Gemini Image Generation

Generate and edit images via Google Gemini's native multimodal image generation.

Model Selection

Model ID Codename Best for Max resolution

gemini-2.5-flash-image

Nano Banana Fast drafts, high-volume, low-latency 1K

gemini-3-pro-image-preview

Nano Banana Pro Studio-quality, text rendering, complex prompts 4K

Default: gemini-3-pro-image-preview (Pro) unless speed/cost is a concern.

Setup

Install (once)

pip install google-genai

from google import genai import os, base64

client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])

If GEMINI_API_KEY is missing, instruct the user to set it as an environment variable. Never ask the user to paste the key in chat.

Text-to-Image

from google import genai from google.genai import types import os

client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])

response = client.models.generate_content( model="gemini-3-pro-image-preview", contents="A photorealistic cat on a rainbow sofa", config=types.GenerateContentConfig( response_modalities=["TEXT", "IMAGE"], ), )

Extract and save

for part in response.candidates[0].content.parts: if part.inline_data is not None: with open("output.png", "wb") as f: f.write(part.inline_data.data) break

Aspect Ratio

Set via image_config :

config=types.GenerateContentConfig( response_modalities=["TEXT", "IMAGE"], image_config=types.ImageConfig( aspect_ratio="16:9", # for slides / widescreen ), )

Supported ratios: 1:1 , 2:3 , 3:2 , 3:4 , 4:3 , 4:5 , 5:4 , 9:16 , 16:9 , 21:9

Common choices:

• Slides / presentations → 16:9

• Social media / portraits → 9:16 or 4:5

• Square thumbnails → 1:1

Image Editing (with reference image)

from google.genai import types from pathlib import Path import base64

ref_bytes = Path("input.jpg").read_bytes()

response = client.models.generate_content( model="gemini-3-pro-image-preview", contents=[ types.Part(inline_data=types.Blob(mime_type="image/jpeg", data=base64.b64encode(ref_bytes).decode())), types.Part(text="Remove the background and replace with a sunset gradient"), ], config=types.GenerateContentConfig( response_modalities=["TEXT", "IMAGE"], ), )

Pro supports up to 14 reference images for multi-image composition and up to 5 human reference images for character/identity consistency.

Batch Generation (for slides)

When generating multiple images (e.g. one per slide), loop sequentially and save with numbered filenames:

import os, time from google import genai from google.genai import types

client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])

prompts = [...] # list of prompt strings

for i, prompt in enumerate(prompts, 1): 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"), ), ) for part in response.candidates[0].content.parts: if part.inline_data is not None: with open(f"slide_{i}.png", "wb") as f: f.write(part.inline_data.data) break time.sleep(1) # rate limit courtesy

Error Handling

• Safety filter block: The model may refuse prompts it deems unsafe. Adjust the prompt to be less ambiguous (remove violent/adult/medical imagery language) and retry.

• Empty response: If response.candidates is empty or has no image parts, the prompt may be too vague. Add concrete scene details and retry.

• Rate limit (429): Back off with exponential delay. Default: time.sleep(2 ** attempt) .

• Timeout: Set a reasonable timeout; Pro model may take 10–30s for complex prompts.

Prompt Best Practices

• Structure: scene → subject → style → composition → constraints

• Always specify art style: "flat vector illustration", "watercolor painting", "3D render", "photorealistic photograph"

• Include lighting and mood: "soft diffused lighting", "dramatic rim light", "golden hour"

• For text in images: quote exact text, specify font style and placement

• For slide illustrations: add "negative space on [side]" to leave room for text overlay

• Use English prompts even for non-English content (better generation quality)

• Keep prompts under 500 words; be specific but not verbose

Style Consistency for Multi-Image Sets

When generating a series (e.g. slide deck), prepend a style prefix to every prompt:

Style prefix: "flat vector illustration, soft pastel color palette, clean lines, minimal detail, 16:9 widescreen"

Slide 1 prompt: "{style_prefix}, a wide establishing shot of a modern office building at sunrise" Slide 2 prompt: "{style_prefix}, a close-up of hands typing on a laptop keyboard"

This ensures visual coherence across all generated images.