nano-banana-2

inference-sh/skills · updated Apr 8, 2026

$npx skills add https://github.com/inference-sh/skills --skill nano-banana-2
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summary

Generate images with Google Gemini 3.1 Flash Image Preview via inference.sh CLI.

skill.md

Nano Banana 2 - Gemini 3.1 Flash Image Preview

Generate images with Google Gemini 3.1 Flash Image Preview via inference.sh CLI.

Quick Start

Requires inference.sh CLI (infsh). Install instructions

infsh login

infsh app run google/gemini-3-1-flash-image-preview --input '{"prompt": "a banana in space, photorealistic"}'

Examples

Basic Text-to-Image

infsh app run google/gemini-3-1-flash-image-preview --input '{
  "prompt": "A futuristic cityscape at sunset with flying cars"
}'

Multiple Images

infsh app run google/gemini-3-1-flash-image-preview --input '{
  "prompt": "Minimalist logo design for a coffee shop",
  "num_images": 4
}'

Custom Aspect Ratio

infsh app run google/gemini-3-1-flash-image-preview --input '{
  "prompt": "Panoramic mountain landscape with northern lights",
  "aspect_ratio": "16:9"
}'

Image Editing (with input images)

infsh app run google/gemini-3-1-flash-image-preview --input '{
  "prompt": "Add a rainbow in the sky",
  "images": ["https://example.com/landscape.jpg"]
}'

High Resolution (4K)

infsh app run google/gemini-3-1-flash-image-preview --input '{
  "prompt": "Detailed illustration of a medieval castle",
  "resolution": "4K"
}'

With Google Search Grounding

infsh app run google/gemini-3-1-flash-image-preview --input '{
  "prompt": "Current weather in Tokyo visualized as an artistic scene",
  "enable_google_search": true
}'

Input Options

Parameter Type Description
prompt string Required. What to generate or change
images array Input images for editing (up to 14). Supported: JPEG, PNG, WebP
num_images integer Number of images to generate
aspect_ratio string Output ratio: "1:1", "16:9", "9:16", "4:3", "3:4", "auto"
resolution string "1K", "2K", "4K" (default: 1K)
output_format string Output format for images
enable_google_search boolean Enable real-time info grounding (weather, news, etc.)

Output

Field Type Description
images array The generated or edited images
description string Text description or response from the model
output_meta object Metadata about inputs/outputs for pricing

Prompt Tips

Styles: photorealistic, illustration, watercolor, oil painting, digital art, anime, 3D render

Composition: close-up, wide shot, aerial view, macro, portrait, landscape

Lighting: natural light, studio lighting, golden hour, dramatic shadows, neon

Details: add specific details about textures, colors, mood, atmosphere

Sample Workflow

# 1. Generate sample input to see all options
infsh app sample google/gemini-3-1-flash-image-preview --save input.json

# 2. Edit the prompt
# 3. Run
infsh app run google/gemini-3-1-flash-image-preview --input input.json

Python SDK

from inferencesh import inference

client = inference()

# Basic generation
result = client.run({
    "app": "google/gemini-3-1-flash-image-preview@0c7ma1ex",
    "input": {
        "prompt": "A banana in space, photorealistic"
    }
})
print(result["output"])

# Stream live updates
for update in client.run({
    "app": "google/gemini-3-1-flash-image-preview@0c7ma1ex",
    "input": {
        "prompt": "A futuristic cityscape at sunset"
    }
}, stream=True):
    if update.get("progress"):
        print(f"progress: {update['progress']}%")
    if update.get("output"):
        print(f"output: {update['output']}")

Related Skills

# Original Nano Banana (Gemini 3 Pro Image, Gemini 2.5 Flash Image)
npx skills add inference-sh/skills@nano-banana

# Full platform skill (all 150+ apps)
npx skills add inference-sh/skills@infsh-cli

# All image generation models
npx skills add inference-sh/skills@ai-image-generation

Browse all image apps: infsh app list --category image

Documentation

Discussion

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general reviews

Ratings

4.837 reviews
  • Evelyn Agarwal· Dec 28, 2024

    nano-banana-2 is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Pratham Ware· Dec 24, 2024

    nano-banana-2 fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Yusuf Wang· Dec 20, 2024

    nano-banana-2 fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Chaitanya Patil· Dec 16, 2024

    Keeps context tight: nano-banana-2 is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Kiara Iyer· Dec 12, 2024

    Keeps context tight: nano-banana-2 is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Michael Sanchez· Nov 19, 2024

    Solid pick for teams standardizing on skills: nano-banana-2 is focused, and the summary matches what you get after install.

  • Kabir Torres· Nov 11, 2024

    Keeps context tight: nano-banana-2 is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Piyush G· Nov 7, 2024

    nano-banana-2 has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Diya Torres· Nov 3, 2024

    nano-banana-2 has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Shikha Mishra· Oct 26, 2024

    Solid pick for teams standardizing on skills: nano-banana-2 is focused, and the summary matches what you get after install.

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