, diffused lighting from the top left. Square image.";
const response = await ai.models.generateContent({
model: "gemini-2.5-flash-image",
contents: prompt,
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("minimalist_design.png", buffer);
console.log("Image saved as minimalist_design.png");
}
}
}
main();
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
result, _ := client.Models.GenerateContent(
ctx,
"gemini-2.5-flash-image",
genai.Text("A minimalist composition featuring a single, delicate red maple leaf positioned in the bottom-right of the frame. The background is a vast, empty off-white canvas, creating significant negative space for text. Soft, diffused lighting from the top left. Square image."),
)
for _, part := range result.Candidates[0].Content.Parts {
if part.Text != "" {
fmt.Println(part.Text)
} else if part.InlineData != nil {
imageBytes := part.InlineData.Data
outputFilename := "minimalist_design.png"
_ = os.WriteFile(outputFilename, imageBytes, 0644)
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"contents": [{
"parts": [
{"text": "A minimalist composition featuring a single, delicate red maple leaf positioned in the bottom-right of the frame. The background is a vast, empty off-white canvas, creating significant negative space for text. Soft, diffused lighting from the top left. Square image."}
]
}]
}'
6. Последовательная графика (комиксные панели / раскадровка)
Основываясь на согласованности характеров и описании сцен, создаются панели для визуального повествования. Для большей точности текста и улучшения навыков рассказывания историй, эти подсказки лучше всего работают с программой Gemini 3 Pro Image Preview.
Шаблон
Make a 3 panel comic in a [style]. Put the character in a [type of scene].
Быстрый
Make a 3 panel comic in a gritty, noir art style with high-contrast black and white inks. Put the character in a humurous scene.
Python
from google import genai
from google.genai import types
from PIL import Image
client = genai.Client()
image_input = Image.open('/path/to/your/man_in_white_glasses.jpg')
text_input = "Make a 3 panel comic in a gritty, noir art style with high-contrast black and white inks. Put the character in a humurous scene."
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents=[text_input, image_input],
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif part.inline_data is not None:
image = part.as_image()
image.save("comic_panel.jpg")
Java
import com.google.genai.Client;
import com.google.genai.types.Content;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
public class ComicPanel {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-3-pro-image-preview",
Content.fromParts(
Part.fromText("""
Make a 3 panel comic in a gritty, noir art style with high-contrast black and white inks. Put the character in a humurous scene.
"""),
Part.fromBytes(
Files.readAllBytes(
Path.of("/path/to/your/man_in_white_glasses.jpg")),
"image/jpeg")),
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("comic_panel.jpg"), blob.data().get());
}
}
}
}
}
}
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const imagePath = "/path/to/your/man_in_white_glasses.jpg";
const imageData = fs.readFileSync(imagePath);
const base64Image = imageData.toString("base64");
const prompt = [
{text: "Make a 3 panel comic in a gritty, noir art style with high-contrast black and white inks. Put the character in a humurous scene."},
{
inlineData: {
mimeType: "image/jpeg",
data: base64Image,
},
},
];
const response = await ai.models.generateContent({
model: "gemini-3-pro-image-preview",
contents: prompt,
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("comic_panel.jpg", buffer);
console.log("Image saved as comic_panel.jpg");
}
}
}
main();
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
imagePath := "/path/to/your/man_in_white_glasses.jpg"
imgData, _ := os.ReadFile(imagePath)
parts := []*genai.Part{
genai.NewPartFromText("Make a 3 panel comic in a gritty, noir art style with high-contrast black and white inks. Put the character in a humurous scene."),
&genai.Part{
InlineData: &genai.Blob{
MIMEType: "image/jpeg",
Data: imgData,
},
},
}
contents := []*genai.Content{
genai.NewContentFromParts(parts, genai.RoleUser),
}
result, _ := client.Models.GenerateContent(
ctx,
"gemini-3-pro-image-preview",
contents,
)
for _, part := range result.Candidates[0].Content.Parts {
if part.Text != "" {
fmt.Println(part.Text)
} else if part.InlineData != nil {
imageBytes := part.InlineData.Data
outputFilename := "comic_panel.jpg"
_ = os.WriteFile(outputFilename, imageBytes, 0644)
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"contents": [{
"parts": [
{"text": "Make a 3 panel comic in a gritty, noir art style with high-contrast black and white inks. Put the character in a humurous scene."},
{"inline_data": {"mime_type": "image/jpeg", "data": "<BASE64_IMAGE_DATA>"}}
]
}]
}'
Вход | Выход |
 |  |
7. Основы работы с поиском Google.
Используйте поиск Google для создания изображений на основе актуальной или текущей информации. Это полезно для новостей, погоды и других тем, требующих оперативного реагирования.
Быстрый
Make a simple but stylish graphic of last night's Arsenal game in the Champion's League
Python
from google import genai
from google.genai import types
prompt = "Make a simple but stylish graphic of last night's Arsenal game in the Champion's League"
aspect_ratio = "16:9" # "1:1","2:3","3:2","3:4","4:3","4:5","5:4","9:16","16:9","21:9"
client = genai.Client()
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents=prompt,
config=types.GenerateContentConfig(
response_modalities=['Text', 'Image'],
image_config=types.ImageConfig(
aspect_ratio=aspect_ratio,
),
tools=[{"google_search": {}}]
)
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif image:= part.as_image():
image.save("football-score.jpg")
Java
import com.google.genai.Client;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.GoogleSearch;
import com.google.genai.types.ImageConfig;
import com.google.genai.types.Part;
import com.google.genai.types.Tool;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Paths;
public class SearchGrounding {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.imageConfig(ImageConfig.builder()
.aspectRatio("16:9")
.build())
.tools(Tool.builder()
.googleSearch(GoogleSearch.builder().build())
.build())
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-3-pro-image-preview", """
Make a simple but stylish graphic of last night's Arsenal game in the Champion's League
""",
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("football-score.jpg"), blob.data().get());
}
}
}
}
}
}
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const prompt = "Make a simple but stylish graphic of last night's Arsenal game in the Champion's League";
const aspectRatio = '16:9';
const resolution = '2K';
const response = await ai.models.generateContent({
model: 'gemini-3-pro-image-preview',
contents: prompt,
config: {
responseModalities: ['TEXT', 'IMAGE'],
imageConfig: {
aspectRatio: aspectRatio,
imageSize: resolution,
},
tools: [{"google_search": {}}],
},
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("football-score.jpg", buffer);
console.log("Image saved as football-score.jpg");
}
}
}
main();
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
pb "google.golang.org/genai/schema"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
defer client.Close()
model := client.GenerativeModel("gemini-3-pro-image-preview")
model.Tools = []*pb.Tool{
pb.NewGoogleSearchTool(),
}
model.GenerationConfig = &pb.GenerationConfig{
ResponseModalities: []pb.ResponseModality{genai.Text, genai.Image},
ImageConfig: &pb.ImageConfig{
AspectRatio: "16:9",
},
}
prompt := "Make a simple but stylish graphic of last night's Arsenal game in the Champion's League"
resp, err := model.GenerateContent(ctx, genai.Text(prompt))
if err != nil {
log.Fatal(err)
}
for _, part := range resp.Candidates[0].Content.Parts {
if txt, ok := part.(genai.Text); ok {
fmt.Printf("%s", string(txt))
} else if img, ok := part.(genai.ImageData); ok {
err := os.WriteFile("football-score.jpg", img.Data, 0644)
if err != nil {
log.Fatal(err)
}
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"contents": [{"parts": [{"text": "Make a simple but stylish graphic of last nights Arsenal game in the Champions League"}]}],
"tools": [{"google_search": {}}],
"generationConfig": {
"responseModalities": ["TEXT", "IMAGE"],
"imageConfig": {"aspectRatio": "16:9"}
}
}'
Подсказки для редактирования изображений
Эти примеры показывают, как добавлять изображения к текстовым подсказкам для редактирования, компоновки и переноса стиля.
1. Добавление и удаление элементов
Предоставьте изображение и опишите необходимые изменения. Модель должна соответствовать стилю, освещению и перспективе оригинального изображения.
Шаблон
Using the provided image of [subject], please [add/remove/modify] [element]
to/from the scene. Ensure the change is [description of how the change should
integrate].
Быстрый
"Using the provided image of my cat, please add a small, knitted wizard hat
on its head. Make it look like it's sitting comfortably and matches the soft
lighting of the photo."
Python
from google import genai
from google.genai import types
from PIL import Image
client = genai.Client()
# Base image prompt: "A photorealistic picture of a fluffy ginger cat sitting on a wooden floor, looking directly at the camera. Soft, natural light from a window."
image_input = Image.open('/path/to/your/cat_photo.png')
text_input = """Using the provided image of my cat, please add a small, knitted wizard hat on its head. Make it look like it's sitting comfortably and not falling off."""
# Generate an image from a text prompt
response = client.models.generate_content(
model="gemini-2.5-flash-image",
contents=[text_input, image_input],
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif part.inline_data is not None:
image = part.as_image()
image.save("cat_with_hat.png")
Java
import com.google.genai.Client;
import com.google.genai.types.Content;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
public class AddRemoveElements {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-2.5-flash-image",
Content.fromParts(
Part.fromText("""
Using the provided image of my cat, please add a small,
knitted wizard hat on its head. Make it look like it's
sitting comfortably and not falling off.
"""),
Part.fromBytes(
Files.readAllBytes(
Path.of("/path/to/your/cat_photo.png")),
"image/png")),
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("cat_with_hat.png"), blob.data().get());
}
}
}
}
}
}
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const imagePath = "/path/to/your/cat_photo.png";
const imageData = fs.readFileSync(imagePath);
const base64Image = imageData.toString("base64");
const prompt = [
{ text: "Using the provided image of my cat, please add a small, knitted wizard hat on its head. Make it look like it's sitting comfortably and not falling off." },
{
inlineData: {
mimeType: "image/png",
data: base64Image,
},
},
];
const response = await ai.models.generateContent({
model: "gemini-2.5-flash-image",
contents: prompt,
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("cat_with_hat.png", buffer);
console.log("Image saved as cat_with_hat.png");
}
}
}
main();
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
imagePath := "/path/to/your/cat_photo.png"
imgData, _ := os.ReadFile(imagePath)
parts := []*genai.Part{
genai.NewPartFromText("Using the provided image of my cat, please add a small, knitted wizard hat on its head. Make it look like it's sitting comfortably and not falling off."),
&genai.Part{
InlineData: &genai.Blob{
MIMEType: "image/png",
Data: imgData,
},
},
}
contents := []*genai.Content{
genai.NewContentFromParts(parts, genai.RoleUser),
}
result, _ := client.Models.GenerateContent(
ctx,
"gemini-2.5-flash-image",
contents,
)
for _, part := range result.Candidates[0].Content.Parts {
if part.Text != "" {
fmt.Println(part.Text)
} else if part.InlineData != nil {
imageBytes := part.InlineData.Data
outputFilename := "cat_with_hat.png"
_ = os.WriteFile(outputFilename, imageBytes, 0644)
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d "{
\"contents\": [{
\"parts\":[
{\"text\": \"Using the provided image of my cat, please add a small, knitted wizard hat on its head. Make it look like it's sitting comfortably and not falling off.\"},
{
\"inline_data\": {
\"mime_type\":\"image/png\",
\"data\": \"<BASE64_IMAGE_DATA>\"
}
}
]
}]
}"
Вход | Выход |
 |  |
2. Заполнение пропущенных областей (семантическая маскировка)
В ходе беседы можно задать «маску» для редактирования определенной части изображения, оставив остальную часть нетронутой.
Шаблон
Using the provided image, change only the [specific element] to [new
element/description]. Keep everything else in the image exactly the same,
preserving the original style, lighting, and composition.
Быстрый
"Using the provided image of a living room, change only the blue sofa to be
a vintage, brown leather chesterfield sofa. Keep the rest of the room,
including the pillows on the sofa and the lighting, unchanged."
Python
from google import genai
from google.genai import types
from PIL import Image
client = genai.Client()
# Base image prompt: "A wide shot of a modern, well-lit living room with a prominent blue sofa in the center. A coffee table is in front of it and a large window is in the background."
living_room_image = Image.open('/path/to/your/living_room.png')
text_input = """Using the provided image of a living room, change only the blue sofa to be a vintage, brown leather chesterfield sofa. Keep the rest of the room, including the pillows on the sofa and the lighting, unchanged."""
# Generate an image from a text prompt
response = client.models.generate_content(
model="gemini-2.5-flash-image",
contents=[living_room_image, text_input],
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif part.inline_data is not None:
image = part.as_image()
image.save("living_room_edited.png")
Java
import com.google.genai.Client;
import com.google.genai.types.Content;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
public class Inpainting {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-2.5-flash-image",
Content.fromParts(
Part.fromBytes(
Files.readAllBytes(
Path.of("/path/to/your/living_room.png")),
"image/png"),
Part.fromText("""
Using the provided image of a living room, change
only the blue sofa to be a vintage, brown leather
chesterfield sofa. Keep the rest of the room,
including the pillows on the sofa and the lighting,
unchanged.
""")),
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("living_room_edited.png"), blob.data().get());
}
}
}
}
}
}
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const imagePath = "/path/to/your/living_room.png";
const imageData = fs.readFileSync(imagePath);
const base64Image = imageData.toString("base64");
const prompt = [
{
inlineData: {
mimeType: "image/png",
data: base64Image,
},
},
{ text: "Using the provided image of a living room, change only the blue sofa to be a vintage, brown leather chesterfield sofa. Keep the rest of the room, including the pillows on the sofa and the lighting, unchanged." },
];
const response = await ai.models.generateContent({
model: "gemini-2.5-flash-image",
contents: prompt,
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("living_room_edited.png", buffer);
console.log("Image saved as living_room_edited.png");
}
}
}
main();
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
imagePath := "/path/to/your/living_room.png"
imgData, _ := os.ReadFile(imagePath)
parts := []*genai.Part{
&genai.Part{
InlineData: &genai.Blob{
MIMEType: "image/png",
Data: imgData,
},
},
genai.NewPartFromText("Using the provided image of a living room, change only the blue sofa to be a vintage, brown leather chesterfield sofa. Keep the rest of the room, including the pillows on the sofa and the lighting, unchanged."),
}
contents := []*genai.Content{
genai.NewContentFromParts(parts, genai.RoleUser),
}
result, _ := client.Models.GenerateContent(
ctx,
"gemini-2.5-flash-image",
contents,
)
for _, part := range result.Candidates[0].Content.Parts {
if part.Text != "" {
fmt.Println(part.Text)
} else if part.InlineData != nil {
imageBytes := part.InlineData.Data
outputFilename := "living_room_edited.png"
_ = os.WriteFile(outputFilename, imageBytes, 0644)
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d "{
\"contents\": [{
\"parts\":[
{
\"inline_data\": {
\"mime_type\":\"image/png\",
\"data\": \"<BASE64_IMAGE_DATA>\"
}
},
{\"text\": \"Using the provided image of a living room, change only the blue sofa to be a vintage, brown leather chesterfield sofa. Keep the rest of the room, including the pillows on the sofa and the lighting, unchanged.\"}
]
}]
}"
Вход | Выход |
 |  |
3. Перенос стиля
Предоставьте изображение и попросите модель воссоздать его содержание в другом художественном стиле.
Шаблон
Transform the provided photograph of [subject] into the artistic style of [artist/art style]. Preserve the original composition but render it with [description of stylistic elements].
Быстрый
"Transform the provided photograph of a modern city street at night into the artistic style of Vincent van Gogh's 'Starry Night'. Preserve the original composition of buildings and cars, but render all elements with swirling, impasto brushstrokes and a dramatic palette of deep blues and bright yellows."
Python
from google import genai
from google.genai import types
from PIL import Image
client = genai.Client()
# Base image prompt: "A photorealistic, high-resolution photograph of a busy city street in New York at night, with bright neon signs, yellow taxis, and tall skyscrapers."
city_image = Image.open('/path/to/your/city.png')
text_input = """Transform the provided photograph of a modern city street at night into the artistic style of Vincent van Gogh's 'Starry Night'. Preserve the original composition of buildings and cars, but render all elements with swirling, impasto brushstrokes and a dramatic palette of deep blues and bright yellows."""
# Generate an image from a text prompt
response = client.models.generate_content(
model="gemini-2.5-flash-image",
contents=[city_image, text_input],
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif part.inline_data is not None:
image = part.as_image()
image.save("city_style_transfer.png")
Java
import com.google.genai.Client;
import com.google.genai.types.Content;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
public class StyleTransfer {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-2.5-flash-image",
Content.fromParts(
Part.fromBytes(
Files.readAllBytes(
Path.of("/path/to/your/city.png")),
"image/png"),
Part.fromText("""
Transform the provided photograph of a modern city
street at night into the artistic style of
Vincent van Gogh's 'Starry Night'. Preserve the
original composition of buildings and cars, but
render all elements with swirling, impasto
brushstrokes and a dramatic palette of deep blues
and bright yellows.
""")),
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("city_style_transfer.png"), blob.data().get());
}
}
}
}
}
}
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const imagePath = "/path/to/your/city.png";
const imageData = fs.readFileSync(imagePath);
const base64Image = imageData.toString("base64");
const prompt = [
{
inlineData: {
mimeType: "image/png",
data: base64Image,
},
},
{ text: "Transform the provided photograph of a modern city street at night into the artistic style of Vincent van Gogh's 'Starry Night'. Preserve the original composition of buildings and cars, but render all elements with swirling, impasto brushstrokes and a dramatic palette of deep blues and bright yellows." },
];
const response = await ai.models.generateContent({
model: "gemini-2.5-flash-image",
contents: prompt,
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("city_style_transfer.png", buffer);
console.log("Image saved as city_style_transfer.png");
}
}
}
main();
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
imagePath := "/path/to/your/city.png"
imgData, _ := os.ReadFile(imagePath)
parts := []*genai.Part{
&genai.Part{
InlineData: &genai.Blob{
MIMEType: "image/png",
Data: imgData,
},
},
genai.NewPartFromText("Transform the provided photograph of a modern city street at night into the artistic style of Vincent van Gogh's 'Starry Night'. Preserve the original composition of buildings and cars, but render all elements with swirling, impasto brushstrokes and a dramatic palette of deep blues and bright yellows."),
}
contents := []*genai.Content{
genai.NewContentFromParts(parts, genai.RoleUser),
}
result, _ := client.Models.GenerateContent(
ctx,
"gemini-2.5-flash-image",
contents,
)
for _, part := range result.Candidates[0].Content.Parts {
if part.Text != "" {
fmt.Println(part.Text)
} else if part.InlineData != nil {
imageBytes := part.InlineData.Data
outputFilename := "city_style_transfer.png"
_ = os.WriteFile(outputFilename, imageBytes, 0644)
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d "{
\"contents\": [{
\"parts\":[
{
\"inline_data\": {
\"mime_type\":\"image/png\",
\"data\": \"<BASE64_IMAGE_DATA>\"
}
},
{\"text\": \"Transform the provided photograph of a modern city street at night into the artistic style of Vincent van Gogh's 'Starry Night'. Preserve the original composition of buildings and cars, but render all elements with swirling, impasto brushstrokes and a dramatic palette of deep blues and bright yellows.\"}
]
}]
}"
Вход | Выход |
 |  |
4. Продвинутая композиция: объединение нескольких изображений.
Предоставьте несколько изображений в качестве контекста для создания новой, составной сцены. Это идеально подходит для макетов продукции или креативных коллажей.
Шаблон
Create a new image by combining the elements from the provided images. Take
the [element from image 1] and place it with/on the [element from image 2].
The final image should be a [description of the final scene].
Быстрый
"Create a professional e-commerce fashion photo. Take the blue floral dress
from the first image and let the woman from the second image wear it.
Generate a realistic, full-body shot of the woman wearing the dress, with
the lighting and shadows adjusted to match the outdoor environment."
Python
from google import genai
from google.genai import types
from PIL import Image
client = genai.Client()
# Base image prompts:
# 1. Dress: "A professionally shot photo of a blue floral summer dress on a plain white background, ghost mannequin style."
# 2. Model: "Full-body shot of a woman with her hair in a bun, smiling, standing against a neutral grey studio background."
dress_image = Image.open('/path/to/your/dress.png')
model_image = Image.open('/path/to/your/model.png')
text_input = """Create a professional e-commerce fashion photo. Take the blue floral dress from the first image and let the woman from the second image wear it. Generate a realistic, full-body shot of the woman wearing the dress, with the lighting and shadows adjusted to match the outdoor environment."""
# Generate an image from a text prompt
response = client.models.generate_content(
model="gemini-2.5-flash-image",
contents=[dress_image, model_image, text_input],
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif part.inline_data is not None:
image = part.as_image()
image.save("fashion_ecommerce_shot.png")
Java
import com.google.genai.Client;
import com.google.genai.types.Content;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
public class AdvancedComposition {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-2.5-flash-image",
Content.fromParts(
Part.fromBytes(
Files.readAllBytes(
Path.of("/path/to/your/dress.png")),
"image/png"),
Part.fromBytes(
Files.readAllBytes(
Path.of("/path/to/your/model.png")),
"image/png"),
Part.fromText("""
Create a professional e-commerce fashion photo.
Take the blue floral dress from the first image and
let the woman from the second image wear it. Generate
a realistic, full-body shot of the woman wearing the
dress, with the lighting and shadows adjusted to
match the outdoor environment.
""")),
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("fashion_ecommerce_shot.png"), blob.data().get());
}
}
}
}
}
}
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const imagePath1 = "/path/to/your/dress.png";
const imageData1 = fs.readFileSync(imagePath1);
const base64Image1 = imageData1.toString("base64");
const imagePath2 = "/path/to/your/model.png";
const imageData2 = fs.readFileSync(imagePath2);
const base64Image2 = imageData2.toString("base64");
const prompt = [
{
inlineData: {
mimeType: "image/png",
data: base64Image1,
},
},
{
inlineData: {
mimeType: "image/png",
data: base64Image2,
},
},
{ text: "Create a professional e-commerce fashion photo. Take the blue floral dress from the first image and let the woman from the second image wear it. Generate a realistic, full-body shot of the woman wearing the dress, with the lighting and shadows adjusted to match the outdoor environment." },
];
const response = await ai.models.generateContent({
model: "gemini-2.5-flash-image",
contents: prompt,
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("fashion_ecommerce_shot.png", buffer);
console.log("Image saved as fashion_ecommerce_shot.png");
}
}
}
main();
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
imgData1, _ := os.ReadFile("/path/to/your/dress.png")
imgData2, _ := os.ReadFile("/path/to/your/model.png")
parts := []*genai.Part{
&genai.Part{
InlineData: &genai.Blob{
MIMEType: "image/png",
Data: imgData1,
},
},
&genai.Part{
InlineData: &genai.Blob{
MIMEType: "image/png",
Data: imgData2,
},
},
genai.NewPartFromText("Create a professional e-commerce fashion photo. Take the blue floral dress from the first image and let the woman from the second image wear it. Generate a realistic, full-body shot of the woman wearing the dress, with the lighting and shadows adjusted to match the outdoor environment."),
}
contents := []*genai.Content{
genai.NewContentFromParts(parts, genai.RoleUser),
}
result, _ := client.Models.GenerateContent(
ctx,
"gemini-2.5-flash-image",
contents,
)
for _, part := range result.Candidates[0].Content.Parts {
if part.Text != "" {
fmt.Println(part.Text)
} else if part.InlineData != nil {
imageBytes := part.InlineData.Data
outputFilename := "fashion_ecommerce_shot.png"
_ = os.WriteFile(outputFilename, imageBytes, 0644)
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d "{
\"contents\": [{
\"parts\":[
{
\"inline_data\": {
\"mime_type\":\"image/png\",
\"data\": \"<BASE64_IMAGE_DATA_1>\"
}
},
{
\"inline_data\": {
\"mime_type\":\"image/png\",
\"data\": \"<BASE64_IMAGE_DATA_2>\"
}
},
{\"text\": \"Create a professional e-commerce fashion photo. Take the blue floral dress from the first image and let the woman from the second image wear it. Generate a realistic, full-body shot of the woman wearing the dress, with the lighting and shadows adjusted to match the outdoor environment.\"}
]
}]
}"
Ввод 1 | Вход 2 | Выход |
 |  |  |
5. Сохранение высокой точности деталей.
Чтобы гарантировать сохранение важных деталей (например, лица или логотипа) во время редактирования, подробно опишите их вместе с вашим запросом на редактирование.
Шаблон
Using the provided images, place [element from image 2] onto [element from
image 1]. Ensure that the features of [element from image 1] remain
completely unchanged. The added element should [description of how the
element should integrate].
Быстрый
"Take the first image of the woman with brown hair, blue eyes, and a neutral
expression. Add the logo from the second image onto her black t-shirt.
Ensure the woman's face and features remain completely unchanged. The logo
should look like it's naturally printed on the fabric, following the folds
of the shirt."
Python
from google import genai
from google.genai import types
from PIL import Image
client = genai.Client()
# Base image prompts:
# 1. Woman: "A professional headshot of a woman with brown hair and blue eyes, wearing a plain black t-shirt, against a neutral studio background."
# 2. Logo: "A simple, modern logo with the letters 'G' and 'A' in a white circle."
woman_image = Image.open('/path/to/your/woman.png')
logo_image = Image.open('/path/to/your/logo.png')
text_input = """Take the first image of the woman with brown hair, blue eyes, and a neutral expression. Add the logo from the second image onto her black t-shirt. Ensure the woman's face and features remain completely unchanged. The logo should look like it's naturally printed on the fabric, following the folds of the shirt."""
# Generate an image from a text prompt
response = client.models.generate_content(
model="gemini-2.5-flash-image",
contents=[woman_image, logo_image, text_input],
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif part.inline_data is not None:
image = part.as_image()
image.save("woman_with_logo.png")
Java
import com.google.genai.Client;
import com.google.genai.types.Content;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
public class HighFidelity {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-2.5-flash-image",
Content.fromParts(
Part.fromBytes(
Files.readAllBytes(
Path.of("/path/to/your/woman.png")),
"image/png"),
Part.fromBytes(
Files.readAllBytes(
Path.of("/path/to/your/logo.png")),
"image/png"),
Part.fromText("""
Take the first image of the woman with brown hair,
blue eyes, and a neutral expression. Add the logo
from the second image onto her black t-shirt.
Ensure the woman's face and features remain
completely unchanged. The logo should look like
it's naturally printed on the fabric, following
the folds of the shirt.
""")),
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("woman_with_logo.png"), blob.data().get());
}
}
}
}
}
}
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const imagePath1 = "/path/to/your/woman.png";
const imageData1 = fs.readFileSync(imagePath1);
const base64Image1 = imageData1.toString("base64");
const imagePath2 = "/path/to/your/logo.png";
const imageData2 = fs.readFileSync(imagePath2);
const base64Image2 = imageData2.toString("base64");
const prompt = [
{
inlineData: {
mimeType: "image/png",
data: base64Image1,
},
},
{
inlineData: {
mimeType: "image/png",
data: base64Image2,
},
},
{ text: "Take the first image of the woman with brown hair, blue eyes, and a neutral expression. Add the logo from the second image onto her black t-shirt. Ensure the woman's face and features remain completely unchanged. The logo should look like it's naturally printed on the fabric, following the folds of the shirt." },
];
const response = await ai.models.generateContent({
model: "gemini-2.5-flash-image",
contents: prompt,
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("woman_with_logo.png", buffer);
console.log("Image saved as woman_with_logo.png");
}
}
}
main();
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
imgData1, _ := os.ReadFile("/path/to/your/woman.png")
imgData2, _ := os.ReadFile("/path/to/your/logo.png")
parts := []*genai.Part{
&genai.Part{
InlineData: &genai.Blob{
MIMEType: "image/png",
Data: imgData1,
},
},
&genai.Part{
InlineData: &genai.Blob{
MIMEType: "image/png",
Data: imgData2,
},
},
genai.NewPartFromText("Take the first image of the woman with brown hair, blue eyes, and a neutral expression. Add the logo from the second image onto her black t-shirt. Ensure the woman's face and features remain completely unchanged. The logo should look like it's naturally printed on the fabric, following the folds of the shirt."),
}
contents := []*genai.Content{
genai.NewContentFromParts(parts, genai.RoleUser),
}
result, _ := client.Models.GenerateContent(
ctx,
"gemini-2.5-flash-image",
contents,
)
for _, part := range result.Candidates[0].Content.Parts {
if part.Text != "" {
fmt.Println(part.Text)
} else if part.InlineData != nil {
imageBytes := part.InlineData.Data
outputFilename := "woman_with_logo.png"
_ = os.WriteFile(outputFilename, imageBytes, 0644)
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d "{
\"contents\": [{
\"parts\":[
{
\"inline_data\": {
\"mime_type\":\"image/png\",
\"data\": \"<BASE64_IMAGE_DATA_1>\"
}
},
{
\"inline_data\": {
\"mime_type\":\"image/png\",
\"data\": \"<BASE64_IMAGE_DATA_2>\"
}
},
{\"text\": \"Take the first image of the woman with brown hair, blue eyes, and a neutral expression. Add the logo from the second image onto her black t-shirt. Ensure the woman's face and features remain completely unchanged. The logo should look like it's naturally printed on the fabric, following the folds of the shirt.\"}
]
}]
}"
Ввод 1 | Вход 2 | Выход |
 |  |  |
6. Оживите что-нибудь.
Загрузите черновой эскиз или рисунок и попросите модель доработать его до готового изображения.
Шаблон
Turn this rough [medium] sketch of a [subject] into a [style description]
photo. Keep the [specific features] from the sketch but add [new details/materials].
Быстрый
"Turn this rough pencil sketch of a futuristic car into a polished photo of the finished concept car in a showroom. Keep the sleek lines and low profile from the sketch but add metallic blue paint and neon rim lighting."
Python
from google import genai
from PIL import Image
client = genai.Client()
# Base image prompt: "A rough pencil sketch of a flat sports car on white paper."
sketch_image = Image.open('/path/to/your/car_sketch.png')
text_input = """Turn this rough pencil sketch of a futuristic car into a polished photo of the finished concept car in a showroom. Keep the sleek lines and low profile from the sketch but add metallic blue paint and neon rim lighting."""
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents=[sketch_image, text_input],
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif part.inline_data is not None:
image = part.as_image()
image.save("car_photo.png")
Java
import com.google.genai.Client;
import com.google.genai.types.Content;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
public class BringToLife {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-3-pro-image-preview",
Content.fromParts(
Part.fromBytes(
Files.readAllBytes(
Path.of("/path/to/your/car_sketch.png")),
"image/png"),
Part.fromText("""
Turn this rough pencil sketch of a futuristic car into a polished photo of the finished concept car in a showroom. Keep the sleek lines and low profile from the sketch but add metallic blue paint and neon rim lighting.
""")),
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("car_photo.png"), blob.data().get());
}
}
}
}
}
}
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const imagePath = "/path/to/your/car_sketch.png";
const imageData = fs.readFileSync(imagePath);
const base64Image = imageData.toString("base64");
const prompt = [
{
inlineData: {
mimeType: "image/png",
data: base64Image,
},
},
{ text: "Turn this rough pencil sketch of a futuristic car into a polished photo of the finished concept car in a showroom. Keep the sleek lines and low profile from the sketch but add metallic blue paint and neon rim lighting." },
];
const response = await ai.models.generateContent({
model: "gemini-3-pro-image-preview",
contents: prompt,
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("car_photo.png", buffer);
console.log("Image saved as car_photo.png");
}
}
}
main();
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
imgData, _ := os.ReadFile("/path/to/your/car_sketch.png")
parts := []*genai.Part{
&genai.Part{
InlineData: &genai.Blob{
MIMEType: "image/png",
Data: imgData,
},
},
genai.NewPartFromText("Turn this rough pencil sketch of a futuristic car into a polished photo of the finished concept car in a showroom. Keep the sleek lines and low profile from the sketch but add metallic blue paint and neon rim lighting."),
}
contents := []*genai.Content{
genai.NewContentFromParts(parts, genai.RoleUser),
}
result, _ := client.Models.GenerateContent(
ctx,
"gemini-3-pro-image-preview",
contents,
)
for _, part := range result.Candidates[0].Content.Parts {
if part.Text != "" {
fmt.Println(part.Text)
} else if part.InlineData != nil {
imageBytes := part.InlineData.Data
outputFilename := "car_photo.png"
_ = os.WriteFile(outputFilename, imageBytes, 0644)
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d "{
\"contents\": [{
\"parts\":[
{
\"inline_data\": {
\"mime_type\":\"image/png\",
\"data\": \"<BASE64_IMAGE_DATA>\"
}
},
{\"text\": \"Turn this rough pencil sketch of a futuristic car into a polished photo of the finished concept car in a showroom. Keep the sleek lines and low profile from the sketch but add metallic blue paint and neon rim lighting.\"}
]
}]
}"
Вход | Выход |
 |  |
7. Единообразие персонажей: обзор на 360 градусов.
Вы можете создавать 360-градусные изображения персонажа, итеративно запрашивая разные ракурсы. Для достижения наилучших результатов включайте ранее созданные изображения в последующие запросы, чтобы обеспечить согласованность. Для сложных поз используйте эталонное изображение желаемой позы.
Шаблон
A studio portrait of [person] against [background], [looking forward/in profile looking right/etc.]
Быстрый
A studio portrait of this man against white, in profile looking right
Python
from google import genai
from google.genai import types
from PIL import Image
client = genai.Client()
image_input = Image.open('/path/to/your/man_in_white_glasses.jpg')
text_input = """A studio portrait of this man against white, in profile looking right"""
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents=[text_input, image_input],
)
for part in response.parts:
if part.text is not None:
print(part.text)
elif part.inline_data is not None:
image = part.as_image()
image.save("man_right_profile.png")
Java
import com.google.genai.Client;
import com.google.genai.types.Content;
import com.google.genai.types.GenerateContentConfig;
import com.google.genai.types.GenerateContentResponse;
import com.google.genai.types.Part;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
public class CharacterConsistency {
public static void main(String[] args) throws IOException {
try (Client client = new Client()) {
GenerateContentConfig config = GenerateContentConfig.builder()
.responseModalities("TEXT", "IMAGE")
.build();
GenerateContentResponse response = client.models.generateContent(
"gemini-3-pro-image-preview",
Content.fromParts(
Part.fromText("""
A studio portrait of this man against white, in profile looking right
"""),
Part.fromBytes(
Files.readAllBytes(
Path.of("/path/to/your/man_in_white_glasses.jpg")),
"image/jpeg")),
config);
for (Part part : response.parts()) {
if (part.text().isPresent()) {
System.out.println(part.text().get());
} else if (part.inlineData().isPresent()) {
var blob = part.inlineData().get();
if (blob.data().isPresent()) {
Files.write(Paths.get("man_right_profile.png"), blob.data().get());
}
}
}
}
}
}
JavaScript
import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";
async function main() {
const ai = new GoogleGenAI({});
const imagePath = "/path/to/your/man_in_white_glasses.jpg";
const imageData = fs.readFileSync(imagePath);
const base64Image = imageData.toString("base64");
const prompt = [
{ text: "A studio portrait of this man against white, in profile looking right" },
{
inlineData: {
mimeType: "image/jpeg",
data: base64Image,
},
},
];
const response = await ai.models.generateContent({
model: "gemini-3-pro-image-preview",
contents: prompt,
});
for (const part of response.candidates[0].content.parts) {
if (part.text) {
console.log(part.text);
} else if (part.inlineData) {
const imageData = part.inlineData.data;
const buffer = Buffer.from(imageData, "base64");
fs.writeFileSync("man_right_profile.png", buffer);
console.log("Image saved as man_right_profile.png");
}
}
}
main();
Идти
package main
import (
"context"
"fmt"
"log"
"os"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
imagePath := "/path/to/your/man_in_white_glasses.jpg"
imgData, _ := os.ReadFile(imagePath)
parts := []*genai.Part{
genai.NewPartFromText("A studio portrait of this man against white, in profile looking right"),
&genai.Part{
InlineData: &genai.Blob{
MIMEType: "image/jpeg",
Data: imgData,
},
},
}
contents := []*genai.Content{
genai.NewContentFromParts(parts, genai.RoleUser),
}
result, _ := client.Models.GenerateContent(
ctx,
"gemini-3-pro-image-preview",
contents,
)
for _, part := range result.Candidates[0].Content.Parts {
if part.Text != "" {
fmt.Println(part.Text)
} else if part.InlineData != nil {
imageBytes := part.InlineData.Data
outputFilename := "man_right_profile.png"
_ = os.WriteFile(outputFilename, imageBytes, 0644)
}
}
}
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d "{
\"contents\": [{
\"parts\":[
{\"text\": \"A studio portrait of this man against white, in profile looking right\"},
{
\"inline_data\": {
\"mime_type\":\"image/jpeg\",
\"data\": \"<BASE64_IMAGE_DATA>\"
}
}
]
}]
}"
Вход | Выход 1 | Выход 2 |
|  |  |
Передовые методы
Чтобы поднять ваши результаты с хорошего до отличного, включите эти профессиональные стратегии в свой рабочий процесс.
- Будьте предельно конкретны: чем больше деталей вы предоставите, тем больше у вас будет контроля. Вместо «фэнтезийные доспехи» опишите их так: «изысканные эльфийские латы, украшенные узорами из серебряной фольги, с высоким воротником и наплечниками в форме соколиных крыльев».
- Укажите контекст и цель: объясните назначение изображения. Понимание контекста моделью повлияет на конечный результат. Например, фраза «Создать логотип для элитного минималистичного бренда средств по уходу за кожей» даст лучшие результаты, чем просто «Создать логотип».
- Итеративный подход и доработка: не ожидайте идеального изображения с первой попытки. Используйте разговорный характер модели для внесения небольших изменений. Задавайте уточняющие вопросы, например: «Отлично, но можно сделать освещение немного теплее?» или «Оставьте все как есть, но измените выражение лица персонажа на более серьезное».
- Используйте пошаговые инструкции: для сложных сцен с множеством элементов разбейте задание на этапы. «Сначала создайте фон безмятежного, туманного леса на рассвете. Затем на переднем плане добавьте покрытый мхом древний каменный алтарь. Наконец, поместите один светящийся меч на вершину алтаря».
- Используйте «семантические отрицательные подсказки»: вместо того, чтобы сказать «нет машин», опишите желаемую сцену в позитивном ключе: «пустая, безлюдная улица без признаков движения транспорта».
- Управляйте камерой: используйте фотографический и кинематографический язык для управления композицией. Применяйте такие термины, как
wide-angle shot,macro shot,low-angle perspective.
Ограничения
- Для достижения наилучших результатов используйте следующие языки: EN, ar-EG, de-DE, es-MX, fr-FR, hi-IN, id-ID, it-IT, ja-JP, ko-KR, pt-BR, ru-RU, ua-UA, vi-VN, zh-CN.
- В функциях генерации изображений отсутствуют аудио- и видеовходы.
- Модель не всегда будет точно соответствовать тому количеству изображений, которое явно запрашивает пользователь.
-
gemini-2.5-flash-imageлучше всего работает с 3 изображениями на входе, в то время какgemini-3-pro-image-previewподдерживает 5 изображений с высоким качеством и до 14 изображений в общей сложности. - При создании текста для изображения Gemini работает лучше всего, если сначала сгенерировать текст, а затем запросить изображение с этим текстом.
- Все сгенерированные изображения содержат водяной знак SynthID .
Дополнительные конфигурации
При желании вы можете настроить режимы ответа и соотношение сторон выходных данных модели в поле config вызовов generate_content .
Типы выходных данных
По умолчанию модель возвращает текстовые и графические ответы (например, response_modalities=['Text', 'Image'] ). Вы можете настроить ответ так, чтобы он возвращал только изображения без текста, используя response_modalities=['Image'] .
Python
response = client.models.generate_content(
model="gemini-2.5-flash-image",
contents=[prompt],
config=types.GenerateContentConfig(
response_modalities=['Image']
)
)
JavaScript
const response = await ai.models.generateContent({
model: "gemini-2.5-flash-image",
contents: prompt,
config: {
responseModalities: ['Image']
}
});
Идти
result, _ := client.Models.GenerateContent(
ctx,
"gemini-2.5-flash-image",
genai.Text("Create a picture of a nano banana dish in a " +
" fancy restaurant with a Gemini theme"),
&genai.GenerateContentConfig{
ResponseModalities: "Image",
},
)
Java
response = client.models.generateContent(
"gemini-2.5-flash-image",
prompt,
GenerateContentConfig.builder()
.responseModalities("IMAGE")
.build());
ОТДЫХ
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"contents": [{
"parts": [
{"text": "Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme"}
]
}],
"generationConfig": {
"responseModalities": ["Image"]
}
}'
Соотношение сторон и размер изображения
По умолчанию модель подбирает размер выходного изображения в соответствии с размером входного изображения или генерирует квадраты в масштабе 1:1. Вы можете управлять соотношением сторон выходного изображения с помощью поля aspect_ratio в image_config в ответе на запрос, как показано здесь:
Python
# For gemini-2.5-flash-image
response = client.models.generate_content(
model="gemini-2.5-flash-image",
contents=[prompt],
config=types.GenerateContentConfig(
image_config=types.ImageConfig(
aspect_ratio="16:9",
)
)
)
# For gemini-3-pro-image-preview
response = client.models.generate_content(
model="gemini-3-pro-image-preview",
contents=[prompt],
config=types.GenerateContentConfig(
image_config=types.ImageConfig(
aspect_ratio="16:9",
image_size="2K",
)
)
)
JavaScript
// For gemini-2.5-flash-image
const response = await ai.models.generateContent({
model: "gemini-2.5-flash-image",
contents: prompt,
config: {
imageConfig: {
aspectRatio: "16:9",
},
}
});
// For gemini-3-pro-image-preview
const response_gemini3 = await ai.models.generateContent({
model: "gemini-3-pro-image-preview",
contents: prompt,
config: {
imageConfig: {
aspectRatio: "16:9",
imageSize: "2K",
},
}
});
Идти
// For gemini-2.5-flash-image
result, _ := client.Models.GenerateContent(
ctx,
"gemini-2.5-flash-image",
genai.Text("Create a picture of a nano banana dish in a " +
" fancy restaurant with a Gemini theme"),
&genai.GenerateContentConfig{
ImageConfig: &genai.ImageConfig{
AspectRatio: "16:9",
},
}
)
// For gemini-3-pro-image-preview
result_gemini3, _ := client.Models.GenerateContent(
ctx,
"gemini-3-pro-image-preview",
genai.Text("Create a picture of a nano banana dish in a " +
" fancy restaurant with a Gemini theme"),
&genai.GenerateContentConfig{
ImageConfig: &genai.ImageConfig{
AspectRatio: "16:9",
ImageSize: "2K",
},
}
)
Java
// For gemini-2.5-flash-image
response = client.models.generateContent(
"gemini-2.5-flash-image",
prompt,
GenerateContentConfig.builder()
.imageConfig(ImageConfig.builder()
.aspectRatio("16:9")
.build())
.build());
// For gemini-3-pro-image-preview
response_gemini3 = client.models.generateContent(
"gemini-3-pro-image-preview",
prompt,
GenerateContentConfig.builder()
.imageConfig(ImageConfig.builder()
.aspectRatio("16:9")
.imageSize("2K")
.build())
.build());
ОТДЫХ
# For gemini-2.5-flash-image
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"contents": [{
"parts": [
{"text": "Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme"}
]
}],
"generationConfig": {
"imageConfig": {
"aspectRatio": "16:9"
}
}
}'
# For gemini-3-pro-image-preview
curl -s -X POST \
"https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"contents": [{
"parts": [
{"text": "Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme"}
]
}],
"generationConfig": {
"imageConfig": {
"aspectRatio": "16:9",
"imageSize": "2K"
}
}
}'
Доступные соотношения сторон и размер создаваемого изображения указаны в следующих таблицах:
Изображение со вспышкой Gemini 2.5
| Соотношение сторон | Разрешение | Токены |
|---|---|---|
| 1:1 | 1024x1024 | 1290 |
| 2:3 | 832x1248 | 1290 |
| 3:2 | 1248x832 | 1290 |
| 3:4 | 864x1184 | 1290 |
| 4:3 | 1184x864 | 1290 |
| 4:5 | 896x1152 | 1290 |
| 5:4 | 1152x896 | 1290 |
| 9:16 | 768x1344 | 1290 |
| 16:9 | 1344x768 | 1290 |
| 21:9 | 1536x672 | 1290 |
Предварительный просмотр изображения Gemini 3 Pro
| Соотношение сторон | разрешение 1K | 1K токенов | разрешение 2K | 2K токенов | разрешение 4K | 4K токенов |
|---|---|---|---|---|---|---|
| 1:1 | 1024x1024 | 1120 | 2048x2048 | 1120 | 4096x4096 | 2000 |
| 2:3 | 848x1264 | 1120 | 1696x2528 | 1120 | 3392x5056 | 2000 |
| 3:2 | 1264x848 | 1120 | 2528x1696 | 1120 | 5056x3392 | 2000 |
| 3:4 | 896x1200 | 1120 | 1792x2400 | 1120 | 3584x4800 | 2000 |
| 4:3 | 1200x896 | 1120 | 2400x1792 | 1120 | 4800x3584 | 2000 |
| 4:5 | 928x1152 | 1120 | 1856x2304 | 1120 | 3712x4608 | 2000 |
| 5:4 | 1152x928 | 1120 | 2304x1856 | 1120 | 4608x3712 | 2000 |
| 9:16 | 768x1376 | 1120 | 1536x2752 | 1120 | 3072x5504 | 2000 |
| 16:9 | 1376x768 | 1120 | 2752x1536 | 1120 | 5504x3072 | 2000 |
| 21:9 | 1584x672 | 1120 | 3168x1344 | 1120 | 6336x2688 | 2000 |
Выбор модели
Выберите модель, наиболее подходящую для ваших конкретных задач.
Gemini 3 Pro Image Preview (Nano Banana Pro Preview) предназначен для профессионального создания графических ресурсов и сложных инструкций. Эта модель имеет реалистичную основу, созданную с помощью поиска Google, стандартный процесс «размышления», который уточняет композицию перед генерацией, и может создавать изображения с разрешением до 4K. Более подробную информацию о ценах и возможностях модели можно найти на соответствующей странице.
Фотоаппарат Gemini 2.5 Flash Image (Nano Banana) разработан для обеспечения высокой скорости и эффективности. Эта модель оптимизирована для обработки больших объемов данных с низкой задержкой и генерирует изображения с разрешением 1024 пикселя. Более подробную информацию о ценах и возможностях модели можно найти на соответствующей странице.
Когда использовать Imagen
Помимо использования встроенных в Gemini возможностей генерации изображений, вы также можете получить доступ к Imagen , нашей специализированной модели генерации изображений, через API Gemini.
При начале работы с Imagen лучше всего выбрать Imagen 4. Для более сложных задач или при необходимости наилучшего качества изображения выбирайте Imagen 4 Ultra (обратите внимание, что за один раз можно сгенерировать только одно изображение).
Что дальше?
- Больше примеров и образцов кода вы найдете в руководстве по использованию .
- Ознакомьтесь с руководством Veo , чтобы узнать, как создавать видео с помощью API Gemini.
- Чтобы узнать больше о моделях Gemini, см. раздел «Модели Gemini» .