Daya API

Image Generation ​

Daya supports invoking image generation models via the Vertex AI protocol. This guide explains how to use Daya to generate images and save them locally.

💡 About Banana Models

Banana is a series of image generation models from Google that can produce high-quality images from text prompts. You can use these models in Daya through the Vertex AI protocol.

Supported Models ​

The currently supported image generation models include (continuously updated):

  • google/gemini-3-pro-image-preview
  • google/gemini-3-pro-image-preview-free
  • google/gemini-2.5-flash-image
  • google/gemini-2.5-flash-image-free

📚 More Models

Visit the Daya model catalog to search and view all available image generation models.

Reference Documentation ​

This guide only covers basic usage. For detailed configuration and advanced usage, refer to the official documentation below:

Usage ​

Python
from google import genai
from google.genai import types

client = genai.Client(
    api_key="$DAYA_API_KEY",  # Replace with your API key
    vertexai=True,
    http_options=types.HttpOptions(
        api_version='v1',
        base_url='https://dayaai.com/api/vertex-ai'
    ),
)

# Streaming call: generate_content_stream
# Non-streaming call: generate_content
prompt = "Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme"

response = client.models.generate_content(
    model="google/gemini-3-pro-image-preview",
    contents=[prompt],
    config=types.GenerateContentConfig(
        response_modalities=["TEXT", "IMAGE"]
    )
)

# Handle text and image responses
for part in response.parts:
    if part.text is not None:
        print(part.text)
    elif part.inline_data is not None:
        # Save the generated image
        image = part.as_image()
        image.save("generated_image.png")
        print("Image saved as generated_image.png")
ts
const genai = require("@google/genai");

const client = new genai.GoogleGenAI({
  apiKey: "$DAYA_API_KEY",  // Replace with your API key
  vertexai: true,
  httpOptions: {
    baseUrl: "https://dayaai.com/api/vertex-ai",
    apiVersion: "v1"
  }
});

// Streaming call: generateContentStream
// Non-streaming call: generateContent
const response = await client.models.generateContent({
  model: "google/gemini-3-pro-image-preview",
  contents: "Generate an image of the Eiffel tower with fireworks in the background",
  config: {
    responseModalities: ["TEXT", "IMAGE"],  // Response modalities must be specified
    // For more configuration options, refer to the Vertex AI official documentation
  }
});

console.log(response);

Configuration ​

Required Parameters ​

  • api_key: Your Daya API key
  • vertexai: Must be set to true to enable the Vertex AI protocol
  • base_url: Daya Vertex AI endpoint https://dayaai.com/api/vertex-ai
  • responseModalities: Response modalities; image generation must include ["TEXT", "IMAGE"]

Invocation Modes ​

Daya supports two invocation modes:

  • Streaming (generate_content_stream / generateContentStream): Ideal for scenarios requiring real-time feedback
  • Non-streaming (generate_content / generateContent): Returns the complete response at once after processing

⚠️ Response Handling

Responses from image generation models may contain both text and images. Iterate over response.parts to process all content parts.

Best Practices ​

  1. Prompt Engineering: Use clear and specific descriptions to achieve better generation quality.
  2. Error Handling: Add exception handling to manage potential API call failures.
  3. Image Saving: The Python SDK provides a convenient as_image() method to convert a response part into a PIL Image object.
  4. Model Selection: Choose the appropriate model based on your needs; free models are suitable for testing, while paid models provide higher quality.