sc-gemini-imagegen

name	sc-gemini-imagegen
description	Generate and edit images using the Gemini API (Nano Banana). This skill SHOULD be used 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

Generate and edit images using Google's Gemini API. The SDK reads GOOGLE_API_KEY by default (GEMINI_API_KEY as fallback). Or pass a key explicitly to genai.Client(api_key=...).

Models

Model	Codename	Best For
gemini-2.5-flash-image	Nano Banana	Most use cases, fast, good quality (default)
gemini-3-pro-image-preview	Nano Banana Pro	High-res (2K/4K), Google Search grounding, precise text
gemini-3.1-flash-image-preview	Nano Banana 2	High volume, extended aspect ratios, 512 size

Start with gemini-2.5-flash-image. Upgrade to Pro for high-res output or search grounding.

Quick Reference

Default Settings

• Model: gemini-2.5-flash-image
• Resolution: 1K (default)
• Aspect Ratio: 1:1 (default)

Available Aspect Ratios

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

3.1 Flash only: 1:4, 4:1, 1:8, 8:1

Available Resolutions

All models: 1K (default), 2K, 4K

3.1 Flash only: 512

Core API Pattern

from google import genai
from google.genai import types

client = genai.Client()  # Reads GOOGLE_API_KEY (or GEMINI_API_KEY fallback)

response = client.models.generate_content(
    model="gemini-2.5-flash-image",
    contents="Your prompt here",
)

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("output.jpg")  # save() takes path only, writes raw bytes

Note: response_modalities is optional. Omit it to let the model decide. Set ['IMAGE'] for image-only output, or ['TEXT', 'IMAGE'] for interleaved text and images.

Custom Resolution & Aspect Ratio

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents=prompt,
    config=types.GenerateContentConfig(
        image_config=types.ImageConfig(
            aspect_ratio="16:9",
            image_size="2K",
        ),
    ),
)

Editing Images (Chat Mode)

Chat mode is recommended for editing. The SDK handles thought signatures automatically across turns.

from PIL import Image

client = genai.Client()
image = Image.open("input.png")

chat = client.chats.create(model="gemini-2.5-flash-image")

# First edit
response = chat.send_message(["Add a sunset to this scene", image])

for i, part in enumerate(response.candidates[0].content.parts):
    if part.text is not None:
        print(part.text)
    elif part.inline_data is not None:
        image = part.as_image()
        image.save(f"edited_{i}.jpg")

# Continue refining
response = chat.send_message("Make the colors warmer")

PIL Image objects, base64 bytes, and file URIs (via client.files.upload()) all work as image inputs.

Google Search Grounding

Generate images informed by real-time data. Requires Pro model.

response = client.models.generate_content(
    model="gemini-3-pro-image-preview",
    contents="Visualize today's weather in Tokyo as an infographic",
    config=types.GenerateContentConfig(
        image_config=types.ImageConfig(
            aspect_ratio="16:9",
            image_size="1K",
        ),
        tools=[types.Tool(google_search=types.GoogleSearch())],
    ),
)

Image search grounding (searching for reference images) is only available on gemini-3.1-flash-image-preview.

Multiple Reference Images

Combine elements from multiple sources. Pass PIL Image objects directly in the contents list.

from PIL import Image

response = client.models.generate_content(
    model="gemini-2.5-flash-image",
    contents=[
        "Create a group photo of these people in an office",
        Image.open("person1.png"),
        Image.open("person2.png"),
        Image.open("person3.png"),
    ],
)

Limits differ by model:

• 3.1 Flash: up to 10 object images + 4 character images (14 total)
• 3 Pro: up to 6 object images + 5 character images (11 total)

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"

File Format & Saving

The API returns JPEG in practice. image.save(path) writes raw bytes from the API response. It takes only a path string (no format kwarg).

# Save as-is (JPEG bytes from the API)
image.save("output.jpg")

To convert formats, use PIL on the raw bytes:

from PIL import Image
import io

for part in response.parts:
    if part.inline_data is not None:
        pil_img = Image.open(io.BytesIO(part.inline_data.data))
        pil_img.save("output.png")  # PIL handles the conversion

Notes

• All generated images include SynthID watermarks (not configurable for Gemini models)
• save(path) writes raw bytes; no format kwarg exists. Use PIL for format conversion
• response_modalities is optional; omit to let the model decide output format
• Multi-turn chat handles thought signatures automatically via the SDK
• Editing via chat mode doesn't support image_config (only modality config)
• For editing, describe changes conversationally; the model understands semantic masking
• Default to 1K for speed; use 2K/4K when quality matters
• person_generation parameter exists on ImageConfig for controlling person depiction in outputs