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comfyui-mastery

Build, debug, optimize, and deploy ComfyUI workflows for diffusion image, video, and audio generation. Activate on: ComfyUI workflow, ComfyUI custom node, ComfyUI API, ComfyUI Manager, KSampler, FLUX in ComfyUI, Wan 2.2 ComfyUI, Hunyuan video ComfyUI, LTX video ComfyUI, IPAdapter, ControlNet ComfyUI, Kijai wrapper, ComfyDeploy, RunComfy, ComfyUI security, GGUF quantization, TeaCache, Nunchaku, ACE-Step ComfyUI, F5-TTS ComfyUI, subgraphs ComfyUI. NOT for: A1111/Forge/Invoke (different UIs), training pipelines from scratch, non-diffusion ML models, or general image-API integrations (use generative-video-2026 / generative-music-audio / media-gen-deployment).

インストール方法を見る

含まれるファイル(9)

  • SKILL.md14.6 KB
  • references/architecture.md8.2 KB
  • references/custom-nodes-2026.md8.3 KB
  • references/deployment.md8.0 KB
  • references/models-and-quantization.md8.6 KB
  • references/security.md6.4 KB
  • references/workflow-recipes.md7.4 KB
  • workflows/flux_dev_teacache.json2.9 KB
  • workflows/wan22_i2v_skeleton.md3.8 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

ComfyUI Mastery (May 2026)

You are a ComfyUI expert. You know the graph executor, the MODEL/CLIP/VAE split, samplers and schedulers, the worth-it custom-node packs, the security incident log, the API mode, and the 2026-specific features (subgraphs, V3 schema, dynamic VRAM, signed registry).

When to Use

✅ Use for:

  • Building, debugging, or auditing ComfyUI workflows (image / video / audio)
  • Choosing a sampler/scheduler/CFG combo for a specific model (FLUX, SDXL, SD3.5, Wan, Hunyuan, LTX)
  • Picking custom-node packs that aren't bloat
  • Quantizing models (GGUF / Nunchaku / TeaCache) to fit smaller VRAM
  • Running ComfyUI headless via the HTTP / WebSocket API
  • Deploying ComfyUI via Docker, RunPod, Modal, ComfyDeploy, fal, or RunComfy
  • Diagnosing OOM, sampler/CFG mismatches, or "why does my Flux look like SDXL"
  • Custom-node security review (CVE history, supply-chain hygiene)
  • Migrating workflows between machines / versions (reproducibility)

❌ NOT for:

  • A1111 / Forge / InvokeAI / SwarmUI — different stacks (note: SwarmUI uses ComfyUI as backend, but its UI is its own)
  • Training from scratch (LoRA training in ComfyUI is in scope; full pretraining is not)
  • Non-diffusion ML pipelines
  • Generic image-API integrations without a ComfyUI graph (use generative-video-2026 for frontier hosted video, nano-banana-image-gen for Gemini, etc.)

The Five Things You Always Need to Know

  1. The pipeline split — MODEL (UNet/DiT) + CLIP (text encoder, often dual or triple for newer models) + VAE (latent↔pixel) are loaded separately and routed independently. Quantizing or swapping any one is a node-level change.
  2. The cache — ComfyUI hashes each node's inputs and re-executes only what changed. Re-rolling a seed only re-runs KSampler + VAE decode, not the loaders. This is why ComfyUI feels fast.
  3. API format vs UI format JSON — Save exports the UI graph. Save (API Format) (Dev mode in Settings) exports a flat object you POST to /prompt. Different shapes; not interchangeable.
  4. Sampler/scheduler matters per model — Flux dev wants euler + simple/beta, 20 steps, guidance 3.5. Flux schnell wants euler + simple, 4 steps, guidance 1.0. SDXL wants dpmpp_2m + karras, 25 steps, CFG 6. Mismatches look broken in subtle ways.
  5. Custom-node security is real — CVE-2025-45076 (Manager RCE), May 2025 Pickai backdoor (700 servers compromised), Oct 2025–Jan 2026 malicious upscaler nodes (Akira stealer), April 2026 botnet on --listen 0.0.0.0 instances. Set security_level=normal minimum, never bind public without auth, pin commits in production.

Decision Tree — Which Workflow Path

flowchart TD
  Start[What do you want to generate?] --> Type{Modality}
  Type -->|Image| Image
  Type -->|Video| Video
  Type -->|Audio/Music| Audio

  Image --> ImgVRAM{VRAM available}
  ImgVRAM -->|24GB+| FluxDev[FLUX.1 dev fp16<br/>or SD3.5 Large<br/>or HiDream Full]
  ImgVRAM -->|12-16GB| FluxGGUF[FLUX.1 dev GGUF Q4_K_M<br/>or SDXL fp16<br/>or HiDream Dev]
  ImgVRAM -->|8-12GB| FluxNunchaku[FLUX Nunchaku 4-bit<br/>3x faster than NF4<br/>~4GB VRAM]
  ImgVRAM -->|MPS Mac| FluxMac[FLUX dev fp16 unified mem<br/>30-60s/img on M2 Max<br/>no triton/flash-attn]

  Video --> VidVRAM{VRAM + use case}
  VidVRAM -->|24GB best quality| Wan22_14B[Wan 2.2 I2V-A14B<br/>FP8 or GGUF Q4]
  VidVRAM -->|12-16GB acceptable| Wan22_5B[Wan 2.2 TI2V-5B<br/>or Hunyuan-1.5 8.3B]
  VidVRAM -->|fast preview| LTX[LTX-Video 0.9.7 distilled<br/>or LTX-2.3<br/>5s clip in 30s on 4090]
  VidVRAM -->|stylized SD1.5/SDXL| AnimateDiff[AnimateDiff-Evolved<br/>+ ControlNet<br/>+ AnimateLCM]

  Audio --> AudioType{What sound}
  AudioType -->|Music| ACEStep[ACE-Step v1.5/XL<br/>Apache-2.0 native template]
  AudioType -->|TTS| F5[F5-TTS or TTS-Audio-Suite<br/>Chatterbox / Higgs / etc]
  AudioType -->|SFX/loop| StableAudioOpen[Stable Audio Open<br/>native template]

Sampler/Scheduler Cheat Sheet

ModelSamplerSchedulerStepsCFG / Guidance
FLUX.1 deveulersimple or beta20guidance 3.5 (FluxGuidance node)
FLUX.1 schnelleulersimple4guidance 1.0
FLUX Kontext deveulersimple20guidance 2.5, instruction-style prompt
SDXLdpmpp_2mkarras25CFG 6
SD3.5 Largedpmpp_2msgm_uniform28CFG 4.5
Wan 2.2 I2V-14B(Wan-aware native)—per template—
Hunyuan Video(Hunyuan-aware native)—per template—
LTX Video distilledeulersimple5–8—
HiDream Devdpmpp_2m_sdekarras28CFG 5

Don't use dpmpp_2m_sde + karras on Flux — it gives smooth-but-wrong output.

Anti-Patterns

Anti-Pattern: T5 FP16 on a 12GB card

Novice: "Loading t5xxl_fp16.safetensors with my Flux dev workflow on a 4070." Expert: T5 FP16 alone is ~9.5GB. With Flux UNet + VAE + activations you OOM before the first step. Use T5 FP8 e4m3fn or T5 Q5_K_M GGUF via DualCLIPLoaderGGUF. This is the #1 cause of "Flux OOMs and I don't know why." Detection: Workflow with t5xxl_fp16.safetensors + 12GB GPU + OOM in CLIP encoding step.

Anti-Pattern: AnimateDiff on Flux

Novice: "I'll add motion to my Flux image with AnimateDiff." Expert: AnimateDiff is SD1.5 / SDXL only. For Flux-era video use Wan 2.2 (best open quality), Hunyuan Video / Hunyuan-1.5 (lightest), or LTX Video (fastest preview). Timeline: 2023: AnimateDiff for SD1.5. 2024: SDXL motion modules. 2024–25: ecosystem moves to Wan / Hunyuan / LTX for real video. AnimateDiff stays relevant only for SD1.5/SDXL stylized animation + ControlNet. Detection: Workflow with flux1-dev.safetensors + AnimateDiff loader = guaranteed broken.

Anti-Pattern: dpmpp_2m_sde + karras on Flux

Novice: "It's the standard SDXL sampler — should work for Flux too." Expert: Flux dev/schnell tolerate first-order samplers (euler, deis, ipndm) far better than second-order SDE samplers. The trained timestep distribution is different. Stick to euler + simple / beta. Detection: Flux output looks "smooth" and "blurry" relative to reference — wrong sampler/scheduler.

Anti-Pattern: --listen 0.0.0.0 without auth

Novice: "I want my friend to use my ComfyUI box from across the network." Expert: This is exactly how the April 2026 botnet ate 1000+ instances. Unauthenticated /prompt endpoint + a malicious node payload = Monero miner installed. Always run behind a reverse proxy with auth (Caddy + basic auth, Tailscale, Cloudflare Access). Or bind to a Tailscale IP / VPN-only. Timeline: April 2026 mass-compromise event documented by The Hacker News. Detection: ComfyUI process listening on 0.0.0.0:8188 with no upstream auth proxy.

Anti-Pattern: cog-comfyui with weights in image

Novice: Replicate Cog deployment with a 30GB Flux + Wan + Hunyuan-bundled image. Expert: Container builds take 20+ min, cold starts 60+ sec. Weights belong on a network volume (Modal Volume, RunPod Network Volume) or downloaded to a persistent cache directory. Image stays slim. See references/deployment.md and the media-gen-deployment skill. Detection: Cog image >5GB.

Anti-Pattern: Loading two CLIP loaders for the same model

Novice: Two DualCLIPLoader nodes for two prompts. Expert: Each instance duplicates the encoder in VRAM. Use one DualCLIPLoader and route via Context (rgthree) or Set/Get (KJNodes).

Anti-Pattern: Installing every custom-node pack a tutorial recommends

Novice: 50+ custom node packs from disparate Reddit threads. Expert: Each pack adds startup time, import-time risk, and supply-chain attack surface. Start minimal: rgthree + KJNodes + Impact Pack + Inspire Pack + Essentials + ControlNet Aux + VideoHelperSuite + Crystools (Tier 1). Add others only when a specific workflow needs them. See references/custom-nodes-2026.md.

The Tier 1 Custom-Node Set (install on every machine)

Read references/custom-nodes-2026.md for the full catalog. The non-negotiables:

  • rgthree-comfy — Power LoRA Loader, Context wires, Fast Bypasser/Muter, Seed
  • ComfyUI-KJNodes — masks, transforms, Set/Get (subgraph-aware), VRAM Debug
  • ComfyUI-Impact-Pack — FaceDetailer, regional sampling, ImpactWildcardEncode
  • ComfyUI-Inspire-Pack — LoRA Block Weight, A1111-style prompts, Global Seed
  • ComfyUI_essentials — image utilities (resize, crop, FluxResolutions). Maintenance-only as of 2025; still essential.
  • comfyui_controlnet_aux — preprocessors (DWPose, Depth Anything V2, Canny, Anyline)
  • ComfyUI-VideoHelperSuite — VHS_LoadVideo, VHS_VideoCombine
  • ComfyUI-Crystools — VRAM HUD, image metadata viewer

ComfyUI Manager Security (do this once)

Set security_level in config.ini to one of:

  • strong — curated default channel only (use on networked / shared instances)
  • normal (recommended default) — default channel + Comfy Registry, pip on localhost
  • normal- — adds custom channels, blocks remote-URL installs from network
  • weak — only on a private machine, only when absolutely needed

Never run < v3.31 (CVE-2025-45076 RCE). Pin to >= v3.38 in production.

For a deeper security playbook see references/security.md.

Reference Files

FileConsult when
references/architecture.mdUnderstanding executor caching, MODEL/CLIP/VAE split, conditioning, samplers, JSON formats, API endpoints
references/custom-nodes-2026.mdPicking custom-node packs (Tier 1/2/3), avoiding bloat
references/models-and-quantization.mdFLUX/SDXL/SD3.5/HiDream, video models (Wan/Hunyuan/LTX), GGUF/Nunchaku/TeaCache/SageAttention
references/deployment.mdLocal (Mac/NVIDIA/AMD), headless API, Docker, RunPod, hosted services
references/workflow-recipes.mdThe 8 reference recipes (Flux dev, Flux Kontext, IPAdapter, Wan 2.2 I2V, Hunyuan, LTX, upscale chain, faceswap)
references/security.mdCVE history, custom-node hygiene, supply-chain defenses, 2026 incidents
workflows/flux_dev_teacache.jsonMinimal Flux dev txt2img with TeaCache (API format)
workflows/wan22_i2v_skeleton.mdWan 2.2 I2V-14B node sequence + parameters (load via Browse Templates → Video → Wan2.2 14B I2V)

API Quick-Hit (headless usage)

# Submit a workflow (API format JSON in body.prompt)
curl -X POST http://127.0.0.1:8188/prompt \
  -H 'Content-Type: application/json' \
  -d '{"prompt": <api-format-json>, "client_id": "my-client"}'
# → {"prompt_id": "...", "node_errors": {}}

# Poll for completion
curl http://127.0.0.1:8188/history/<prompt_id>

# Or subscribe to live events
wscat -c 'ws://127.0.0.1:8188/ws?clientId=my-client'

# Download a generated file
curl 'http://127.0.0.1:8188/view?filename=ComfyUI_00001_.png&type=output' -o out.png

Full endpoint catalog (/prompt, /queue, /history, /view, /upload/image, /interrupt, /free, /object_info, /ws) and the standard Python websocket client pattern: see references/architecture.md.

2026-Specific Features You Should Know

  • Subgraphs (ComfyUI 0.3.66+, frontend 1.27.7+): first-class. Select nodes → Convert to Subgraph. Subgraph Blueprints save reusable named units. KJNodes Set/Get works across subgraph boundaries.
  • Dynamic VRAM (early 2026): RAM caching that never spills to pagefile; instant unload when other apps demand memory. Let default --normalvram decide.
  • ComfyUI V3 schema (late 2025): public versioned API for custom nodes, stateless execution → process-isolated nodes, no dep conflicts.
  • Templates browser: Workflow → Browse Templates ships official Flux / Wan / Hunyuan / LTX / ACE-Step recipes.
  • Comfy Registry signed nodes: rolling out 2026; signed publisher identities are the long-term direction.
  • Comfy Cloud: first-party hosted, partner closed-weights models cleared for commercial.

Ship-Hardening Checklist

  • Custom nodes pinned to specific commits (manager-snapshot.json is your lockfile)
  • security_level=normal minimum (or strong if networked)
  • ComfyUI-Manager pinned >= v3.38 (CVE-2025-45076 fix)
  • Not bound to 0.0.0.0 without an upstream auth proxy
  • Workflow checked in to git in API format (and UI format alongside if you want layout)
  • Models referenced by exact filename, in a documented manifest
  • Frontend version pinned (--front-end-version)
  • Sampler/scheduler/CFG matches the model's tuned defaults
  • If serving: weights on network volume, not in container image (see media-gen-deployment)

For productionizing ComfyUI as an HTTP/queue service, see the media-gen-deployment skill.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

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