Generate an image from text

using Google.Cloud.AIPlatform.V1;
using System;
using System.IO;
using System.Linq;
using System.Threading.Tasks;
using Value = Google.Protobuf.WellKnownTypes.Value;

public class GenerateImage
{
    public async Task<FileInfo> Generate(
        string projectId = "your-project-id")
    {
        var predictionServiceClient = new PredictionServiceClientBuilder
        {
            Endpoint = "us-central1-aiplatform.googleapis.com"
        }.Build();


        string prompt = "a dog reading a newspaper";
        string outputFileName = "dog_newspaper.png";
        string model = "imagegeneration@006";

        var predictRequest = new PredictRequest
        {
            EndpointAsEndpointName = EndpointName.FromProjectLocationPublisherModel(projectId, "us-central1", "google", model),
            Instances =
            {
                Value.ForStruct(new()
                {
                    Fields =
                    {
                        ["prompt"] = Value.ForString(prompt)
                    }
                })
            },
            Parameters = Value.ForStruct(new()
            {
                Fields =
                {
                    ["sampleCount"] = Value.ForNumber(1)
                }
            })
        };

        PredictResponse response = await predictionServiceClient.PredictAsync(predictRequest);
        byte[] imageBytes = Convert.FromBase64String(response.Predictions.First().StructValue.Fields["bytesBase64Encoded"].StringValue);

        File.WriteAllBytes(outputFileName, imageBytes);
        FileInfo fileInfo = new FileInfo(Path.GetFullPath(outputFileName));

        Console.WriteLine($"Created output image {fileInfo.FullName} with {fileInfo.Length} bytes");
        return fileInfo;
    }
}

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.util.Base64;
import java.util.Collections;
import java.util.HashMap;
import java.util.Map;

public class GenerateImageSample {

  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 prompt = ""; // The text prompt describing what you want to see.

    generateImage(projectId, location, prompt);
  }

  // Generate an image using a text prompt using an Imagen model
  public static PredictResponse generateImage(String projectId, String location, 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", "imagen-3.0-generate-001");

      Map<String, Object> instancesMap = new HashMap<>();
      instancesMap.put("prompt", prompt);
      Value instances = mapToValue(instancesMap);

      Map<String, Object> paramsMap = new HashMap<>();
      paramsMap.put("sampleCount", 1);
      // You can't use a seed value and watermark at the same time.
      // paramsMap.put("seed", 100);
      // paramsMap.put("addWatermark", true);
      paramsMap.put("aspectRatio", "1:1");
      paramsMap.put("safetyFilterLevel", "block_some");
      paramsMap.put("personGeneration", "allow_adult");
      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();
  }
}

import vertexai
from vertexai.preview.vision_models import ImageGenerationModel

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
# output_file = "my-output.png"
# prompt = "" # The text prompt describing what you want to see.

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

model = ImageGenerationModel.from_pretrained("imagen-3.0-generate-001")

images = model.generate_images(
    prompt=prompt,
    # Optional parameters
    number_of_images=1,
    language="en",
    # You can't use a seed value and watermark at the same time.
    # add_watermark=False,
    # seed=100,
    aspect_ratio="1:1",
    safety_filter_level="block_some",
    person_generation="allow_adult",
)

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

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

print(f"Created output image using {len(images[0]._image_bytes)} bytes")