在 Amazon Bedrock 上调用 Amazon Titan Image 来生成图片

以下代码示例展示了如何在 Amazon Bedrock 上调用 Amazon Titan Image 来生成图像。

Go

SDK适用于 Go V2

注意

还有更多相关信息 GitHub。查找完整示例，学习如何在 AWS 代码示例存储库中进行设置和运行。

使用 Amazon Titan 图像生成器创建图片。

type TitanImageRequest struct {
	TaskType              string                `json:"taskType"`
	TextToImageParams     TextToImageParams     `json:"textToImageParams"`
	ImageGenerationConfig ImageGenerationConfig `json:"imageGenerationConfig"`
}
type TextToImageParams struct {
	Text string `json:"text"`
}
type ImageGenerationConfig struct {
	NumberOfImages int     `json:"numberOfImages"`
	Quality        string  `json:"quality"`
	CfgScale       float64 `json:"cfgScale"`
	Height         int     `json:"height"`
	Width          int     `json:"width"`
	Seed           int64   `json:"seed"`
}

type TitanImageResponse struct {
	Images []string `json:"images"`
}

// Invokes the Titan Image model to create an image using the input provided
// in the request body.
func (wrapper InvokeModelWrapper) InvokeTitanImage(prompt string, seed int64) (string, error) {
	modelId := "amazon.titan-image-generator-v1"

	body, err := json.Marshal(TitanImageRequest{
		TaskType: "TEXT_IMAGE",
		TextToImageParams: TextToImageParams{
			Text: prompt,
		},
		ImageGenerationConfig: ImageGenerationConfig{
			NumberOfImages: 1,
			Quality:        "standard",
			CfgScale:       8.0,
			Height:         512,
			Width:          512,
			Seed:           seed,
		},
	})

	if err != nil {
		log.Fatal("failed to marshal", err)
	}

	output, err := wrapper.BedrockRuntimeClient.InvokeModel(context.TODO(), &bedrockruntime.InvokeModelInput{
		ModelId:     aws.String(modelId),
		ContentType: aws.String("application/json"),
		Body:        body,
	})

	if err != nil {
		ProcessError(err, modelId)
	}

	var response TitanImageResponse
	if err := json.Unmarshal(output.Body, &response); err != nil {
		log.Fatal("failed to unmarshal", err)
	}

	base64ImageData := response.Images[0]

	return base64ImageData, nil

}

• 有关API详细信息，请参阅 “AWS SDK for Go API参考 InvokeModel” 中的。

Java

SDK适用于 Java 2.x

注意

还有更多相关信息 GitHub。查找完整示例，学习如何在 AWS 代码示例存储库中进行设置和运行。

使用 Amazon Titan 图像生成器创建图片。

// Create an image with the Amazon Titan Image Generator.

import org.json.JSONObject;
import org.json.JSONPointer;
import software.amazon.awssdk.auth.credentials.DefaultCredentialsProvider;
import software.amazon.awssdk.core.SdkBytes;
import software.amazon.awssdk.core.exception.SdkClientException;
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.bedrockruntime.BedrockRuntimeClient;

import java.math.BigInteger;
import java.security.SecureRandom;

import static com.example.bedrockruntime.libs.ImageTools.displayImage;

public class InvokeModel {

    public static String invokeModel() {

        // Create a Bedrock Runtime client in the AWS Region you want to use.
        // Replace the DefaultCredentialsProvider with your preferred credentials provider.
        var client = BedrockRuntimeClient.builder()
                .credentialsProvider(DefaultCredentialsProvider.create())
                .region(Region.US_EAST_1)
                .build();

        // Set the model ID, e.g., Titan Image G1.
        var modelId = "amazon.titan-image-generator-v1";

        // The InvokeModel API uses the model's native payload.
        // Learn more about the available inference parameters and response fields at:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-image.html
        var nativeRequestTemplate = """
                {
                    "taskType": "TEXT_IMAGE",
                    "textToImageParams": { "text": "{{prompt}}" },
                    "imageGenerationConfig": { "seed": {{seed}} }
                }""";

        // Define the prompt for the image generation.
        var prompt = "A stylized picture of a cute old steampunk robot";

        // Get a random 31-bit seed for the image generation (max. 2,147,483,647).
        var seed = new BigInteger(31, new SecureRandom());

        // Embed the prompt and seed in the model's native request payload.
        var nativeRequest = nativeRequestTemplate
                .replace("{{prompt}}", prompt)
                .replace("{{seed}}", seed.toString());

        try {
            // Encode and send the request to the Bedrock Runtime.
            var response = client.invokeModel(request -> request
                    .body(SdkBytes.fromUtf8String(nativeRequest))
                    .modelId(modelId)
            );

            // Decode the response body.
            var responseBody = new JSONObject(response.body().asUtf8String());

            // Retrieve the generated image data from the model's response.
            var base64ImageData = new JSONPointer("/images/0").queryFrom(responseBody).toString();

            return base64ImageData;

        } catch (SdkClientException e) {
            System.err.printf("ERROR: Can't invoke '%s'. Reason: %s", modelId, e.getMessage());
            throw new RuntimeException(e);
        }
    }

    public static void main(String[] args) {
        System.out.println("Generating image. This may take a few seconds...");

        String base64ImageData = invokeModel();

        displayImage(base64ImageData);
    }
}

• 有关API详细信息，请参阅 “AWS SDK for Java 2.x API参考 InvokeModel” 中的。

PHP

SDK for PHP

注意

还有更多相关信息 GitHub。查找完整示例，学习如何在 AWS 代码示例存储库中进行设置和运行。

使用 Amazon Titan 图像生成器创建图片。

    public function invokeTitanImage(string $prompt, int $seed)
    {
        # The different model providers have individual request and response formats.
        # For the format, ranges, and default values for Titan Image models refer to:
        # https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-image.html

        $base64_image_data = "";

        try {
            $modelId = 'amazon.titan-image-generator-v1';

            $request = json_encode([
                'taskType' => 'TEXT_IMAGE',
                'textToImageParams' => [
                    'text' => $prompt
                ],
                'imageGenerationConfig' => [
                    'numberOfImages' => 1,
                    'quality' => 'standard',
                    'cfgScale' => 8.0,
                    'height' => 512,
                    'width' => 512,
                    'seed' => $seed
                ]
            ]);

            $result = $this->bedrockRuntimeClient->invokeModel([
                'contentType' => 'application/json',
                'body' => $request,
                'modelId' => $modelId,
            ]);

            $response_body = json_decode($result['body']);

            $base64_image_data = $response_body->images[0];
        } catch (Exception $e) {
            echo "Error: ({$e->getCode()}) - {$e->getMessage()}\n";
        }

        return $base64_image_data;
    }

• 有关API详细信息，请参阅 “AWS SDK for PHP API参考 InvokeModel” 中的。

Python

SDK适用于 Python (Boto3)

注意

还有更多相关信息 GitHub。查找完整示例，学习如何在 AWS 代码示例存储库中进行设置和运行。

使用 Amazon Titan 图像生成器创建图片。

# Use the native inference API to create an image with Amazon Titan Image Generator

import base64
import boto3
import json
import os
import random

# Create a Bedrock Runtime client in the AWS Region of your choice.
client = boto3.client("bedrock-runtime", region_name="us-east-1")

# Set the model ID, e.g., Titan Image Generator G1.
model_id = "amazon.titan-image-generator-v1"

# Define the image generation prompt for the model.
prompt = "A stylized picture of a cute old steampunk robot."

# Generate a random seed.
seed = random.randint(0, 2147483647)

# Format the request payload using the model's native structure.
native_request = {
    "taskType": "TEXT_IMAGE",
    "textToImageParams": {"text": prompt},
    "imageGenerationConfig": {
        "numberOfImages": 1,
        "quality": "standard",
        "cfgScale": 8.0,
        "height": 512,
        "width": 512,
        "seed": seed,
    },
}

# Convert the native request to JSON.
request = json.dumps(native_request)

# Invoke the model with the request.
response = client.invoke_model(modelId=model_id, body=request)

# Decode the response body.
model_response = json.loads(response["body"].read())

# Extract the image data.
base64_image_data = model_response["images"][0]

# Save the generated image to a local folder.
i, output_dir = 1, "output"
if not os.path.exists(output_dir):
    os.makedirs(output_dir)
while os.path.exists(os.path.join(output_dir, f"titan_{i}.png")):
    i += 1

image_data = base64.b64decode(base64_image_data)

image_path = os.path.join(output_dir, f"titan_{i}.png")
with open(image_path, "wb") as file:
    file.write(image_data)

print(f"The generated image has been saved to {image_path}")

• 有关API详细信息，请参阅InvokeModel中的 AWS SDKPython (Boto3) API 参考。

有关 AWS SDK开发者指南和代码示例的完整列表，请参阅将此服务与 AWS SDK。本主题还包括有关入门的信息以及有关先前SDK版本的详细信息。