comfyui skill

ComfyUI

Generate images, video, audio, and 3D content through ComfyUI using the official comfy-cli for setup/lifecycle and direct REST/WebSocket API for workflow execution.

What's in this skill

Reference docs (references/):

• official-cli.md — every comfy ... command, with flags
• rest-api.md — REST + WebSocket endpoints (local + cloud), payload schemas
• workflow-format.md — API-format JSON, common node types, param mapping

Scripts (scripts/):

Example workflows (workflows/): SD 1.5, SDXL, Flux Dev, SDXL img2img, SDXL inpaint, ESRGAN upscale, AnimateDiff video, Wan T2V. See workflows/README.md.

When to Use

• User asks to generate images with Stable Diffusion, SDXL, Flux, SD3, etc.
• User wants to run a specific ComfyUI workflow file
• User wants to chain generative steps (txt2img → upscale → face restore)
• User needs ControlNet, inpainting, img2img, or other advanced pipelines
• User asks to manage ComfyUI queue, check models, or install custom nodes
• User wants video/audio/3D generation via AnimateDiff, Hunyuan, Wan, AudioCraft, etc.

Architecture: Two Layers

┌─────────────────────────────────────────────────────┐
│ Layer 1: comfy-cli (official lifecycle tool)        │
│   Setup, server lifecycle, custom nodes, models     │
│   → comfy install / launch / stop / node / model    │
└─────────────────────────┬───────────────────────────┘
                          │
┌─────────────────────────▼───────────────────────────┐
│ Layer 2: REST/WebSocket API + skill scripts         │
│   Workflow execution, param injection, monitoring   │
│   POST /api/prompt, GET /api/view, WS /ws           │
│   → run_workflow.py, run_batch.py, ws_monitor.py    │
└─────────────────────────────────────────────────────┘

Why two layers? The official CLI is excellent for installation and server management but has minimal workflow execution support. The REST/WS API fills that gap — the scripts handle param injection, execution monitoring, and output download that the CLI doesn't do.

Quick Start

Detect environment

# What's available?
command -v comfy >/dev/null 2>&1 && echo "comfy-cli: installed"
curl -s http://127.0.0.1:8188/system_stats 2>/dev/null && echo "server: running"

# Can this machine run ComfyUI locally? (GPU/VRAM/disk check)
python3 scripts/hardware_check.py

If nothing is installed, see Setup & Onboarding below — but always run the hardware check first.

One-line health check

python3 scripts/health_check.py
# → JSON: comfy_cli on PATH? server reachable? at least one checkpoint? smoke-test passes?

Core Workflow

Step 1: Get a workflow JSON in API format

Workflows must be in API format (each node has class_type). They come from:

the legacy "Save (API Format)" button (older UI)

• ComfyUI web UI → Workflow → Export (API) (newer UI) or

must be loaded into ComfyUI then re-exported

• This skill's workflows/ directory (ready-to-run examples)
• Community downloads (civitai, Reddit, Discord) — usually editor format,

Editor format (top-level nodes and links arrays) is not directly executable. The scripts detect this and tell you to re-export.

Step 2: See what's controllable

python3 scripts/extract_schema.py workflow_api.json --summary-only
# → {"parameter_count": 12, "has_negative_prompt": true, "has_seed": true, ...}

python3 scripts/extract_schema.py workflow_api.json
# → full schema with parameters, model deps, embedding refs

Step 3: Run with parameters

# Local (defaults to http://127.0.0.1:8188)
python3 scripts/run_workflow.py \
  --workflow workflow_api.json \
  --args '{"prompt": "a beautiful sunset over mountains", "seed": -1, "steps": 30}' \
  --output-dir ./outputs

# Cloud (export API key once; uses correct /api routing automatically)
export COMFY_CLOUD_API_KEY="comfyui-..."
python3 scripts/run_workflow.py \
  --workflow workflow_api.json \
  --args '{"prompt": "..."}' \
  --host https://cloud.comfy.org \
  --output-dir ./outputs

# Real-time progress via WebSocket (requires `pip install websocket-client`)
python3 scripts/run_workflow.py \
  --workflow flux_dev.json \
  --args '{"prompt": "..."}' \
  --ws

# img2img / inpaint: pass --input-image to upload + reference automatically
python3 scripts/run_workflow.py \
  --workflow sdxl_img2img.json \
  --input-image image=./photo.png \
  --args '{"prompt": "make it watercolor", "denoise": 0.6}'

# Batch / sweep: 8 random seeds, parallel up to cloud tier limit
python3 scripts/run_batch.py \
  --workflow sdxl.json \
  --args '{"prompt": "abstract"}' \
  --count 8 --randomize-seed --parallel 3 \
  --output-dir ./outputs/batch

-1 for seed (or omitting it with --randomize-seed) generates a fresh random seed per run.

Step 4: Present results

The scripts emit JSON to stdout describing every output file:

{
  "status": "success",
  "prompt_id": "abc-123",
  "outputs": [
    {"file": "./outputs/sdxl_00001_.png", "node_id": "9",
     "type": "image", "filename": "sdxl_00001_.png"}
  ]
}

Decision Tree

Setup & Onboarding

When a user asks to set up ComfyUI, the FIRST thing to do is ask whether they want Comfy Cloud (hosted, zero install, API key) or Local (install ComfyUI on their machine). Don't start running install commands or hardware checks until they've answered.

Official docs: https://docs.comfy.org/installation CLI docs: https://docs.comfy.org/comfy-cli/getting-started Cloud docs: https://docs.comfy.org/getstarted/cloud Cloud API:** https://docs.comfy.org/development/cloud/overview

Step 0: Ask Local vs Cloud (ALWAYS FIRST)

Suggested script:

"Do you want to run ComfyUI locally on your machine, or use Comfy Cloud?

- Comfy Cloud — hosted on RTX 6000 Pro GPUs, all common models pre-installed,

zero setup. Requires an API key (paid subscription required to actually run

workflows; free tier is read-only). Best if you don't have a capable GPU.

- Local — free, but your machine MUST meet the hardware requirements:

- NVIDIA GPU with ≥6 GB VRAM (≥8 GB for SDXL, ≥12 GB for Flux/video), OR

- AMD GPU with ROCm support (Linux), OR

- Apple Silicon Mac (M1+) with ≥16 GB unified memory (≥32 GB recommended).

- Intel Macs and machines with no GPU will NOT work — use Cloud instead.

Which would you like?"

Routing:

• Cloud → skip to Path A.
• Local → run hardware check first, then pick a path from Paths B–E based on the verdict.
• Unsure → run the hardware check and let the verdict decide.

Step 1: Verify Hardware (ONLY if user chose local)

python3 scripts/hardware_check.py --json
# Optional: also probe `torch` for actual CUDA/MPS:
python3 scripts/hardware_check.py --json --check-pytorch

The script also surfaces wsl: true (WSL2 with NVIDIA passthrough) and rosetta: true (x86_64 Python on Apple Silicon — must reinstall as ARM64).

If verdict is cloud but the user wants local, do not proceed silently. Show the notes array verbatim and ask whether they want to (a) switch to Cloud or (b) force a local install (will OOM or be unusably slow on modern models).

Choosing an Installation Path

Use the hardware check first. The table below is the fallback for when the user has already told you their hardware:

For the fully automated path (hardware check → install → launch → verify):

bash scripts/comfyui_setup.sh
# Or with overrides:
bash scripts/comfyui_setup.sh --m-series --port=8190 --workspace=/data/comfy