🫧 GPT Image Edit — Pro Pack on RunComfy

Edit images with OpenAI GPT Image 2 (the `/edit` endpoint of ChatGPT Images 2.0) on RunComfy — bundled with the model's documented prompting patterns so the skill gets sharper output than naive prompting against the same model. Documents GPT Image Edit's strengths (preservation language, multilingual in-image text editing, multi-reference up to 10 images, layout / typography precision), the schema, and when to route to Nano Banana Edit / Flux Kontext / GPT Image 2 t2i instead. Calls `runcomfy run openai/gpt-image-2/edit` through the local RunComfy CLI. Triggers on "gpt image edit", "gpt-image-edit", "chatgpt image edit", "edit with gpt image 2", or any explicit ask to edit with this model.

runcomfy.com · docs · Edit endpoint · Text-to-image sibling

OpenAI GPT Image 2 — /edit endpoint (ChatGPT Images 2.0 image-to-image) on the RunComfy Model API. Strongest in its class at preserving identity through targeted edits and rewriting embedded text in any script (Latin, kana, CJK, Cyrillic, Arabic).

When to pick this model (vs siblings)

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

1. RunComfy CLI — npm i -g @runcomfy/cli
2. RunComfy account — runcomfy login opens a browser device-code flow.
3. 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.