comfyui-workflow-builder

---

name: comfyui-workflow-builder description: Generate, build, create, or design ComfyUI workflow JSON from natural language descriptions. Produces valid node graphs with correct class_types, connections, output indices, and model-appropriate settings. Handles txt2img, img2img, inpainting, ControlNet, LoRA stacking, upscaling, and face detailing pipelines. Does NOT cover ComfyUI installation, custom node development, Python scripting, model training, hardware advice, or architectural explanations. user-invocable: true metadata: {"openclaw":{"emoji":"🔧","os":["darwin","linux","win32"],"requires":{"anyBins":["curl","wget"]},"primaryEnv":"COMFYUI_URL"}}

ComfyUI Workflow Builder

Translates natural language requests into executable ComfyUI workflow JSON. Always validates against inventory before generating.

Workflow Generation Process

Step 1: Understand the Request

Parse the user's intent into:

• Output type: Image, video, or audio
• Source material: Text-only, reference image(s), existing video
• Identity method: None, zero-shot (InstantID/PuLID), LoRA, Kontext
• Quality level: Draft (fast iteration) vs production (maximum quality)
• Special requirements: ControlNet, inpainting, upscaling, lip-sync

Step 2: Check Inventory

Read state/inventory.json to determine:

• Available checkpoints → select best match for task
• Available identity models → determine which methods are possible
• Available ControlNet models → enable pose/depth control if available
• Custom nodes installed → verify all required nodes exist
• VRAM available → optimize settings accordingly

Step 3: Select Pipeline Pattern

Based on request + inventory, choose from:

Pattern	When	Key Nodes
Text-to-Image	Simple generation	Checkpoint → CLIP → KSampler → VAE
Identity-Preserved Image	Character consistency	+ InstantID/PuLID/IP-Adapter
LoRA Character	Trained character	+ LoRA Loader
Image-to-Video (Wan)	High-quality video	Diffusion Model → Wan I2V → Video Combine
Image-to-Video (AnimateDiff)	Fast video, motion control	+ AnimateDiff Loader + Motion LoRAs
Talking Head	Character speaks	Image → Video → Voice → Lip-Sync
Upscale	Enhance resolution	Image → UltimateSDUpscale → Save
Inpainting	Edit regions	Image + Mask → Inpaint Model → KSampler

Step 4: Generate Workflow JSON

ComfyUI workflow format:

{
  "{node_id}": {
    "class_type": "{NodeClassName}",
    "inputs": {
      "{param_name}": "{value}",
      "{connected_param}": ["{source_node_id}", {output_index}]
    }
  }
}

Rules:

• Node IDs are strings (typically "1", "2", "3"...)
• Connected inputs use array format: ["source_node_id", output_index]
• Output index is 0-based integer
• Filenames must match exactly what's in inventory
• Seed values: use random large integer or fixed for reproducibility

Step 5: Validate

Before presenting to user:

1. Every class_type exists in inventory's node list
2. Every model filename exists in inventory's model list
3. All required connections are present (no dangling inputs)
4. VRAM estimate doesn't exceed available VRAM
5. Resolution is compatible with chosen model (512 for SD1.5, 1024 for SDXL/FLUX)

Step 6: Output

If online mode: Queue via comfyui-api skill If offline mode: Save JSON to projects/{project}/workflows/ with descriptive name

Workflow Templates

Basic Text-to-Image (FLUX)

{
  "1": {
    "class_type": "LoadCheckpoint",
    "inputs": {"ckpt_name": "flux1-dev.safetensors"}
  },
  "2": {
    "class_type": "CLIPTextEncode",
    "inputs": {"text": "{positive_prompt}", "clip": ["1", 1]}
  },
  "3": {
    "class_type": "CLIPTextEncode",
    "inputs": {"text": "{negative_prompt}", "clip": ["1", 1]}
  },
  "4": {
    "class_type": "EmptyLatentImage",
    "inputs": {"width": 1024, "height": 1024, "batch_size": 1}
  },
  "5": {
    "class_type": "KSampler",
    "inputs": {
      "seed": 42,
      "steps": 25,
      "cfg": 3.5,
      "sampler_name": "euler",
      "scheduler": "normal",
      "denoise": 1.0,
      "model": ["1", 0],
      "positive": ["2", 0],
      "negative": ["3", 0],
      "latent_image": ["4", 0]
    }
  },
  "6": {
    "class_type": "VAEDecode",
    "inputs": {"samples": ["5", 0], "vae": ["1", 2]}
  },
  "7": {
    "class_type": "SaveImage",
    "inputs": {"filename_prefix": "output", "images": ["6", 0]}
  }
}

With Identity Preservation (InstantID + IP-Adapter)

Extends basic template by adding:

• Load reference image node
• InstantID Model Loader + Apply InstantID
• IPAdapter Unified Loader + Apply IPAdapter
• FaceDetailer post-processing

See references/workflows.md for complete node settings.

Video Generation (Wan I2V)

Uses different loader chain:

• Load Diffusion Model (not LoadCheckpoint)
• Wan I2V Conditioning
• EmptySD3LatentImage (with frame count)
• Video Combine (VHS)

See references/workflows.md Workflow 4 for complete settings.

VRAM Estimation

Component	Approximate VRAM
FLUX FP16	16GB
FLUX FP8	8GB
SDXL	6GB
SD1.5	4GB
InstantID	+4GB
IP-Adapter	+2GB
ControlNet (each)	+1.5GB
Wan 14B	20GB
Wan 1.3B	5GB
AnimateDiff	+3GB
FaceDetailer	+2GB

Common Mistakes to Avoid

1. Wrong output index: CheckpointLoader outputs [model, clip, vae] at indices [0, 1, 2]
2. CFG too high for InstantID: Use 4-5, not default 7-8
3. Wrong resolution for model: FLUX/SDXL=1024, SD1.5=512
4. Missing VAE: FLUX needs explicit VAE (ae.safetensors)
5. Wrong model in wrong loader: Diffusion models need LoadDiffusionModel, not LoadCheckpoint

Reference Files

• references/workflows.md - Detailed node-by-node templates
• references/models.md - Model files and paths
• references/prompt-templates.md - Model-specific prompts
• state/inventory.json - Current inventory cache