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, multilingua

GPT Image Edit — Pro Pack on RunComfy [runcomfy.com](https://www.runcomfy.com/?utm source=skills.sh&utm medium=skill&utm campaign=gpt image edit) · [Edit endpoint](https://www.runcomfy.com/models/openai/gpt image 2/edit?utm source=skills.sh&utm medium=skill&utm campaign=gpt image edit) · [Text to image sibling](https://www.runcomfy.com/models/openai/gpt image 2/text to image?utm source=skills.sh&utm medium=skill&utm campaign=gpt image edit) · [GitHub](https://github.com/agentspace so/runcomfy skills/tree/main/gpt image edit) 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 ](../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: Multilingual text rewrite (preserve everything except the headline): Multi ref composition: 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): Multilingual variant: Multi ref composition: 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](https://docs.runcomfy.com/cli/troubleshooting?utm source=skills.sh&utm medium=skill&utm campaign=gpt image edit). 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.

gpt-image-edit