Image Generation

ZenMux supports invoking image generation models via the Vertex AI protocol. This guide explains how to use ZenMux 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 ZenMux 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 ZenMux 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:

• Vertex AI Official Documentation
• Vertex AI Nano-Banana Notebook

Usage

Python

from google import genai
from google.genai import types

client = genai.Client(
    api_key="$ZENMUX_API_KEY",  # Replace with your API key
    vertexai=True,
    http_options=types.HttpOptions(
        api_version='v1',
        base_url='https://zenmux.ai/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")

TypeScript

const genai = require("@google/genai");

const client = new genai.GoogleGenAI({
  apiKey: "$ZENMUX_API_KEY",  // Replace with your API key
  vertexai: true,
  httpOptions: {
    baseUrl: "https://zenmux.ai/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 ZenMux API key
• vertexai: Must be set to true to enable the Vertex AI protocol
• base_url: ZenMux Vertex AI endpoint https://zenmux.ai/api/vertex-ai
• responseModalities: Response modalities; image generation must include ["TEXT", "IMAGE"]

Invocation Modes

ZenMux 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.