nano-banana（Nano Banana 图像生成）

Nano Banana Skill

Python scripting with Gemini image generation using uv. Write small, focused scripts using heredocs for quick tasks—no files needed for one-off operations.

Choosing Your Approach

Quick image generation: Use heredoc with inline Python for one-off image requests.

Complex workflows: When multiple steps are needed (generate -> refine -> save), break into separate scripts and iterate.

Scripting tasks: For non-image Python tasks, use the same heredoc pattern with uv run.

Writing Scripts

Execute Python inline using heredocs with inline script metadata for dependencies:

uv run - << 'EOF'
# /// script
# dependencies = ["google-genai", "pillow"]
# ///
from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=["A cute banana character with sunglasses"],
    config=types.GenerateContentConfig(
        response_modalities=['IMAGE']
    )
)

for part in response.parts:
    if part.inline_data is not None:
        image = part.as_image()
        image.save("tmp/generated.png")
        print("Image saved to tmp/generated.png")
EOF

The # /// script block declares dependencies inline using TOML syntax. This makes scripts self-contained and reproducible.

Why these dependencies:

• google-genai - Gemini API client
• pillow - Required for .as_image() method (converts base64 to PIL Image) and saving images

Only write to files when:

• The script needs to be reused multiple times
• The script is complex and requires iteration
• The user explicitly asks for a saved script

Basic Template

uv run - << 'EOF'
# /// script
# dependencies = ["google-genai", "pillow"]
# ///
from google import genai
from google.genai import types

client = genai.Client()

# Generate image
response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=["YOUR PROMPT HERE"],
    config=types.GenerateContentConfig(
        response_modalities=['IMAGE']
    )
)

# Save result
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("tmp/output.png")
        print("Saved: tmp/output.png")
EOF

Key Principles

1. Small scripts: Each script should do ONE thing (generate, refine, save)
2. Evaluate output: Always save images and print status to decide next steps
3. Use tmp/: Save generated images to tmp/ directory by default
4. Stateless execution: Each script runs independently, no cleanup needed

Workflow Loop

Follow this pattern for complex tasks:

1. Write a script to generate/process one image
2. Run it and observe the output
3. Evaluate - did it work? Check the saved image
4. Decide - refine prompt or task complete?
5. Repeat until satisfied

Image Configuration

Configure aspect ratio and resolution:

config=types.GenerateContentConfig(
    response_modalities=['IMAGE'],
    image_config=types.ImageConfig(
        aspect_ratio="16:9",  # "1:1", "16:9", "9:16", "4:3", "3:4"
        image_size="2K"       # "1K", "2K", "4K" (uppercase required)
    )
)

Models

• gemini-3-pro-image-preview - Fast, general purpose image generation
• gemini-3-pro-image-preview - Advanced, professional asset production (Nano Banana Pro)

Default to gemini-3-pro-image-preview (Nano Banana Pro) for all image generation unless:

• The user explicitly requests a different model
• The user wants to save budget/costs
• The user specifies a simpler or quick generation task

Nano Banana Pro provides higher quality results and should be the recommended choice.

Text + Image Output

To receive both text explanation and image:

config=types.GenerateContentConfig(
    response_modalities=['TEXT', 'IMAGE']
)

Image Editing

Edit existing images by including them in the request:

uv run - << 'EOF'
# /// script
# dependencies = ["google-genai", "pillow"]
# ///
from google import genai
from google.genai import types
from PIL import Image

client = genai.Client()

# Load existing image
img = Image.open("input.png")

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=[
        "Add a party hat to this character",
        img
    ],
    config=types.GenerateContentConfig(
        response_modalities=['IMAGE']
    )
)

for part in response.parts:
    if part.inline_data is not None:
        part.as_image().save("tmp/edited.png")
        print("Saved: tmp/edited.png")
EOF

Debugging Tips

1. Print response.parts to see what was returned
2. Check for text parts - model may include explanations
3. Save images immediately to verify output visually
4. Use Read tool to view saved images after generation

Error Recovery

If a script fails:

1. Check error message for API issues
2. Verify GOOGLE_API_KEY is set
3. Try simpler prompt to isolate the issue
4. Check image format compatibility for edits

Advanced Scenarios

For complex workflows including thinking process, Google Search grounding, multi-turn conversations, and professional asset production, load references/guide.md.