/create-image-fal

name: create-image-fal
description: Generate or edit an image via any FAL image model (nano-banana edit, gpt-image, flux, ...), routed through the GooseWorks proxy, so the call is charged to your GooseWorks credits. image_urls must be public URLs; --image puts a local file on the fal CDN first. The recipe names the model + prompt. Use for keyframes, flat-cover transforms, product hero edits. (For the OpenAI gpt-image family specifically, create-image-gpt-image-fal also exists.)
status: active

create-image-fal

**GPT Image 2 quality follows the customer's tier.** `gen_image.py` always sends an explicit quality: `--tier standard` (the default) is `medium`, `--tier best` is `high`; a payload's own `quality`, or `--quality`, wins. Above the tier is a one-shot upgrade, only after a failed check at the cheaper setting or when the customer asks (see create-creator-takes-h3's quality tier). `--query '{"shot_scene": 3, "upgrade_reason": ...}'` sends the call's GooseWorks query fields.

**Never hand-write a person prompt.** Any image with a person in it is made by create-creator-takes-h3's `scripts/make_character.py` (GPT Image 2, phone capture, deep focus, no retouching), unless the recipe prescribes its own creator still; read [the avatar-generation guide](references/avatar-generation.md) first. Nano Banana is for cartoons only, never for real people or real products, and never use Flux for a face.

Generate or edit an image via any FAL image model (nano-banana edit, gpt-image, flux, ...), routed through the GooseWorks proxy, so the call is charged to your GooseWorks credits. image_urls must be public URLs; --image puts a local file on the fal CDN first. The recipe names the model + prompt. Use for keyframes, flat-cover transforms, product hero edits. (For the OpenAI gpt-image family specifically, create-image-gpt-image-fal also exists.)

Run

gen_image.py --model fal-ai/nano-banana/edit --payload '{...}' --out keyframe.png — charged to your GooseWorks credits; returns the *.fal.media URL.

The brand's own product

**Never ask for a blank pack of the advertised product.** "No brand text in a prompt" bans *typing* label text; it never means removing the product's real label. A test video asked for a pouch with "its unprinted back facing the camera" and "no printed words", sent no product photo, and the pack in the hands read as a different product.

• **Refused words.** When the object is the brand's product, a prompt with "unprinted", "blank"

(pack, pouch, box, label...), "unlabeled", "no printed words" (or "without a label") or "back facing the camera" is refused. Pass `--brand-product static` or `--brand-product held` and `gen_image.py` runs `scripts/brand_pack_lint.py` before any upload or paid call.

• **Static or flat:** on a counter or shelf, the real photo usually goes on afterwards with an overlay.
• **Held, opened, poured or used on screen:** `openai/gpt-image-2/edit` with the real product photo

as image 1 (`--product-image pack-front.jpg`, put first), its front facing the camera. The script adds that line to the prompt. Never type the label's words into the prompt.

• **Check the label at 2x against the photo.** If it garbles, reframe so the hands cover the label

area while the shape and colours still match, or cut to the real photo during the action. A blank, unprinted, back-facing or unlabeled pack of the advertised product is a P0 defect.

• **Each visible state** (closed pack, open pack, contents, cooked food) comes from a real brand

photo sent as the edit reference, never from prose. No photo of a state: don't show it.

gen_image.py --model openai/gpt-image-2/edit --brand-product held --product-image pack-front.jpg \
    --image presenter.png --payload '{"prompt": "She tips the open pouch into the pot of boiling water"}' \
    --out pour.png

Contract

• Paid calls route through the GooseWorks proxies (charged to your GooseWorks credits) via the

bundled `media_proxy.py` — never a provider SDK's default host.

• The template recipe (DB) supplies the model + params; this capability is generic.

Edit an image (Nano Banana edit)

`fal-ai/nano-banana/edit` is the default editor for a prompt-driven edit: a restage or scene swap, a subject swap in the same pose, a product placed into a scene. It keeps subject geometry, the room and the light. Use create-image-gpt-image-fal instead when lettering is the point of the edit (logos, wordmarks, signage) or for a transparent cutout. Small text inside the source can come back mangled with either model; check it.

gen_image.py --model fal-ai/nano-banana/edit --image product.png --image https://.../scene.jpg \
    --payload '{"prompt": "Put the bottle on the counter; keep everything else unchanged", "aspect_ratio": "9:16"}' \
    --out edit.png

• `--image` is repeatable and keeps its order; a public url passes through, a local file is put on

the fal CDN through the GooseWorks storage proxy (free). Urls already in the payload's `image_urls` come first.

• Payload: `prompt` (required); `aspect_ratio` `auto` (the input's), `21:9`, `16:9`, `3:2`, `4:3`,

`5:4`, `1:1`, `4:5`, `3:4`, `2:3` or `9:16`; `output_format` `png`, `jpeg` or `webp`; `seed`. The script saves the first image, so leave `num_images` at 1.

• There is no mask. For a region-locked edit, say "keep everything else unchanged" in the prompt.
• Keep the long edge of each input at about 2048 px or less.
• Check the result: it decodes, its size matches the asked aspect, and no watermark, garbled text

or extra fingers appear.

Creator references

Whenever the image will contain a person (creator, presenter, interviewee or any face in focus), with or without a template recipe, read the bundled [avatar-generation guide](references/avatar-generation.md) before any paid call. Unless the selected recipe prescribes its own creator still, make the person with create-creator-takes-h3's scripts/make_character.py, which calls this sk