התאמה אישית של תמונות על סמך אמצעי בקרה באמצעות Imagen

// Using this SDK to access Imagen models is a Preview release and requires opt-in
@OptIn(PublicPreviewAPI::class)
suspend fun customizeImage() {
    // Initialize the Agent Platform Gemini API backend service (formerly branded Vertex AI).
    // Optionally specify the location to access the model (for example, `us-central1`)
    val ai = Firebase.ai(backend = GenerativeBackend.vertexAI(location = "us-central1"))

    // Create an `ImagenModel` instance with an Imagen "capability" model
    val model = ai.imagenModel("imagen-3.0-capability-001")

    // This example assumes 'referenceImage' is a pre-loaded Bitmap.
    // In a real app, this might come from the user's device or a URL.
    val referenceImage: Bitmap = TODO("Load your reference image Bitmap here")

    // Define the subject reference using the reference image.
    val controlReference = ImagenControlReference(
        image = referenceImage,
        referenceID = 1,
        controlType = CONTROL_TYPE_SCRIBBLE
    )

    // Provide a prompt that describes the final image.
    // The "[1]" links the prompt to the subject reference with ID 1.
    val prompt = "A cat flying through outer space arranged like the space scribble[1]"

    // Use the editImage API to perform the controlled customization.
    // Pass the list of references, the prompt, and an editing configuration.
    val editedImage = model.editImage(
        referenceImages = listOf(controlReference),
        prompt = prompt,
        config = ImagenEditingConfig(
            editSteps = 50 // Number of editing steps, a higher value can improve quality
        )
    )

    // Process the result
}

// Initialize the Agent Platform Gemini API backend service (formerly branded Vertex AI).
// Optionally specify the location to access the model (for example, `us-central1`)
// Create an `ImagenModel` instance with an Imagen "capability" model
ImagenModel imagenModel = FirebaseAI.getInstance(GenerativeBackend.vertexAI("us-central1"))
        .imagenModel(
                /* modelName */ "imagen-3.0-capability-001");

ImagenModelFutures model = ImagenModelFutures.from(imagenModel);

// This example assumes 'referenceImage' is a pre-loaded Bitmap.
// In a real app, this might come from the user's device or a URL.
Bitmap referenceImage = null; // TODO("Load your image Bitmap here");

// Define the subject reference using the reference image.
ImagenControlReference controlReference = new ImagenControlReference.Builder()
        .setImage(referenceImage)
        .setReferenceID(1)
        .setControlType(CONTROL_TYPE_SCRIBBLE)
        .build();

// Provide a prompt that describes the final image.
// The "[1]" links the prompt to the subject reference with ID 1.
String prompt = "A cat flying through outer space arranged like the space scribble[1]";

// Define the editing configuration.
ImagenEditingConfig imagenEditingConfig = new ImagenEditingConfig.Builder()
        .setEditSteps(50) // Number of editing steps, a higher value can improve quality
        .build();

// Use the editImage API to perform the controlled customization.
// Pass the list of references, the prompt, and an editing configuration.
Futures.addCallback(model.editImage(Collections.singletonList(controlReference), prompt, imagenEditingConfig), new FutureCallback<ImagenGenerationResponse>() {
    @Override
    public void onSuccess(ImagenGenerationResponse result) {
        if (result.getImages().isEmpty()) {
            Log.d("TAG", "No images generated");
        }
        Bitmap bitmap = ((ImagenInlineImage) result.getImages().get(0)).asBitmap();
        // Use the bitmap to display the image in your UI
    }

    @Override
    public void onFailure(Throwable t) {
        // ...
    }
}, Executors.newSingleThreadExecutor());

import 'dart:typed_data';
import 'package:firebase_ai/firebase_ai.dart';
import 'package:firebase_core/firebase_core.dart';
import 'firebase_options.dart';

// Initialize FirebaseApp
await Firebase.initializeApp(
  options: DefaultFirebaseOptions.currentPlatform,
);

// Initialize the Agent Platform Gemini API backend service (formerly branded Vertex AI).
// Optionally specify a location to access the model (for example, `us-central1`)
final ai = FirebaseAI.vertexAI(location: 'us-central1');

// Create an `ImagenModel` instance with an Imagen "capability" model
final model = ai.imagenModel(model: 'imagen-3.0-capability-001');

// This example assumes 'referenceImage' is a pre-loaded Uint8List.
// In a real app, this might come from the user's device or a URL.
final Uint8List referenceImage = Uint8List(0); // TODO: Load your reference image data here

// Define the control reference using the reference image.
final controlReference = ImagenControlReference(
  image: referenceImage,
  referenceId: 1,
    controlType: ImagenControlType.scribble,
);

// Provide a prompt that describes the final image.
// The "[1]" links the prompt to the subject reference with ID 1.
final prompt = "A cat flying through outer space arranged like the space scribble[1]";

try {
  // Use the editImage API to perform the controlled customization.
  // Pass the list of references, the prompt, and an editing configuration.
  final response = await model.editImage(
    [controlReference],
    prompt,
    config: ImagenEditingConfig(
      editSteps: 50, // Number of editing steps, a higher value can improve quality
    ),
  );

  // Process the result.
  if (response.images.isNotEmpty) {
    final editedImage = response.images.first.bytes;
    // Use the editedImage (a Uint8List) to display the image, save it, etc.
    print('Image successfully generated!');
  } else {
    // Handle the case where no images were generated.
    print('Error: No images were generated.');
  }
} catch (e) {
  // Handle any potential errors during the API call.
  print('An error occurred: $e');
}