Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/majesticlabs-dev/majestic-marketplace --skill gemini-image-coderAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
Gemini Image Generation
Generate and edit images using Google's Gemini API. Requires GEMINI_API_KEY environment variable.
Quick Reference
| Setting | Default | Options |
|---|---|---|
| Model | gemini-3-pro-image-preview | Use this for all generation |
| Resolution | 1K | 1K, 2K, 4K |
| Aspect Ratio | 1:1 | 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9 |
CLI Scripts
Generate Image
bash
python scripts/generate_image.py "A cat in space" output.jpg
python scripts/generate_image.py "Epic landscape" landscape.jpg --aspect 16:9 --size 2K
python scripts/generate_image.py "Logo for Acme Corp" logo.jpg --aspect 1:1Edit Image
bash
python scripts/edit_image.py input.jpg "Add a rainbow" output.jpg
python scripts/edit_image.py photo.jpg "Make it look like Van Gogh" artistic.jpgCore API Pattern
python
import os
from google import genai
from google.genai import types
client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])
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.jpg") # Always use .jpg!Custom Resolution & Aspect Ratio
python
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",
image_size="2K"
),
)
)Editing Images
python
from PIL import Image
img = Image.open("input.jpg")
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'],
),
)Multi-Turn Refinement
python
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...Prompting Best Practices
| Style | Prompt Pattern |
|---|---|
| Photorealistic | Include camera: lens, lighting, angle, mood |
| Stylized Art | Specify style explicitly: "kawaii-style", "cel-shading" |
| Text in Images | Be explicit: font style, placement, colors |
| Product Mockups | Describe lighting setup and surface |
Examples
# Photorealistic
"A photorealistic close-up portrait, 85mm lens, soft golden hour light, shallow depth of field"
# Stylized
"A kawaii-style sticker of a happy red panda, bold outlines, cel-shading, white background"
# Logo with text
"Create a logo with text 'Daily Grind' in clean sans-serif, black and white, coffee bean motif"
# Product mockup
"Studio-lit product photo on polished concrete, three-point softbox setup, 45-degree angle"Advanced Features
Google Search Grounding
python
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": {}}]
)
)Multiple Reference Images (Up to 14)
python
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.jpg"),
Image.open("person2.jpg"),
Image.open("person3.jpg"),
],
config=types.GenerateContentConfig(
response_modalities=['TEXT', 'IMAGE'],
),
)Critical: File Format
Gemini returns JPEG by default. Always use `.jpg` extension.
python
# CORRECT
image.save("output.jpg")
# WRONG - causes "Image does not match media type" errors
image.save("output.png") # Creates JPEG with PNG extension!If PNG is Required
python
from PIL import Image
for part in response.parts:
if part.inline_data:
img = part.as_image()
img.save("output.png", format="PNG") # Explicit conversionMulti-Image Consistency
When generating a set of images that must look like the same scene (e.g., room makeovers, product variations, before/after sequences):
Lock the architecture, vary only the style.
- Write one detailed base description: dimensions, camera angle, window count/position, door location, furniture size, ceiling height, floor type
- Copy the base description identically into every prompt
- Change only the style portion: colors, materials, decor, lighting fixtures
Common failures without this technique:
- Windows appear/disappear between images
- Room dimensions change, furniture moves
- Result looks like N different rooms, not one room in N styles
Prompt structure:
[Camera/phone type]. [Detailed room architecture — identical across all images].
[Natural lighting description]. [Orientation].
**[STYLE VARIATION — only this part changes per image]**Tips:
- Include "iPhone photo" and "realistic lighting" for photorealistic output
- Add signs of life (mugs, remotes, books) so spaces feel inhabited, not staged
- "Before" images should look modern but tired, not derelict
- Always use portrait orientation (9:16 / 2:3) for social media slideshows
Notes
- All generated images include SynthID watermarks
- Default to 1K for speed; use 2K/4K when quality is critical
- For editing, describe changes conversationally—the model understands semantic masking
- Image-only mode won't work with Google Search grounding
