---
name: 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.
The helper scripts default to this model as well. If the API rejects the preview model for the current account or region, rerun with an explicit `--model` override and record that fallback; do not silently change the default.

## 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

```python
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.jpg")
```

## Custom Resolution & Aspect Ratio

```python
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

```python
# 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

```python
# 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:

```python
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:

```python
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:

```python
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:

```python
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

The Gemini API commonly returns JPEG inline image data. Prefer `.jpg` for generated outputs unless the caller explicitly needs another format. PIL chooses the saved file format from the output extension when `format` is omitted, so saving to `.png` writes a PNG file rather than a JPEG-with-PNG-extension.

```python
# Recommended default for Gemini image outputs
image.save("output.jpg")

# Also valid when PNG output is explicitly needed; PIL writes PNG here
image.save("output.png")
```

### Choosing an Explicit Format

If downstream tooling requires a specific media type, pass `format` explicitly and make the extension match:

```python
from PIL import Image

# Generate with Gemini
for part in response.parts:
    if part.inline_data:
        img = part.as_image()
        img.save("output.png", format="PNG")
        img.convert("RGB").save("output.jpg", format="JPEG")
```

### Verifying Image Format

Check actual format vs extension with the `file` command:

```bash
file image.png
# If output and extension disagree, regenerate or resave with matching format.
```

## Notes

- All generated images include SynthID watermarks
- Gemini commonly returns JPEG inline data; prefer `.jpg` by default, and pass an explicit PIL `format` when another media type is required
- 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