gpt-image-edit

$29

You want

Use

Edit multilingual / embedded text in image

GPT Image Edit

Identity preservation through translated headline variants

GPT Image Edit

Layout-precise edit (move headline, swap CTA, etc.)

GPT Image Edit

Up to 10 reference images

GPT Image Edit

Batch up to 20 images consistently

Nano Banana Edit

Single-shot precise local edit, source-fidelity-first

Flux Kontext

Generate from scratch with GPT Image 2

sibling gpt-image-2 skill

Batch SKU galleries with stable identity

Nano Banana Edit

Prerequisites

• RunComfy CLI — npm i -g @runcomfy/cli

• RunComfy account — runcomfy login opens a browser device-code flow.

• CI / containers — set RUNCOMFY_TOKEN=<token> instead of runcomfy login.

Endpoints + input schema

openai/gpt-image-2/edit

Field

Type

Required

Default

Notes

prompt

string

yes

—

Edit instruction. Lead with preservation, end with the change.

images

string[]

yes

—

Up to 10 publicly-fetchable HTTPS URLs. First is primary; rest are auxiliary.

size

enum

no

auto

auto (preserve input), 1024_1024 (1:1), 1024_1536 (2:3 portrait), 1536_1024 (3:2 landscape).

size=auto preserves the input ratio — strongly recommended unless the edit explicitly changes framing.

How to invoke

Single-ref preservation edit:

runcomfy run openai/gpt-image-2/edit \

  --input '{

    "prompt": "Keep the person'\''s face, pose, and brand mark unchanged. Replace the background with a soft warm-grey studio sweep and a gentle floor shadow.",

    "images": ["https://.../portrait.jpg"]

  }' \

  --output-dir <absolute/path>

Multilingual text rewrite (preserve everything except the headline):

runcomfy run openai/gpt-image-2/edit \

  --input '{

    "prompt": "Keep the photograph, layout, and brand mark exactly as in the input. Replace only the in-image headline. The new headline reads \"今日のおすすめ\" in bold Japanese kana, same position and font weight as before.",

    "images": ["https://.../poster-en.jpg"]

  }' \

  --output-dir <absolute/path>

Multi-ref composition:

runcomfy run openai/gpt-image-2/edit \

  --input '{

    "prompt": "Compose subject from image 1 into the room from image 2. Match the lighting and color palette of image 2. Keep image 1 subject identity (face, pose, clothing) unchanged.",

    "images": ["https://.../subject.jpg", "https://.../room.jpg"]

  }' \

  --output-dir <absolute/path>

Prompting — what actually works

Lead with preservation goals. Always: "Keep [face / pose / clothing / brand / framing] unchanged." Then state the change. The model honors what's stated up front.

Multilingual text — quote the characters, name the script. "the headline reads \"コーヒー\" in bold Japanese kana", "the label says \"АРОМА\" in Cyrillic, white on black", "the right-margin caption reads \"تخفيض\" in Arabic right-to-left". Don't paraphrase — quote.

Directional language for spatial edits. Concrete spatial scopes work: "move the headline from top-right to bottom-center", "remove the leftmost object only", "replace the watermark in the bottom-right corner".

Multi-ref numbering. When passing multiple images, refer to them by number: "subject from image 1, lighting from image 2, color palette from image 3". The model routes cues correctly.

**Use size: "auto" to preserve input ratio.** Only override when the edit explicitly changes framing (e.g. cropping a 16:9 to 1:1).

Anti-patterns:

• Long compound edit instructions ("change A and B and C and D") → drift increases per added scope.

• Missing preservation goals → model subtly rewrites the face / brand / framing.

• Paraphrasing in-image text instead of quoting it → text comes out different.

• Asking for size outside the 3 fixed values + auto → 422.

Where it shines

Use case

Why GPT Image Edit

Multilingual ad localization

One source asset → many language variants of the same headline

Brand-safe headline / CTA swaps

Layout precision + preservation language hold the rest stable

Multi-ref composition (subject from one, scene from another)

Numbered refs route cues correctly

Layout-precise repositioning

Directional language ("top-right to bottom-center") honored

Identity preservation across signage edits

Strongest in class for face / brand preservation through targeted edits

Sample prompts (verified to produce strong results)

Background swap with full preservation (page example):

Turn the background into a bright minimal white-to-soft-gray studio

sweep with gentle floor shadow; add a large headline in-image that

reads "OPEN STUDIO" in a bold clean sans-serif, high contrast, centered;

keep the main person or product, pose, and face identity unchanged

Multilingual variant:

Keep the photograph, layout, lighting, and brand mark exactly as in the

input. Replace only the in-image headline.

The new headline reads "コーヒー" in bold Japanese kana, same position

and font weight as before.

Multi-ref composition:

Compose subject from image 1 into the kitchen from image 2.

Match the warm window light and color palette of image 2.

Keep subject identity (face, pose, clothing) from image 1 unchanged.

Limitations

• **size: 3 fixed values + auto** — anything else 422s.

• **images: up to 10** — first is primary, rest are auxiliary cues.

• Long compound prompts drift — split into multiple passes when needed.

• For batch consistency across many SKU images, Nano Banana Edit (up to 20) is better.

• Photorealism on portraits — Nano Banana Pro wins head-to-head.

Exit codes

code

meaning

0

success

64

bad CLI args

65

bad input JSON / schema mismatch

69

upstream 5xx

75

retryable: timeout / 429

77

not signed in or token rejected

Full reference: docs.runcomfy.com/cli/troubleshooting.

How it works

The skill invokes runcomfy run openai/gpt-image-2/edit with a JSON body matching the schema. The CLI POSTs to https://model-api.runcomfy.net/v1/models/openai/gpt-image-2/edit, polls the request, fetches the result, and downloads any .runcomfy.net/.runcomfy.com URL into --output-dir. Ctrl-C cancels the remote request before exit.

Security & Privacy

• Token storage: runcomfy login writes the API token to ~/.config/runcomfy/token.json with mode 0600 (owner-only read/write). Set RUNCOMFY_TOKEN env var to bypass the file entirely in CI / containers.

• Input boundary: the user prompt is passed as a JSON string to the CLI via --input. The CLI does NOT shell-expand the prompt; it transmits the JSON body directly to the Model API over HTTPS. No shell injection surface from prompt content.

• Third-party content: image / mask / video URLs you pass are fetched by the RunComfy model server, not by the CLI on your machine. Treat external URLs as untrusted; image-based prompt injection is a known risk for any image-edit / video-edit model.

• Outbound endpoints: only model-api.runcomfy.net (request submission) and *.runcomfy.net / *.runcomfy.com (download whitelist for generated outputs). No telemetry, no callbacks.

• Generated-file size cap: the CLI aborts any single download > 2 GiB to prevent disk-fill from a malicious or runaway model output.