Nano Banana Image Generation

For authentication, see Overview.

What is Nano Banana

Nano Banana is Gemini’s multimodal image generation capability, using the same generateContent endpoint as text models.

Model	Model ID	Features
Nano Banana	`gemini-2.5-flash-image`	Speed optimized for high-volume tasks
Nano Banana Pro	`gemini-3-pro-image-preview`	Professional production, advanced reasoning, high-fidelity text rendering, up to 4K resolution

Endpoints

Same as text models:

Type	Endpoint
Non-Streaming	`POST /v1beta/models/{model}:generateContent`
Streaming	`POST /v1beta/models/{model}:streamGenerateContent`

Text to Image

cURL
Python
Node.js

curl -s -X POST \
  "https://api.pipellm.com/v1beta/models/gemini-2.5-flash-image:generateContent" \
  -H "x-goog-api-key: $PIPELLM_API_KEY" \
  -H "Content-Type: application/json" \
  -d @- << 'EOF'
{
  "contents": [{
    "parts": [
      {"text": "Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme"}
    ]
  }]
}
EOF

from google import genai
from PIL import Image

client = genai.Client(
  api_key=os.getenv('PIPELLM_API_KEY'),
  http_options={'base_url': 'https://api.pipellm.com'}
)

response = client.models.generate_content(
  model="gemini-2.5-flash-image",
  contents=["Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme"],
)

for part in response.parts:
  if part.text is not None:
    print(part.text)
  elif part.inline_data is not None:
    image = part.as_image()
    image.save("generated_image.png")

import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";

const ai = new GoogleGenAI({
  apiKey: process.env.PIPELLM_API_KEY,
  httpOptions: { baseUrl: 'https://api.pipellm.com' }
});

const response = await ai.models.generateContent({
  model: "gemini-2.5-flash-image",
  contents: "Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme",
});

for (const part of response.candidates[0].content.parts) {
  if (part.text) {
    console.log(part.text);
  } else if (part.inlineData) {
    const buffer = Buffer.from(part.inlineData.data, "base64");
    fs.writeFileSync("generated_image.png", buffer);
  }
}

Image to Image (with Reference)

Upload a reference image and generate a new one with text prompts:

cURL
Python
Node.js

IMG_BASE64=$(base64 -w0 /path/to/cat_image.jpeg)

curl -s -X POST \
  "https://api.pipellm.com/v1beta/models/gemini-2.5-flash-image:generateContent" \
  -H "x-goog-api-key: $PIPELLM_API_KEY" \
  -H "Content-Type: application/json" \
  -d @- << EOF
{
  "contents": [{
    "parts": [
      {"text": "Create a picture of my cat eating a nano-banana in a fancy restaurant under the Gemini constellation"},
      {"inline_data": {"mime_type": "image/jpeg", "data": "$IMG_BASE64"}}
    ]
  }]
}
EOF

from google import genai
from PIL import Image

client = genai.Client(
  api_key=os.getenv('PIPELLM_API_KEY'),
  http_options={'base_url': 'https://api.pipellm.com'}
)

image = Image.open('/path/to/cat_image.png')

response = client.models.generate_content(
  model="gemini-2.5-flash-image",
  contents=[
    "Create a picture of my cat eating a nano-banana in a fancy restaurant under the Gemini constellation",
    image
  ],
)

for part in response.parts:
  if part.text is not None:
    print(part.text)
  elif part.inline_data is not None:
    image = part.as_image()
    image.save("edited_image.png")

import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";

const ai = new GoogleGenAI({
  apiKey: process.env.PIPELLM_API_KEY,
  httpOptions: { baseUrl: 'https://api.pipellm.com' }
});

const imageData = fs.readFileSync("cat_image.png");
const base64Image = imageData.toString("base64");

const response = await ai.models.generateContent({
  model: "gemini-2.5-flash-image",
  contents: [
    { text: "Create a picture of my cat eating a nano-banana in a fancy restaurant under the Gemini constellation" },
    { inlineData: { mimeType: "image/png", data: base64Image } }
  ],
});

for (const part of response.candidates[0].content.parts) {
  if (part.text) {
    console.log(part.text);
  } else if (part.inlineData) {
    const buffer = Buffer.from(part.inlineData.data, "base64");
    fs.writeFileSync("edited_image.png", buffer);
  }
}

Response Format

Image generation responses are multimodal, containing text and base64-encoded image data:

{
  "candidates": [{
    "content": {
      "parts": [
        { "text": "Here is your generated image..." },
        {
          "inlineData": {
            "mimeType": "image/png",
            "data": "<BASE64_IMAGE_DATA>"
          }
        }
      ]
    }
  }]
}

More Features

Nano Banana Documentation

Supports multi-turn editing, 4K resolution, multi-image composition, and more

Quick Start

Use Cases

Platform Guide

Nano Banana Image Generation

What is Nano Banana

Endpoints

Text to Image

Image to Image (with Reference)

Response Format

More Features

Nano Banana Documentation

Quick Start

Use Cases

Platform Guide

​What is Nano Banana

​Endpoints

​Text to Image

​Image to Image (with Reference)

​Response Format

​More Features

Nano Banana Documentation

What is Nano Banana

Endpoints

Text to Image

Image to Image (with Reference)

Response Format

More Features