luma-imagegen

name	luma-imagegen
description	Use when the user asks to generate images via the Luma AI API (Dream Machine / Photon); collects a prompt and options interactively, then calls the API using the bundled script. Requires LUMA_API_KEY — will prompt the user if missing.
author	lumalabs

Luma Image Generation Skill

Generates images using the Luma AI Photon model (Dream Machine API). Handles API key detection, interactive prompt collection, parameter selection, async polling, and final image download — all via the bundled scripts/luma_imagegen.py CLI.

When to use

• Generate a new image from a text description using Luma AI (Photon / Photon Flash)
• Use a reference image to guide style, structure, or character consistency
• Modify or stylize an existing image using Luma's modify_image_ref

Workflow

1. Check API key — detect LUMA_API_KEY in environment. If missing, guide the user (see below).
2. Collect inputs — ask the user for: prompt, aspect ratio, model choice, and any optional reference images.
3. Build the structured prompt — augment the user's description into a labeled spec (see prompt template below).
4. Run the bundled CLI — execute scripts/luma_imagegen.py with the collected parameters.
5. Poll until complete — the script handles async polling automatically; wait for state: completed.
6. Display result — show the final image URL and download the image to output/luma/.
7. Iterate — if the result doesn't match expectations, adjust the prompt and re-run.

API key detection & setup

Before any API call, check for the key:

python3 ${CLAUDE_SKILL_DIR}/scripts/luma_imagegen.py --check-key

If LUMA_API_KEY is missing:

1. Tell the user the key is not set.
2. Direct them to generate one: https://lumalabs.ai/dream-machine/api/keys
3. Ask them to add it to their .env file or export it in their shell:

   export LUMA_API_KEY=your_key_here
   

4. Never ask the user to paste the key in chat. Ask them to set it locally and confirm when ready.
5. Once confirmed, retry the --check-key command to verify.

Interactive questions to ask the user

Ask these questions before running the generation:

1. Prompt (required): "What image do you want to generate? Describe the scene, subject, style, and any important details."
2. Aspect ratio (optional, default 16:9): "What aspect ratio? Options: 1:1, 3:4, 4:3, 9:16, 16:9 (default), 9:21, 21:9"
3. Model (optional, default photon-1): "Use photon-1 (higher quality) or photon-flash-1 (faster and cheaper)?"
4. Reference image (optional): "Do you have a reference image URL for style or structure guidance?"

Only ask what's needed — skip questions the user has already answered in their message.

Running the CLI

python3 ${CLAUDE_SKILL_DIR}/scripts/luma_imagegen.py \
  --prompt "YOUR AUGMENTED PROMPT" \
  --aspect-ratio 16:9 \
  --model photon-1 \
  [--image-ref "https://example.com/ref.jpg" --image-ref-weight 0.85] \
  [--out output/luma/]

All flags:

Flag	Default	Description
--prompt	(required)	Text description of the image
--aspect-ratio	16:9	1:1, 3:4, 4:3, 9:16, 16:9, 9:21, 21:9
--model	photon-1	photon-1 or photon-flash-1
--image-ref	—	Public URL for style/structure reference
--image-ref-weight	0.85	Weight of reference image (0.0–1.0)
--modify-ref	—	Base image URL to modify
--modify-ref-weight	0.5	Weight for modification fidelity
--out	output/luma/	Output directory for downloaded images
--poll-interval	3	Seconds between polling requests
--check-key	—	Verify LUMA_API_KEY is set and exit

Output conventions

• Save final images to output/luma/ with descriptive filenames (e.g., photon1_hero_16x9.png). The output directory is relative to the current working directory when the script is invoked.
• Log the generation ID for reference (useful to retrieve the image later).
• If the generation fails, show the failure_reason from the API response.

Prompt augmentation

Reformat the user's description into a structured spec. Only make implied details explicit — do not invent new requirements.

Template (include only relevant lines):

Primary request: <user's main prompt>
Scene/background: <environment or setting>
Subject: <main subject>
Style/medium: <photo/illustration/3D/cinematic/etc>
Composition/framing: <wide/close-up/overhead; subject placement>
Lighting/mood: <lighting type and emotional tone>
Color palette: <dominant colors or palette notes>
Aspect ratio: <e.g., 16:9 landscape>
Avoid: <elements to exclude>

Augmentation rules:

• Keep it concise — add only what the user implied or provided.
• Always include "Avoid:" to prevent common quality issues (watermarks, logos, blur).
• For modification requests, explicitly list what should change and what must stay the same.

Example augmented prompts

Landscape hero image

Primary request: a misty mountain lake at sunrise
Scene/background: alpine lake surrounded by pine trees, light morning fog
Style/medium: photorealistic nature photography
Composition/framing: wide panoramic, lake centered, mountains in background
Lighting/mood: golden hour, warm and serene
Aspect ratio: 16:9 landscape
Avoid: people, boats, watermarks, oversaturation

Product shot

Primary request: a ceramic coffee mug on a wooden table
Scene/background: warm kitchen interior, soft bokeh background
Subject: minimalist white ceramic mug, steam rising
Style/medium: clean product photography
Lighting/mood: soft diffused window light
Aspect ratio: 1:1 square
Avoid: text, logos, harsh shadows, clutter

Prompting best practices

• Describe scene → subject → style → composition → lighting.
• Mention the intended use (hero image, social post, product shot) to calibrate detail level.
• Use "Avoid:" to eliminate common defects (watermarks, blur, stock-photo clichés).
• For modifications, list invariants explicitly ("change only the background; keep the mug unchanged").
• Start with photon-flash-1 for quick iteration; switch to photon-1 for final quality.
• If the result isn't satisfactory, make one targeted change per iteration.

Models reference

Model	Speed	Quality	Best for
photon-1	Slower	Higher	Final assets, complex scenes
photon-flash-1	Fast	Good	Rapid iteration, drafts

Dependencies

The script uses only the Python standard library. No additional packages are required.