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
• template-integrity.md — converting comfyui-workflow-templates from editor format to API format: Reroute bypass, dotted dynamic-input keys (values.a, resize_type.width), Cloud quirks (302 redirect, 1 concurrent free-tier job, 1080p VRAM ceiling), Discord-compatible ffmpeg stitch. Authored by @purzbeats. Load this whenever you're starting from an official template.

Scripts (scripts/):

Script	Purpose
_common.py	Shared HTTP, cloud routing, node catalogs (don't run directly)
hardware_check.py	Probe GPU/VRAM/disk → recommend local vs Comfy Cloud
comfyui_setup.sh	Hardware check + comfy-cli + ComfyUI install + launch + verify
extract_schema.py	Read a workflow → list controllable params + model deps
check_deps.py	Check workflow against running server → list missing nodes/models
auto_fix_deps.py	Run check_deps then comfy node install / comfy model download
run_workflow.py	Inject params, submit, monitor, download outputs (HTTP or WS)
run_batch.py	Submit a workflow N times with sweeps, parallel up to your tier
ws_monitor.py	Real-time WebSocket viewer for executing jobs (live progress)
health_check.py	Verification checklist runner — comfy-cli + server + models + smoke test
fetch_logs.py	Pull traceback / status messages for a given prompt_id

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)
python scripts/hardware_check.py

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

One-line health check

python 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:

• ComfyUI web UI → Workflow → Export (API) (newer UI) or the legacy "Save (API Format)" button (older UI)
• This skill's workflows/ directory (ready-to-run examples)
• Community downloads (civitai, Reddit, Discord) — usually editor format, must be loaded into ComfyUI then re-exported

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

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

python 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)
python 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-..."
python 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`)
python scripts/run_workflow.py \
  --workflow flux_dev.json \
  --args '{"prompt": "..."}' \
  --ws

# img2img / inpaint: pass --input-image to upload + reference automatically
python 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
python 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

User says	Tool	Command
Lifecycle (use comfy-cli)
"install ComfyUI"	comfy-cli	bash scripts/comfyui_setup.sh
"start ComfyUI"	comfy-cli	comfy launch --background
"stop ComfyUI"	comfy-cli	comfy stop
"install X node"	comfy-cli	comfy node install <name>
"download X model"	comfy-cli	comfy model download --url <url> --relative-path models/checkpoints
"list installed models"	comfy-cli	comfy model list
"list installed nodes"	comfy-cli	comfy node show installed
Execution (use scripts)
"is everything ready?"	script	health_check.py (optionally with --workflow X --smoke-test)
"what can I change in this workflow?"	script	extract_schema.py W.json
"check if W's deps are met"	script	check_deps.py W.json
"fix missing deps"	script	auto_fix_deps.py W.json
"generate an image"	script	run_workflow.py --workflow W --args '{...}'
"use this image" (img2img)	script	run_workflow.py --input-image image=./x.png ...
"8 variations with random seeds"	script	run_batch.py --count 8 --randomize-seed ...
"show me live progress"	script	ws_monitor.py --prompt-id <id>
"fetch the error from job X"	script	fetch_logs.py <prompt_id>
Direct REST
"what's in the queue?"	REST	curl http://HOST:8188/queue (local) or

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/get_started/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)

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

Verdict	Meaning	Action
ok	≥8 GB VRAM (discrete) OR ≥32 GB unified (Apple Silicon)	Local install — use comfy_cli_flag from report
marginal	SD1.5 works; SDXL tight; Flux/video unlikely	Local OK for light workflows, else Path A (Cloud)
cloud	No usable GPU, <6 GB VRAM, <16 GB Apple unified, Intel Mac, Rosetta Python	Switch to Cloud unless user explicitly forces local

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:

Situation	Recommended Path
verdict: cloud from hardware check	Path A: Comfy Cloud
No GPU / want to try without commitment	Path A: Comfy Cloud
Windows + NVIDIA + non-technical	Path B: ComfyUI Desktop
Windows + NVIDIA + technical	Path C: Portable or Path D: comfy-cli
Linux + any GPU	Path D: comfy-cli (easiest)
macOS + Apple Silicon	Path B: Desktop or Path D: comfy-cli
Headless / server / CI / agents	Path D: comfy-cli

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

It runs hardware_check.py internally, refuses to install locally when the verdict is cloud (unless --force-cloud-override), picks the right comfy-cli flag, and prefers pipx/uvx over global pip to avoid polluting system Python.

---

Path A: Comfy Cloud (No Local Install)

For users without a capable GPU or who want zero setup. Hosted on RTX 6000 Pro.

Docs: https://docs.comfy.org/get_started/cloud

1. Sign up at https://comfy.org/cloud
2. Generate an API key at https://platform.comfy.org/login
3. Set the key:

   export COMFY_CLOUD_API_KEY="your-comfyui-key"
   

4. Run workflows:

   python scripts/run_workflow.py \
     --workflow workflows/flux_dev_txt2img.json \
     --args '{"prompt": "..."}' \
     --host https://cloud.comfy.org \
     --output-dir ./outputs
   

Pricing: https://www.comfy.org/cloud/pricing Concurrent jobs: Free/Standard 1, Creator 3, Pro 5. Free tier cannot run workflows via API — only browse models. Paid subscription required for /api/prompt, /api/upload/*, /api/view, etc.

---

Path B: ComfyUI Desktop (Windows / macOS)

One-click installer for non-technical users. Currently Beta.

Docs: https://docs.comfy.org/installation/desktop

• Windows (NVIDIA): https://download.comfy.org/windows/nsis/x64
• macOS (Apple Silicon): https://comfy.org

Linux is not supported for Desktop — use Path D.

---

Path C: ComfyUI Portable (Windows Only)

Docs: https://docs.comfy.org/installation/comfyui_portable_windows

Download from https://github.com/comfyanonymous/ComfyUI/releases, extract, run run_nvidia_gpu.bat. Update via update/update_comfyui_stable.bat.

---

Path D: comfy-cli (All Platforms — Recommended for Agents)

The official CLI is the best path for headless/automated setups.

Docs: https://docs.comfy.org/comfy-cli/getting-started

Install comfy-cli

# Recommended:
pipx install comfy-cli
# Or use uvx without installing:
uvx --from comfy-cli comfy --help
# Or (if pipx/uvx unavailable):
pip install --user comfy-cli

Disable analytics non-interactively:

comfy --skip-prompt tracking disable

Install ComfyUI

comfy --skip-prompt install --nvidia              # NVIDIA (CUDA)
comfy --skip-prompt install --amd                 # AMD (ROCm, Linux)
comfy --skip-prompt install --m-series            # Apple Silicon (MPS)
comfy --skip-prompt install --cpu                 # CPU only (slow)
comfy --skip-prompt install --nvidia --fast-deps  # uv-based dep resolution

Default location: ~/comfy/ComfyUI (Linux), ~/Documents/comfy/ComfyUI (macOS/Win). Override with comfy --workspace /custom/path install.

Launch / verify

comfy launch --background                       # background daemon on :8188
comfy launch -- --listen 0.0.0.0 --port 8190    # LAN-accessible custom port
curl -s http://127.0.0.1:8188/system_stats      # health check

---

Path E: Manual Install (Advanced / Unsupported Hardware)

For Ascend NPU, Cambricon MLU, Intel Arc, or other unsupported hardware.

Docs: https://docs.comfy.org/installation/manual_install

git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu130
pip install -r requirements.txt
python main.py

---

Post-Install: Download Models

# SDXL (general purpose, ~6.5 GB)
comfy model download \
  --url "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors" \
  --relative-path models/checkpoints

# SD 1.5 (lighter, ~4 GB, good for 6 GB cards)
comfy model download \
  --url "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors" \
  --relative-path models/checkpoints

# Flux Dev fp8 (smaller variant, ~12 GB)
comfy model download \
  --url "https://huggingface.co/Comfy-Org/flux1-dev/resolve/main/flux1-dev-fp8.safetensors" \
  --relative-path models/checkpoints

# CivitAI (set token first):
comfy model download \
  --url "https://civitai.com/api/download/models/128713" \
  --relative-path models/checkpoints \
  --set-civitai-api-token "YOUR_TOKEN"

List installed: comfy model list.

Post-Install: Install Custom Nodes

comfy node install comfyui-impact-pack             # popular utility pack
comfy node install comfyui-animatediff-evolved     # video generation
comfy node install comfyui-controlnet-aux          # ControlNet preprocessors
comfy node install comfyui-essentials              # common helpers
comfy node update all
comfy node install-deps --workflow=workflow.json   # install everything a workflow needs

Post-Install: Verify

python scripts/health_check.py
# → comfy_cli on PATH? server reachable? checkpoints? smoke test?

python scripts/check_deps.py my_workflow.json
# → are this workflow's nodes/models/embeddings installed?

python scripts/run_workflow.py \
  --workflow workflows/sd15_txt2img.json \
  --args '{"prompt": "test", "steps": 4}' \
  --output-dir ./test-outputs

Image Upload (img2img / Inpainting)

The simplest way is to use --input-image with run_workflow.py:

python scripts/run_workflow.py \
  --workflow workflows/sdxl_img2img.json \
  --input-image image=./photo.png \
  --args '{"prompt": "make it cyberpunk", "denoise": 0.6}'

The flag uploads photo.png, then injects its server-side filename into whatever schema parameter is named image. For inpainting, pass both:

python scripts/run_workflow.py \
  --workflow workflows/sdxl_inpaint.json \
  --input-image image=./photo.png \
  --input-image mask_image=./mask.png \
  --args '{"prompt": "fill with flowers"}'

Manual upload via REST:

curl -X POST "http://127.0.0.1:8188/upload/image" \
  -F "image=@photo.png" -F "type=input" -F "overwrite=true"
# Returns: {"name": "photo.png", "subfolder": "", "type": "input"}

# Cloud equivalent:
curl -X POST "https://cloud.comfy.org/api/upload/image" \
  -H "X-API-Key: $COMFY_CLOUD_API_KEY" \
  -F "image=@photo.png" -F "type=input" -F "overwrite=true"

Cloud Specifics

• Base URL: https://cloud.comfy.org
• Auth: X-API-Key header (or ?token=KEY for WebSocket)
• API key: set $COMFY_CLOUD_API_KEY once and the scripts pick it up automatically
• Output download: /api/view returns a 302 to a signed URL; the scripts follow it and strip X-API-Key before fetching from the storage backend (don't leak the API key to S3/CloudFront).
• Endpoint differences from local ComfyUI:
  • /api/object_info, /api/queue, /api/userdata — 403 on free tier; paid only.
  • /history is renamed to /history_v2 on cloud (the scripts route automatically).
  • /models/<folder> is renamed to /experiment/models/<folder> on cloud (the scripts route automatically).
  • clientId in WebSocket is currently ignored — all connections for a user receive the same broadcast. Filter by prompt_id client-side.
  • subfolder is accepted on uploads but ignored — cloud has a flat namespace.
• Concurrent jobs: Free/Standard: 1, Creator: 3, Pro: 5. Extras queue automatically. Use run_batch.py --parallel N to saturate your tier.

Queue & System Management

# Local
curl -s http://127.0.0.1:8188/queue | python -m json.tool
curl -X POST http://127.0.0.1:8188/queue -d '{"clear": true}'    # cancel pending
curl -X POST http://127.0.0.1:8188/interrupt                      # cancel running
curl -X POST http://127.0.0.1:8188/free \
  -H "Content-Type: application/json" \
  -d '{"unload_models": true, "free_memory": true}'

# Cloud — same paths under /api/, plus:
python scripts/fetch_logs.py --tail-queue --host https://cloud.comfy.org

Pitfalls

1. API format required — every script and the /api/prompt endpoint expect API-format workflow JSON. The scripts detect editor format (top-level nodes and links arrays) and tell you to re-export via "Workflow → Export (API)" (newer UI) or "Save (API Format)" (older UI).

2. Server must be running — all execution requires a live server. comfy launch --background starts one. Verify with curl http://127.0.0.1:8188/system_stats.

3. Model names are exact — case-sensitive, includes file extension. check_deps.py does fuzzy matching (with/without extension and folder prefix), but the workflow itself must use the canonical name. Use comfy model list to discover what's installed.

4. Missing custom nodes — "class_type not found" means a required node isn't installed. check_deps.py reports which package to install; auto_fix_deps.py runs the install for you.

5. Working directory — comfy-cli auto-detects the ComfyUI workspace. If commands fail with "no workspace found", use comfy --workspace /path/to/ComfyUI <command> or comfy set-default /path/to/ComfyUI.

6. Cloud free-tier API limits — /api/prompt, /api/view, /api/upload/*, /api/object_info all return 403 on free accounts. health_check.py and check_deps.py handle this gracefully and surface a clear message.

7. Timeout for video/audio workflows — auto-detected when an output node is VHS_VideoCombine, SaveVideo, etc.; the default jumps from 300 s to 900 s. Override explicitly with --timeout 1800.

8. Path traversal in output filenames — server-supplied filenames are passed through safe_path_join to refuse anything escaping --output-dir. Keep this protection on — workflows with custom save nodes can produce arbitrary paths.

9. Workflow JSON is arbitrary code — custom nodes run Python, so submitting an unknown workflow has the same trust profile as eval. Inspect workflows from untrusted sources before running.

10. Auto-randomized seed — pass seed: -1 in (or use and omit the seed) to get a fresh seed per run. The actual seed is logged to stderr.

Verification Checklist

Use python scripts/health_check.py to run the whole list at once. Manual:

• hardware_check.py verdict is ok OR the user explicitly chose Comfy Cloud
• comfy --version works (or uvx --from comfy-cli comfy --help)
• curl http://HOST:PORT/system_stats returns JSON
• comfy model list shows at least one checkpoint (local) OR /api/experiment/models/checkpoints returns models (cloud)
• Workflow JSON is in API format
• check_deps.py reports is_ready: true (or only node_check_skipped on cloud free tier)
• Test run with a small workflow completes; outputs land in --output-dir