Edit images with Gemini

Gemini 2.5 Flash Image ( gemini-2.5-flash-image ) supports improved editing of images and multi-turn editing, and contains updated safety filters that provide a more flexible and less restrictive user experience.

For more information about model capabilities, see Gemini models .

Edit an image

Console

To edit images, 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.
Click Insert media ( ) and select a source from the menu, then follow the dialog's instructions.
Write what edits you want to make to the image in the Write a prompt text area.
Click the Prompt ( ) button.

Gemini generates an edited version of the provided 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

  from 
  
 google 
  
 import 
 genai 
 from 
  
 google.genai.types 
  
 import 
 GenerateContentConfig 
 , 
 Modality 
 from 
  
 PIL 
  
 import 
 Image 
 from 
  
 io 
  
 import 
 BytesIO 
 client 
 = 
 genai 
 . 
 Client 
 () 
 # Using an image of Eiffel tower, with fireworks in the background. 
 image 
 = 
 Image 
 . 
 open 
 ( 
 "test_resources/example-image-eiffel-tower.png" 
 ) 
 response 
 = 
 client 
 . 
 models 
 . 
 generate_content 
 ( 
 model 
 = 
 "gemini-3-pro-image-preview" 
 , 
 contents 
 = 
 [ 
 image 
 , 
 "Edit this image to make it look like a cartoon." 
 ], 
 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 
 ))) 
 image 
 . 
 save 
 ( 
 "output_folder/bw-example-image.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.ByteArrayInputStream 
 ; 
 import 
  
 java.io.File 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.nio.file.Files 
 ; 
 import 
  
 java.nio.file.Paths 
 ; 
 import 
  
 java.util.ArrayList 
 ; 
 import 
  
 java.util.List 
 ; 
 import 
  
 javax.imageio.ImageIO 
 ; 
 public 
 class 
  
 ImageGenMmFlashEditImageWithTextAndImage 
 { 
 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/bw-example-image.png" 
 ; 
 generateContent 
 ( 
 modelId 
 , 
 outputFile 
 ); 
 } 
 // 
 Edits 
 an 
 image 
 with 
 image 
 and 
 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 
 ()) 
 { 
 byte 
 [] 
 localImageBytes 
 = 
 Files 
 . 
 readAllBytes 
 ( 
 Paths 
 . 
 get 
 ( 
 "resources/example-image-eiffel-tower.png" 
 )); 
 GenerateContentResponse 
 response 
 = 
 client 
 . 
 models 
 . 
 generateContent 
 ( 
 modelId 
 , 
 Content 
 . 
 fromParts 
 ( 
 Part 
 . 
 fromBytes 
 ( 
 localImageBytes 
 , 
 "image/png" 
 ), 
 Part 
 . 
 fromText 
 ( 
 "Edit this image to make it look like a cartoon." 
 )), 
 GenerateContentConfig 
 . 
 builder 
 () 
 . 
 responseModalities 
 ( 
 "TEXT" 
 , 
 "IMAGE" 
 ) 
 . 
 build 
 ()); 
 // 
 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 
 : 
 // 
 No 
 problem 
 ! 
 Here 
 's the image in a cartoon style... 
 // 
 // 
 Content 
 written 
 to 
 : 
 resources 
 / 
 output 
 / 
 bw 
 - 
 example 
 - 
 image 
 . 
 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" 
 ) 
 // 
 generateImageMMFlashEditWithTextImg 
 demonstrates 
 editing 
 an 
 image 
 with 
 text 
 and 
 image 
 inputs 
 . 
 func 
 generateImageMMFlashEditWithTextImg 
 ( 
 w 
 io 
 . 
 Writer 
 ) 
 error 
 { 
 // 
 TODO 
 ( 
 developer 
 ): 
 Update 
 below 
 lines 
 outputFile 
 := 
 "bw-example-image.png" 
 inputFile 
 := 
 "example-image-eiffel-tower.png" 
 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 
 ) 
 } 
 image 
 , 
 err 
 := 
 os 
 . 
 ReadFile 
 ( 
 inputFile 
 ) 
 if 
 err 
 != 
 nil 
 { 
 return 
 fmt 
 . 
 Errorf 
 ( 
 "failed to read image: %w" 
 , 
 err 
 ) 
 } 
 modelName 
 := 
 "gemini-2.5-flash-image" 
 prompt 
 := 
 "Edit this image to make it look like a cartoon." 
 contents 
 := 
 [] 
 * 
 genai 
 . 
 Content 
 { 
 { 
 Role 
 : 
 "user" 
 , 
 Parts 
 : 
 [] 
 * 
 genai 
 . 
 Part 
 { 
 { 
 Text 
 : 
 prompt 
 }, 
 { 
 InlineData 
 : 
& genai 
 . 
 Blob 
 { 
 MIMEType 
 : 
 "image/png" 
 , 
 Data 
 : 
 image 
 , 
 }}, 
 }, 
 }, 
 } 
 resp 
 , 
 err 
 := 
 client 
 . 
 Models 
 . 
 GenerateContent 
 ( 
 ctx 
 , 
 modelName 
 , 
 contents 
 , 
& genai 
 . 
 GenerateContentConfig 
 { 
 ResponseModalities 
 : 
 [] 
 string 
 { 
 string 
 ( 
 genai 
 . 
 ModalityText 
 ), 
 string 
 ( 
 genai 
 . 
 ModalityImage 
 ), 
 }, 
 }, 
 ) 
 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 content was generated" 
 ) 
 } 
 for 
 _ 
 , 
 part 
 := 
 range 
 resp 
 . 
 Candidates 
 [ 
 0 
 ] 
 . 
 Content 
 . 
 Parts 
 { 
 if 
 part 
 . 
 Text 
 != 
 "" 
 { 
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
 part 
 . 
 Text 
 ) 
 } 
 else 
 if 
 part 
 . 
 InlineData 
 != 
 nil 
 { 
 if 
 len 
 ( 
 part 
 . 
 InlineData 
 . 
 Data 
 ) 
> 0 
 { 
 if 
 err 
 := 
 os 
 . 
 WriteFile 
 ( 
 outputFile 
 , 
 part 
 . 
 InlineData 
 . 
 Data 
 , 
 0644 
 ); 
 err 
 != 
 nil 
 { 
 return 
 fmt 
 . 
 Errorf 
 ( 
 "failed to save image: %w" 
 , 
 err 
 ) 
 } 
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
 outputFile 
 ) 
 } 
 } 
 } 
 // 
 Example 
 response 
 : 
 // 
 Here 
 's the image of the Eiffel Tower and fireworks, cartoonized for you! 
 // 
 Cartoon 
 - 
 style 
 edit 
 : 
 // 
 - 
 Simplified 
 the 
 Eiffel 
 Tower 
 with 
 bolder 
 lines 
 and 
 slightly 
 exaggerated 
 proportions 
 . 
 // 
 - 
 Brightened 
 and 
 saturated 
 the 
 colors 
 of 
 the 
 sky 
 , 
 fireworks 
 , 
 and 
 foliage 
 for 
 a 
 more 
 vibrant 
 , 
 cartoonish 
 look 
 . 
 // 
 .... 
 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' 
 ; 
 const 
  
 FILE_NAME 
  
 = 
  
 'test-data/example-image-eiffel-tower.png' 
 ; 
 async 
  
 function 
  
 generateImage 
 ( 
  
 projectId 
  
 = 
  
 GOOGLE_CLOUD_PROJECT 
 , 
  
 location 
  
 = 
  
 GOOGLE_CLOUD_LOCATION 
 ) 
  
 { 
  
 const 
  
 client 
  
 = 
  
 new 
  
 GoogleGenAI 
 ({ 
  
 vertexai 
 : 
  
 true 
 , 
  
 project 
 : 
  
 projectId 
 , 
  
 location 
 : 
  
 location 
 , 
  
 }); 
  
 const 
  
 imageBytes 
  
 = 
  
 fs 
 . 
 readFileSync 
 ( 
 FILE_NAME 
 ); 
  
 const 
  
 response 
  
 = 
  
 await 
  
 client 
 . 
 models 
 . 
 generateContent 
 ({ 
  
 model 
 : 
  
 'gemini-2.5-flash-image' 
 , 
  
 contents 
 : 
  
 [ 
  
 { 
  
 role 
 : 
  
 'user' 
 , 
  
 parts 
 : 
  
 [ 
  
 { 
  
 inlineData 
 : 
  
 { 
  
 mimeType 
 : 
  
 'image/png' 
 , 
  
 data 
 : 
  
 imageBytes 
 . 
 toString 
 ( 
 'base64' 
 ), 
  
 }, 
  
 }, 
  
 { 
  
 text 
 : 
  
 'Edit this image to make it look like a cartoon' 
 , 
  
 }, 
  
 ], 
  
 }, 
  
 ], 
  
 config 
 : 
  
 { 
  
 responseModalities 
 : 
  
 [ 
 Modality 
 . 
 TEXT 
 , 
  
 Modality 
 . 
 IMAGE 
 ], 
  
 }, 
  
 }); 
  
 for 
  
 ( 
 const 
  
 part 
  
 of 
  
 response 
 . 
 candidates 
 [ 
 0 
 ] 
 . 
 content 
 . 
 parts 
 ) 
  
 { 
  
 if 
  
 ( 
 part 
 . 
 text 
 ) 
  
 { 
  
 console 
 . 
 log 
 ( 
 ` 
 $ 
 { 
 part 
 . 
 text 
 } 
 ` 
 ); 
  
 } 
  
 else 
  
 if 
  
 ( 
 part 
 . 
 inlineData 
 ) 
  
 { 
  
 const 
  
 outputDir 
  
 = 
  
 'output-folder' 
 ; 
  
 if 
  
 ( 
 ! 
 fs 
 . 
 existsSync 
 ( 
 outputDir 
 )) 
  
 { 
  
 fs 
 . 
 mkdirSync 
 ( 
 outputDir 
 , 
  
 { 
 recursive 
 : 
  
 true 
 }); 
  
 } 
  
 const 
  
 imageBytes 
  
 = 
  
 Buffer 
 . 
 from 
 ( 
 part 
 . 
 inlineData 
 . 
 data 
 , 
  
 'base64' 
 ); 
  
 const 
  
 filename 
  
 = 
  
 ` 
 $ 
 { 
 outputDir 
 } 
 / 
 bw 
 - 
 example 
 - 
 image 
 . 
 png 
 ` 
 ; 
  
 fs 
 . 
 writeFileSync 
 ( 
 filename 
 , 
  
 imageBytes 
 ); 
  
 } 
  
 } 
  
 // 
  
 Example 
  
 response 
 : 
  
 // 
  
 Okay 
 , 
  
 I 
  
 will 
  
 edit 
  
 this 
  
 image 
  
 to 
  
 give 
  
 it 
  
 a 
  
 cartoonish 
  
 style 
 , 
  
 with 
  
 bolder 
  
 outlines 
 , 
  
 simplified 
  
 details 
 , 
  
 and 
  
 more 
  
 vibrant 
  
 colors 
 . 
  
 return 
  
 response 
 ; 
 }

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": [ 
 {"fileData": { 
 "mimeType": "image/jpg", 
 "fileUri": " FILE_NAME 
" 
 } 
 }, 
 {"text": "Convert this photo to black and white, in a cartoonish style."}, 
 ] 
 }, 
 "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 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.

Multi-turn image editing

Gemini 2.5 Flash Image and Gemini 3 Pro Image support improved multi-turn editing, which lets you respond to the model with changes after receiving an edited image response.

We recommended limiting the entire request file size to 50MB maximum.

To test out multi-turn image editing, see the following notebooks:

To see code samples related to multi-turn image creation and editing using Gemini 3 Pro Image, see Example of multi-turn image editing using thought signatures .

What's next?

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

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

Edit an image

Console

Python

Install

Java

Go

Node.js

Install

REST

Multi-turn image editing

What's next?

Edit images with Gemini