Edit images with Flux 1 Kontext Pro (Black Forest Labs' precise local image-edit model) on RunComfy — bundled with the model's documented prompting patterns so the skill gets sharper output than naive prompting against the same model. Documents Flux Kontext's strengths (single-reference precise loca

Flux Kontext Pro — Pro Pack on RunComfy [runcomfy.com](https://www.runcomfy.com/?utm source=skills.sh&utm medium=skill&utm campaign=flux kontext) · [Model page](https://www.runcomfy.com/models/blackforestlabs/flux 1 kontext pro/image to image?utm source=skills.sh&utm medium=skill&utm campaign=flux kontext) · [GitHub](https://github.com/agentspace so/runcomfy skills/tree/main/flux kontext) Black Forest Labs' Flux 1 Kontext Pro — single reference precise local image edit — hosted on the RunComfy Model API . Strong prompt control, consistent outputs, high fidelity. When to pick this model (vs siblings) You want Use Single image precise local edit ("she's now holding X") Flux Kontext High fidelity preservation of source identity Flux Kontext Batch edits across 1–20 images Nano Banana Edit Edit multilingual / embedded text in image GPT Image 2 edit Generate from scratch, no source image Flux 2 Klein If the user said "Flux Kontext" / "kontext" / "BFL Kontext" explicitly, route here regardless. 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 blackforestlabs/flux 1 kontext/pro/edit Field Type Required Default Notes prompt string yes — Single declarative edit instruction. image string yes — Single source image URL (publicly fetchable HTTPS). aspect ratio enum no (input) Pick from supported W:H options on the model page. seed int no — Reuse for variant comparisons. The schema is intentionally minimal — Kontext leans on prompt + single ref. For multi image or web grounded edits, route to Nano Banana Edit. How to invoke Default — local edit, preserve everything else: With seed for reproducible variant series: Prompting — what actually works One declarative instruction. Kontext shines on prompts shaped like the docs example: "She is now holding an orange umbrella and smiling" . Imperative mood, single change. Preservation first. Lead with "Keep [identity / pose / framing / brand] unchanged." Then the change. Models honor what's stated up front. Single ref only — pick the right one. No multi image fanout here. If you have multiple references, decide which is primary and pass that one. For multi image flows, route to Nano Banana Edit. Iterate on small changes. If Kontext drifts, split a compound edit into sequential single instruction passes (pass 1: change background, pass 2: change clothing). Aspect ratio — pick from the supported enum. Out of list values 422 or crop. Anti patterns: Compound prompts ("change A and add B and remove C") → drift. Trying to fan out to multiple source images → wrong model (use Nano Banana Edit). Prompts written in passive voice → less reliable. Asking for novel composition without a source image → wrong model (use Flux 2 Klein t2i). Where it shines Use case Why Flux Kontext Single shot precise local edit Specifically designed for this; high fidelity Preserve source identity through targeted change Strong preservation under explicit instruction Brand asset text or color swap Quoted text + preservation lead in works well Quick iteration on one image Short prompts + single ref = fast result loop Sample prompts (verified to produce strong results) Page example: Preservation led brand edit: Compositional micro edit: Limitations Single source image only. For multi image flows, use Nano Banana Edit (1–20). Public RunComfy docs are minimal — schema fields beyond prompt + image + aspect ratio + seed may exist; check the [model page](https://www.runcomfy.com/models/blackforestlabs/flux 1 kontext pro/image to image?utm source=skills.sh&utm medium=skill&utm campaign=flux kontext) for the latest field list. Compound prompts drift — split into sequential passes. For multilingual / embedded text editing, GPT Image 2 edit usually wins. 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=flux kontext). How it works The skill invokes runcomfy run blackforestlabs/flux 1 kontext/pro/edit with a JSON body matching the schema. The CLI POSTs to https://model api.runcomfy.net/v1/models/blackforestlabs/flux 1 kontext/pro/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.

flux-kontext