Generate images with Gemini

You can use Gemini to generate images from a text prompt. Supported interfaces include the Google Cloud console and the Agent Platform API.

The following Gemini models support image generation:

Click to expand supported models

For more information about Gemini model capabilities, see Gemini models.

Generate images

The following shows how to generate images using either Agent Studio or using the API.

For more information about best practices for prompting, see Design multimodal prompts.

Console

To generate images with Gemini, do the following:

Open Agent Studio > Create prompt.
Click Switch model and select one of the displayed models.
In the Outputs panel, select Image and text from the drop-down menu.
Write a description of the image you want to generate in the text area of the Write a prompt text area.
Click the Prompt () button.

Gemini generates an image based on your description. This process takes a few seconds, but can be comparatively slower depending on capacity.

Python

Install

pip install --upgrade google-genai

To learn more, see the SDK reference documentation.

Set environment variables to use the Google Gen AI SDK with Vertex AI:

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_ENTERPRISE=True

import os
from io import BytesIO

from google import genai
from google.genai.types import GenerateContentConfig, Modality
from PIL import Image

client = genai.Client()

response = client.models.generate_content(
    model="gemini-3.1-flash-image",
    contents=("Generate an image of the Eiffel tower with fireworks in the background."),
    config=GenerateContentConfig(
        response_modalities=[Modality.TEXT, Modality.IMAGE],
    ),
)
for part in response.candidates[0].content.parts:
    if part.text:
        print(part.text)
    elif part.inline_data:
        image = Image.open(BytesIO((part.inline_data.data)))
        # Ensure the output directory exists
        output_dir = "output_folder"
        os.makedirs(output_dir, exist_ok=True)
        image.save(os.path.join(output_dir, "example-image-eiffel-tower.png"))

Go

Learn how to install or update the Go.

To learn more, see the SDK reference documentation.

Set environment variables to use the Google Gen AI SDK with Vertex AI:

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_ENTERPRISE=True

import (
	"context"
	"fmt"
	"io"
	"os"

	"google.golang.org/genai"
)

// generateMMFlashWithText demonstrates how to generate both text and image outputs.
func generateMMFlashWithText(w io.Writer) error {
	ctx := context.Background()

	client, err := genai.NewClient(ctx, &genai.ClientConfig{
		HTTPOptions: genai.HTTPOptions{APIVersion: "v1"},
	})
	if err != nil {
		return fmt.Errorf("failed to create genai client: %w", err)
	}

	modelName := "gemini-2.5-flash-image"
	contents := []*genai.Content{
		{
			Parts: []*genai.Part{
				{Text: "Generate an image of the Eiffel tower with fireworks in the background."},
			},
			Role: genai.RoleUser,
		},
	}

	resp, err := client.Models.GenerateContent(ctx,
		modelName,
		contents,
		&genai.GenerateContentConfig{
			ResponseModalities: []string{
				string(genai.ModalityText),
				string(genai.ModalityImage),
			},
			CandidateCount: int32(1),
			SafetySettings: []*genai.SafetySetting{
				{Method: genai.HarmBlockMethodProbability},
				{Category: genai.HarmCategoryDangerousContent},
				{Threshold: genai.HarmBlockThresholdBlockMediumAndAbove},
			},
		},
	)
	if err != nil {
		return fmt.Errorf("failed to generate content: %w", err)
	}

	if len(resp.Candidates) == 0 || resp.Candidates[0].Content == nil {
		return fmt.Errorf("no candidates returned")
	}
	var fileName string
	for _, part := range resp.Candidates[0].Content.Parts {
		if part.Text != "" {
			fmt.Fprintln(w, part.Text)
		} else if part.InlineData != nil {
			fileName = "example-image-eiffel-tower.png"
			if err := os.WriteFile(fileName, part.InlineData.Data, 0o644); err != nil {
				return fmt.Errorf("failed to save image: %w", err)
			}
		}
	}
	fmt.Fprintln(w, fileName)

	// Example response:
	// I will generate an image of the Eiffel Tower at night, with a vibrant display of
	// colorful fireworks exploding in the dark sky behind it.
	// ....
	return nil
}

Node.js

Install

npm install @google/genai

To learn more, see the SDK reference documentation.

Set environment variables to use the Google Gen AI SDK with Vertex AI:

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_ENTERPRISE=True

const fs = require('fs');
const {GoogleGenAI, Modality} = require('@google/genai');

const GOOGLE_CLOUD_PROJECT = process.env.GOOGLE_CLOUD_PROJECT;
const GOOGLE_CLOUD_LOCATION =
  process.env.GOOGLE_CLOUD_LOCATION || 'us-central1';

async function generateImage(
  projectId = GOOGLE_CLOUD_PROJECT,
  location = GOOGLE_CLOUD_LOCATION
) {
  const client = new GoogleGenAI({
    vertexai: true,
    project: projectId,
    location: location,
  });

  const response = await client.models.generateContentStream({
    model: 'gemini-2.5-flash-image',
    contents:
      'Generate an image of the Eiffel tower with fireworks in the background.',
    config: {
      responseModalities: [Modality.TEXT, Modality.IMAGE],
    },
  });

  const generatedFileNames = [];
  let imageIndex = 0;

  for await (const chunk of response) {
    const text = chunk.text;
    const data = chunk.data;
    if (text) {
      console.debug(text);
    } else if (data) {
      const outputDir = 'output-folder';
      if (!fs.existsSync(outputDir)) {
        fs.mkdirSync(outputDir, {recursive: true});
      }
      const fileName = `${outputDir}/generate_content_streaming_image_${imageIndex++}.png`;
      console.debug(`Writing response image to file: ${fileName}.`);
      try {
        fs.writeFileSync(fileName, data);
        generatedFileNames.push(fileName);
      } catch (error) {
        console.error(`Failed to write image file ${fileName}:`, error);
      }
    }
  }

  // Example response:
  //  I will generate an image of the Eiffel Tower at night, with a vibrant display of
  //  colorful fireworks exploding in the dark sky behind it. The tower will be
  //  illuminated, standing tall as the focal point of the scene, with the bursts of
  //  light from the fireworks creating a festive atmosphere.

  return generatedFileNames;
}

Java

Learn how to install or update the Java.

To learn more, see the SDK reference documentation.

Set environment variables to use the Google Gen AI SDK with Vertex AI:

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_ENTERPRISE=True


import com.google.genai.Client;
import com.google.genai.types.Blob;
import com.google.genai.types.Candidate;
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 com.google.genai.types.SafetySetting;
import java.awt.image.BufferedImage;
import java.io.ByteArrayInputStream;
import java.io.File;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import javax.imageio.ImageIO;

public class ImageGenMmFlashWithText {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String modelId = "gemini-2.5-flash-image";
    String outputFile = "resources/output/example-image-eiffel-tower.png";
    generateContent(modelId, outputFile);
  }

  // Generates an image with text input
  public static void generateContent(String modelId, String outputFile) throws IOException {
    // Client Initialization. Once created, it can be reused for multiple requests.
    try (Client client = Client.builder().location("global").vertexAI(true).build()) {

      GenerateContentConfig contentConfig =
          GenerateContentConfig.builder()
              .responseModalities("TEXT", "IMAGE")
              .candidateCount(1)
              .safetySettings(
                  SafetySetting.builder()
                      .method("PROBABILITY")
                      .category("HARM_CATEGORY_DANGEROUS_CONTENT")
                      .threshold("BLOCK_MEDIUM_AND_ABOVE")
                      .build())
              .build();

      GenerateContentResponse response =
          client.models.generateContent(
              modelId,
              "Generate an image of the Eiffel tower with fireworks in the background.",
              contentConfig);

      // Get parts of the response
      List<Part> parts =
          response
              .candidates()
              .flatMap(candidates -> candidates.stream().findFirst())
              .flatMap(Candidate::content)
              .flatMap(Content::parts)
              .orElse(new ArrayList<>());

      // For each part print text if present, otherwise read image data if present and
      // write it to the output file
      for (Part part : parts) {
        if (part.text().isPresent()) {
          System.out.println(part.text().get());
        } else if (part.inlineData().flatMap(Blob::data).isPresent()) {
          BufferedImage image =
              ImageIO.read(new ByteArrayInputStream(part.inlineData().flatMap(Blob::data).get()));
          ImageIO.write(image, "png", new File(outputFile));
        }
      }

      System.out.println("Content written to: " + outputFile);
      // Example response:
      // Here is the Eiffel Tower with fireworks in the background...
      //
      // Content written to: resources/output/example-image-eiffel-tower.png
    }
  }
}

REST

Run the following command in the terminal to create or overwrite this file in the current directory:

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://${API_ENDPOINT}:generateContent \
  -d '{
    "contents": {
      "role": "USER",
      "parts": [
        {
          "text": "Create a tutorial explaining how to make a peanut butter and jelly sandwich in three easy steps."
        }
      ]
    },
    "generationConfig": {
      "responseModalities": ["TEXT", "IMAGE"],
      "imageConfig": {
        "aspectRatio": "16:9",
      },
     },
     "safetySettings": {
      "method": "PROBABILITY",
      "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
      "threshold": "BLOCK_MEDIUM_AND_ABOVE"
    },
  }' 2>/dev/null >response.json

Gemini generates an image based on your description. This process takes a few seconds, but can be comparatively slower depending on capacity.

Generate interleaved images and text

You can use Gemini to generate interleaved images with text responses. For example, you can generate images for each step of a generated recipe without having to make separate requests to the model.

The following Gemini models support generating interleaved images and text:

Click to expand supported models

Console

To generate interleaved images with text responses, do the following:

Open Agent Studio > Create prompt.
Click Switch model and select one of the displayed models.
In the Outputs panel, select Image and text from the drop-down menu.
Write a description of the image you want to generate in the text area of the Write a prompt text area. For example, "Create a tutorial explaining how to make a peanut butter and jelly sandwich in three steps. For each step, provide a title with the number of the step, an explanation, and also generate an image, generate each image in a 1:1 aspect ratio."
Click the Prompt () button.

Gemini generates a response based on your description. This process takes a few seconds, but can be comparatively slower depending on capacity.

Python

Install

pip install --upgrade google-genai

To learn more, see the SDK reference documentation.

Set environment variables to use the Google Gen AI SDK with Vertex AI:

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_ENTERPRISE=True

from google import genai
from google.genai.types import GenerateContentConfig, Modality
from PIL import Image
from io import BytesIO

client = genai.Client()

response = client.models.generate_content(
    model="gemini-3.1-flash-image",
    contents=(
        "Generate an illustrated recipe for a paella."
        "Create images to go alongside the text as you generate the recipe"
    ),
    config=GenerateContentConfig(response_modalities=[Modality.TEXT, Modality.IMAGE]),
)
with open("output_folder/paella-recipe.md", "w") as fp:
    for i, part in enumerate(response.candidates[0].content.parts):
        if part.text is not None:
            fp.write(part.text)
        elif part.inline_data is not None:
            image = Image.open(BytesIO((part.inline_data.data)))
            image.save(f"output_folder/example-image-{i+1}.png")
            fp.write(f"![image](example-image-{i+1}.png)")

Java

Learn how to install or update the Java.

To learn more, see the SDK reference documentation.

Set environment variables to use the Google Gen AI SDK with Vertex AI:

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_ENTERPRISE=True


import com.google.genai.Client;
import com.google.genai.types.Blob;
import com.google.genai.types.Candidate;
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.awt.image.BufferedImage;
import java.io.BufferedWriter;
import java.io.ByteArrayInputStream;
import java.io.File;
import java.io.FileWriter;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import javax.imageio.ImageIO;

public class ImageGenMmFlashTextAndImageWithText {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String modelId = "gemini-2.5-flash-image";
    String outputFile = "resources/output/paella-recipe.md";
    generateContent(modelId, outputFile);
  }

  // Generates text and image with text input
  public static void generateContent(String modelId, String outputFile) throws IOException {
    // Client Initialization. Once created, it can be reused for multiple requests.
    try (Client client = Client.builder().location("global").vertexAI(true).build()) {

      GenerateContentResponse response =
          client.models.generateContent(
              modelId,
              Content.fromParts(
                  Part.fromText("Generate an illustrated recipe for a paella."),
                  Part.fromText(
                      "Create images to go alongside the text as you generate the recipe.")),
              GenerateContentConfig.builder().responseModalities("TEXT", "IMAGE").build());

      try (BufferedWriter writer = new BufferedWriter(new FileWriter(outputFile))) {

        // Get parts of the response
        List<Part> parts =
            response
                .candidates()
                .flatMap(candidates -> candidates.stream().findFirst())
                .flatMap(Candidate::content)
                .flatMap(Content::parts)
                .orElse(new ArrayList<>());

        int index = 1;
        // For each part print text if present, otherwise read image data if present and
        // write it to the output file
        for (Part part : parts) {
          if (part.text().isPresent()) {
            writer.write(part.text().get());
          } else if (part.inlineData().flatMap(Blob::data).isPresent()) {
            BufferedImage image =
                ImageIO.read(new ByteArrayInputStream(part.inlineData().flatMap(Blob::data).get()));
            ImageIO.write(
                image, "png", new File("resources/output/example-image-" + index + ".png"));
            writer.write("![image](example-image-" + index + ".png)");
          }
          index++;
        }

        System.out.println("Content written to: " + outputFile);

        // Example response:
        // A markdown page for a Paella recipe(`paella-recipe.md`) has been generated.
        // It includes detailed steps and several images illustrating the cooking process.
        //
        // Content written to:  resources/output/paella-recipe.md
      }
    }
  }
}

Go

Learn how to install or update the Go.

To learn more, see the SDK reference documentation.

Set environment variables to use the Google Gen AI SDK with Vertex AI:

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_ENTERPRISE=True

import (
	"context"
	"fmt"
	"io"
	"os"
	"path/filepath"

	"google.golang.org/genai"
)

// generateMMFlashTxtImgWithText demonstrates how to generate an illustrated recipe
// combining text and image outputs into a markdown file.
func generateMMFlashTxtImgWithText(w io.Writer) error {
	ctx := context.Background()

	client, err := genai.NewClient(ctx, &genai.ClientConfig{
		HTTPOptions: genai.HTTPOptions{APIVersion: "v1"},
	})
	if err != nil {
		return fmt.Errorf("failed to create genai client: %w", err)
	}

	modelName := "gemini-2.5-flash-image"
	contents := []*genai.Content{
		{
			Parts: []*genai.Part{
				{Text: "Generate an illustrated recipe for a paella. " +
					"Create images to go alongside the text as you generate the recipe."},
			},
			Role: genai.RoleUser,
		},
	}

	resp, err := client.Models.GenerateContent(ctx,
		modelName,
		contents,
		&genai.GenerateContentConfig{
			ResponseModalities: []string{
				string(genai.ModalityText),
				string(genai.ModalityImage),
			},
			CandidateCount: int32(1),
		},
	)
	if err != nil {
		return fmt.Errorf("failed to generate content: %w", err)
	}

	if len(resp.Candidates) == 0 || resp.Candidates[0].Content == nil {
		return fmt.Errorf("no candidates returned")
	}

	outputFolder := ""

	// Create the markdown file
	mdFile := filepath.Join(outputFolder, "paella-recipe.md")
	fp, err := os.Create(mdFile)
	if err != nil {
		return fmt.Errorf("failed to create markdown file: %w", err)
	}
	defer fp.Close()

	for i, part := range resp.Candidates[0].Content.Parts {
		if part.Text != "" {
			if _, err := fp.WriteString(part.Text); err != nil {
				return fmt.Errorf("failed to write text: %w", err)
			}
		} else if part.InlineData != nil {
			imgFile := filepath.Join(outputFolder, fmt.Sprintf("example-image-%d.png", i+1))
			if err := os.WriteFile(imgFile, part.InlineData.Data, 0644); err != nil {
				return fmt.Errorf("failed to save image: %w", err)
			}
			if _, err := fp.WriteString(fmt.Sprintf("![image](%s)", filepath.Base(imgFile))); err != nil {
				return fmt.Errorf("failed to write image reference: %w", err)
			}
		}
	}

	fmt.Fprintln(w, mdFile)

	// Example response:
	//  A markdown page for a Paella recipe (`paella-recipe.md`) has been generated.
	//  It includes detailed steps and several images illustrating the cooking process.
	return nil
}

Node.js

Install

npm install @google/genai

To learn more, see the SDK reference documentation.

Set environment variables to use the Google Gen AI SDK with Vertex AI:

# Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values
# with appropriate values for your project.
export GOOGLE_CLOUD_PROJECT=GOOGLE_CLOUD_PROJECT
export GOOGLE_CLOUD_LOCATION=global
export GOOGLE_GENAI_USE_ENTERPRISE=True

const fs = require('fs');
const {GoogleGenAI, Modality} = require('@google/genai');

const GOOGLE_CLOUD_PROJECT = process.env.GOOGLE_CLOUD_PROJECT;
const GOOGLE_CLOUD_LOCATION =
  process.env.GOOGLE_CLOUD_LOCATION || 'us-central1';

async function savePaellaRecipe(response) {
  const parts = response.candidates[0].content.parts;

  let mdText = '';
  const outputDir = 'output-folder';

  for (let i = 0; i < parts.length; i++) {
    const part = parts[i];

    if (part.text) {
      mdText += part.text + '\n';
    } else if (part.inlineData) {
      if (!fs.existsSync(outputDir)) {
        fs.mkdirSync(outputDir, {recursive: true});
      }
      const imageBytes = Buffer.from(part.inlineData.data, 'base64');
      const imagePath = `example-image-${i + 1}.png`;
      const saveImagePath = `${outputDir}/${imagePath}`;

      fs.writeFileSync(saveImagePath, imageBytes);
      mdText += `![image](./${imagePath})\n`;
    }
  }
  const mdFile = `${outputDir}/paella-recipe.md`;

  fs.writeFileSync(mdFile, mdText);
  console.log(`Saved recipe to: ${mdFile}`);
}

async function generateImage(
  projectId = GOOGLE_CLOUD_PROJECT,
  location = GOOGLE_CLOUD_LOCATION
) {
  const client = new GoogleGenAI({
    vertexai: true,
    project: projectId,
    location: location,
  });

  const response = await client.models.generateContent({
    model: 'gemini-2.5-flash-image',
    contents:
      'Generate an illustrated recipe for a paella. Create images to go alongside the text as you generate the recipe',
    config: {
      responseModalities: [Modality.TEXT, Modality.IMAGE],
    },
  });
  console.log(response);

  await savePaellaRecipe(response);

  return response;
}
// Example response:
//  A markdown page for a Paella recipe(`paella-recipe.md`) has been generated.
//  It includes detailed steps and several images illustrating the cooking process.

REST

Run the following command in the terminal to create or overwrite this file in the current directory:

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://${API_ENDPOINT}:generateContent \
  -d '{
    "contents": {
      "role": "USER",
      "parts": [
        {
          "text": "Generate an illustrated recipe for a paella. Create images to
          go alongside the text as you generate the recipe."
        }
      ]
    },
    "generationConfig": {
      "responseModalities": ["TEXT", "IMAGE"],
      "imageConfig": {
        "aspectRatio": "16:9",
      },
    },
    "safetySettings": {
      "method": "PROBABILITY",
      "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
      "threshold": "BLOCK_MEDIUM_AND_ABOVE"
    },
  }' 2>/dev/null >response.json

Gemini generates an image based on your description. This process takes a few seconds, but can be comparatively slower depending on capacity.

Save generated images to Cloud Storage

Instead of returning raw base64-encoded image bytes inline in the response, you can configure generateContent and streamGenerateContent requests to write generated images directly to a Cloud Storage bucket and return the object URI in fileData.fileUri.

The following Gemini models support Cloud Storage output persistence:

Gemini Nano Banana 2.1 (gemini-nano-banana-2.1)
Gemini 3.1 Flash Image (gemini-3.1-flash-image)
Gemini 3.1 Flash-Lite Image (gemini-3.1-flash-lite-image)
Gemini 3 Pro Image (gemini-3-pro-image)

Prerequisites

Before sending a request with Cloud Storage output persistence, verify that the principal authenticating the request has the following Identity and Access Management permission on the destination Cloud Storage bucket:

storage.objects.create (included in roles such as the Storage Object Creator role, roles/storage.objectCreator)

Before generating images, the service verifies storage.objects.create permission on the target Cloud Storage bucket using the caller's credentials. If the Cloud Storage bucket does not exist, the caller lacks write permission, or a VPC Service Controls perimeter blocks the write, the request fails before generation with a 403 PERMISSION_DENIED error.

Send a request with Cloud Storage output

To write generated images directly to Cloud Storage, configure both of the following fields in your generateContent or streamGenerateContent request:

Set outputConfig.outputUriPrefix to your destination Cloud Storage bucket or folder prefix (for example, gs://BUCKET_NAME/PREFIX/).
Set generationConfig.responseFormat[].image.delivery to "URI".

The following REST request generates an image and writes it directly to a Cloud Storage bucket:

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  "https://aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/global/publishers/google/models/gemini-nano-banana-2.1:generateContent" \
  -d '{
    "contents": [
      {
        "role": "USER",
        "parts": [
          {
            "text": "Create an architectural rendering of a sustainable glass greenhouse in alpine snow."
          }
        ]
      }
    ],
    "generationConfig": {
      "responseModalities": ["TEXT", "IMAGE"],
      "responseFormat": [
        {
          "image": {
            "delivery": "URI"
          }
        }
      ]
    },
    "outputConfig": {
      "outputUriPrefix": "gs://BUCKET_NAME/PREFIX/"
    }
  }'

Replace the following:

PROJECT_ID: Your Google Cloud project ID.
BUCKET_NAME: The name of the destination Cloud Storage bucket.
PREFIX: Optional folder path prefix inside the Cloud Storage bucket (for example, outputs).

The service writes each generated image part to Cloud Storage using the naming pattern gs://BUCKETNAME/PREFIX/imageGENERATIONID.EXTENSION (appending CANDIDATEINDEX when a response contains multiple candidates, and IMAGE_INDEX when a candidate contains multiple images) and returns the URI in fileData:

{
  "candidates": [
    {
      "content": {
        "role": "model",
        "parts": [
          {
            "fileData": {
              "mimeType": "image/png",
              "fileUri": "gs://my-bucket/outputs/image_1234567890.png"
            }
          }
        ]
      }
    }
  ]
}

When you configure Cloud Storage output delivery, the service handles responses as follows:

Mixed text and image responses: Text parts in a TEXT and IMAGE response remain inline in parts[].text. Only generated image parts are written to Cloud Storage and returned as parts[].fileData objects.
Thinking images: When generationConfig.thinkingConfig.includeThoughts is set to true, intermediate thinking images (parts[].thought: true) are also written to Cloud Storage using the thoughtimageGENERATION_ID prefix and returned as parts[].fileData objects.
Streaming responses (streamGenerateContent): Each generated image is written to Cloud Storage before the response chunk referencing its fileData.fileUri is sent.
No-image responses: If a request returns only text or is blocked by safety filters, no object is written to Cloud Storage.
Encryption and retention: Written objects inherit the destination Cloud Storage bucket's encryption configuration, including Customer-managed encryption keys (CMEK), and follow the Cloud Storage bucket's lifecycle management rules.

Limitations

Cloud Storage output has the following limitation:

Multi-turn editing with cross-project Cloud Storage buckets: The service writes generated images using the credentials of the principal that sent the request. However, when a later request in a multi-turn conversation references a generated image by its fileData.fileUri, the service reads the object using the Gemini Enterprise Agent Platform service agent of the calling project (service-PROJECT_NUMBER@gcp-sa-aiplatform.iam.gserviceaccount.com). If the destination Cloud Storage bucket is in a different project and the service agent doesn't have the storage.objects.get permission on that Cloud Storage bucket, the follow-up request fails with a 403 PERMISSION_DENIED error even though the original write succeeded.

To use generated images as input in multi-turn editing, grant the service agent read access to the destination Cloud Storage bucket (for example, the Storage Object Viewer role, roles/storage.objectViewer).

What's next

See the following links for more information about Gemini image generation:

Generate images with Gemini Stay organized with collections Save and categorize content based on your preferences.

Click to expand supported models

Generate images

Console

Python

Install

Go

Node.js

Install

Java

REST

Generate interleaved images and text

Click to expand supported models

Console

Python

Install

Java

Go

Node.js

Install

REST

Save generated images to Cloud Storage

Prerequisites

Send a request with Cloud Storage output

Limitations

What's next

Generate images with Gemini