Agent skill · AI & Agents

flux-image

Generate images with FLUX models (Black Forest Labs) via inference.sh CLI. Models: FLUX Dev LoRA, FLUX.2 Klein LoRA with custom style adaptation. Capabilities: text-to-image, image-to-image, LoRA fine-tuning, custom styles. Triggers: flux, flux.2, flux dev, flux schnell, flux pro, black forest labs, flux image, flux ai, flux model, flux lora

majiayu000github.com/majiayu000GitHub ↗
claude-coderead-onlyMIT
Install
npx skills add majiayu000/claude-skill-registry --skill flux-image-balkonsen-ha-ai-gen-workflow --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 3 KB
Bundled scripts: none
Allowed tools: Bash(infsh*)
Path: skills/ai-ml/flux-image-balkonsen-ha-ai-gen-workflow/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# FLUX Image Generation Generate images with FLUX models via [inference.sh](https://inference.sh) CLI. ![FLUX Image Generation](https://cloud.inference.sh/app/files/u/4mg21r6ta37mpaz6ktzwtt8krr/01kg0v0nz7wv0qwqjtq1cam52z.jpeg) ## Quick Start > Requires inference.sh CLI (`infsh`). Get installation instructions: `npx skills add inference-sh/skills@agent-tools` ```bash infsh login infsh app run falai/flux-dev-lora --input '{"prompt": "a futuristic city at night"}' ``` ## FLUX Models | Model | App ID | Speed | Quality | Use Case | |-------|--------|-------|---------|----------| | FLUX Dev LoRA | `falai/flux-dev-lora` | Medium | Highest | Production, custom styles | | FLUX.2 Klein LoRA | `falai/flux-2-klein-lora` | Fastest | Good | Fast iteration, 4B/9B sizes | | **FLUX Dev (Pruna)** | `pruna/flux-dev` | Fast | High | Optimized, speed modes | | **FLUX Dev LoRA (Pruna)** | `pruna/flux-dev-lora` | Fast | High | LoRA with optimization | | **FLUX Klein 4B (Pruna)** | `pruna/flux-klein-4b` | Fastest | Good | Ultra-cheap ($0.0001/img) | ## Examples ### High-Quality Generation ```bash infsh app run falai/flux-dev-lora --input '{ "prompt": "professional product photo of headphones, studio light

What's inside
Steps it walks through
  1. Quick Start
  2. FLUX Models
  3. Examples
  4. High-Quality Generation
  5. Fast Generation (Klein)
  6. With LoRA Custom Styles
  7. Image-to-Image
  8. For Other Image Tasks
  9. Related Skills
  10. Documentation
Ships with 1 file
  • metadata.json
Commands it runs
infsh login
infsh app run falai/flux-dev-lora --input '{"prompt": "a futuristic city at night"}'
infsh app run falai/flux-dev-lora --input '{
infsh app run falai/flux-2-klein-lora --input '{"prompt": "abstract art, colorful"}'
infsh app sample falai/flux-dev-lora --save input.json
Edit to add lora_url for custom style
infsh app run falai/flux-dev-lora --input input.json
Image editing with natural language
infsh app run falai/reve --input '{"prompt": "change background to beach"}'
Upscaling
More from claude-skill-registry
All skills →
About this skill
What does the flux-image skill do?

Generate images with FLUX models (Black Forest Labs) via inference.sh CLI. Models: FLUX Dev LoRA, FLUX.2 Klein LoRA with custom style adaptation. Capabilities: text-to-image, image-to-image, LoRA fine-tuning, custom styles. Triggers: flux, flux.2, flux dev, flux schnell, flux pro, black forest labs, flux image, flux ai, flux model, flux lora

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill flux-image-balkonsen-ha-ai-gen-workflow --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going