ce-gemini-imagegen

name

ce-gemini-imagegen

description

This skill should be used when generating and editing images using the Gemini API (Nano Banana Pro). It applies when creating images from text prompts, editing existing images, applying style transfers, generating logos with text, creating stickers, product mockups, or any image generation/manipulation task. Supports text-to-image, image editing, multi-turn refinement, and composition from multiple reference images.

Gemini Image Generation (Nano Banana Pro)

Generate and edit images using Google's Gemini API. The environment variable GEMINI_API_KEY must be set.

Default Model

Model	Resolution	Best For
gemini-3-pro-image-preview	1K-4K	All image generation (default)

Note: Always use this Pro model. Only use a different model if explicitly requested.

Quick Reference

Default Settings

• Model: gemini-3-pro-image-preview
• Resolution: 1K (default, options: 1K, 2K, 4K)
• Aspect Ratio: 1:1 (default)

Available Aspect Ratios

1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9

Available Resolutions

1K (default), 2K, 4K

Core API Pattern

import os
from google import genai
from google.genai import types

client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])

# Basic generation (1K, 1:1 - defaults)
response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=["Your prompt here"],
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE']))

for part in response.parts:
    if part.text:
        print(part.text)
    elif part.inline_data:
        image = part.as_image
        image.save("output.png")

Custom Resolution & Aspect Ratio

from google.genai import types

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=[prompt],
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE'],
        image_config=types.ImageConfig(
            aspect_ratio="16:9", # Wide format
            image_size="2K" # Higher resolution
        ))
)

Resolution Examples

# 1K (default) - Fast, good for previews
image_config=types.ImageConfig(image_size="1K")

# 2K - Balanced quality/speed
image_config=types.ImageConfig(image_size="2K")

# 4K - Maximum quality, slower
image_config=types.ImageConfig(image_size="4K")

Aspect Ratio Examples

# Square (default)
image_config=types.ImageConfig(aspect_ratio="1:1")

# Landscape wide
image_config=types.ImageConfig(aspect_ratio="16:9")

# Ultra-wide panoramic
image_config=types.ImageConfig(aspect_ratio="21:9")

# Portrait
image_config=types.ImageConfig(aspect_ratio="9:16")

# Photo standard
image_config=types.ImageConfig(aspect_ratio="4:3")

Editing Images

Pass existing images with text prompts:

from PIL import Image

img = Image.open("input.png")
response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=["Add a sunset to this scene", img],
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE']))

Multi-Turn Refinement

Use chat for iterative editing:

from google.genai import types

chat = client.chats.create(
    model="gemini-3-pro-image-preview",
    config=types.GenerateContentConfig(response_modalities=['TEXT', 'IMAGE'])
)

response = chat.send_message("Create a logo for 'Acme Corp'")
# Save first image...

response = chat.send_message("Make the text bolder and add a blue gradient")
# Save refined image...

Prompting Best Practices

Photorealistic Scenes

Include camera details: lens type, lighting, angle, mood.

"A photorealistic close-up portrait, 85mm lens, soft golden hour light, shallow depth of field"

Stylized Art

Specify style explicitly:

"A kawaii-style sticker of a happy red panda, bold outlines, cel-shading, white background"

Text in Images

Be explicit about font style and placement:

"Create a logo with text 'Daily Grind' in clean sans-serif, black and white, coffee bean motif"

Product Mockups

Describe lighting setup and surface:

"Studio-lit product photo on polished concrete, three-point softbox setup, 45-degree angle"

Advanced Features

Google Search Grounding

Generate images based on real-time data:

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=["Visualize today's weather in Tokyo as an infographic"],
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE'],
        tools=[{"google_search": {}}]
    )
)

Multiple Reference Images (Up to 14)

Combine elements from multiple sources:

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=[
        "Create a group photo of these people in an office",
        Image.open("person1.png"),
        Image.open("person2.png"),
        Image.open("person3.png"),
    ],
    config=types.GenerateContentConfig(
        response_modalities=['TEXT', 'IMAGE']))

Important: File Format & Media Type

CRITICAL: The Gemini API returns images in JPEG format by default. When saving, always use .jpg extension to avoid media type mismatches.

# CORRECT - Use .jpg extension (Gemini returns JPEG)
image.save("output.jpg")

# WRONG - Will cause "Image does not match media type" errors
image.save("output.png") # Creates JPEG with PNG extension!

Converting to PNG (if needed)

If you specifically need PNG format:

from PIL import Image

# Generate with Gemini
for part in response.parts:
    if part.inline_data:
        img = part.as_image
        # Convert to PNG by saving with explicit format
        img.save("output.png", format="PNG")

Verifying Image Format

Check actual format vs extension with the file command:

file image.png
# If output shows "JPEG image data" - rename to .jpg!

Notes

• All generated images include SynthID watermarks
• Gemini returns JPEG format by default - always use .jpg extension
• Image-only mode (responseModalities: ["IMAGE"]) won't work with Google Search grounding
• For editing, describe changes conversationally—the model understands semantic masking
• Default to 1K resolution for speed; use 2K/4K when quality is critical