From safe-engineering
Generates and edits images using Google's Gemini API via Python. Supports text-to-image, image editing, style transfers, logos, stickers, mockups, custom resolutions, aspect ratios, multi-turn refinement.
npx claudepluginhub safe-global/safe-engineering-pluginThis skill uses the workspace's default tool permissions.
Generate and edit images using Google's Gemini API. The environment variable `GEMINI_API_KEY` must be set.
Searches, retrieves, and installs Agent Skills from prompts.chat registry using MCP tools like search_skills and get_skill. Activates for finding skills, browsing catalogs, or extending Claude.
Searches prompts.chat for AI prompt templates by keyword or category, retrieves by ID with variable handling, and improves prompts via AI. Use for discovering or enhancing prompts.
Guides TDD-style skill creation: pressure scenarios as tests, baseline agent failures, write docs to enforce compliance, verify with RED-GREEN-REFACTOR.
Generate and edit images using Google's Gemini API. The environment variable GEMINI_API_KEY must be set.
| 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.
gemini-3-pro-image-preview1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9
1K (default), 2K, 4K
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")
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
),
)
)
# 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")
# 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")
Pass existing images with text prompts:
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'],
),
)
Use chat for iterative editing:
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...
Include camera details: lens type, lighting, angle, mood.
"A photorealistic close-up portrait, 85mm lens, soft golden hour light, shallow depth of field"
Specify style explicitly:
"A kawaii-style sticker of a happy red panda, bold outlines, cel-shading, white background"
Be explicit about font style and placement:
"Create a logo with text 'Daily Grind' in clean sans-serif, black and white, coffee bean motif"
Describe lighting setup and surface:
"Studio-lit product photo on polished concrete, three-point softbox setup, 45-degree angle"
Generate images based on real-time data:
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": {}}]
)
)
Combine elements from multiple sources:
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'],
),
)
CRITICAL: The Gemini API returns images in JPEG format by default. When saving, always use .jpg extension to avoid media type mismatches.
# 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!
If you specifically need PNG format:
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")
Check actual format vs extension with the file command:
file image.png
# If output shows "JPEG image data" - rename to .jpg!
.jpg extensionresponseModalities: ["IMAGE"]) won't work with Google Search grounding