GPT Image Edit

# 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).

```bash npx skills add agentspace-so/runcomfy-skills --skill gpt-image-edit -g ```

## 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:**

```bash 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):**

```bash 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:**

```bash 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 ass