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Image edit on RunComfy. This image edit skill transforms an existing image โ background swap, object removal, in-image text rewrite, mask- driven region repl...
---
name: image-edit-runcomfy
displayName: "๐ซง Image Edit โ Pro Pack on RunComfy"
description: >
Image edit on RunComfy. This image edit skill transforms an existing
image โ background swap, object removal, in-image text rewrite, mask-
driven region replacement, or any other image edit task โ by routing
the image edit request to the right model in the RunComfy catalog.
Image edit supports single-image edit, batch image edit (up to 20),
multi-reference image edit, and mask-based image edit at up to 4K.
Calls `runcomfy run <model>/edit` through the local RunComfy CLI.
Triggers on "image edit", "edit image", "image-to-image", "i2i",
"image editing", "swap background", "remove object", "rewrite
headline", or any explicit ask to edit an image.
emoji: "๐ซง"
homepage: https://www.runcomfy.com
license: MIT
clawdis:
requires:
bins:
- runcomfy
env:
- RUNCOMFY_TOKEN
config:
- ~/.config/runcomfy
---
# ๐ซง Image Edit โ Pro Pack on RunComfy
[runcomfy.com](https://www.runcomfy.com/?utm_source=clawhub&utm_medium=skill&utm_campaign=image-edit-runcomfy) ยท [docs](https://docs.runcomfy.com/cli/introduction?utm_source=clawhub&utm_medium=skill&utm_campaign=image-edit-runcomfy) ยท [Image edit models](https://www.runcomfy.com/models?utm_source=clawhub&utm_medium=skill&utm_campaign=image-edit-runcomfy)
**Image edit on RunComfy.** This skill is the canonical image edit entry point for the RunComfy Model API: give it a source image and an edit instruction, and it returns the edited image. Image edit on RunComfy means transforming an existing still โ swap background, remove an object, rewrite a headline, mask-fill a region โ without re-shooting.
## What "image edit" means here
Image edit is the task of taking a source image and producing a transformed image that preserves identity, framing, or layout where you want, while changing what you specify. Image edit is distinct from text-to-image (no input) and from image-to-video (output is a clip). Common image edit operations include:
- **Background image edit** โ swap the background of a portrait, product, or scene while preserving the foreground identity.
- **Object-removal image edit** โ remove cables, watermarks, distracting elements, leaving the rest of the image edit untouched.
- **Object-addition image edit** โ add a new element (umbrella, sign, accessory) to an existing image edit subject.
- **Text-rewrite image edit** โ replace an in-image headline, label, or signage, including multilingual image edit.
- **Mask-driven image edit** โ fill or replace a specific masked region with strength control.
- **Multi-ref composition image edit** โ combine subject from one image with scene/lighting from another.
- **Batch image edit** โ apply the same image edit instruction across 1โ20 inputs (SKU galleries, A/B variants).
This skill picks the right image edit endpoint for the user's intent and calls `runcomfy run <model>/edit` with the matching schema.
## When to use image edit on RunComfy
Pick image edit on RunComfy whenever:
- You have an **existing image** and want to **change** something about it โ image edit is the right task.
- You want **identity-stable image edit** โ the subject, brand, or product from the input must survive into the edited image.
- You're producing **batch image edit at scale** โ SKU galleries, multi-language variant image edit, A/B testing.
- You need **mask-precise image edit** โ region replacement, watermark removal, region fill.
- The user said "image edit", "edit image", "image-to-image", "swap the background", "remove the watermark", "rewrite the headline", or showed an image and asked to transform it โ route here.
## Image edit routes
| User intent | Image edit model | Why |
|---|---|---|
| Default image edit โ single or batch (up to 20), background swap, object remove/add | `google/nano-banana-2/edit` | Most flexible image edit; identity preservation; batch up to 20 |
| Multilingual in-image text rewrite, layout-precise image edit | `openai/gpt-image-2/edit` | Strongest in-image typography for image edit; multi-ref composition (up to 10) |
| Single-shot precise local image edit ("she's holding an orange umbrella") | `blackforestlabs/flux-1-kontext/pro/edit` | Single-instruction, single-ref, high-fidelity image edit |
| Mask-driven image edit (object removal, region fill, region replace) | `tongyi-mai/z-image/turbo/inpainting` | Mask-based image edit with strength control |
The agent reads this table, classifies the user's image edit intent, and picks the matching endpoint.
## Prerequisites
1. **RunComfy CLI** โ `npm i -g @runcomfy/cli`
2. **RunComfy account** โ `runcomfy login`.
3. **CI / containers** โ set `RUNCOMFY_TOKEN=<token>`.
## Default image edit โ Nano Banana Edit
The default image edit endpoint. Use for any general image edit task: background swap, object removal, object addition, batch image edit. Up to 20 inputs per image edit call, up to 4K resolution.
### Schema
| Field | Type | Required | Default | Notes |
|---|---|---|---|---|
| `prompt` | string | yes | โ | Image edit instruction. Lead with preservation, then state the change. |
| `image_urls` | array | yes | โ | 1โ20 source images for the image edit. HTTPS URLs. |
| `number_of_images` | int | no | 1 | 1โ4 image edit outputs per call. |
| `aspect_ratio` | enum | no | `auto` | `auto` follows input; lock for batch image edit consistency. |
| `resolution` | enum | no | `1K` | `0.5K` / `1K` / `2K` / `4K` for the image edit output. |
| `output_format` | enum | no | `png` | `png` / `jpeg` / `webp`. |
| `seed` | int | no | โ | Reproducibility for image edit variants. |
| `enable_web_search` | bool | no | false | Web-grounded image edit (extra latency). |
### Invoke
**Background-swap image edit:**
```bash
runcomfy run google/nano-banana-2/edit \
--input '{
"prompt": "Keep the subject identity, pose, and clothing unchanged. Convert the background into a rainy neon cyberpunk street.",
"image_urls": ["https://.../portrait.jpg"]
}' \
--output-dir <absolute/path>
```
**Batch image edit (lock aspect + resolution):**
```bash
runcomfy run google/nano-banana-2/edit \
--input '{
"prompt": "Replace the watermark in the bottom-right with the text \"AURA\" in clean white sans-serif. Keep everything else exactly as in the input.",
"image_urls": ["https://.../sku-1.jpg", "https://.../sku-2.jpg", "https://.../sku-3.jpg"],
"aspect_ratio": "1:1",
"resolution": "1K"
}' \
--output-dir <absolute/path>
```
## Multilingual image edit โ GPT Image 2 Edit
Use when the image edit involves rewriting in-image text (especially non-Latin scripts) or composing from multiple references with layout precision.
| Field | Type | Required | Default | Notes |
|---|---|---|---|---|
| `prompt` | string | yes | โ | Image edit instruction; lead with preservation. |
| `images` | string[] | yes | โ | Up to 10 reference images for the image edit. First is primary. |
| `size` | enum | no | `auto` | `auto`, `1024_1024`, `1024_1536`, `1536_1024`. |
**Multilingual text-rewrite image edit:**
```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 \"ไปๆฅใฎใใใใ\" in bold Japanese kana, same position and font weight.",
"images": ["https://.../poster-en.jpg"]
}' \
--output-dir <absolute/path>
```
**Multi-ref composition image edit:**
```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 unchanged.",
"images": ["https://.../subject.jpg", "https://.../room.jpg"]
}' \
--output-dir <absolute/path>
```
## Single-shot precise image edit โ Flux Kontext Pro
Use when the image edit is a single declarative instruction on a single reference image โ the most surgical image edit option.
| Field | Type | Required | Notes |
|---|---|---|---|
| `prompt` | string | yes | One declarative image edit instruction. |
| `image` | string | yes | Single source image for the image edit. |
| `aspect_ratio` | enum | no | Pick from supported W:H values. |
| `seed` | int | no | Reproducibility. |
```bash
runcomfy run blackforestlabs/flux-1-kontext/pro/edit \
--input '{
"prompt": "Keep the person'\''s face, pose, and clothing unchanged. Add an orange umbrella in her left hand and a slight smile.",
"image": "https://.../portrait.jpg"
}' \
--output-dir <absolute/path>
```
## Mask-driven image edit โ Z-Image Turbo Inpaint
Use when the image edit is constrained to a specific masked region โ object removal, region fill, region replacement. Mask-driven image edit gives the cleanest results when you can supply a precise mask.
| Field | Type | Required | Notes |
|---|---|---|---|
| `prompt` | string | yes | What to fill / replace; preservation constraints for the unmasked surround. |
| `image` | string | yes | Source image for the image edit. |
| `mask_image` | string | yes | Grayscale mask URL (white = inpaint, black = preserve). |
| `strength` | float | no | 0.3โ0.6 retouching image edit, 0.7โ1.0 full replacement image edit. |
| `control_scale` | float | no | 0.6โ0.9 typical. |
| `aspect_ratio` | enum | no | W:H output ratio. |
| `seed` | int | no | Reproducibility. |
**Object-removal image edit:**
```bash
runcomfy run tongyi-mai/z-image/turbo/inpainting \
--input '{
"prompt": "Remove overhead cables; preserve rooflines and sky gradient; thin clean sky.",
"image": "https://.../street.jpg",
"mask_image": "https://.../cables-mask.png",
"strength": 0.5,
"control_scale": 0.8
}' \
--output-dir <absolute/path>
```
**Region-replacement image edit:**
```bash
runcomfy run tongyi-mai/z-image/turbo/inpainting \
--input '{
"prompt": "Replace busy backdrop with smooth light gray studio paper; mask background only.",
"image": "https://.../product.jpg",
"mask_image": "https://.../bg-mask.png",
"strength": 0.9
}' \
--output-dir <absolute/path>
```
## Prompting image edit โ what works
Image edit prompts behave differently from text-to-image prompts. The source image already fixes most of the look โ your image edit prompt should drive the change, not redescribe the source.
- **Lead with preservation goals.** `"Keep [identity / pose / framing / brand] unchanged. Then state the image edit change."` Tell the image edit model what NOT to change.
- **One image edit direction per call.** Compound image edits drift. Pick one bucket โ background OR object OR text OR layout โ per image edit call.
- **Spatial scope language.** "background only", "the left object", "upper-right quadrant" โ image edit models honor concrete locations.
- **Quote in-image text exactly.** For text-rewrite image edit, put the literal characters in quotes. Name the script for non-Latin: "Japanese kana", "Cyrillic", "Arabic".
- **Number multi-refs in image edit prompts.** "Subject from image 1, lighting from image 2" โ image edit models route cues correctly when refs are numbered.
- **Mask-edge softness.** For mask-driven image edit, a 1โ3px blur on the mask edge blends cleaner than a sharp binary mask.
- **Iterate small.** Split compound image edit into multiple shorter passes; consistency is better across passes than within a single overstuffed prompt.
## Image edit FAQ
**What's the max batch size for image edit?** 20 inputs per call on the default image edit endpoint (Nano Banana Edit). Other image edit routes are single-input.
**What image formats does image edit accept?** JPEG, PNG, WebP. Source URLs must be publicly fetchable HTTPS.
**Does image edit preserve subject identity?** Yes โ all four image edit routes are designed for identity preservation. Always state the goal: `"keep face identity unchanged"`.
**Can image edit rewrite text in non-Latin scripts?** Yes โ route to GPT Image 2 Edit. It handles Japanese kana, Cyrillic, Arabic, Hangul, Chinese, etc.
**What's the highest resolution available for image edit?** 4K on Nano Banana Edit. Other image edit routes cap at their respective sizes.
**Image edit vs text-to-image on RunComfy?** Image edit transforms an existing image. Text-to-image starts from a prompt only. Use image edit when you have a source; use text-to-image for novel content.
**Can I do mask-free region image edit?** Yes โ most image edit routes work without an explicit mask. Use spatial language ("upper-right corner", "the background only"). For surgical region image edit, provide a mask via the Z-Image inpaint route.
**Can I run multiple image edits in one call?** Within Nano Banana Edit's batch (1โ20 inputs with the same instruction), yes. For different image edit instructions, chain calls.
## Limitations
- **Each image edit route inherits its model's limits.** Nano Banana Edit: 1โ20 inputs, 1โ4 outputs. GPT Image 2 Edit: up to 10 refs, 4 fixed sizes. Flux Kontext Pro: single ref. Z-Image Inpaint: mask required.
- **No multi-route image edit blending.** This skill picks one image edit model per call.
- **Brand-specific overrides** โ if the user named a specific model, route to the corresponding brand skill (`gpt-image-edit`, `flux-kontext`, `nano-banana-edit`) instead of forcing it through this image edit router.
## Exit codes
| code | meaning |
|---|---|
| 0 | image edit succeeded |
| 64 | bad CLI args |
| 65 | bad input JSON for image edit / 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=clawhub&utm_medium=skill&utm_campaign=image-edit-runcomfy).
## How it works
The skill picks one of four image edit endpoints (Nano Banana Edit / GPT Image 2 Edit / Flux Kontext Pro / Z-Image Turbo Inpaint) based on user intent, and invokes `runcomfy run <model>/edit` with the matching JSON body. The CLI POSTs to the RunComfy Model API, polls the image edit request status every 2 seconds, and downloads the resulting image edit output from the `*.runcomfy.net` / `*.runcomfy.com` URL into `--output-dir`. `Ctrl-C` cancels the in-flight image edit request.
## Security & Privacy
- **Token storage**: `runcomfy login` writes the API token to `~/.config/runcomfy/token.json` with mode 0600.
- **Input boundary**: the image edit prompt is passed as JSON via `--input`. No shell injection.
- **Third-party content**: source images and masks are fetched by the RunComfy server. Treat external URLs as untrusted โ image-based prompt injection is a known risk for any image edit model.
- **Outbound endpoints**: only `model-api.runcomfy.net` and `*.runcomfy.net` / `*.runcomfy.com`.
- **Generated-file size cap**: 2 GiB.
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