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name: create-image-gpt-image-fal description: Generate a single photoreal or designed image with OpenAI gpt-image via fal.ai. Supports gpt-image-1 (default, fixed sizes — the FAL fallback for Higgsfield's gpt_image_2) and gpt-image-2 (openai/gpt-image-2, custom output sizes up to 3840px). Routes to text-to-image or the edit variant depending on whether a reference image is provided. Use for photoreal character anchors, scene keyframes, and designed sheets (e.g. storyboards) where precise layout and legible text matter.

create-image-gpt-image-fal

Purpose

Generate one image via fal.ai's OpenAI gpt-image endpoints. Two model families are supported through a single --model flag:

• gpt-image-1 (default) — fal-ai/gpt-image-1. The FAL fallback for Higgsfield's gpt_image_2. Fixed output sizes only. Used by:
  • video-orchestrator/lock-character Phase 0 (anchor portrait) and Phase 1 (angle keyframes via /edit)
  • video-orchestrator/create-clips Phase 1 for photoreal scenes
  • the orchestrator's generate_with_fallback.py router on Higgsfield failure
• gpt-image-2 — openai/gpt-image-2. The newer model; accepts custom output sizes (any multiple of 16, up to 3840px) and renders dense text/layouts well. Used for designed sheets such as ad storyboards.

The default stays gpt-image-1 so existing callers and the lock-character anchor-parity contract are unaffected. Opt into the newer model with --model gpt-image-2.

Inputs

Required:

• --prompt — text prompt. A verbatim character descriptor block goes here for character work.
• --output — local PNG destination.

Optional:

• --model — gpt-image-1 (default) or gpt-image-2.
• --aspect-ratio — 9:16 (default), 16:9, 1:1, 2:3, 3:2. gpt-image-2 also accepts 3:4, 4:3, 4:5. Used when --image-size is not given.
• --image-size — explicit WIDTHxHEIGHT (e.g. 1728x2304). gpt-image-2 only — values are rounded to multiples of 16 and capped at 3840px. On gpt-image-1 a custom size is ignored with a warning and the aspect-ratio mapping is used instead.
• --quality — low | medium | high (default medium; use high for finals).
• --ref-image / --ref-url — a PUBLIC image URL for the /edit variant. Repeatable — pass it twice to send multiple refs (e.g. identity + style). The proxy does not upload local files, so a local path is rejected — host the image first (the MCP media_upload, or any public URL) and pass that URL. When present, routes to the model's /edit variant so the model can match the references. Order matters: pass identity (character) first, then style refs.
• --with-logs — stream fal queue logs.

Credentials (proxy-routed — NOT a raw FAL key):

• The bundled scripts/media_proxy.py routes every call through the GooseWorks fal-proxy, so every call is charged to your GooseWorks credits. It reads ~/.gooseworks/credentials.json (api_base, api_key, agent_id), written by the GooseWorks CLI. Do not set FAL_API_KEY: an agent (cal_) token is not a FAL key and 401s against fal directly.
• Set GW_PROJECT_ID=<ad project id> in the env so the generation's spend attributes to that ad project (per-project cost shows in the app).

Credentials at run time

A cloud sandbox injects GW_MEDIA_PROXY_TOKEN; a terminal uses the CLI's credentials file. With neither, the bundled helper relays each paid call through the agent instead (see media-proxy), so the script never asks for a key or a sign-in.

Workflow

# Text-to-image, default model (gpt-image-1)
python3 skills/video/create-image-gpt-image-fal/scripts/generate.py \
  --prompt "..." \
  --output /path/to/anchor.png \
  --aspect-ratio 9:16 \
  --quality medium

# Edit-from-reference (anchor -> angle). --ref-image must be a PUBLIC URL,
# NOT a local path (the proxy does not upload local files):
python3 .../generate.py \
  --prompt "..." \
  --output /path/to/angle-3q-left.png \
  --ref-image "https://.../anchor.png" \
  --aspect-ratio 9:16

# gpt-image-2 with a custom output size (e.g. a designed storyboard sheet)
python3 .../generate.py \
  --prompt "..." \
  --output /path/to/storyboard.png \
  --model gpt-image-2 \
  --image-size 1728x2304 \
  --quality high

The script:

1. Loads the agent credentials from ~/.gooseworks/credentials.json via the bundled media_proxy.py (proxy-routed; charged to your GooseWorks credits).
2. Resolves the model family (--model) and output size (--image-size if given and supported, else the aspect-ratio mapping).
3. If one or more --ref-image / --ref-url flags are set, passes them as image_urls=[url1, url2, ...] (they must already be PUBLIC URLs) and routes to the model's /edit variant. Otherwise routes to the /text-to-image variant.
4. Submits through the GooseWorks fal-proxy and polls the queue to completion — host-swapping the queue.fal.run status/response URLs to the proxy base (see media_proxy.py); never polls queue.fal.run directly.
5. Downloads the first result image to --output.
6. Writes <output>.meta.json with gateway: "fal-proxy", model id, model_family, request, and cost.

Output

• <output_path> — PNG (≥ 1 KB).
• <output_path>.meta.json — request + result metadata + cost, including model_family (gpt-image-1 or gpt-image-2).

Quality Checks

• Output file exists and is > 1 KB.
• For character anchors: visually inspect against the descriptor block (hair, shirt color, age).
• meta.json includes gateway: "fal-proxy", the resolved model id, model_family, image_size, and quality.
• For gpt-image-2 custom sizes: confirm the output dimensions match the requested WIDTHxHEIGHT.
• No readable text in the prompt that should appear in the image. AI image models mangle short brand text, URLs, code tokens, captions, and wordmarks even with explicit prompting. Examples observed: "ffmpeg" → "ffmmg"; "claritude" → "clarytude"; "therapists" → "therapits". Use PIL or ffmpeg drawtext for any overlay containing readable text. Reserve image gen for purely visual content (characters, scenes, backgrounds). Repeats LEARNINGS L4.

Failure Modes

| Symptom | Likely cause | Fix | |---|---|---| | 401 Unauthorized from fal | Calling fal directly with an agent token, or polling queue.fal.run instead of the proxy | This atom is proxy-routed — it uses the ~/.gooseworks/credentials.json agent token via media_proxy.py, never a raw FAL_API_KEY. Without that file the helper relays the call instead. | | ERROR: ref images must be PUBLIC URLs | Passed a local path to --ref-image / --ref-url | The proxy does not upload local files. Host it (the MCP media_upload) and pass the resulting public URL. | | 429 Too Many Requests | RPS limit | Drop concurrency to 2-3. | | Custom size ignored | --image-size passed with --model gpt-image-1 | gpt-image-1 only supports fixed sizes; use --model gpt-image-2 for custom sizes. | | Aspect-ratio drift (gpt-image-1) | gpt-image-1 only supports 1024x1024, 1024x1536, 1536x1024 | The script maps aspect ratios to these internally. | | Size rejected (gpt-image-2) | Dimension not a multiple of 16, or > 3840px | The script rounds to /16 and caps at 3840; pass a smaller size. | | Anchor reference ignored | /text-to-image variant doesn't accept refs | Pass --ref-image to force the /edit variant. | | Skin / face looks "AI-stock" | gpt-image's failure mode | Add anti-AI cues to the prompt: "natural skin texture with pores, slight asymmetry, no perfect teeth". |

Model notes

• Product lettering. openai/gpt-image-2/edit off a real product photo keeps proportions and exact lettering far better than nano-banana, so use it for product-hero frames. Cheaper and pixel-exact: cut the real photo out and composite it instead of redrawing the product.
• /edit can ignore the aspect ratio and return a 1024x1024 square; center-cropping that to 9:16 chops the subject. Pass --image-size (gpt-image-2) or verify the returned size before using it.
• No contact shadow under a soft key on a bright seamless floor. That is physically right for the lighting, so re-prompting cannot fix it: ask for a harder key from a steeper angle, a floor several stops darker than the wall, or composite the shadow afterwards.
• Safe zones: see create-image-fal; it holds the notes that apply to every image model.

Cross-provider parity note

When this atom generates a character anchor (lock-character Phase 0), the anchor approved here MUST be pinned for all downstream angle gens, and the same --model must be used for those angle gens. Mixing model families (or mixing FAL-gpt-image with Higgsfield-gpt_image_2) introduces aesthetic drift. The orchestrator's generate_with_fallback.py inherits gateway/model_family from the anchor's .meta.json for subsequent calls.

References

• fal.ai/models/fal-ai/gpt-image-1
• fal.ai/models/openai/gpt-image-2
• Sibling Higgsfield path: mcp__higgsfield__generate_image with model="gpt_image_2"
• Shared helper: scripts/media_proxy.py (proxy-routed FAL/ElevenLabs; charged to your GooseWorks credits — the helper generate.py actually imports). scripts/fal_helpers.py is a LEGACY raw-FAL helper kept for reference only; generate.py does not use it (it would need a real FAL_KEY).
• Storyboard sheets for video ads are made with this skill's gpt-image-2 mode.