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name: image-generation description: > Generate or edit images with Gemini Pro. Use when user says "generate an image", "create a picture", "make me a logo", "edit this image", "remove the background", "change the style", "combine these images", "add text to image", "style transfer", "make a sticker", "product mockup", or any image creation/manipulation request. Handles t2i (text-to-image), i2i (image-to-image editing), and multi-reference composition.

Image Generation

Generate and edit images using Google's Gemini Pro Image API. Requires GEMINI_API_KEY environment variable.

Default Output & Logging

When the user doesn't specify a location, save images to:

Copy/Users/samarthgupta/Documents/generated images/

Every generated image gets a companion .md file with the prompt used (e.g., logo.png → logo.md).

When gathering parameters (aspect ratio, resolution), offer the option to specify a custom output location.

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Core Prompting Principle

Describe scenes narratively, don't list keywords. Gemini has deep language understanding—write prompts like prose, not tags.

Copy❌ "cat, wizard hat, magical, fantasy, 4k, detailed"

✓ "A fluffy orange tabby sits regally on a velvet cushion, wearing an ornate
   purple wizard hat embroidered with silver stars. Soft candlelight illuminates
   the scene from the left. The mood is whimsical yet dignified."

The Formula

Copy[Subject + Adjectives] doing [Action] in [Location/Context].
[Composition/Camera]. [Lighting/Atmosphere]. [Style/Media]. [Constraint].

Not every prompt needs every element—match detail to intent.

Prescriptive vs Open Prompting

Prescriptive (user has specific vision): Detailed descriptions, exact specifications Open (exploring/want model creativity): General direction, let model decide details

Both are valid. Ask the user's intent if unclear.

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Capability Patterns

Photorealistic Scenes

Think like a photographer: describe lens, light, moment.

• Specify camera (85mm portrait, 24mm wide), aperture (f/1.8 bokeh, f/11 sharp throughout)
• Describe lighting direction and quality (golden hour from camera-left, three-point softbox)
• Include mood and format (serene, vertical portrait)

Product Photography

• Isolation: Clean white backdrop, soft even lighting, e-commerce ready
• Lifestyle: Product in use context, natural setting, aspirational but authentic
• Hero shots: Cinematic framing, dramatic lighting, space for text overlay

Logos & Text

• Put text in quotes: 'Morning Brew Coffee Co'
• Describe typography: "clean bold sans-serif with generous letter-spacing"
• Specify color scheme, shape constraints, design intent
• Iterate with follow-up edits for refinement

Stylized Illustration

• Name the style: "kawaii-style sticker", "anime-influenced", "vintage travel poster"
• Describe design language: "bold outlines, flat colors, cel-shading"
• Include format constraints: "white background", "die-cut sticker format"

Editing Images

• Acknowledge subject: "Using the provided image of my cat..."
• Explicit preservation: "Keep everything unchanged except..."
• Realistic integration: "should look naturally printed on the fabric"
• Image ordering: Main image to edit should be last in --input list

Pattern: Acknowledge → specify change → describe integration → preserve the rest

Multi-Image Composition

• State output goal first
• Assign elements: "Take X from first image, Y from second"
• Describe integration requirements (lighting match, realistic shadows)
• Supports up to 14 reference images

Character Consistency

• Use follow-up edits for multiple views of the same character
• Reference distinctive features explicitly in follow-ups
• Include "exact same character" or "maintain all design details"
• Save successful designs as reference for future prompts

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Invoking Aesthetics Through Naming

Names invoke aesthetics. The model learned associations for film stocks, cameras, studios, artists, and styles. Instead of describing characteristics, reference the name directly.

Copy"Portrait at golden hour, shot on Kodak Portra 400"
→ Warm skin tones, pastel highlights, fine grain

"Studio Ghibli forest scene"
→ Lush nature, soft lighting, whimsical atmosphere

"Fashion editorial, Hasselblad medium format"
→ Exceptional detail, shallow DOF, that medium format look

This works for photography, animation, illustration, game art, graphic design, fine art—anything with a recognizable visual identity.

See STYLE_REFERENCE.md for comprehensive lexicon of film stocks, cameras, studios, artists, and styles.

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Configuration

Aspect Ratios

1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9

Resolutions

• 1K (~1024px) — default, fast
• 2K (~2048px) — high quality
• 4K (~4096px) — maximum detail

Defaults: 1K resolution, 1:1 aspect. Confirm with user before changing.

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Advanced Features

Google Search Grounding

Enable with --grounding flag when real-time data helps:

• Weather visualizations
• Current events infographics
• Real-world data charts

Semantic Masking

No manual masking needed. Describe changes conversationally:

• "Change the sofa to red leather"
• "Replace the background with a sunset beach"
• "Remove the power lines from the sky"

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Script Usage

One unified script handles all modes: t2i, i2i, and multi-reference composition.

Copy# Text-to-image (t2i)
uv run {baseDir}/scripts/generate.py --prompt "A serene mountain lake at dawn" --output landscape.png

# Image-to-image editing (i2i)
uv run {baseDir}/scripts/generate.py --prompt "Make it sunset colors" --input photo.png --output edited.png

# Multi-reference composition (up to 14 images)
uv run {baseDir}/scripts/generate.py --prompt "Combine the cat from image 1 with the background from image 2" --input cat.png --input background.png --output composite.png

# With options
uv run {baseDir}/scripts/generate.py --prompt "Logo for 'Acme Corp'" --output logo.png --aspect 1:1 --resolution 2K

# With Google Search grounding
uv run {baseDir}/scripts/generate.py --prompt "Current weather in Tokyo visualized" --output weather.png --grounding

# Batch generation (up to 4 images, 2 parallel requests)
uv run {baseDir}/scripts/generate.py --prompt "A cat in different poses" --output cat.png --batch 4
# Outputs: cat-1.png, cat-2.png, cat-3.png, cat-4.png

Script Options

Auto-Resolution Detection

When editing images, the script automatically detects appropriate resolution from input dimensions:

• Input ≥3000px → 4K output
• Input ≥1500px → 2K output
• Otherwise → 1K output

Override with explicit --resolution flag.

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Recommended Defaults

Unless the user specifies otherwise, use:

• Resolution: 2K (good balance of quality and speed)
• Batch: 3 (gives variety without overwhelming)

Only use 4K when high detail is explicitly needed (large prints, zoom-in requirements).

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Pre-Generation Confirmation

Before running the script, show the user: (1) the exact prompt, (2) input images in order if editing/composing, (3) resolution and aspect ratio. Ask for confirmation before proceeding.

Quick Checklist

Before generating:

• Narrative description (not keyword list)?
• Camera/lighting details for photorealism?
• Text in quotes, font style described?
• Aspect ratio appropriate for use case?
• User preference: prescriptive or open?