AI Skill
Review
Audit score 70

flux-kontext

doany-ai/skills

Precise local image edits with Flux 1 Kontext Pro on RunComfy—single-reference, high-fidelity edits via CLI.

What is flux-kontext?

Edit images with Black Forest Labs' Flux 1 Kontext Pro model hosted on RunComfy. Use this for single-image precise local edits (e.g., "add an umbrella", "change label text") where you want to preserve the source identity and get consistent, high-fidelity output. Routes to other models for batch edits, text-heavy images, or generation from scratch.

  • Invoke Flux 1 Kontext Pro's image-edit endpoint via RunComfy CLI with a single source image and declarative prompt
  • Preserve source identity (face, pose, clothing, framing) while making targeted local edits
  • Support seed-based reproducibility for variant comparisons
  • Handle aspect-ratio selection from supported enum options
  • Execute edits with strong prompt control and consistent output quality

How to install flux-kontext

npx skills add https://github.com/doany-ai/skills --skill flux-kontext
Prerequisites
  • RunComfy CLI: npm i -g @runcomfy/cli
  • RunComfy account created via runcomfy login (or RUNCOMFY_TOKEN env var for CI/containers)
  • Publicly accessible HTTPS URL for the source image
Claude Code
Cursor
Windsurf
Cline

How to use flux-kontext

  1. 1.Install the skill: npx skills add agentspace-so/runcomfy-skills --skill flux-kontext -g
  2. 2.Authenticate: runcomfy login (opens browser device-code flow) or set RUNCOMFY_TOKEN env var
  3. 3.Prepare a publicly accessible HTTPS image URL and a single declarative edit instruction
  4. 4.Call runcomfy run blackforestlabs/flux-1-kontext/pro/edit with --input JSON containing prompt and image URL
  5. 5.Specify --output-dir for the result; optionally add seed for reproducibility
  6. 6.Review the output; if the edit drifted, split compound changes into sequential single-instruction passes

Use cases

Good for
  • Add or modify objects in a portrait while keeping the person's face and pose unchanged
  • Swap brand text or colors on product packaging while preserving bottle shape and lighting
  • Apply micro-compositional edits like adding a shoulder bag or changing background details
  • Generate reproducible variants of an edit by reusing the same seed
  • Iterate quickly on single-image edits with short, declarative prompts
Who it's for
  • Product photographers and e-commerce teams doing asset edits
  • Brand teams managing label or packaging text changes
  • Portrait editors making targeted adjustments
  • Anyone needing fast, high-fidelity single-image edits without multi-reference complexity

flux-kontext FAQ

When should I use Flux Kontext vs. other edit models?

Use Flux Kontext for single-image precise local edits with high-fidelity preservation (e.g., "add an umbrella"). Use Nano Banana Edit for batch edits across 1–20 images, GPT Image 2 edit for multilingual/embedded text, and Flux 2 Klein for generation from scratch without a source image.

What makes a good Flux Kontext prompt?

Lead with preservation ("Keep the person's face, pose, and clothing unchanged"), then state one declarative change in active voice ("Add an orange umbrella"). Avoid compound edits ("change A and add B and remove C") in a single prompt; split them into sequential passes instead.

Can I edit multiple images at once?

No. Flux Kontext accepts a single source image per request. For batch edits across multiple images, route to Nano Banana Edit instead.

How do I get reproducible variants?

Pass the same seed value in the input JSON across multiple runs. This lets you compare variants while keeping the random generation deterministic.

What if my edit drifts or doesn't match the prompt?

Check that your prompt is declarative and single-focused. If you're trying to make multiple changes, split them into sequential passes (pass 1: change background, pass 2: change clothing). Avoid passive voice and compound instructions.

Full instructions (SKILL.md)

Source of truth, from doany-ai/skills.


name: flux-kontext displayName: "Flux Kontext Pro — Pro Pack on RunComfy" description: > 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 local edits, strong prompt control, consistent high-fidelity outputs), the schema (single image + prompt), and when to route to Nano Banana Edit / GPT Image 2 edit / Flux 2 Klein instead. Calls runcomfy run blackforestlabs/flux-1-kontext/pro/edit through the local RunComfy CLI. Triggers on "flux kontext", "flux-kontext", "flux 1 kontext", "kontext", "BFL kontext", or any explicit ask to edit with this model. homepage: https://www.runcomfy.com license: MIT

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)

You wantUse
Single-image precise local edit ("she's now holding X")Flux Kontext
High-fidelity preservation of source identityFlux Kontext
Batch edits across 1–20 imagesNano Banana Edit
Edit multilingual / embedded text in imageGPT Image 2 edit
Generate from scratch, no source imageFlux 2 Klein

If the user said "Flux Kontext" / "kontext" / "BFL Kontext" explicitly, route here regardless.

Prerequisites

  1. RunComfy CLInpm i -g @runcomfy/cli
  2. RunComfy accountruncomfy 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

FieldTypeRequiredDefaultNotes
promptstringyesSingle declarative edit instruction.
imagestringyesSingle source image URL (publicly fetchable HTTPS).
aspect_ratioenumno(input)Pick from supported W:H options on the model page.
seedintnoReuse 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:

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>

With seed for reproducible variant series:

runcomfy run blackforestlabs/flux-1-kontext/pro/edit \
  --input '{
    "prompt": "Keep the bottle, label, and lighting unchanged. Replace the brand text on the label from \"ALPHA\" to \"AURA\".",
    "image": "https://.../bottle.jpg",
    "seed": 42
  }' \
  --output-dir <absolute/path>

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 caseWhy Flux Kontext
Single-shot precise local editSpecifically designed for this; high fidelity
Preserve source identity through targeted changeStrong preservation under explicit instruction
Brand-asset text or color swapQuoted text + preservation lead-in works well
Quick iteration on one imageShort prompts + single ref = fast result loop

Sample prompts (verified to produce strong results)

Page example:

She is now holding an orange umbrella and smiling

Preservation-led brand edit:

Keep the bottle silhouette, table, and lighting exactly as in the input.
Replace only the brand text on the label, from "ALPHA" to "AURA".
Same font weight, white on black, centered.

Compositional micro-edit:

Keep the person's face, pose, and clothing unchanged. Add a leather
shoulder bag, dark brown, hanging on the right shoulder.

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

codemeaning
0success
64bad CLI args
65bad input JSON / schema mismatch
69upstream 5xx
75retryable: timeout / 429
77not signed in or token rejected

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.