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    Generate Videos from Images using Veo

    Create videos from images, using Veo , a generative AI model for video generation.

    Explore further

    For detailed documentation that includes this code sample, see the following:

    Code sample

    Go

    Before trying this sample, follow the Go setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Go API reference documentation .

    To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      import 
  
 ( 
  
 "context" 
  
 "fmt" 
  
 "io" 
  
 "time" 
  
 "google.golang.org/genai" 
 ) 
 // generateVideoFromImage shows how to gen video from img. 
 func 
  
 generateVideoFromImage 
 ( 
 w 
  
 io 
 . 
 Writer 
 , 
  
 outputGCSURI 
  
 string 
 ) 
  
 error 
  
 { 
  
 //outputGCSURI = "gs://your-bucket/your-prefix" 
  
 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 
  
 := 
  
& genai 
 . 
 Image 
 { 
  
 GCSURI 
 : 
  
 "gs://cloud-samples-data/generative-ai/image/flowers.png" 
 , 
  
 MIMEType 
 : 
  
 "image/png" 
 , 
  
 } 
  
 config 
  
 := 
  
& genai 
 . 
 GenerateVideosConfig 
 { 
  
 AspectRatio 
 : 
  
 "16:9" 
 , 
  
 OutputGCSURI 
 : 
  
 outputGCSURI 
 , 
  
 } 
  
 modelName 
  
 := 
  
 "veo-3.0-generate-preview" 
  
 prompt 
  
 := 
  
 "Extreme close-up of a cluster of vibrant wildflowers swaying gently in a sun-drenched meadow." 
  
 operation 
 , 
  
 err 
  
 := 
  
 client 
 . 
 Models 
 . 
 GenerateVideos 
 ( 
 ctx 
 , 
  
 modelName 
 , 
  
 prompt 
 , 
  
 image 
 , 
  
 config 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "failed to start video generation: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 // Polling until the operation is done 
  
 for 
  
 ! 
 operation 
 . 
 Done 
  
 { 
  
 time 
 . 
 Sleep 
 ( 
 15 
  
 * 
  
 time 
 . 
 Second 
 ) 
  
 operation 
 , 
  
 err 
  
 = 
  
 client 
 . 
 Operations 
 . 
 GetVideosOperation 
 ( 
 ctx 
 , 
  
 operation 
 , 
  
 nil 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "failed to get operation status: %w" 
 , 
  
 err 
 ) 
  
 } 
  
 } 
  
 if 
  
 operation 
 . 
 Response 
  
 != 
  
 nil 
 && 
 len 
 ( 
 operation 
 . 
 Response 
 . 
 GeneratedVideos 
 ) 
 > 
 0 
  
 { 
  
 videoURI 
  
 := 
  
 operation 
 . 
 Response 
 . 
 GeneratedVideos 
 [ 
 0 
 ]. 
 Video 
 . 
 URI 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 videoURI 
 ) 
  
 return 
  
 nil 
  
 } 
  
 // Example response: 
  
 // gs://your-bucket/your-prefix/videoURI 
  
 return 
  
 fmt 
 . 
 Errorf 
 ( 
 "video generation failed or returned no results" 
 ) 
 }

    Java

    Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Java API reference documentation .

    To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      import 
  
 com.google.genai.Client 
 ; 
 import 
  
 com.google.genai.types.GenerateVideosConfig 
 ; 
 import 
  
 com.google.genai.types.GenerateVideosOperation 
 ; 
 import 
  
 com.google.genai.types.GenerateVideosResponse 
 ; 
 import 
  
 com.google.genai.types.GeneratedVideo 
 ; 
 import 
  
 com.google.genai.types.GetOperationConfig 
 ; 
 import 
  
 com.google.genai.types.Image 
 ; 
 import 
  
 com.google.genai.types.Video 
 ; 
 import 
  
 java.util.concurrent.TimeUnit 
 ; 
 public 
  
 class 
 VideoGenWithImg 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 InterruptedException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 modelId 
  
 = 
  
 "veo-3.0-generate-preview" 
 ; 
  
 String 
  
 outputGcsUri 
  
 = 
  
 "gs://your-bucket/your-prefix" 
 ; 
  
 generateContent 
 ( 
 modelId 
 , 
  
 outputGcsUri 
 ); 
  
 } 
  
 // Generates a video with an image and a text prompt. 
  
 public 
  
 static 
  
 String 
  
 generateContent 
 ( 
 String 
  
 modelId 
 , 
  
 String 
  
 outputGcsUri 
 ) 
  
 throws 
  
 InterruptedException 
  
 { 
  
 // Client Initialization. Once created, it can be reused for multiple requests. 
  
 try 
  
 ( 
 Client 
  
 client 
  
 = 
  
 Client 
 . 
 builder 
 (). 
 location 
 ( 
 "global" 
 ). 
 vertexAI 
 ( 
 true 
 ). 
 build 
 ()) 
  
 { 
  
 GenerateVideosOperation 
  
 operation 
  
 = 
  
 client 
 . 
 models 
 . 
 generateVideos 
 ( 
  
 modelId 
 , 
  
 "Extreme close-up of a cluster of vibrant wildflowers" 
  
 + 
  
 " swaying gently in a sun-drenched meadow." 
 , 
  
 Image 
 . 
 builder 
 () 
  
 . 
 gcsUri 
 ( 
 "gs://cloud-samples-data/generative-ai/image/flowers.png" 
 ) 
  
 . 
 mimeType 
 ( 
 "image/png" 
 ) 
  
 . 
 build 
 (), 
  
 GenerateVideosConfig 
 . 
 builder 
 () 
  
 . 
 aspectRatio 
 ( 
 "16:9" 
 ) 
  
 . 
 outputGcsUri 
 ( 
 outputGcsUri 
 ) 
  
 . 
 build 
 ()); 
  
 while 
  
 ( 
 ! 
 operation 
 . 
 done 
 (). 
 orElse 
 ( 
 false 
 )) 
  
 { 
  
 TimeUnit 
 . 
 SECONDS 
 . 
 sleep 
 ( 
 15 
 ); 
  
 operation 
  
 = 
  
 client 
 . 
 operations 
 . 
 getVideosOperation 
 ( 
 operation 
 , 
  
 GetOperationConfig 
 . 
 builder 
 (). 
 build 
 ()); 
  
 } 
  
 String 
  
 generatedVideoUri 
  
 = 
  
 operation 
  
 . 
 response 
 () 
  
 . 
 flatMap 
 ( 
 GenerateVideosResponse 
 :: 
 generatedVideos 
 ) 
  
 . 
 flatMap 
 ( 
 videos 
  
 - 
>  
 videos 
 . 
 stream 
 (). 
 findFirst 
 ()) 
  
 . 
 flatMap 
 ( 
 GeneratedVideo 
 :: 
 video 
 ) 
  
 . 
 flatMap 
 ( 
 Video 
 :: 
 uri 
 ) 
  
 . 
 orElseThrow 
 ( 
  
 () 
  
 - 
>  
 new 
  
 IllegalStateException 
 ( 
  
 "Could not get the URI from the generated video" 
 )); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Generated video URI: " 
  
 + 
  
 generatedVideoUri 
 ); 
  
 // Example response: 
  
 // Generated video URI: gs://your-bucket/your-prefix/generated-video-123.mp4 
  
 return 
  
 generatedVideoUri 
 ; 
  
 } 
  
 } 
 }

    Node.js

    Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Node.js API reference documentation .

    To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      const 
  
 { 
 GoogleGenAI 
 } 
  
 = 
  
 require 
 ( 
 '@google/genai' 
 ); 
 const 
  
 GOOGLE_CLOUD_PROJECT 
  
 = 
  
 process 
 . 
 env 
 . 
 GOOGLE_CLOUD_PROJECT 
 ; 
 const 
  
 GOOGLE_CLOUD_LOCATION 
  
 = 
  
 process 
 . 
 env 
 . 
 GOOGLE_CLOUD_LOCATION 
  
 || 
  
 'global' 
 ; 
 async 
  
 function 
  
 generateVideo 
 ( 
  
 outputGcsUri 
 , 
  
 projectId 
  
 = 
  
 GOOGLE_CLOUD_PROJECT 
 , 
  
 location 
  
 = 
  
 GOOGLE_CLOUD_LOCATION 
 ) 
  
 { 
  
 const 
  
 client 
  
 = 
  
 new 
  
 GoogleGenAI 
 ({ 
  
 vertexai 
 : 
  
 true 
 , 
  
 project 
 : 
  
 projectId 
 , 
  
 location 
 : 
  
 location 
 , 
  
 }); 
  
 let 
  
 operation 
  
 = 
  
 await 
  
 client 
 . 
 models 
 . 
 generateVideos 
 ({ 
  
 model 
 : 
  
 'veo-3.1-fast-generate-001' 
 , 
  
 prompt 
 : 
  
 'Extreme close-up of a cluster of vibrant wildflowers swaying gently in a sun-drenched meadow' 
 , 
  
 image 
 : 
  
 { 
  
 gcsUri 
 : 
  
 'gs://cloud-samples-data/generative-ai/image/flowers.png' 
 , 
  
 mimeType 
 : 
  
 'image/png' 
 , 
  
 }, 
  
 config 
 : 
  
 { 
  
 aspectRatio 
 : 
  
 '16:9' 
 , 
  
 outputGcsUri 
 : 
  
 outputGcsUri 
 , 
  
 }, 
  
 }); 
  
 while 
  
 ( 
 ! 
 operation 
 . 
 done 
 ) 
  
 { 
  
 await 
  
 new 
  
 Promise 
 ( 
 resolve 
  
 = 
>  
 setTimeout 
 ( 
 resolve 
 , 
  
 15000 
 )); 
  
 operation 
  
 = 
  
 await 
  
 client 
 . 
 operations 
 . 
 get 
 ({ 
 operation 
 : 
  
 operation 
 }); 
  
 console 
 . 
 log 
 ( 
 operation 
 ); 
  
 } 
  
 if 
  
 ( 
 operation 
 . 
 response 
 ) 
  
 { 
  
 console 
 . 
 log 
 ( 
 operation 
 . 
 response 
 . 
 generatedVideos 
 [ 
 0 
 ]. 
 video 
 . 
 uri 
 ); 
  
 } 
  
 return 
  
 operation 
 ; 
 }

    Python

    Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Python API reference documentation .

    To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      import 
  
 time 
 from 
  
 google 
  
 import 
 genai 
 from 
  
 google.genai.types 
  
 import 
 GenerateVideosConfig 
 , 
 Image 
 client 
 = 
 genai 
 . 
 Client 
 () 
 # TODO(developer): Update and un-comment below line 
 # output_gcs_uri = "gs://your-bucket/your-prefix" 
 operation 
 = 
 client 
 . 
 models 
 . 
 generate_videos 
 ( 
 model 
 = 
 "veo-3.1-generate-001" 
 , 
 prompt 
 = 
 "Extreme close-up of a cluster of vibrant wildflowers swaying gently in a sun-drenched meadow." 
 , 
 image 
 = 
 Image 
 ( 
 gcs_uri 
 = 
 "gs://cloud-samples-data/generative-ai/image/flowers.png" 
 , 
 mime_type 
 = 
 "image/png" 
 , 
 ), 
 config 
 = 
 GenerateVideosConfig 
 ( 
 aspect_ratio 
 = 
 "16:9" 
 , 
 output_gcs_uri 
 = 
 output_gcs_uri 
 , 
 ), 
 ) 
 while 
 not 
 operation 
 . 
 done 
 : 
 time 
 . 
 sleep 
 ( 
 15 
 ) 
 operation 
 = 
 client 
 . 
 operations 
 . 
 get 
 ( 
 operation 
 ) 
 print 
 ( 
 operation 
 ) 
 if 
 operation 
 . 
 response 
 : 
 print 
 ( 
 operation 
 . 
 result 
 . 
 generated_videos 
 [ 
 0 
 ] 
 . 
 video 
 . 
 uri 
 ) 
 # Example response: 
 # gs://your-bucket/your-prefix

    What's next

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