Generate images with Flux 2 Klein (Black Forest Labs' distilled fast variant of Flux 2) 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 2 Klein's strengths (sub-second latency, multi-ref

Flux 2 Klein — Pro Pack on RunComfy [runcomfy.com](https://www.runcomfy.com/?utm source=skills.sh&utm medium=skill&utm campaign=flux 2 klein) · [9B model](https://www.runcomfy.com/models/blackforestlabs/flux 2 klein/9b/text to image?utm source=skills.sh&utm medium=skill&utm campaign=flux 2 klein) · [4B model](https://www.runcomfy.com/models/blackforestlabs/flux 2 klein/4b/text to image?utm source=skills.sh&utm medium=skill&utm campaign=flux 2 klein) · [GitHub](https://github.com/agentspace so/runcomfy skills/tree/main/flux 2 klein) Black Forest Labs' Flux 2 Klein (the distilled, low latency variant of Flux 2) hosted on the RunComfy Model API — no API key, async REST. When to pick this model (vs siblings) Flux 2 Klein's distinct strength is latency first creative iteration : sub second feedback enables live art direction sessions and rapid product visualization that batch style models can't sustain. Pick it when iteration speed matters more than ceiling resolution . You want Use Real time / live art direction sessions Flux 2 Klein 4B Fast iteration with strong detail at the end Flux 2 Klein 9B Multi reference brand styling with consistent looks Flux 2 Klein 2K–4K hero images, max resolution Seedream 5 Maximum prompt adherence + extreme detail Flux 2 Pro Embedded text, logos, multilingual signage GPT Image 2 Hyperrealistic portrait Nano Banana Pro If the user said "Flux 2 Klein" / "BFL Klein" / "flux klein" explicitly, route here regardless. If they said "Flux 2" generically, ask whether they want Klein (fast) or Pro (max quality) before defaulting. 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 Two variants, same endpoint shape, same prompt grammar. blackforestlabs/flux 2 klein/9b/text to image The fidelity first variant. Use for polish / final output. Field Type Required Default Notes prompt string yes — Up to ~512 tokens. Longer degrades. steps int no 25 4–50. Step distilled architecture — 4–8 enough for concepting; ~25 for polish; 25 buys little. width int no 1024 512–1536 typical. Aspect ratio capped at 16:9 , max ~2K total. height int no 1024 Match width 's aspect intent. blackforestlabs/flux 2 klein/4b/text to image The latency first variant. Sub second 4 step inference. Use for live iteration / concepting. Same field set as 9B. Default steps is effectively 4 — the variant is built for that step count. Reference images (both variants) Up to 4 simultaneous reference images are supported on the same endpoint for style transfer / guided composition. The exact field name in the JSON body is documented on the [model's API tab](https://www.runcomfy.com/models/blackforestlabs/flux 2 klein/9b/text to image?utm source=skills.sh&utm medium=skill&utm campaign=flux 2 klein) — pass it through the CLI verbatim. Reference image use enables editing style workflows without a separate /edit endpoint. How to invoke Fast concepting (4B, sub second): Polish / final (9B, ~25 steps): Wide format poster: The CLI submits, polls every 2s until terminal, then downloads any .runcomfy.net / .runcomfy.com URL from the result into output dir . Stdout is the result JSON. Stderr is progress. For pipe friendly usage: Prompting — what actually works These are model specific patterns that empirically improve output quality. Subject first declarative grammar. The structure Flux 2 Klein was trained on is "Subject + action + scene + style + lighting + camera + quality" . Front load the subject; trail with directives. Example: "A vibrant hummingbird mid flight sipping nectar from a bright pink hibiscus, iridescent feathers in morning sun, soft bokeh tropical garden, macro photography, razor sharp detail, cinematic lighting" . Specificity wins over flowery language. "4k product photo, softbox lighting, reflective table, 35mm, f/2.8" guides predictably. "A really pretty product image" doesn't. Step count by phase. Concepting : 4–8 steps on the 4B variant — sub second feedback for live exploration. Refinement : 8–15 steps still on 4B, locking in subject + framing. Polish : ~25 steps on the 9B variant — texture, microdetail, fine typography. Multi reference alignment. When passing reference images, keep their aesthetics aligned . Mixing a watercolor + a photoreal + a 3D render in the same call confuses the editor. Pick one consistent visual register across all refs. Conditional edits : state what stays, then what changes. "Same composition and lighting as reference, but change the background from beach to mountain studio." This pattern holds composition stable. For text rendering (Klein has the 8B Qwen3 embedder, decent but not GPT Image 2 territory): add "crisp typography, high contrast label" and bump steps to ~25 if the text comes out soft. For heavy in image text or multilingual rendering, route to GPT Image 2 instead. Anti patterns : Don't conflict adjectives. "minimalist + ornate" cancels. Don't exceed ~512 tokens. The model degrades, doesn't truncate gracefully. Don't ask for 4K — the model's resolution ceiling is ~2K. Don't ask for ultra wide ( 16:9) — the model crops. Where it shines Use case Why Flux 2 Klein Live art direction sessions Sub second feedback (4B) enables real time iteration Interactive product visualization Fast UI previews and product comps without batch waits Multi reference brand styling Strong style consistency across references for unified asset packs Rapid concepting → polish workflow 4B for exploration, 9B for the final pass — same prompt grammar throughout Consumer GPU friendly inference 4B variant runs on modest hardware; relevant for self host comparisons but RunComfy hosted is fine Sample prompts (verified to produce strong results) From the model page (BFL example): Product photo pattern: Brand consistent pair (multi ref): Limitations Resolution ceiling ~2K — for higher native res, route to Seedream 5. Aspect ratio cap 16:9 — extreme wide/tall ratios get cropped. Prompt cap ~512 tokens — longer degrades quality; doesn't truncate gracefully. Reference image cap 4 — more than 4 increases latency and dilutes guidance. Text rendering — the 8B Qwen3 embedder helps but GPT Image 2 still wins for embedded text precision. Exit codes The runcomfy CLI uses sysexits style codes: code meaning 0 success 64 bad CLI args 65 bad input JSON / schema mismatch (e.g. width: 4096 would 422) 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 2 klein). How it works 1. The skill invokes runcomfy run blackforestlabs/flux 2 klein/<variant /text to image with a JSON body matching the schema. 2. The CLI POSTs to https://model api.runcomfy.net/v1/models/blackforestlabs/flux 2 klein/<variant /text to image with the user's bearer token. 3. The Model API returns a request id ; the CLI polls GET .../requests/<id /status every 2 seconds. 4. On terminal status, the CLI fetches GET .../requests/<id /result and downloads any URL whose host ends with .runcomfy.net or .runcomfy.com into output dir . Other URLs are listed but not fetched. 5. Ctrl C while polling sends POST .../requests/<id /cancel so you don't get billed for GPU you stopped. What this skill is not Not a self hosted Flux runner. Not a capability grant — depends on a working RunComfy account. Not multi tenant. 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-2-klein