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