Does this skill work with languages other than Python?

While the core concepts apply generally, the specific implementation references and code patterns provided are designed for the Python google-genai library.

Which Google model does this skill use?

This skill is specifically optimized for the gemini-3-pro-image-preview model, offering high-fidelity image generation and editing.

Can I use reference images for style consistency?

Yes, the skill supports style transfer by passing 5 or more reference images to maintain high-fidelity visual consistency across generations.

What image resolutions are supported?

The skill supports three primary resolutions: 1K (~1024px), 2K (~2048px), and 4K (~4096px). Note that 2K is the recommended standard output.

Gemini Image Generation

Name: Gemini Image Generation
Author: tyrchen

bytyrchen

•

105

Ciencia de Datos y ML

Generates and edits high-quality images using Google's Gemini-3-pro-image-preview model via the google-genai Python library.

Acerca de

The gemini-image skill provides developers with a comprehensive implementation guide for integrating Google's advanced image generation capabilities into their projects. It offers standardized patterns for text-to-image generation, high-fidelity style transfer using multiple reference images, and sophisticated image editing. By providing precise configuration options for resolutions up to 4K and various aspect ratios, this skill ensures developers can implement production-ready AI image workflows with best practices for prompt engineering and error handling.

Características Principales

High-fidelity style transfer using up to 5+ reference images
Text-to-image generation with 10+ supported aspect ratios
Optimized Python implementation patterns using google-genai
Support for 1K, 2K, and 4K high-resolution outputs
Advanced image editing and modification via text instructions
105 GitHub stars

Casos de Uso

Automating multi-resolution asset creation for social media and marketing
Building AI-powered design tools with consistent branding via style transfer
Developing image editing applications that utilize generative AI for inpainting

Acerca de

Características Principales

High-fidelity style transfer using up to 5+ reference images
Text-to-image generation with 10+ supported aspect ratios
Optimized Python implementation patterns using google-genai
Support for 1K, 2K, and 4K high-resolution outputs
Advanced image editing and modification via text instructions
105 GitHub stars

Casos de Uso

Automating multi-resolution asset creation for social media and marketing
Building AI-powered design tools with consistent branding via style transfer
Developing image editing applications that utilize generative AI for inpainting