Comfyui Core

ComfyUI Core Knowledge

Workflow JSON Format (API Format)

ComfyUI workflows are JSON objects mapping string node IDs to node definitions:

{
  "1": {
    "class_type": "CheckpointLoaderSimple",
    "inputs": { "ckpt_name": "sd_xl_base_1.0.safetensors" },
    "_meta": { "title": "Load Checkpoint" }
  },
  "2": {
    "class_type": "CLIPTextEncode",
    "inputs": { "text": "a cat", "clip": ["1", 1] },
    "_meta": { "title": "Positive Prompt" }
  }
}

Key Rules

• Node IDs are strings of integers ("1", "2", etc.)
• class_type is the exact Python class name of the node
• inputs contains both widget values (scalars) and connections (arrays)
• Connections use the format ["sourceNodeId", outputIndex], a 2-element array where:
  • the first element is the string node ID of the source node
  • the second element is the integer index into the source node's output list (0-based)
• _meta is optional and used for display titles only

Connection Examples

"model": ["1", 0]       // Connect to node 1's first output (MODEL)
"clip": ["1", 1]        // Connect to node 1's second output (CLIP)
"vae": ["1", 2]         // Connect to node 1's third output (VAE)
"positive": ["2", 0]    // Connect to node 2's first output (CONDITIONING)
"samples": ["5", 0]     // Connect to node 5's first output (LATENT)
"images": ["6", 0]      // Connect to node 6's first output (IMAGE)

Important: API Format vs Web UI Format

• API format (for execution/analysis) is { "1": { class_type, inputs }, "2": { ... } }. It is compact and used by enqueue_workflow, create_workflow (action:"validate"), create_workflow (action:"modify"), etc.
• Web UI format (for saving and frontend editing) is { "nodes": [...], "links": [...] }. It includes layout positions, sizes, groups, and visual metadata so ComfyUI's canvas can open and edit it
• Execution tools expect and return API format
• Save in Web UI format so saved workflows stay readable and editable in the ComfyUI frontend. A raw API-format save is not canvas-editable. It "exists" in the library but loads blank in the canvas, which strands users and tempts agents into creating yet another new workflow instead of reopening the old one. Because of this, save_workflow auto-converts API-format input to Web UI format with a generated layout. Prefer passing real Web UI format (from get_workflow(action="get", filename=…, format="ui")), since a generated layout loses the original node positions and groups
• get_workflow defaults to format="api" for analysis/execution; use format="ui" when loading a workflow to re-save or edit in the canvas
• Muted/bypassed nodes are preserved with _meta.mode: "muted". They are inactive but visible for understanding the workflow
• Get/Set virtual wire nodes are preserved with _meta.title and Constant key for tracing data flow

Workflow Library Tools

• get_workflow(action="analyze", filename=…) is the first call for understanding any saved workflow. It returns a structured text summary with sections, node IDs, key settings, virtual wires, and connection graph. No raw JSON, just what you need to reason about the workflow. Supports views: summary (default), overview (mermaid), detail (section mermaid), list, flat.
• get_workflow (action:"list") lists all saved workflows in ComfyUI's user library
• get_workflow(action="get", filename=…) loads raw workflow JSON. Only use it when you need the actual JSON for enqueue_workflow, create_workflow (action:"modify"), or save_workflow. Use action="analyze" instead for understanding. When the JSON is headed back to save_workflow, request format="ui" so the workflow stays editable in the frontend.
• save_workflow(action="save", filename=…, workflow=…) saves a workflow to the user library. Pass Web UI format ({ nodes, links }) so it keeps its real layout in ComfyUI's canvas. API-format graphs are accepted and auto-converted to Web UI format (with a generated layout) precisely because a raw API-format save is not canvas-editable; the frontend cannot open it. When re-saving an existing workflow, load it with get_workflow(action="get", filename=…, format="ui") and edit that, so positions and groups survive.

Data Types

ComfyUI nodes pass typed data through connections:

Standard Pipeline Patterns

Text-to-Image (txt2img)

CheckpointLoaderSimple → MODEL, CLIP, VAE
  ├─ CLIP → CLIPTextEncode (positive) → CONDITIONING
  ├─ CLIP → CLIPTextEncode (negative) → CONDITIONING
  │
EmptyLatentImage → LATENT
  │
KSampler (model, positive, negative, latent_image) → LATENT
  │
VAEDecode (samples, vae) → IMAGE
  │
SaveImage (images)

Node IDs typically: 1=Checkpoint, 2=Positive, 3=Negative, 4=EmptyLatent, 5=KSampler, 6=VAEDecode, 7=SaveImage

Image-to-Image (img2img)

Same as txt2img but replace EmptyLatentImage with:

LoadImage → IMAGE
VAEEncode (pixels, vae) → LATENT → KSampler.latent_image

Set KSampler.denoise to 0.5 to 0.8 (lower = closer to input image).

Upscale

LoadImage → IMAGE
UpscaleModelLoader → UPSCALE_MODEL
ImageUpscaleWithModel (upscale_model, image) → IMAGE
SaveImage (images)

Inpaint

LoadImage (image) → IMAGE → VAEEncode → LATENT
LoadImage (mask) → MASK
SetLatentNoiseMask (samples, mask) → LATENT → KSampler.latent_image

MCP Tool Usage Guide

Quick Generation

1. create_workflow with template "txt2img" and your params
2. enqueue_workflow(action="enqueue") with the returned JSON. It returns prompt_id immediately
3. Poll queue (action:"status") with the prompt_id until done is true
4. Use get_image (action:"list_outputs") (limit 1) to find the generated image, then Read to display it

Inspect & Modify

• create_workflow (action:"node_info") queries what nodes are available and their schemas
• create_workflow (action:"modify") patches an existing workflow (set_input, add_node, remove_node, connect, insert_between)
• visualize_workflow shows a workflow as a mermaid diagram

Reverse Engineering

• visualize_workflow turns workflow JSON into a mermaid diagram
• visualize_workflow (action:"mermaid") turns a mermaid diagram into workflow JSON (uses /object_info for schema resolution)

Model Management

• list_local_models shows what's installed
• download_model action:"search" finds models on HuggingFace
• download_model downloads to ComfyUI's models directory

Never ask the user to manually download models. If a required model is missing, search for it and download it yourself:

1. Check list_local_models first
2. If missing, search HuggingFace via download_model action:"search" or CivitAI via their REST API
3. Use download_model to install it directly to the correct subfolder

CivitAI API (when the CIVITAI_API_TOKEN env var is available):

• Search: GET https://civitai.com/api/v1/models?query={query}&types=Checkpoint&sort=Most+Downloaded&limit=5
• Details: GET https://civitai.com/api/v1/models/{modelId}
• Download: GET https://civitai.com/api/download/models/{modelVersionId}?token={token}

CivitAI is preferred for fine-tuned models, community-rated checkpoints, and specialized LoRAs. HuggingFace is preferred for official/base models (SDXL, Flux, SD 1.5).

Custom Nodes

• search_custom_nodes searches the ComfyUI Registry (action: "search") or gets one pack's details (action: "details")
• list_packs (action: "generate_skill") auto-generates a skill file for a node pack

Workflow Execution

enqueue_workflow submits to ComfyUI's queue and returns prompt_id + queue position immediately. It does not block.

Background Progress Monitoring

After enqueuing one or more workflows, use a background Bash task to monitor progress silently:

# Single job
Bash(run_in_background: true):
node "${CLAUDE_PLUGIN_ROOT}/scripts/monitor-progress.mjs" <prompt_id>

# Multiple jobs (batch)
Bash(run_in_background: true):
node "${CLAUDE_PLUGIN_ROOT}/scripts/monitor-progress.mjs" <id1> <id2> <id3>

The script connects to ComfyUI's WebSocket and reports:

• Step-by-step progress (e.g., KSampler step 12/20 (60%))
• Success with output filenames and timing
• Errors with node details and messages

The standard generation pattern:

1. create_workflow or build workflow JSON + enqueue_workflow(action="enqueue") (repeat for batch)
2. Start background monitor with all prompt_ids
3. Continue conversation. Results appear when jobs finish
4. Use get_image (action:"list_outputs") or Read to display the generated images

Do not poll queue (action:"status") in a loop. The background monitor replaces polling entirely.

If the monitor script is unavailable, fall back to queue (action:"status") and poll until done is true.

Queue Management

One tool, queue, driven by its action parameter:

• queue (action:"list") shows running/pending job counts and prompt_ids
• queue (action:"status") checks if a specific prompt_id is running, pending, or done
• queue (action:"cancel") interrupts a running job (pass optional prompt_id to target a specific one)
• queue (action:"cancel_queued") removes a specific pending job from the queue by prompt_id
• queue (action:"clear") removes all pending jobs (does not stop the currently running job)

When to use queue tools:

• To check status, use queue (action:"status") for a quick boolean check (prefer the background monitor for ongoing tracking)
• To abort, queue (action:"cancel") stops what's running now and queue (action:"cancel_queued") removes a pending one
• To start fresh, queue (action:"clear") then optionally queue (action:"cancel")

Monitoring & Recovery

• get_system_stats reports GPU, VRAM, Python version, OS details
• queue (action:"list") shows running/pending jobs (also listed above under Queue Management)

When ComfyUI is unresponsive or crashed:

1. Try get_system_stats. If it fails, ComfyUI is down
2. Use restart_comfyui with action: "restart" (preserves launch args from a prior action: "stop")
3. If restart fails (no saved process info), use restart_comfyui with action: "start" or ask the user to start it manually
4. After ComfyUI is back, re-enqueue any failed/lost workflows

When a job appears hung (monitor shows [STALL]):

1. Check get_system_stats and look at VRAM usage (OOM causes hangs)
2. Try queue (action:"cancel") to interrupt the stuck job
3. If cancel fails, use restart_comfyui to force-restart
4. Use clear_vram after restart to free GPU memory before retrying

KSampler Parameters

Mermaid Visualization Conventions

The visualize_workflow tool produces mermaid flowcharts with:

• Subgraphs grouping nodes by category: loading, conditioning, sampling, image, output
• Edge labels showing data types: -->|MODEL|, -->|CLIP|, -->|LATENT|, etc.
• Node labels showing class_type and optionally widget values
• Direction LR (left-to-right) by default, TB (top-to-bottom) for large workflows

The visualize_workflow (action:"mermaid") tool parses mermaid back into workflow JSON, using connection type labels to resolve the correct input/output slots via /object_info schemas.

Common Mistakes to Avoid

1. Wrong connection format. Use ["1", 0] not [1, 0]; node IDs are strings
2. Web UI format. Don't pass { nodes: [], links: [] }; use API format
3. Missing VAE. CheckpointLoaderSimple has 3 outputs: MODEL(0), CLIP(1), VAE(2)
4. Wrong output index. Check the node's output list order via create_workflow (action:"node_info")
5. Seed handling. enqueue_workflow randomizes seeds by default unless disable_random_seed: true

Sources

• Official: ComfyUI workflow/API conventions from https://github.com/comfyanonymous/ComfyUI and https://docs.comfy.org
• Empirical: MCP tool recipes and KSampler default tables are product/empirical notes, not a vendor prompting guide.