flux-kontext

Flux Kontext Pro — Pro Pack on RunComfy

runcomfy.com · Model page · GitHub

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.

npx skills add agentspace-so/runcomfy-skills --skill flux-kontext -g

When to pick this model (vs siblings)

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

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:

runcomfy run blackforestlabs/flux-1-kontext/pro/edit \
  --input

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

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 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

Full reference: docs.runcomfy.com/cli/troubleshooting.

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.