Verwenden Sie Amazon Nova Reel, um ein Video aus einer Textaufforderung zu generieren

Verwenden Sie Amazon Nova Reel, um ein Video aus einer Textaufforderung zu generieren.

"""
This example demonstrates how to use Amazon Nova Reel to generate a video from a text prompt.

It shows how to:
- Set up the Amazon Bedrock runtime client
- Configure a text-to-video request
- Submit an asynchronous job for video generation
- Poll for job completion status
- Access the generated video from S3
"""

import random
import time

import boto3

# Replace with your own S3 bucket to store the generated video
# Format: s3://your-bucket-name
OUTPUT_S3_URI = "s3://REPLACE-WITH-YOUR-S3-BUCKET-NAME"


def start_text_to_video_generation_job(bedrock_runtime, prompt, output_s3_uri):
    """
    Starts an asynchronous text-to-video generation job using Amazon Nova Reel.

    :param bedrock_runtime: The Bedrock runtime client
    :param prompt: The text description of the video to generate
    :param output_s3_uri: S3 URI where the generated video will be stored

    :return: The invocation ARN of the async job
    """
    # Specify the model ID for text-to-video generation
    model_id = "amazon.nova-reel-v1:0"

    # Generate a random seed between 0 and 2,147,483,646
    # This helps ensure unique video generation results
    seed = random.randint(0, 2147483646)

    # Configure the video generation request with additional parameters
    model_input = {
        "taskType": "TEXT_VIDEO",
        "textToVideoParams": {"text": prompt},
        "videoGenerationConfig": {
            "fps": 24,
            "durationSeconds": 6,
            "dimension": "1280x720",
            "seed": seed,
        },
    }

    # Specify the S3 location for the output video
    output_config = {"s3OutputDataConfig": {"s3Uri": output_s3_uri}}

    # Invoke the model asynchronously
    response = bedrock_runtime.start_async_invoke(
        modelId=model_id, modelInput=model_input, outputDataConfig=output_config
    )

    invocation_arn = response["invocationArn"]

    return invocation_arn


def query_job_status(bedrock_runtime, invocation_arn):
    """
    Queries the status of an asynchronous video generation job.

    :param bedrock_runtime: The Bedrock runtime client
    :param invocation_arn: The ARN of the async invocation to check

    :return: The runtime response containing the job status and details
    """
    return bedrock_runtime.get_async_invoke(invocationArn=invocation_arn)


def main():
    """
    Main function that demonstrates the complete workflow for generating
    a video from a text prompt using Amazon Nova Reel.
    """
    # Create a Bedrock Runtime client
    # Note: Credentials will be loaded from the environment or AWS CLI config
    bedrock_runtime = boto3.client("bedrock-runtime", region_name="us-east-1")

    # Configure the text prompt and output location
    prompt = "Closeup of a cute old steampunk robot. Camera zoom in."

    # Verify the S3 URI has been set to a valid bucket
    if "REPLACE-WITH-YOUR-S3-BUCKET-NAME" in OUTPUT_S3_URI:
        print("ERROR: You must replace the OUTPUT_S3_URI with your own S3 bucket URI")
        return

    print("Submitting video generation job...")
    invocation_arn = start_text_to_video_generation_job(
        bedrock_runtime, prompt, OUTPUT_S3_URI
    )
    print(f"Job started with invocation ARN: {invocation_arn}")

    # Poll for job completion
    while True:
        print("\nPolling job status...")
        job = query_job_status(bedrock_runtime, invocation_arn)
        status = job["status"]

        if status == "Completed":
            bucket_uri = job["outputDataConfig"]["s3OutputDataConfig"]["s3Uri"]
            print(f"\nSuccess! The video is available at: {bucket_uri}/output.mp4")
            break
        elif status == "Failed":
            print(
                f"\nVideo generation failed: {job.get('failureMessage', 'Unknown error')}"
            )
            break
        else:
            print("In progress. Waiting 15 seconds...")
            time.sleep(15)


if __name__ == "__main__":
    main()

Verwenden Sie Amazon Nova Reel, um ein Video aus einer Textaufforderung zu generieren.

// This example demonstrates how to use Amazon Nova Reel to generate a video from a text prompt.
// It shows how to:
// - Set up the Amazon Bedrock runtime client
// - Configure a text-to-video request
// - Submit an asynchronous job for video generation
// - Poll for job completion status
// - Access the generated video from S3

import AWSBedrockRuntime
import Foundation
import Smithy

func startTextToVideoGenerationJob(
    bedrockRuntimeClient: BedrockRuntimeClient, prompt: String, outputS3Uri: String
) async throws -> String? {
    // Specify the model ID for text-to-video generation
    let modelId = "amazon.nova-reel-v1:0"

    // Configure the video generation request with additional parameters
    let modelInputSource: [String: Any] = [
        "taskType": "TEXT_VIDEO",
        "textToVideoParams": [
            "text": "\(prompt)"
        ],
        "videoGenerationConfig": [
            "durationSeconds": 6,
            "fps": 24,
            "dimension": "1280x720",
        ],
    ]

    let modelInput = try Document.make(from: modelInputSource)

    let input = StartAsyncInvokeInput(
        modelId: modelId,
        modelInput: modelInput,
        outputDataConfig: .s3outputdataconfig(
            BedrockRuntimeClientTypes.AsyncInvokeS3OutputDataConfig(
                s3Uri: outputS3Uri
            )
        )
    )

    // Invoke the model asynchronously
    let output = try await bedrockRuntimeClient.startAsyncInvoke(input: input)
    return output.invocationArn
}

func queryJobStatus(
    bedrockRuntimeClient: BedrockRuntimeClient, 
    invocationArn: String?
) async throws -> GetAsyncInvokeOutput {
    try await bedrockRuntimeClient.getAsyncInvoke(
        input: GetAsyncInvokeInput(invocationArn: invocationArn))
}

func main() async throws {
    // Create a Bedrock Runtime client
    let config =
        try await BedrockRuntimeClient.BedrockRuntimeClientConfiguration(
            region: "us-east-1"
        )
    let client = BedrockRuntimeClient(config: config)

    // Specify the S3 location for the output video
    let bucket = "s3://REPLACE-WITH-YOUR-S3-BUCKET-NAM"

    print("Submitting video generation job...")
    let invocationArn = try await startTextToVideoGenerationJob(
        bedrockRuntimeClient: client,
        prompt: "A pomegranate juice in a railway station",
        outputS3Uri: bucket
    )
    print("Job started with invocation ARN: \(String(describing:invocationArn))")

    // Poll for job completion
    var status: BedrockRuntimeClientTypes.AsyncInvokeStatus?
    var isReady = false
    var hasFailed = false

    while !isReady && !hasFailed {
        print("\nPolling job status...")
        status = try await queryJobStatus(
            bedrockRuntimeClient: client, invocationArn: invocationArn
        ).status
        switch status {
        case .completed:
            isReady = true
            print("Video is ready\nCheck S3 bucket: \(bucket)")
        case .failed:
            hasFailed = true
            print("Something went wrong")
        case .inProgress:
            print("Job is in progress...")
            try await Task.sleep(nanoseconds: 15 * 1_000_000_000)  // 15 seconds
        default:
            isReady = true
        }
    }
}

do {
    try await main()
} catch {
    print("An error occurred: \(error)")
}