Gemini Image

Generate or edit images using Gemini API with multiple reference image support. Use for image generation, style transfer, combining references, UI mockups, or any visual creation task.

Gemini Image Generation

Generate images with support for multiple reference images (up to 14).

Setup

Required: GEMINI_API_KEY environment variable

Get your API key at: https://aistudio.google.com/apikey

Set it:

bash

export GEMINI_API_KEY="your-key-here"

Or in your OpenClaw config (~/.openclaw/openclaw.json):

json

{
  "skills": {
    "entries": {
      "gemini-image": {
        "env": { "GEMINI_API_KEY": "your-key-here" }
      }
    }
  }
}

Usage

bash

# Generate from prompt
uv run {baseDir}/scripts/generate.py -p "a sunset over mountains" -o sunset.png

# Edit/transform one image
uv run {baseDir}/scripts/generate.py -p "make this pop art style" -i photo.png -o popart.png

# Combine multiple references (up to 14)
uv run {baseDir}/scripts/generate.py -p "combine style of first with content of second" \
  -i style-ref.png -i content.png -o combined.png

# With aspect ratio
uv run {baseDir}/scripts/generate.py -p "wide landscape" -a "16:9" -o wide.png

# Higher resolution
uv run {baseDir}/scripts/generate.py -p "detailed portrait" -r 2K -o portrait.png

Options

Flag	Description
-p, --prompt	Image prompt (required)
-o, --output	Output filename (required)
-i, --input-image	Reference image (repeatable, up to 14)
-m, --model	pro (default), flash2, flash, or exp
-r, --resolution	1K (default), 512, 2K, 4K (pro/flash2)
-a, --aspect-ratio	See ratios below (pro/flash2)
-t, --thinking	minimal (default), high, dynamic (pro/flash2)

Models

• pro (gemini-3-pro-image-preview) — Highest quality, thinking mode, up to 14 refs, aspect ratio + resolution
• flash2 (gemini-3.1-flash-image-preview) — NEW: Nano Banana 2. Best price/perf, thinking levels, great text rendering, localization, aspect ratio + resolution + 512px
• flash (gemini-2.5-flash-image) — Older Flash, simpler config, no aspect ratio/resolution control
• exp (gemini-2.0-flash-exp) — Experimental, good for edits

When to use flash2 vs pro:

• flash2 — Fast iterations, text-heavy images, localization, bulk generation, great quality at lower cost
• pro — Maximum quality, complex multi-reference compositions, highest fidelity

Aspect Ratios (pro/flash2)

1:1 · 2:3 · 3:2 · 3:4 · 4:3 · 4:5 · 5:4 · 9:16 · 16:9 · 21:9 · 4:1 · 1:4 · 8:1 · 1:8

Thinking Levels (pro/flash2)

Control how much the model reasons before generating:

• minimal (default) — Fast, good for simple prompts
• high — Better quality for complex multi-element prompts
• dynamic — Model decides how much thinking is needed

bash

# Complex scene with high thinking
uv run {baseDir}/scripts/generate.py -p "detailed city scene with specific text overlay" -m flash2 -t high -o city.png

Reference Image Limits

Model	Max Images	Notes
pro	14 total	5 high-fidelity, 6 objects, 5 humans
flash	3	Best with ≤3 inputs
exp	varies	Experimental

Batch Generation (50% cheaper)

For bulk jobs that can wait 24hr, use the batch API.

"Batch Submit" Workflow

When user says "batch submit", follow this automated workflow:

1. Submit batch job — Create JSONL, run batch.py submit
2. Create hourly cron — Check batch status every hour
3. On completion — Download images to specified output directory
4. Deliver to originating channel — Send all images to the channel where the request was made (Telegram, Discord, Signal, etc.)
5. Self-disable cron — Remove the cron job after successful delivery

Cron job text template:

text

Check gemini-image batch [BATCH_ID]. If complete: download to [OUTPUT_DIR], send all images to [CHANNEL], then disable this cron job.

Manual Batch Commands

1. Create requests JSONL (one per line):

json

{"key": "sunset", "prompt": "a sunset over mountains", "aspect_ratio": "16:9", "resolution": "2K"}
{"key": "portrait", "prompt": "corporate headshot", "input_images": ["/path/to/ref.png"]}

2. Submit batch:

bash

uv run {baseDir}/scripts/batch.py submit requests.jsonl

3. Check status (typically ready within 24hr):

bash

uv run {baseDir}/scripts/batch.py status

4. Download when done:

bash

uv run {baseDir}/scripts/batch.py download -o ./images/

When to use batch vs instant:

• Need it now → generate.py (instant, full price)
• Bulk generation, can wait → batch.py (24hr, 50% off)

Prompting Guide

See references/prompting.md for detailed strategies:

Generation:

• Photorealistic scenes (camera/lens/lighting terms)
• Stylized illustrations & stickers (transparent backgrounds)
• Text rendering (logos, typography)
• Product mockups (commercial photography)
• Minimalist/negative space design
• Sequential art (comics, storyboards)
• Google Search grounding (real-time info)
• iOS wireframes & UI mockups

Editing:

• Adding/removing elements
• Inpainting (semantic masking)
• Style transfer
• Combining multiple images
• High-fidelity detail preservation
• Sketch → finished image
• Character consistency (360 views)

Core principle: Describe the scene narratively, don't just list keywords.

Tips

• Images go first in context, prompt last — order -i flags intentionally
• For style transfer: first image = style reference, second = content
• Pro model uses "Thinking" phase to plan composition before generating
• Timestamps in filenames: 2026-01-26-sunset.png
• Script outputs MEDIA: line for Clawdbot auto-attach

Draft Mode (Preview Before Generate)

For complex generations — especially UI mockups and wireframes — use a two-step workflow:

Step 1: Draft the JSON Prompt

When user asks for a wireframe or complex image, show the structured prompt first instead of generating immediately:

text

Here's the prompt I'll use:

{
  "image_type": "UI mockup",
  "device": {"frame": "iPhone 16 Pro", "orientation": "portrait"},
  "design_system": {
    "style": "iOS 18 native",
    "corners": "rounded, 16px radius",
    "shadows": "soft drop shadows",
    "spacing": "8pt grid",
    "font": "SF Pro"
  },
  "layout": {
    "header": "Navigation bar with back button and title 'Settings'",
    "content": "List of settings items with icons and toggles",
    "bottom": "Tab bar with 4 items"
  }
}

Want me to generate this, or should I adjust anything first?

Step 2: User Approves or Tweaks

• "looks good" / "generate" → Run the generation
• "make the header bigger" / "add a search bar" → Update JSON, show again
• "use flash model" → Note the model preference

Step 3: Generate

Convert JSON to narrative prompt and run:

bash

uv run {baseDir}/scripts/generate.py \
  -p "[narrative version of JSON]" \
  -o mockup.png \
  -a "9:16" \
  -m pro

When to Use Draft Mode

Use Draft Mode	Skip Draft (Generate Direct)
UI mockups / wireframes (new)	Simple images ("sunset over mountains")
Complex multi-element scenes	Style transfer with clear reference
User says "let me see the prompt first"	User says "just make it"
Previous generation failed/missed the mark	Simple edits (color swap, minor tweaks)
Structural changes to existing image	—

Trigger phrases for draft mode:

• "wireframe", "mockup", "UI", "screen design"
• "let me review the prompt"
• "show me what you'll generate"
• "draft first"

Edit iterations:

• Simple tweaks (color change, font swap) → Generate directly, no approval needed
• Structural changes (new elements, layout changes, removing sections) → Show change summary for approval

iOS Wireframes

Quick example:

bash

uv run {baseDir}/scripts/generate.py \
  -p "Convert this wireframe to high-fidelity iOS 18 UI. iPhone 16 Pro frame, rounded corners, soft shadows, SF Pro font. Interpret: scribbles→images, rectangles→buttons, lines→text" \
  -i wireframe-sketch.png \
  -o mockup.png \
  -a "9:16"

See references/prompting.md for detailed templates including JSON-structured prompts and multi-screen flows.