在 Amazon Bedrock 上叫用 Stability.ai 穩定擴散 XL 以產生映像

使用穩定擴散建立映像。

// Create an image with Stable Diffusion.

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., Stable Diffusion XL v1.
        var modelId = "stability.stable-diffusion-xl-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-diffusion-1-0-text-image.html
        var nativeRequestTemplate = """
                {
                    "text_prompts": [{ "text": "{{prompt}}" }],
                    "style_preset": "{{style}}",
                    "seed": {{seed}}
                }""";

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

        // Get a random 32-bit seed for the image generation (max. 4,294,967,295).
        var seed = new BigInteger(31, new SecureRandom());

        // Choose a style preset.
        var style = "cinematic";

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

        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("/artifacts/0/base64")
                    .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);
    }


}

使用穩定擴散建立映像。

    public function invokeStableDiffusion(string $prompt, int $seed, string $style_preset)
    {
        // The different model providers have individual request and response formats.
        // For the format, ranges, and available style_presets of Stable Diffusion models refer to:
        // https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-stability-diffusion.html

        $base64_image_data = "";
        try {
            $modelId = 'stability.stable-diffusion-xl-v1';
            $body = [
                'text_prompts' => [
                    ['text' => $prompt]
                ],
                'seed' => $seed,
                'cfg_scale' => 10,
                'steps' => 30
            ];
            if ($style_preset) {
                $body['style_preset'] = $style_preset;
            }

            $result = $this->bedrockRuntimeClient->invokeModel([
                'contentType' => 'application/json',
                'body' => json_encode($body),
                'modelId' => $modelId,
            ]);
            $response_body = json_decode($result['body']);
            $base64_image_data = $response_body->artifacts[0]->base64;
        } catch (Exception $e) {
            echo "Error: ({$e->getCode()}) - {$e->getMessage()}\n";
        }

        return $base64_image_data;
    }

使用穩定擴散建立映像。

# Use the native inference API to create an image with Stability.ai Stable Diffusion

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., Stable Diffusion XL 1.
model_id = "stability.stable-diffusion-xl-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, 4294967295)

# Format the request payload using the model's native structure.
native_request = {
    "text_prompts": [{"text": prompt}],
    "style_preset": "photographic",
    "seed": seed,
    "cfg_scale": 10,
    "steps": 30,
}

# 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["artifacts"][0]["base64"]

# 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"stability_{i}.png")):
    i += 1

image_data = base64.b64decode(base64_image_data)

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

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

使用穩定擴散建立映像。

    "Stable Diffusion Input Parameters should be in a format like this:
*   {
*     "text_prompts": [
*       {"text":"Draw a dolphin with a mustache"},
*       {"text":"Make it photorealistic"}
*     ],
*     "cfg_scale":10,
*     "seed":0,
*     "steps":50
*   }
    TYPES: BEGIN OF prompt_ts,
             text TYPE /aws1/rt_shape_string,
           END OF prompt_ts.

    DATA: BEGIN OF ls_input,
            text_prompts TYPE STANDARD TABLE OF prompt_ts,
            cfg_scale    TYPE /aws1/rt_shape_integer,
            seed         TYPE /aws1/rt_shape_integer,
            steps        TYPE /aws1/rt_shape_integer,
          END OF ls_input.

    APPEND VALUE prompt_ts( text = iv_prompt ) TO ls_input-text_prompts.
    ls_input-cfg_scale = 10.
    ls_input-seed = 0. "or better, choose a random integer.
    ls_input-steps = 50.

    DATA(lv_json) = /ui2/cl_json=>serialize(
      data = ls_input
                pretty_name   = /ui2/cl_json=>pretty_mode-low_case ).

    TRY.
        DATA(lo_response) = lo_bdr->invokemodel(
          iv_body = /aws1/cl_rt_util=>string_to_xstring( lv_json )
          iv_modelid = 'stability.stable-diffusion-xl-v1'
          iv_accept = 'application/json'
          iv_contenttype = 'application/json' ).

        "Stable Diffusion Result Format:
*       {
*         "result": "success",
*         "artifacts": [
*           {
*             "seed": 0,
*             "base64": "iVBORw0KGgoAAAANSUhEUgAAAgAAA....
*             "finishReason": "SUCCESS"
*           }
*         ]
*       }
        TYPES: BEGIN OF artifact_ts,
                 seed         TYPE /aws1/rt_shape_integer,
                 base64       TYPE /aws1/rt_shape_string,
                 finishreason TYPE /aws1/rt_shape_string,
               END OF artifact_ts.

        DATA: BEGIN OF ls_response,
                result    TYPE /aws1/rt_shape_string,
                artifacts TYPE STANDARD TABLE OF artifact_ts,
              END OF ls_response.

        /ui2/cl_json=>deserialize(
          EXPORTING jsonx = lo_response->get_body( )
                    pretty_name = /ui2/cl_json=>pretty_mode-camel_case
          CHANGING  data  = ls_response ).
        IF ls_response-artifacts IS NOT INITIAL.
          DATA(lv_image) = cl_http_utility=>if_http_utility~decode_x_base64( ls_response-artifacts[ 1 ]-base64 ).
        ENDIF.
      CATCH /aws1/cx_bdraccessdeniedex INTO DATA(lo_ex).
        WRITE / lo_ex->get_text( ).
        WRITE / |Don't forget to enable model access at https://console.aws.amazon.com/bedrock/home?#/modelaccess|.

    ENDTRY.

叫用 Stability.ai 穩定擴散 XL 基礎模型，以使用 L2 高階用戶端產生映像。

    TRY.
        DATA(lo_bdr_l2_sd) = /aws1/cl_bdr_l2_factory=>create_stable_diffusion_xl_1( lo_bdr ).
        " iv_prompt contains a prompt like 'Show me a picture of a unicorn reading an enterprise financial report'.
        DATA(lv_image) = lo_bdr_l2_sd->text_to_image( iv_prompt ).
      CATCH /aws1/cx_bdraccessdeniedex INTO DATA(lo_ex).
        WRITE / lo_ex->get_text( ).
        WRITE / |Don't forget to enable model access at https://console.aws.amazon.com/bedrock/home?#/modelaccess|.

    ENDTRY.