---name: ai-image-generatordescription: Generate AI images using Gemini or GPT APIs directly. Covers model selection (Gemini for scenes; GPT Image 2 for text rendering, batch variations, multi-reference compositing; GPT Image 1.5 for transparent icons), the 5-part prompting framework, API calling patterns, multi-turn editing, and quality assurance. Produces photorealistic scenes, icons, illustrations, OG images, posters, infographics, and product shots. Use when building websites that need images, creating marketing assets, or generating visual content. Triggers: 'generate image', 'ai image', 'create hero image', 'make an icon', 'generate illustration', 'create og image', 'poster', 'infographic', 'image variations', 'gpt-image-2', 'ai art', 'image generation'.allowed-tools: compatibility: claude-code-only---

AI Image Generator

Generate images using AI APIs (Google Gemini and OpenAI GPT). This skill teaches the prompting patterns and API mechanics for producing professional images directly from Claude Code.

Managed alternative: If you don't want to manage API keys, ImageBot provides a managed image generation service with album templates and brand kit support.

Model Selection

Choose the right model for the job:

Need	Model	Why
Photorealistic scenes / stock photos	Gemini 3.1 Flash Image	Best depth, complexity, environmental context
Final client scenes (higher detail)	Gemini 3 Pro Image	Higher detail, better style consistency
Text on images (posters, OG with copy, infographics)	GPT Image 2	Text rendering actually works — including multi-script
10-variation style exploration	GPT Image 2	Native batch — one prompt, 10 variants sharing composition + palette
Multi-reference compositing (product + lifestyle)	GPT Image 2	Handles lighting, scale, perspective across references
Transparent icons / logos	GPT Image 1.5	Native RGBA alpha — GPT Image 2 cannot do transparency
Quick drafts / iteration	Gemini 2.5 Flash Image	Free tier (~500/day)

Rule of thumb: any image with readable text → GPT Image 2 (unless you need transparency, then GPT 1.5). Otherwise → Gemini.

Model IDs

Model	API ID	Provider
Gemini 3.1 Flash Image	gemini-3.1-flash-image-preview	Google AI
Gemini 3 Pro Image	gemini-3-pro-image-preview	Google AI
Gemini 2.5 Flash Image	gemini-2.5-flash-image	Google AI
GPT Image 2 (default)	gpt-image-2	OpenAI
GPT Image 2 (ChatGPT-parity output)	chatgpt-image-latest	OpenAI
GPT Image 1.5 (transparency-only)	gpt-image-1.5	OpenAI

Verify model IDs before use — they change frequently:

curl -s "https://generativelanguage.googleapis.com/v1beta/models?key=$GEMINI_API_KEY" | python3 -c "import sys,json; [print(m['name']) for m in json.load(sys.stdin)['models'] if 'image' in m['name'].lower()]"

GPT Image 2 Specifics

Released 2026-04-22. Three capabilities that change when you'd reach for it.

1. Text rendering actually works

Posters, OG images with headlines, infographics with labels, UI mockups, pricing cards. Text is rendered reliably, including non-Latin scripts (Japanese, Korean, Hindi, Bengali). Primary reason to switch from Gemini — Gemini doesn't render readable text at all.

2. Multi-variation batching

One prompt, up to 10 images in a single call. Variants share composition and palette but differ in detail. Good for style exploration before committing, A/B options for a client, rapid ideation.

3. Multi-reference compositing

Feed reference images alongside your prompt — product shots, lifestyle scenes, logos. The model places the product into the scene with correct lighting, scale, perspective. Enables "product in context" workflows without multi-turn editing.

Modes

• Instant (default, all plans) — generates without a planning pass. Fast, good enough for most cases.
• Thinking (Plus/Pro/Business plans) — plans layout before drawing. Use when element counts matter ("3 icons in a row", "5 feature bullets") or text must land in specific regions. Fewer re-rolls on complex compositions.

Aspect ratios

3:1 ultra-wide through 1:3 ultra-tall, plus 1:1, 3:2, 2:3, 16:9, 9:16. Wider range than other models — useful for website banners (ultra-wide hero) or mobile story formats (ultra-tall).

Resolution

Up to 2K on the long edge standard. 4K in beta.

Generation time

Up to 2 minutes on complex prompts. Build async UX — don't block on the response. Show progress or spin off and poll.

Constraints

• No transparent backgrounds. Fall back to gpt-image-1.5 when you need PNG transparency.
• API Org Verification may be required before the endpoint fires — enable in your OpenAI account settings if you hit auth errors on first call.

Pricing (per 1024×1024 image)

Quality	Cost
Low	$0.006
Medium	$0.053
High	$0.211

Token pricing: $5/M text in, $10/M text out, $8/M image in, $30/M image out.

The 5-Part Prompting Framework

Build prompts in this order for consistent results:

1. Image Type

Set the genre: "A photorealistic photograph", "An isometric illustration", "A flat vector icon"

2. Subject

Who or what, with specific details: "of a warm, approachable Australian woman in her early 30s, smiling naturally"

3. Environment

Setting and spatial relationships: "in a bright modern home with terracotta decor on wooden shelves behind her"

4. Technical Specs

Camera and lighting: "Shot at 85mm f/2.0, natural window light, head and shoulders framing"

5. Constraints

What to exclude: "Photorealistic, no text, no watermarks, no logos"

Example (Good vs Bad)

BAD — keyword soup:
"professional woman, spa, warm lighting, high quality, 4K"

GOOD — narrative direction:
"A professional skin treatment scene in a warm clinical setting.
A practitioner wearing blue medical gloves uses a microneedling pen
on the client's forehead. The client lies on a white treatment bed,
eyes closed, relaxed. Warm golden-hour light from a window to the
left. Terracotta-toned wall visible in the background. Shot at
85mm f/2.0, shallow depth of field. No text, no watermarks."

Workflow

1. Determine Image Need

Purpose	Aspect Ratio	Model
Hero banner (no text)	16:9 or 21:9	Gemini
Hero banner with headline copy	16:9 or 3:1 ultra-wide	GPT Image 2
Service card	4:3 or 3:4	Gemini
Profile / avatar	1:1	Gemini
Icon / badge (transparent)	1:1	GPT Image 1.5
OG / social share (no text)	1.91:1	Gemini
OG / social share with copy	1.91:1	GPT Image 2
Poster / infographic / pricing card / any typography-heavy	varies	GPT Image 2
Style exploration (10 variants of one concept)	any	GPT Image 2 (batch)
Instagram post	1:1 or 4:5	Gemini
Mobile hero	9:16	Gemini

2. Build the Prompt

Use the 5-part framework. Refer to references/prompting-guide.md for detailed photography parameters.

3. Generate via API

Gemini (Python — handles shell escaping correctly)

python3 << 'PYEOF'
import json, base64, urllib.request, os, sys

GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
if not GEMINI_API_KEY:
    print("Set GEMINI_API_KEY environment variable"); sys.exit(1)

model = "gemini-3.1-flash-image-preview"
url = f"https://generativelanguage.googleapis.com/v1beta/models/{model}:generateContent?key={GEMINI_API_KEY}"

prompt = """A professional photograph of a modern co-working space in
Newcastle, Australia. Natural light floods through floor-to-ceiling
windows. Three people collaborate at a standing desk — one pointing
at a laptop screen. Exposed brick wall, potted fiddle-leaf fig,
coffee cups on the desk. Shot at 35mm f/4.0, environmental portrait
style. No text, no watermarks, no logos."""

payload = json.dumps({
    "contents": [{"parts": [{"text": prompt}]}],
    "generationConfig": {
        "responseModalities": ["TEXT", "IMAGE"],
        "temperature": 0.8
    }
}).encode()

req = urllib.request.Request(url, data=payload, headers={
    "Content-Type": "application/json",
    "User-Agent": "ImageGen/1.0"
})

resp = urllib.request.urlopen(req, timeout=120)
result = json.loads(resp.read())

# Extract image from response
for part in result["candidates"][0]["content"]["parts"]:
    if "inlineData" in part:
        img_data = base64.b64decode(part["inlineData"]["data"])
        output_path = "hero-image.png"
        with open(output_path, "wb") as f:
            f.write(img_data)
        print(f"Saved: {output_path} ({len(img_data):,} bytes)")
        break
PYEOF

GPT Image 1.5 — Transparent Icons

Use gpt-image-1.5 specifically for the transparent PNG case. GPT Image 2 cannot do transparency.

python3 << 'PYEOF'
import json, base64, urllib.request, os, sys

OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
if not OPENAI_API_KEY:
    print("Set OPENAI_API_KEY environment variable"); sys.exit(1)

url = "https://api.openai.com/v1/images/generations"

payload = json.dumps({
    "model": "gpt-image-1.5",
    "prompt": "A minimal, clean plumbing wrench icon. Flat design, single consistent stroke weight, modern style. On a transparent background.",
    "n": 1,
    "size": "1024x1024",
    "background": "transparent",
    "output_format": "png"
}).encode()

req = urllib.request.Request(url, data=payload, headers={
    "Content-Type": "application/json",
    "Authorization": f"Bearer {OPENAI_API_KEY}"
})

resp = urllib.request.urlopen(req, timeout=120)
result = json.loads(resp.read())

img_data = base64.b64decode(result["data"][0]["b64_json"])
with open("icon-wrench.png", "wb") as f:
    f.write(img_data)
print(f"Saved: icon-wrench.png ({len(img_data):,} bytes)")
PYEOF

GPT Image 2 — Text-heavy or Batch Variations

Use gpt-image-2 when text has to render readably, or when you want 10 variants in one call. No transparency — if you need transparent bg, use 1.5 above.

python3 << 'PYEOF'
import json, base64, urllib.request, os, sys, pathlib

OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
if not OPENAI_API_KEY:
    print("Set OPENAI_API_KEY environment variable"); sys.exit(1)

url = "https://api.openai.com/v1/images/generations"

# 10-variation batch of a pricing card with rendered text
payload = json.dumps({
    "model": "gpt-image-2",
    "prompt": (
        "A modern pricing card for a web hosting plan. "
        "Headline 'Starter' in bold sans-serif. "
        "Price '$29/month' directly below in large type. "
        "Three feature lines: 'Unlimited bandwidth', 'SSD storage', 'Free SSL'. "
        "Clean flat design, soft drop shadow, deep blue accent colour. "
        "White card on light grey background."
    ),
    "n": 10,
    "size": "1024x1024",
    "quality": "medium",
    "output_format": "png"
}).encode()

req = urllib.request.Request(url, data=payload, headers={
    "Content-Type": "application/json",
    "Authorization": f"Bearer {OPENAI_API_KEY}"
})

# Timeout: up to 2 min for complex prompts
resp = urllib.request.urlopen(req, timeout=180)
result = json.loads(resp.read())

pathlib.Path("variations").mkdir(exist_ok=True)
for i, item in enumerate(result["data"], 1):
    img_data = base64.b64decode(item["b64_json"])
    path = f"variations/pricing-card-{i:02d}.png"
    with open(path, "wb") as f:
        f.write(img_data)
    print(f"Saved: {path} ({len(img_data):,} bytes)")

print(f"\nGenerated {len(result['data'])} variants. Pick the best; delete the rest.")
PYEOF

Batch workflow: generate 10 → review them side-by-side → pick 1-2 → optionally regenerate with tighter prompt on the winning direction. Faster than single-shot + iterate.

4. Save and Optimise

Save generated images to .jez/artifacts/ or the user's specified path.

Post-processing (optional):

# Convert to WebP for web use
python3 -c "
from PIL import Image
img = Image.open('hero-image.png')
img.save('hero-image.webp', 'WEBP', quality=85)
print(f'WebP: {img.size[0]}x{img.size[1]}')
"

# Trim whitespace from transparent icons
python3 -c "
from PIL import Image
img = Image.open('icon.png')
trimmed = img.crop(img.getbbox())
trimmed.save('icon-trimmed.png')
"

5. Quality Check (Optional)

Send the generated image back to a vision model for QA:

# Send to Gemini Flash for critique
critique_prompt = """Review this image for:
1. AI artifacts (extra fingers, floating objects, text errors)
2. Technical accuracy (wrong equipment, unsafe positioning)
3. Composition issues (awkward cropping, cluttered background)
4. Style consistency with a professional stock photo

List any issues found, or say 'PASS' if the image is production-ready."""

If issues are found, append them as negative guidance to the original prompt and regenerate.

Multi-Turn Editing

Gemini supports editing a generated image across conversation turns. The key requirement: preserve thought signatures from model responses.

# Turn 1: Generate base image
contents = [{"role": "user", "parts": [{"text": "Scene prompt..."}]}]

# The response includes thoughtSignature on parts — preserve them ALL

# Turn 2: Edit the image
contents = [
    {"role": "user", "parts": [{"text": "Original prompt"}]},
    {"role": "model", "parts": response_parts_with_signatures},  # Keep intact
    {"role": "user", "parts": [{"text": "Edit: change the wall colour to blue. Keep everything else exactly the same."}]}
]

Edit prompt pattern: Always specify what to KEEP unchanged, not just what to change. The model treats unlisted elements as free to modify.

GOOD: "Edit this image: keep the people, desk, and window unchanged.
Only change: wall colour from terracotta to ocean blue."

BAD: "Now make the wall blue."
(Model may change everything else too)

API Key Setup

Provider	Get key at	Env variable
Google Gemini	aistudio.google.com	GEMINI_API_KEY
OpenAI	platform.openai.com	OPENAI_API_KEY

export GEMINI_API_KEY="your-key-here"
export OPENAI_API_KEY="your-key-here"

Common Mistakes

Mistake	Fix
Using curl for Gemini prompts	Use Python — shell escaping breaks on apostrophes
"Beautiful, professional, high quality"	Use concrete specs: "85mm f/1.8, golden hour light"
Not specifying what to exclude	Always end with "No text, no watermarks, no logos"
Requesting transparent PNG from Gemini	Gemini cannot do transparency — use GPT Image 1.5 with background: "transparent"
Requesting transparent PNG from GPT Image 2	GPT Image 2 cannot do transparency — fall back to gpt-image-1.5 for this case only
Using GPT Image 1.5 for text on images	GPT Image 1.5 text rendering is unreliable — use gpt-image-2 for any readable text
Blocking a request to GPT Image 2	Generation can take up to 2 min on complex prompts — use 180s timeout, build async UX
American defaults for AU businesses	Explicitly specify "Australian" + local architecture, vegetation
Generic data for model ID	Verify current model IDs — they change frequently