Amazon Bedrock에서 Stability.ai 스테이블 디퓨전 XL을 호출하여 이미지를 생성합니다.

다음 코드 예제는 Amazon Bedrock에서 Stability.ai 스테이블 디퓨전 XL을 호출하여 이미지를 생성하는 방법을 보여줍니다.

.NET

AWS SDK for .NET

참고

더 많은 정보가 있습니다 GitHub. AWS 코드 예제 리포지토리에서 전체 예제를 찾고 설정 및 실행하는 방법을 배워보세요.

Stability.ai 스테이블 디퓨전 XL 기반 모델을 비동기적으로 호출하여 이미지를 생성합니다.

        /// <summary>
        /// Asynchronously invokes the Stability.ai Stable Diffusion XLmodel to run an inference based on the provided input.
        /// </summary>
        /// <param name="prompt">The prompt that describes the image Stability.ai Stable Diffusion XL has to generate.</param>
        /// <returns>A base-64 encoded image generated by model</returns>
        /// <remarks>
        /// The different model providers have individual request and response formats.
        /// For the format, ranges, and default values for Stability.ai Stable Diffusion XL, refer to:
        ///     https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-stability-diffusion.html
        /// </remarks>
        public static async Task<string?> InvokeStableDiffusionXLG1Async(string prompt, int seed, string? stylePreset = null)
        {
            string stableDiffusionXLModelId = "stability.stable-diffusion-xl";

            AmazonBedrockRuntimeClient client = new(RegionEndpoint.USEast1);

            var jsonPayload = new JsonObject()
            {
                { "text_prompts", new JsonArray() {
                    new JsonObject()
                        {
                            { "text", prompt }
                        }
                    }
                },
                { "seed", seed }
            };

            if (!string.IsNullOrEmpty(stylePreset))
            {
                jsonPayload.Add("style_preset", stylePreset);
            }

            string payload = jsonPayload.ToString();

            try
            {
                InvokeModelResponse response = await client.InvokeModelAsync(new InvokeModelRequest()
                {
                    ModelId = stableDiffusionXLModelId,
                    Body = AWSSDKUtils.GenerateMemoryStreamFromString(payload),
                    ContentType = "application/json",
                    Accept = "application/json"
                });

                if (response.HttpStatusCode == System.Net.HttpStatusCode.OK)
                {
                    var results = JsonNode.ParseAsync(response.Body).Result?["artifacts"]?.AsArray();

                    return results?[0]?["base64"]?.GetValue<string>();
                }
                else
                {
                    Console.WriteLine("InvokeModelAsync failed with status code " + response.HttpStatusCode);
                }
            }
            catch (AmazonBedrockRuntimeException e)
            {
                Console.WriteLine(e.Message);
            }
            return null;
        }

• API 세부 정보는 API 참조를 참조하십시오. InvokeModelAWS SDK for .NET

Java

SDK for Java 2.x

참고

자세한 내용은 다음과 같습니다 GitHub. AWS 코드 예제 리포지토리에서 전체 예제를 찾고 설정 및 실행하는 방법을 배워보세요.

Stability.ai Stable Diffusion XL 파운데이션 모델을 비동기식으로 간접 호출하여 이미지를 생성합니다.

    /**
     * Asynchronously invokes the Stability.ai Stable Diffusion XL model to create
     * an image based on the provided input.
     *
     * @param prompt      The prompt that guides the Stable Diffusion model.
     * @param seed        The random noise seed for image generation (use 0 or omit
     *                    for a random seed).
     * @param stylePreset The style preset to guide the image model towards a
     *                    specific style.
     * @return A Base64-encoded string representing the generated image.
     */
    public static String invokeStableDiffusion(String prompt, long seed, String stylePreset) {
        /*
         * 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
         */

        String stableDiffusionModelId = "stability.stable-diffusion-xl-v1";

        BedrockRuntimeAsyncClient client = BedrockRuntimeAsyncClient.builder()
                .region(Region.US_EAST_1)
                .credentialsProvider(ProfileCredentialsProvider.create())
                .build();

        JSONArray wrappedPrompt = new JSONArray().put(new JSONObject().put("text", prompt));
        JSONObject payload = new JSONObject()
                .put("text_prompts", wrappedPrompt)
                .put("seed", seed);

        if (stylePreset != null && !stylePreset.isEmpty()) {
            payload.put("style_preset", stylePreset);
        }

        InvokeModelRequest request = InvokeModelRequest.builder()
                .body(SdkBytes.fromUtf8String(payload.toString()))
                .modelId(stableDiffusionModelId)
                .contentType("application/json")
                .accept("application/json")
                .build();

        CompletableFuture<InvokeModelResponse> completableFuture = client.invokeModel(request)
                .whenComplete((response, exception) -> {
                    if (exception != null) {
                        System.out.println("Model invocation failed: " + exception);
                    }
                });

        String base64ImageData = "";
        try {
            InvokeModelResponse response = completableFuture.get();
            JSONObject responseBody = new JSONObject(response.body().asUtf8String());
            base64ImageData = responseBody
                    .getJSONArray("artifacts")
                    .getJSONObject(0)
                    .getString("base64");

        } catch (InterruptedException e) {
            Thread.currentThread().interrupt();
            System.err.println(e.getMessage());
        } catch (ExecutionException e) {
            System.err.println(e.getMessage());
        }

        return base64ImageData;
    }

Stability.ai Stable Diffusion XL 파운데이션 모델을 간접 호출하여 이미지를 생성합니다.

        /**
         * Invokes the Stability.ai Stable Diffusion XL model to create an image based
         * on the provided input.
         *
         * @param prompt      The prompt that guides the Stable Diffusion model.
         * @param seed        The random noise seed for image generation (use 0 or omit
         *                    for a random seed).
         * @param stylePreset The style preset to guide the image model towards a
         *                    specific style.
         * @return A Base64-encoded string representing the generated image.
         */
        public static String invokeStableDiffusion(String prompt, long seed, String stylePreset) {
                /*
                 * 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
                 */

                String stableDiffusionModelId = "stability.stable-diffusion-xl";

                BedrockRuntimeClient client = BedrockRuntimeClient.builder()
                                .region(Region.US_EAST_1)
                                .credentialsProvider(ProfileCredentialsProvider.create())
                                .build();

                JSONArray wrappedPrompt = new JSONArray().put(new JSONObject().put("text", prompt));

                JSONObject payload = new JSONObject()
                                .put("text_prompts", wrappedPrompt)
                                .put("seed", seed);

                if (!(stylePreset == null || stylePreset.isEmpty())) {
                        payload.put("style_preset", stylePreset);
                }

                InvokeModelRequest request = InvokeModelRequest.builder()
                                .body(SdkBytes.fromUtf8String(payload.toString()))
                                .modelId(stableDiffusionModelId)
                                .contentType("application/json")
                                .accept("application/json")
                                .build();

                InvokeModelResponse response = client.invokeModel(request);

                JSONObject responseBody = new JSONObject(response.body().asUtf8String());

                String base64ImageData = responseBody
                                .getJSONArray("artifacts")
                                .getJSONObject(0)
                                .getString("base64");

                return base64ImageData;
        }

• API 세부 정보는 AWS SDK for Java 2.x API InvokeModel참조를 참조하십시오.

PHP

SDK for PHP

참고

자세한 내용은 다음과 같습니다 GitHub. AWS 코드 예제 리포지토리에서 전체 예제를 찾고 설정 및 실행하는 방법을 배워보세요.

Stability.ai Stable Diffusion XL 파운데이션 모델을 간접 호출하여 이미지를 생성합니다.

    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';

            $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;
    }

• API 세부 정보는 AWS SDK for PHP API InvokeModel참조를 참조하십시오.

Python

SDK for Python(Boto3)

참고

자세한 내용은 다음과 같습니다 GitHub. AWS 코드 예제 리포지토리에서 전체 예제를 찾고 설정 및 실행하는 방법을 배워보세요.

Stability.ai Stable Diffusion XL 파운데이션 모델을 간접 호출하여 이미지를 생성합니다.

# 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}")

• API에 대한 자세한 내용은 파이썬용AWS SDK (Boto3) API 레퍼런스를 참조하십시오 InvokeModel.

SAP ABAP

SDK for SAP ABAP

참고

자세한 내용은 여기에서 확인할 수 있습니다. GitHub AWS 코드 예제 리포지토리에서 전체 예제를 찾고 설정 및 실행하는 방법을 배워보세요.

Stability.ai Stable Diffusion XL 파운데이션 모델을 간접 호출하여 이미지를 생성합니다.

    "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-v0'
          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.

Stability.ai 스테이블 디퓨전 XL 기반 모델을 호출하여 L2 하이 레벨 클라이언트를 사용하여 이미지를 생성하십시오.

    TRY.
        DATA(lo_bdr_l2_sd) = /aws1/cl_bdr_l2_factory=>create_stable_diffusion_10( 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.

• API에 대한 자세한 내용은 SAP ABAP API용AWS SDK 레퍼런스를 참조하십시오 InvokeModel.

AWS SDK 개발자 가이드 및 코드 예제의 전체 목록은 을 참조하십시오. AWS SDK와 함께 이 서비스 사용 이 주제에는 시작하기에 대한 정보와 이전 SDK 버전에 대한 세부 정보도 포함되어 있습니다.