open-image 0.3.2

Tiny CLI for OpenAI image generation. Prompt in, PNG out. Model-agnostic.

open-image

Tiny CLI for OpenAI image generation. Prompt in, PNG out. Model-agnostic.

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Why another CLI?

Every serious image-gen workflow needs a stable, forgettable command — one you can pipe into, script around, and re-run six months later without rewriting. The official SDKs are fine for apps; they're heavy for "just give me a PNG."

open-image is one file, ~290 lines, pure stdlib + openai. No framework, no config, no lock-in to a specific model.

pip install open-image
export OPENAI_API_KEY=sk-...
open-image --prompt "a red fox in a snowy forest, cinematic"
# → /abs/path/output/20260423-223012-a1b2c3d4.png

That's it.

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Features

Four ways to feed a prompt

Method	Example
Inline	open-image --prompt "a red fox in snow"
File	open-image --prompt-file prompts/scene.txt
Stdin	echo "a blue cat" | open-image
Editor	open-image (no args in a TTY → opens $EDITOR, or notepad on Windows, vi otherwise)

The resolver picks them in that order. Lines starting with # in the editor buffer are stripped — write notes to yourself without polluting the prompt.

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Model-agnostic by design

--model is a flag, not a constant. The day a new image model ships, swap the string — no code change, no version bump, no fork:

open-image --model gpt-image-2   --prompt "..."   # default; requires org verification
open-image --model gpt-image-1   --prompt "..."   # transparency, output_format support
open-image --model future-model  --prompt "..."   # whenever it arrives

Default is gpt-image-2. Change per call, or alias open-image='open-image --model gpt-image-1' in your shell if you prefer a different default.

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--extra escape hatch

Any keyword the API accepts, --extra forwards verbatim to openai.images.generate(**params). Zero client-side validation — the API is the source of truth:

open-image \
  --model gpt-image-2 \
  --extra '{"size":"1024x1024","quality":"high"}' \
  --prompt "a lone surfer at dawn, Hokusai woodblock style"

open-image \
  --model gpt-image-1 \
  --extra '{"size":"1024x1024","output_format":"png","transparency":true}' \
  --prompt "a minimalist cat icon on a transparent background"

If you pass a wrong key, the API error surfaces verbatim — exactly what you want for debugging. No wrapper in the way.

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Install

From PyPI (recommended)

pip install open-image

With pipx (isolated global command)

pipx install open-image

From source

git clone https://github.com/tvtdev94/open-image
cd open-image
pip install -e .

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Setup

Set your OpenAI API key (must have image-generation credit):

# Option A — environment variable (recommended)
export OPENAI_API_KEY=sk-...

# Option B — per-call flag
open-image --api-key sk-... --prompt "..."

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Flags

Flag	Default	Purpose
--prompt	—	Inline prompt text
--prompt-file	—	Path to a file containing the prompt
--model	gpt-image-2	Any OpenAI image model (gpt-image-2, gpt-image-1, dall-e-3, dall-e-2, …)
--extra	{}	JSON object forwarded to images.generate
--out-dir	./output	Where to save PNGs (auto-created)
--api-key	$OPENAI_API_KEY	Override via flag if not in env
--keep	50	Keep only N newest PNGs in --out-dir after save; 0 disables pruning
--list-models	—	List known OpenAI image models with notes, then exit
--install-skill	—	Re-install Claude Code skill at ~/.claude/skills/open-image/ (overwrites)

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Output

./output/{YYYYMMDD-HHMMSS}-{uuid8}.png

One PNG per response.data item (so n=4 → four files). Absolute path(s) printed to stdout, one per line — friendly to xargs, fzf, wl-copy, whatever you pipe into.

open-image --prompt "a corgi" | tee -a log.txt
open-image --prompt "a corgi" | head -n1 | xargs -I{} open {}    # macOS preview

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Gallery

All generated by open-image with gpt-image-2:

A close-up cinematic macro of a bee hovering over a lotus at sunrise.	A bustling night market in a cyberpunk Hanoi alleyway.

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Error handling

Every error path exits with a clear, actionable message:

• No API key → ERROR: No API key. Set OPENAI_API_KEY env or pass --api-key.
• --extra not valid JSON → parser error with column offset
• Empty prompt → ERROR: Empty prompt.
• API failure (auth, model access, invalid params) → API error string forwarded verbatim
• Un-writable --out-dir → PermissionError surfaced with the path

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Models supported

The CLI is model-agnostic — --model accepts any string. These are the models known at write time; pass any future model ID without a code change.

Model	Notes
gpt-image-2	Default. Requires org verification on OpenAI dashboard. Returns b64_json.
gpt-image-1	Newer GPT image model. Supports input_fidelity, transparency, output_format.
dall-e-3	n=1 only. Sizes: 1024x1024 / 1792x1024 / 1024x1792. quality: standard / hd. style: vivid / natural. Pass response_format=b64_json via --extra for offline storage.
dall-e-2	n>1 supported. Sizes: 256x256 / 512x512 / 1024x1024.

Run open-image --list-models to print this table at any time.

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Claude Code integration

If you use Claude Code, open-image ships a Claude skill that teaches the agent how to use this CLI — no manual prompt setup.

• Auto-install: First time you run any open-image command, the skill is silently installed at ~/.claude/skills/open-image/SKILL.md (only if ~/.claude/ exists, never overwrites existing customization).
• Re-install / update: After upgrading the package, refresh the skill content:

  open-image --install-skill
  

Once installed, Claude Code knows when to call open-image, which models exist, how --extra works, and how to capture the stdout paths. If you don't use Claude Code, nothing happens — the auto-install gracefully no-ops when ~/.claude/ is absent.

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Philosophy

Three principles, one file:

• YAGNI — no MCP server, no HTTP wrapper, no runtime plugins. The optional Claude Code skill is just markdown — Claude reads it, no daemon, no IPC. If your agent has a shell, it can use this.
• KISS — argparse + stdlib + one SDK call. Zero abstractions between you and the API.
• DRY — --extra means the tool never needs a new flag per new API param.

The whole tool fits in your head. When a future model adds a parameter, you already know how to use it.

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License

MIT © 2026 tvtdev94