ce-gemini-imagegen

SKILL.md

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

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