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- 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
How to use flux-kontext
- 1.Install the skill: npx skills add agentspace-so/runcomfy-skills --skill flux-kontext -g
- 2.Authenticate: runcomfy login (opens browser device-code flow) or set RUNCOMFY_TOKEN env var
- 3.Prepare a publicly accessible HTTPS image URL and a single declarative edit instruction
- 4.Call runcomfy run blackforestlabs/flux-1-kontext/pro/edit with --input JSON containing prompt and image URL
- 5.Specify --output-dir for the result; optionally add seed for reproducibility
- 6.Review the output; if the edit drifted, split compound changes into sequential single-instruction passes
Use cases
- 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
- 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
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.
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.
No. Flux Kontext accepts a single source image per request. For batch edits across multiple images, route to Nano Banana Edit instead.
Pass the same seed value in the input JSON across multiple runs. This lets you compare variants while keeping the random generation deterministic.
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 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
- RunComfy CLI —
npm i -g @runcomfy/cli - RunComfy account —
runcomfy loginopens a browser device-code flow. - CI / containers — set
RUNCOMFY_TOKEN=<token>instead ofruncomfy 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:
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 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:
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
| 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.
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 loginwrites the API token to~/.config/runcomfy/token.jsonwith mode 0600 (owner-only read/write). SetRUNCOMFY_TOKENenv 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.
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