Panggil Stability.ai Stable Diffusion XL di Amazon Bedrock untuk menghasilkan gambar

Contoh kode berikut menunjukkan cara memanggil Stability.ai Stable Diffusion XL di Amazon Bedrock untuk menghasilkan gambar.

.NET

AWS SDK for .NET

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Secara asinkron memanggil model fondasi Stability.ai Stable Diffusion XL untuk menghasilkan gambar.

        /// <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;
        }

• Untuk detail API, lihat InvokeModeldi Referensi AWS SDK for .NET API.

Java

SDK untuk Java 2.x

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Secara asinkron memanggil model dasar Stability.ai Stable Diffusion XL untuk menghasilkan gambar.

    /**
     * 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;
    }

Memanggil model dasar Stability.ai Stable Diffusion XL untuk menghasilkan gambar.

        /**
         * 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;
        }

• Untuk detail API, lihat InvokeModeldi Referensi AWS SDK for Java 2.x API.

PHP

SDK untuk PHP

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Memanggil model dasar Stability.ai Stable Diffusion XL untuk menghasilkan gambar.

    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;
    }

• Untuk detail API, lihat InvokeModeldi Referensi AWS SDK for PHP API.

Python

SDK untuk Python (Boto3)

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Memanggil model dasar Stability.ai Stable Diffusion XL untuk menghasilkan gambar.

# 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}")

• Untuk detail API, lihat InvokeModeldi AWS SDK for Python (Boto3) Referensi API.

SAP ABAP

SDK untuk SAP ABAP

catatan

Ada lebih banyak tentang GitHub. Temukan contoh lengkapnya dan pelajari cara mengatur dan menjalankannya di AWS Repositori Contoh Kode.

Memanggil model dasar Stability.ai Stable Diffusion XL untuk menghasilkan gambar.

    "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.

Panggil model pondasi Stability.ai Stable Diffusion XL untuk menghasilkan gambar menggunakan klien tingkat tinggi L2.

    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.

• Untuk detail API, lihat InvokeModeldi AWS SDK untuk referensi SAP ABAP API.

Untuk daftar lengkap panduan pengembang AWS SDK dan contoh kode, lihatMenggunakan layanan ini dengan AWS SDK. Topik ini juga mencakup informasi tentang memulai dan detail tentang versi SDK sebelumnya.