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