Generate images with Gemini

The following Gemini models support the ability to generate images in addition to text:

Gemini 2.5 Flash Image, otherwise known as Gemini 2.5 Flash (with Nano Banana)
Gemini 3 Pro Image (preview), otherwise known as Gemini 3 Pro (with Nano Banana)

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

Generate images

The following shows how to generate images using either Vertex AI 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 Vertex AI Studio > Create prompt .
Click Switch model and select one of the following models from the menu:
- gemini-2.5-flash-image
- gemini-3-pro-image-preview
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 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_VERTEXAI 
 = 
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-pro-image-preview" 
 , 
 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 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_VERTEXAI 
 = 
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 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_VERTEXAI 
 = 
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 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_VERTEXAI 
 = 
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

Note: Gemini 2.5 Flash Image supports the following aspect ratios: 1:1 , 3:2 , 2:3 , 3:4 , 4:3 , 4:5 , 5:4 , 9:16 , 16:9 , and 21:9 .

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

Gemini 2.5 Flash Image supports generating interleaved images with text responses. For example, Gemini 2.5 Flash Image lets you generate images for each each step of a generated recipe without having to make separate requests to the model.

Console

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

Open Vertex AI Studio > Create prompt .
Click Switch model and select one of the following models from the menu:
- gemini-2.5-flash-image
- gemini-3-pro-image-preview
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 easy 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 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_VERTEXAI 
 = 
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-pro-image-preview" 
 , 
 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 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_VERTEXAI 
 = 
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 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_VERTEXAI 
 = 
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 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_VERTEXAI 
 = 
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": "Create a tutorial explaining how to make a peanut butter and jelly sandwich in three easy 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." 
 } 
 ] 
 }, 
 "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

Note: Gemini 2.5 Flash Image and Gemini 3 Pro Image support the following aspect ratios: 1:1 , 3:2 , 2:3 , 3:4 , 4:3 , 4:5 , 5:4 , 9:16 , 16:9 , and 21:9 .

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

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.

Generate images

Console

Python

Install

Go

Node.js

Install

Java

REST

Generate interleaved images and text

Console

Python

Install

Java

Go

Node.js

Install

REST

What's next?

Generate images with Gemini