---
name: gemini-imagegen
description: "Generate or edit images using the Gemini API. Supports text-to-image, image editing, multi-turn refinement, style transfer, logos with text, stickers, product mockups, and composition from multiple reference images."
triggers:
- gemini image
- image generation
- text to image
- generate image
- gemini api image
- imagegen
---

# Gemini Image Generation (Nano Banana Pro)

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

## Phase 0: Pre-Flight Checks

Before generating images, verify both environment and quota:

1. **Environment:** Confirm `GEMINI_API_KEY` is set (the scripts check this)
2. **Python dependencies:** Install in a venv (`python3 -m venv .venv && source .venv/bin/activate && pip install -r requirements.txt`) -- bare `pip install` is blocked by PEP 668 on modern Linux; versions are pinned to exact releases for supply chain security
3. **Image generation quota:** Free-tier API keys authenticate successfully but may have zero quota for image generation. Run a minimal test request before building the full pipeline:

```bash
python3 -c "
import os
from google import genai
from google.genai import types
client = genai.Client(api_key=os.environ['GEMINI_API_KEY'])
try:
    r = client.models.generate_content(
        model='gemini-2.5-flash-image',
        contents=['Generate a 1x1 pixel red square'],
        config=types.GenerateContentConfig(response_modalities=['TEXT', 'IMAGE']),
    )
    print('[ok] Image generation quota available')
except Exception as e:
    print(f'[FAIL] Image generation quota: {e}')
"
```

If quota is unavailable, fall back to Pillow-only generation (solid/gradient backgrounds with text overlay).

## 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

```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.png")
```

## 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

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

```python
# 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:

```python
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:

```bash
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