Modificare i contenuti delle immagini utilizzando la modifica senza maschera con Imagen v.002

Questo esempio mostra come utilizzare il modello Imagen per la modifica delle immagini senza maschere. Specifica le modifiche utilizzando un prompt di testo.

Esempio di codice

Java

Prima di provare questo esempio, segui le istruzioni di configurazione Java riportate nella guida rapida all'utilizzo delle librerie client di Vertex AI. Per ulteriori informazioni, consulta la documentazione di riferimento dell'API Java di Vertex AI.

Per autenticarti a Vertex AI, configura le Credenziali predefinite dell'applicazione. Per ulteriori informazioni, consulta Configurare l'autenticazione per un ambiente di sviluppo locale.

import com.google.api.gax.rpc.ApiException;
import com.google.cloud.aiplatform.v1.EndpointName;
import com.google.cloud.aiplatform.v1.PredictResponse;
import com.google.cloud.aiplatform.v1.PredictionServiceClient;
import com.google.cloud.aiplatform.v1.PredictionServiceSettings;
import com.google.gson.Gson;
import com.google.protobuf.InvalidProtocolBufferException;
import com.google.protobuf.Value;
import com.google.protobuf.util.JsonFormat;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.Base64;
import java.util.Collections;
import java.util.HashMap;
import java.util.Map;

public class EditImageMaskFreeSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "my-project-id";
    String location = "us-central1";
    String inputPath = "/path/to/my-input.png";
    String prompt = ""; // The text prompt describing what you want to see.

    editImageMaskFree(projectId, location, inputPath, prompt);
  }

  // Edit an image without using a mask. The edit is applied to the entire image and is saved to a
  // new file.
  public static PredictResponse editImageMaskFree(
      String projectId, String location, String inputPath, String prompt)
      throws ApiException, IOException {
    final String endpoint = String.format("%s-aiplatform.googleapis.com:443", location);
    PredictionServiceSettings predictionServiceSettings =
        PredictionServiceSettings.newBuilder().setEndpoint(endpoint).build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests.
    try (PredictionServiceClient predictionServiceClient =
        PredictionServiceClient.create(predictionServiceSettings)) {

      final EndpointName endpointName =
          EndpointName.ofProjectLocationPublisherModelName(
              projectId, location, "google", "imagegeneration@002");

      // Convert the image to Base64 and create the image map
      String imageBase64 =
          Base64.getEncoder().encodeToString(Files.readAllBytes(Paths.get(inputPath)));
      Map<String, String> imageMap = new HashMap<>();
      imageMap.put("bytesBase64Encoded", imageBase64);

      Map<String, Object> instancesMap = new HashMap<>();
      instancesMap.put("prompt", prompt); // [ "prompt", "<my-prompt>" ]
      instancesMap.put(
          "image", imageMap); // [ "image", [ "bytesBase64Encoded", "iVBORw0KGgo...==" ] ]
      Value instances = mapToValue(instancesMap);

      Map<String, Object> paramsMap = new HashMap<>();
      // Optional parameters
      paramsMap.put("seed", 1);
      // Controls the strength of the prompt.
      // 0-9 (low strength), 10-20 (medium strength), 21+ (high strength)
      paramsMap.put("guidanceScale", 21);
      paramsMap.put("sampleCount", 1);
      Value parameters = mapToValue(paramsMap);

      PredictResponse predictResponse =
          predictionServiceClient.predict(
              endpointName, Collections.singletonList(instances), parameters);

      for (Value prediction : predictResponse.getPredictionsList()) {
        Map<String, Value> fieldsMap = prediction.getStructValue().getFieldsMap();
        if (fieldsMap.containsKey("bytesBase64Encoded")) {
          String bytesBase64Encoded = fieldsMap.get("bytesBase64Encoded").getStringValue();
          Path tmpPath = Files.createTempFile("imagen-", ".png");
          Files.write(tmpPath, Base64.getDecoder().decode(bytesBase64Encoded));
          System.out.format("Image file written to: %s\n", tmpPath.toUri());
        }
      }
      return predictResponse;
    }
  }

  private static Value mapToValue(Map<String, Object> map) throws InvalidProtocolBufferException {
    Gson gson = new Gson();
    String json = gson.toJson(map);
    Value.Builder builder = Value.newBuilder();
    JsonFormat.parser().merge(json, builder);
    return builder.build();
  }
}

Node.js

Prima di provare questo esempio, segui le istruzioni di configurazione Node.js riportate nella guida rapida all'utilizzo delle librerie client di Vertex AI. Per ulteriori informazioni, consulta la documentazione di riferimento dell'API Node.js di Vertex AI.

Per autenticarti a Vertex AI, configura le Credenziali predefinite dell'applicazione. Per ulteriori informazioni, consulta Configurare l'autenticazione per un ambiente di sviluppo locale.

/**
 * TODO(developer): Update these variables before running the sample.
 */
const projectId = process.env.CAIP_PROJECT_ID;
const location = 'us-central1';
const inputFile = 'resources/cat.png';
const prompt = 'dog';

const aiplatform = require('@google-cloud/aiplatform');

// Imports the Google Cloud Prediction Service Client library
const {PredictionServiceClient} = aiplatform.v1;

// Import the helper module for converting arbitrary protobuf.Value objects
const {helpers} = aiplatform;

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: `${location}-aiplatform.googleapis.com`,
};

// Instantiates a client
const predictionServiceClient = new PredictionServiceClient(clientOptions);

async function editImageMaskFree() {
  const fs = require('fs');
  const util = require('util');
  // Configure the parent resource
  const endpoint = `projects/${projectId}/locations/${location}/publishers/google/models/imagegeneration@002`;

  const imageFile = fs.readFileSync(inputFile);
  // Convert the image data to a Buffer and base64 encode it.
  const encodedImage = Buffer.from(imageFile).toString('base64');

  const promptObj = {
    prompt: prompt, // The text prompt describing what you want to see
    image: {
      bytesBase64Encoded: encodedImage,
    },
  };
  const instanceValue = helpers.toValue(promptObj);
  const instances = [instanceValue];

  const parameter = {
    // Optional parameters
    seed: 100,
    // Controls the strength of the prompt
    // 0-9 (low strength), 10-20 (medium strength), 21+ (high strength)
    guidanceScale: 21,
    sampleCount: 1,
  };
  const parameters = helpers.toValue(parameter);

  const request = {
    endpoint,
    instances,
    parameters,
  };

  // Predict request
  const [response] = await predictionServiceClient.predict(request);
  const predictions = response.predictions;
  if (predictions.length === 0) {
    console.log(
      'No image was generated. Check the request parameters and prompt.'
    );
  } else {
    let i = 1;
    for (const prediction of predictions) {
      const buff = Buffer.from(
        prediction.structValue.fields.bytesBase64Encoded.stringValue,
        'base64'
      );
      // Write image content to the output file
      const writeFile = util.promisify(fs.writeFile);
      const filename = `output${i}.png`;
      await writeFile(filename, buff);
      console.log(`Saved image ${filename}`);
      i++;
    }
  }
}
await editImageMaskFree();

Python

Prima di provare questo esempio, segui le istruzioni di configurazione Python riportate nella guida rapida all'utilizzo delle librerie client di Vertex AI. Per ulteriori informazioni, consulta la documentazione di riferimento dell'API Python di Vertex AI.

Per autenticarti a Vertex AI, configura le Credenziali predefinite dell'applicazione. Per ulteriori informazioni, consulta Configurare l'autenticazione per un ambiente di sviluppo locale.

import vertexai
from vertexai.preview.vision_models import Image, ImageGenerationModel

# TODO(developer): Update and un-comment below lines
# PROJECT_ID = "your-project-id"
# input_file = "input-image.png"
# output_file = "output-image.png"
# prompt = "" # The text prompt describing what you want to see.

vertexai.init(project=PROJECT_ID, location="us-central1")

model = ImageGenerationModel.from_pretrained("imagegeneration@002")
base_img = Image.load_from_file(location=input_file)

images = model.edit_image(
    base_image=base_img,
    prompt=prompt,
    # Optional parameters
    seed=1,
    # Controls the strength of the prompt.
    # -- 0-9 (low strength), 10-20 (medium strength), 21+ (high strength)
    guidance_scale=21,
    number_of_images=1,
)

images[0].save(location=output_file, include_generation_parameters=False)

# Optional. View the edited image in a notebook.
# images[0].show()

print(f"Created output image using {len(images[0]._image_bytes)} bytes")
# Example response:
# Created output image using 1234567 bytes

Passaggi successivi

Per cercare e filtrare i sample di codice per altri Google Cloud prodotti, consulta il Google Cloud browser di sample.