comfyui-nodes

name	comfyui-nodes
description	Use this skill when creating, modifying, or debugging ComfyUI custom nodes. Triggers on: 'ComfyUI custom node', 'ComfyUI node development', 'ComfyUI plugin', 'INPUT_TYPES', 'NODE_CLASS_MAPPINGS', 'RETURN_TYPES', 'define_schema', 'io.ComfyNode', or any request to build a new node for ComfyUI. Covers both V1 (current) and V3 (new) node APIs.
license	MIT
metadata	{"author":"marduk191","version":"2.1.0"}

ComfyUI Custom Node Development Skill

Comprehensive guide to creating custom nodes for ComfyUI. Covers both the current V1 API and the new V3 schema API.

Quick Reference

Official Resources:

• Repository: https://github.com/comfyanonymous/ComfyUI
• Documentation: https://docs.comfy.org/custom-nodes
• V3 Migration: https://docs.comfy.org/custom-nodes/v3_migration
• Example Nodes: https://github.com/comfyanonymous/ComfyUI/tree/master/comfy_extras

---

File Structure

Custom nodes go in ComfyUI/custom_nodes/:

ComfyUI/
└── custom_nodes/
    └── my_custom_nodes/
        ├── __init__.py          # REQUIRED: Node registration
        ├── nodes.py             # Node class definitions
        ├── requirements.txt     # Optional: pip dependencies
        └── web/                 # Optional: Frontend JS extensions
            └── js/
                └── my_extension.js

---

Basic Node Template

ALL FOUR class attributes (CATEGORY, RETURN_TYPES, FUNCTION, INPUT_TYPES) are REQUIRED. Missing any one will cause silent failure.

class MyCustomNode:
    """Description of what this node does."""

    # ── ALL FOUR REQUIRED ATTRIBUTES ──────────────────────────
    CATEGORY = "my_nodes/utilities"       # REQUIRED: Menu location
    RETURN_TYPES = ("IMAGE",)             # REQUIRED: Output types as TUPLE (NOT a method!)
    RETURN_NAMES = ("output_image",)      # Optional: Display names for outputs
    FUNCTION = "process"                  # REQUIRED: Name of the method ComfyUI will call
    OUTPUT_NODE = False                   # Optional: True for terminal nodes (save, preview)
    # ──────────────────────────────────────────────────────────

    @classmethod
    def INPUT_TYPES(cls):                 # REQUIRED: Must be a @classmethod
        return {
            "required": {
                "image": ("IMAGE",),
                "strength": ("FLOAT", {
                    "default": 1.0,
                    "min": 0.0,
                    "max": 2.0,
                    "step": 0.1,
                    "display": "slider",
                    "tooltip": "Effect strength"
                }),
            },
            "optional": {
                "mask": ("MASK",),
            },
            "hidden": {
                "node_id": "UNIQUE_ID",
            }
        }

    # Method name MUST match FUNCTION value. Parameters MUST match INPUT_TYPES keys.
    # Do NOT use execute(**kwargs). Use explicit named parameters.
    def process(self, image, strength, mask=None, node_id=None):
        # Your processing logic here
        result = image * strength
        return (result,)  # MUST return a tuple matching RETURN_TYPES (NOT a dict!)

---

Node Registration (init.py)

Keep __init__.py minimal. ComfyUI auto-discovers nodes via NODE_CLASS_MAPPINGS. No registration API calls needed.

# ✅ CORRECT: Use RELATIVE imports (from .nodes, NOT from nodes)
from .nodes import MyCustomNode, AnotherNode

# Maps internal name -> class (use unique prefixes to avoid conflicts!)
NODE_CLASS_MAPPINGS = {
    "MyProject_CustomNode": MyCustomNode,
    "MyProject_AnotherNode": AnotherNode,
}

# Maps internal name -> display name in UI (ALWAYS include this!)
NODE_DISPLAY_NAME_MAPPINGS = {
    "MyProject_CustomNode": "My Custom Node",
    "MyProject_AnotherNode": "Another Node",
}

# For JS extensions (only if you have web/ directory)
WEB_DIRECTORY = "./web/js"

__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', 'WEB_DIRECTORY']

NEVER do any of these in __init__.py:

• from nodes import ... (bare import — conflicts with ComfyUI's nodes module)
• sys.path.insert(0, ...) (path manipulation — causes import conflicts)
• register_custom_node() or register_node() (these APIs don't exist)
• sys.modules['NODE_CLASS_MAPPINGS'] = ... (nonsensical)
• Runtime CATEGORY patching with hasattr checks

---

INPUT_TYPES Reference

Input Categories

@classmethod
def INPUT_TYPES(cls):
    return {
        "required": { ... },   # Must be connected/set
        "optional": { ... },   # Can be left empty
        "hidden": { ... }      # Not shown in UI
    }

Primitive Types

INT

"count": ("INT", {
    "default": 1,
    "min": 0,
    "max": 100,
    "step": 1,
    "display": "number",  # or "slider"
    "tooltip": "Number of iterations"
})

FLOAT

"strength": ("FLOAT", {
    "default": 1.0,
    "min": 0.0,
    "max": 10.0,
    "step": 0.1,
    "round": 0.01,
    "display": "slider",
    "tooltip": "Effect strength"
})

STRING

"prompt": ("STRING", {
    "default": "",
    "multiline": True,
    "placeholder": "Enter text...",
    "dynamicPrompts": True,
    "tooltip": "Text input"
})

BOOLEAN

"enabled": ("BOOLEAN", {
    "default": True,
    "label_on": "Enabled",
    "label_off": "Disabled",
    "tooltip": "Toggle feature"
})

COMBO (Dropdown)

"mode": (["option1", "option2", "option3"], {"default": "option1"})

COLOR

"color": ("INT", {
    "default": 0xFF0000,
    "min": 0,
    "max": 0xFFFFFF,
    "display": "color"
})

Common Input Options

Option	Type	Description
default	any	Initial value
min / max	number	Value bounds
step	number	Increment step
round	number	Decimal precision
display	str	"number", "slider", "color"
tooltip	str	Hover help text
forceInput	bool	Force socket (no widget)
lazy	bool	Enable lazy evaluation
multiline	bool	Multi-line text (STRING)

Hidden Inputs

"hidden": {
    "node_id": "UNIQUE_ID",           # Node's unique ID
    "prompt": "PROMPT",                # Full workflow prompt
    "extra_pnginfo": "EXTRA_PNGINFO",  # PNG metadata
}

---

Data Types

Tensor Types

Type	Shape	Description
IMAGE	[B,H,W,C]	Image batch (C=3 RGB, values 0-1)
MASK	[B,H,W]	Grayscale mask (values 0-1)
LATENT	dict {"samples": [B,C,H,W]}	Latent space (channel-first)
AUDIO	dict {"waveform": [B,C,T]}	Audio data

Model Types

Type	Description
MODEL	Diffusion model (UNet)
CLIP	Text encoder
VAE	Variational autoencoder
CONDITIONING	Text/image conditioning
CONTROL_NET	ControlNet model
CLIP_VISION	Vision encoder

Sampling Types

Type	Description
SAMPLER	Sampling algorithm
SIGMAS	Noise schedule
NOISE	Noise generator
GUIDER	Guidance strategy

Custom Types

# Define your own type
RETURN_TYPES = ("MY_CUSTOM_DATA",)

# Accept in another node
"my_input": ("MY_CUSTOM_DATA", {"forceInput": True})

# Wildcard (accept any type)
"any_input": ("*",)

---

Working with Tensors

Image Format

import torch

def process(self, image):
    # IMAGE: [Batch, Height, Width, Channels] - channel-LAST
    batch, height, width, channels = image.shape

    # Process single image
    single = image[0]  # [H, W, C]

    # Add batch dimension back
    result = single.unsqueeze(0)  # [1, H, W, C]

    return (result,)

Mask Format

def process(self, mask):
    # MASK: [B, H, W] or [H, W]
    if mask.dim() == 2:
        mask = mask.unsqueeze(0)  # Add batch dim

    inverted = 1.0 - mask
    return (inverted,)

Latent Format

def process(self, latent):
    # LATENT: dict with "samples" key
    # samples: [B, C, H, W] - channel-FIRST, 1/8 image size
    samples = latent["samples"]

    processed = samples * 0.5

    return ({"samples": processed},)

Format Conversion

# Channel-last to channel-first (IMAGE -> model input)
chw = image.permute(0, 3, 1, 2)  # [B,H,W,C] -> [B,C,H,W]

# Channel-first to channel-last (model output -> IMAGE)
hwc = tensor.permute(0, 2, 3, 1)  # [B,C,H,W] -> [B,H,W,C]

---

Advanced Features

IS_CHANGED (Cache Control)

@classmethod
def IS_CHANGED(cls, image, seed):
    # Return different value when node should re-execute
    # NaN = always re-execute
    return float("NaN")

    # Or hash inputs
    return hash(seed)

WARNING: Do NOT return bool. Returning True means "unchanged"!

VALIDATE_INPUTS

@classmethod
def VALIDATE_INPUTS(cls, image, strength):
    if strength < 0:
        return "Strength must be non-negative"
    return True

Lazy Evaluation

@classmethod
def INPUT_TYPES(cls):
    return {
        "required": {
            "image1": ("IMAGE", {"lazy": True}),
            "image2": ("IMAGE", {"lazy": True}),
            "blend": ("FLOAT", {"default": 0.5}),
        }
    }

def check_lazy_status(self, image1, image2, blend):
    needed = []
    if blend > 0 and image1 is None:
        needed.append("image1")
    if blend < 1 and image2 is None:
        needed.append("image2")
    return needed

def process(self, image1, image2, blend):
    return (image1 * blend + image2 * (1 - blend),)

List Processing

class BatchProcessor:
    INPUT_IS_LIST = True
    OUTPUT_IS_LIST = (True,)
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "process"

    @classmethod
    def INPUT_TYPES(cls):
        return {"required": {"images": ("IMAGE",)}}

    def process(self, images):
        # images is a list of tensors
        results = [process_single(img) for img in images]
        return (results,)

Progress Updates

from server import PromptServer

def process(self, count, node_id):
    for i in range(count):
        PromptServer.instance.send_sync(
            "progress",
            {"node": node_id, "value": i, "max": count}
        )
        do_work(i)
    return (count,)

Return UI Data

def process(self, image):
    result = process_image(image)

    return {
        "ui": {"message": ["Processing complete!"]},
        "result": (result,)
    }

---

JavaScript Extensions

// web/js/my_extension.js
import { app } from "../../scripts/app.js";

app.registerExtension({
    name: "my.custom.extension",

    async setup() {
        console.log("Extension loaded");

        app.api.addEventListener("my.custom.message", (event) => {
            console.log("Received:", event.detail);
        });
    },

    async beforeRegisterNodeDef(nodeType, nodeData, app) {
        if (nodeData.name === "MyProject_CustomNode") {
            const onNodeCreated = nodeType.prototype.onNodeCreated;
            nodeType.prototype.onNodeCreated = function() {
                if (onNodeCreated) onNodeCreated.apply(this, arguments);
                // Custom initialization
            };
        }
    }
});

---

Complete Example: Image Filter Node

# nodes.py
import torch

class ImageBrightnessContrast:
    """Adjust image brightness and contrast."""

    CATEGORY = "image/adjustments"
    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("image",)
    FUNCTION = "adjust"

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "image": ("IMAGE",),
                "brightness": ("FLOAT", {
                    "default": 0.0,
                    "min": -1.0,
                    "max": 1.0,
                    "step": 0.05,
                    "display": "slider",
                    "tooltip": "Brightness adjustment (-1 to 1)"
                }),
                "contrast": ("FLOAT", {
                    "default": 1.0,
                    "min": 0.0,
                    "max": 3.0,
                    "step": 0.1,
                    "display": "slider",
                    "tooltip": "Contrast multiplier"
                }),
            }
        }

    def adjust(self, image, brightness, contrast):
        # Apply contrast (around midpoint 0.5)
        result = (image - 0.5) * contrast + 0.5

        # Apply brightness
        result = result + brightness

        # Clamp to valid range
        result = torch.clamp(result, 0.0, 1.0)

        return (result,)


class ImageBlend:
    """Blend two images together."""

    CATEGORY = "image/composite"
    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("blended",)
    FUNCTION = "blend"

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "image1": ("IMAGE",),
                "image2": ("IMAGE",),
                "blend_mode": (["normal", "multiply", "screen", "overlay"],),
                "opacity": ("FLOAT", {
                    "default": 0.5,
                    "min": 0.0,
                    "max": 1.0,
                    "step": 0.05,
                    "display": "slider"
                }),
            },
            "optional": {
                "mask": ("MASK",),
            }
        }

    def blend(self, image1, image2, blend_mode, opacity, mask=None):
        if blend_mode == "normal":
            blended = image2
        elif blend_mode == "multiply":
            blended = image1 * image2
        elif blend_mode == "screen":
            blended = 1 - (1 - image1) * (1 - image2)
        elif blend_mode == "overlay":
            blended = torch.where(
                image1 < 0.5,
                2 * image1 * image2,
                1 - 2 * (1 - image1) * (1 - image2)
            )

        # Apply opacity
        result = image1 * (1 - opacity) + blended * opacity

        # Apply mask if provided
        if mask is not None:
            if mask.dim() == 2:
                mask = mask.unsqueeze(0)
            mask = mask.unsqueeze(-1)  # [B,H,W] -> [B,H,W,1]
            result = image1 * (1 - mask) + result * mask

        return (result,)

# __init__.py
from .nodes import ImageBrightnessContrast, ImageBlend

NODE_CLASS_MAPPINGS = {
    "MyNodes_BrightnessContrast": ImageBrightnessContrast,
    "MyNodes_Blend": ImageBlend,
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "MyNodes_BrightnessContrast": "Brightness/Contrast",
    "MyNodes_Blend": "Image Blend",
}

__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

---

Best Practices

1. Use unique prefixes in NODE_CLASS_MAPPINGS to avoid conflicts
2. Add tooltips to all inputs for better UX
3. Validate inputs with VALIDATE_INPUTS for clear error messages
4. Handle batch dimensions - always expect [B,H,W,C] for images
5. Clamp outputs to valid ranges (0-1 for images)
6. Use lazy evaluation for expensive optional inputs
7. Document dependencies in requirements.txt
8. Test with various batch sizes and edge cases

---

⚠️ CRITICAL: Common Mistakes That Break Nodes

These mistakes will cause nodes to silently fail to register, not appear in workflows, or crash at runtime. Every single one has been encountered in real node development.

Mistake 1: Using OUTPUT_TYPES Instead of RETURN_TYPES

WRONG — Node will not register:

class MyNode:
    @classmethod
    def OUTPUT_TYPES(cls):  # ❌ WRONG NAME, WRONG FORMAT
        return {"result": ("IMAGE",)}  # ❌ Dict, not tuple

CORRECT:

class MyNode:
    RETURN_TYPES = ("IMAGE",)  # ✅ Class attribute, tuple
    RETURN_NAMES = ("result",)  # ✅ Optional display names

Key points:

• It's RETURN_TYPES, never OUTPUT_TYPES
• It's a class attribute (tuple), NOT a classmethod
• For multiple outputs: RETURN_TYPES = ("IMAGE", "IMAGE") with RETURN_NAMES = ("left", "right")

Mistake 2: Missing FUNCTION Attribute

WRONG — ComfyUI doesn't know which method to call:

class MyNode:
    RETURN_TYPES = ("IMAGE",)
    # ❌ No FUNCTION attribute!

    def execute(self, image):  # ComfyUI will never call this
        return (image,)

CORRECT:

class MyNode:
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "process"  # ✅ Tells ComfyUI which method to call

    def process(self, image):  # ✅ Method name matches FUNCTION
        return (image,)

Mistake 3: Missing CATEGORY Attribute

WRONG — Node won't appear in the menu:

class MyNode:
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "process"
    # ❌ No CATEGORY! Node is invisible in menus

CORRECT:

class MyNode:
    CATEGORY = "image/restoration"  # ✅ Shows in Add Node menu
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "process"

Mistake 4: Using execute(**kwargs) Instead of Named Parameters

WRONG — Parameters won't be received correctly:

class MyNode:
    FUNCTION = "execute"

    def execute(self, **kwargs):  # ❌ Generic kwargs
        image = kwargs.get("image")  # ❌ Won't work reliably
        strength = kwargs.get("strength", 1.0)
        return (image * strength,)

CORRECT:

class MyNode:
    FUNCTION = "process"

    def process(self, image, strength=1.0):  # ✅ Explicit named params
        return (image * strength,)           # matching INPUT_TYPES keys

Key points:

• Method name must match the FUNCTION attribute value
• Parameter names must match the keys in INPUT_TYPES
• Optional inputs should have default values (e.g., mask=None)
• Don't use **kwargs — ComfyUI passes arguments by name

Mistake 5: Returning Dicts Instead of Tuples

WRONG — ComfyUI expects tuples:

def process(self, image):
    result = do_something(image)
    return {"result": result}  # ❌ Dict return

CORRECT:

def process(self, image):
    result = do_something(image)
    return (result,)  # ✅ Tuple matching RETURN_TYPES

For multiple outputs:

RETURN_TYPES = ("IMAGE", "IMAGE")
RETURN_NAMES = ("left", "right")

def process(self, image):
    return (left_result, right_result)  # ✅ Tuple with one element per RETURN_TYPES entry

Mistake 6: Bad __init__.py — Bare Import from nodes

WRONG — Conflicts with ComfyUI's own nodes module:

# ❌ __init__.py
import sys, os
NODES_DIR = os.path.dirname(os.path.abspath(__file__))
if NODES_DIR not in sys.path:
    sys.path.insert(0, NODES_DIR)  # ❌ sys.path manipulation
from nodes import NODE_CLASS_MAPPINGS  # ❌ Bare import collides with ComfyUI's nodes module

CORRECT:

# ✅ __init__.py (3 lines is all you need!)
from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS

__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

Key points:

• ALWAYS use relative imports (.nodes, not nodes)
• NEVER manipulate sys.path — it will cause import conflicts
• NEVER use bare from nodes import — nodes is a ComfyUI core module

Mistake 7: Calling Non-Existent Registration APIs

WRONG — These functions do not exist in ComfyUI:

# ❌ None of these exist!
from comfy.utils import register_custom_node  # ❌ Does not exist
register_custom_node(name, cls)               # ❌ Does not exist

from nodes import register_node               # ❌ Does not exist
register_node(cls)                            # ❌ Does not exist

# ❌ Also wrong:
sys.modules['NODE_CLASS_MAPPINGS'] = mappings  # ❌ Nonsensical

CORRECT — ComfyUI auto-discovers nodes via NODE_CLASS_MAPPINGS:

# ✅ Just export these dicts from __init__.py — that's it!
NODE_CLASS_MAPPINGS = {
    "MyProject_NodeName": MyNodeClass,
}
NODE_DISPLAY_NAME_MAPPINGS = {
    "MyProject_NodeName": "My Node Name",
}

ComfyUI reads NODE_CLASS_MAPPINGS from your package's __init__.py automatically. No registration function calls are needed.

Mistake 8: Missing NODE_DISPLAY_NAME_MAPPINGS

WRONG — Nodes show ugly internal names in UI:

NODE_CLASS_MAPPINGS = {
    "NAFNetDenoiseNode": NAFNetDenoiseNode,
}
# ❌ No NODE_DISPLAY_NAME_MAPPINGS — UI shows "NAFNetDenoiseNode"

CORRECT:

NODE_CLASS_MAPPINGS = {
    "NAFNet_Denoise": NAFNetDenoiseNode,
}
NODE_DISPLAY_NAME_MAPPINGS = {
    "NAFNet_Denoise": "NAFNet Denoise",  # ✅ Clean UI name
}

---

V1 Node Required Attributes Checklist

Every V1 node class MUST have ALL of these:

class MyNode:
    CATEGORY = "my_category"          # ✅ REQUIRED - menu location
    RETURN_TYPES = ("IMAGE",)         # ✅ REQUIRED - output types tuple
    FUNCTION = "my_method"            # ✅ REQUIRED - method name string

    @classmethod
    def INPUT_TYPES(cls):             # ✅ REQUIRED - classmethod
        return {"required": { ... }}

    def my_method(self, param1, param2):  # ✅ REQUIRED - matches FUNCTION
        return (result,)                   # ✅ REQUIRED - returns tuple

Missing ANY of these will cause silent failures.

---

Minimal Valid __init__.py

from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

Never add:

• sys.path manipulation
• register_custom_node() or register_node() calls
• sys.modules manipulation
• Manual CATEGORY patching via hasattr checks
• WEB_DIRS manual registration
• Bare from nodes import (always use from .nodes import)

---

Debugging

1. Check ComfyUI console for Python errors
2. Use print() statements (output goes to terminal)
3. Browser DevTools (F12) for JS extension errors
4. Verify tensor shapes match expected formats
5. Test with minimal workflows first

---

V3 Node API (New)

The V3 API is the future of ComfyUI node development. It uses a centralized schema approach instead of scattered class attributes. V3 nodes are stateless classmethods, making them compatible with isolated/distributed environments.

V3 Basic Template

from comfy.nodes.common import io

class MyV3Node(io.ComfyNode):
    """Description of what this node does."""

    @classmethod
    def define_schema(cls) -> io.Schema:
        return io.Schema(
            node_id="MyProject_V3Node",
            display_name="My V3 Node",
            category="my_nodes/utilities",
            description="A node that processes images",
            inputs=[
                io.Image.Input("image"),
                io.Float.Input("strength",
                    default=1.0, min=0.0, max=2.0, step=0.1,
                    tooltip="Effect strength"
                ),
                io.Mask.Input("mask", optional=True),
            ],
            outputs=[
                io.Image.Output("image"),
            ],
            is_output_node=False,
        )

    @classmethod
    def execute(cls, image, strength, mask=None) -> io.NodeOutput:
        result = image * strength
        if mask is not None:
            if mask.dim() == 2:
                mask = mask.unsqueeze(0)
            mask = mask.unsqueeze(-1)
            result = image * (1 - mask) + result * mask
        return io.NodeOutput(result)

V3 Extension Registration

from comfy.nodes.common import io

class MyExtension(io.ComfyExtension):
    async def get_node_list(self) -> list[type[io.ComfyNode]]:
        return [MyV3Node, AnotherV3Node]

async def comfy_entrypoint() -> MyExtension:
    return MyExtension()

V3 Input/Output Types

V1 Format	V3 Equivalent
("INT", {...})	io.Int.Input("name", default=0, min=0, max=100)
("FLOAT", {...})	io.Float.Input("name", default=1.0, min=0.0, max=10.0)
("STRING", {...})	io.String.Input("name", multiline=True)
("BOOLEAN", {...})	io.Boolean.Input("name", default=True)
("IMAGE",)	io.Image.Input("name")
("MASK",)	io.Mask.Input("name")
("LATENT",)	io.Latent.Input("name")
("MODEL",)	io.Model.Input("name")
("CLIP",)	io.Clip.Input("name")
("VAE",)	io.Vae.Input("name")
("CONDITIONING",)	io.Conditioning.Input("name")
(["opt1", "opt2"],)	io.Combo.Input("name", options=["opt1", "opt2"])
Custom type	io.Custom("MY_TYPE").Input("name")

V3 Special Methods

V1 Method	V3 Method	Purpose
INPUT_TYPES()	define_schema()	Node configuration
VALIDATE_INPUTS()	validate_inputs()	Input validation
IS_CHANGED()	fingerprint_inputs()	Cache control
check_lazy_status()	check_lazy_status()	Lazy evaluation (now classmethod)
FUNCTION = "process"	execute()	Always named execute

V3 UI Output

@classmethod
def execute(cls, image) -> io.NodeOutput:
    result = process_image(image)
    return io.NodeOutput(result, ui=io.ui.PreviewImage(result, cls=cls))

V3 Custom API Routes

from aiohttp import web
from server import PromptServer

class MyExtension(io.ComfyExtension):
    async def get_node_list(self):
        return [MyNode]

    async def init_custom_routes(self):
        @PromptServer.instance.routes.get("/my-extension/status")
        async def get_status(request):
            return web.json_response({"status": "ok"})

---

Error Handling Patterns

Graceful Input Validation

@classmethod
def VALIDATE_INPUTS(cls, image, strength):
    if strength < 0:
        return "Strength must be non-negative"
    if strength > 10:
        return "Strength too high (max 10)"
    return True

Try/Catch in Processing

def process(self, image, **kwargs):
    try:
        result = complex_operation(image)
        return (result,)
    except RuntimeError as e:
        if "out of memory" in str(e):
            # Try with lower resolution
            small = torch.nn.functional.interpolate(
                image.permute(0, 3, 1, 2),
                scale_factor=0.5,
                mode='bilinear'
            ).permute(0, 2, 3, 1)
            result = complex_operation(small)
            return (result,)
        raise

---

Device & Memory Management

import torch
import comfy.model_management as mm

def process(self, image):
    device = mm.get_torch_device()
    offload_device = mm.unet_offload_device()

    # Move to GPU for processing
    image = image.to(device)

    result = heavy_computation(image)

    # Move back to CPU to free VRAM
    result = result.to(offload_device)

    return (result,)

---

Publishing Custom Nodes

ComfyUI Manager Registration

Create a pyproject.toml in your node pack root:

[project]
name = "comfyui-my-nodes"
description = "Description of your node pack"
version = "1.0.0"
license = "MIT"
requires-python = ">=3.9"
dependencies = ["torch", "numpy"]

[project.urls]
Repository = "https://github.com/user/comfyui-my-nodes"

[tool.comfy]
PublisherId = "your-publisher-id"
DisplayName = "My Custom Nodes"
Icon = "https://example.com/icon.png"

Submit to ComfyUI Registry

# Install comfy-cli
pip install comfy-cli

# Login
comfy node login

# Publish
comfy node publish

---

Resources

• Official Docs: https://docs.comfy.org/custom-nodes
• V3 Migration Guide: https://docs.comfy.org/custom-nodes/v3_migration
• ComfyUI Source: https://github.com/comfyanonymous/ComfyUI
• Built-in Nodes: https://github.com/comfyanonymous/ComfyUI/tree/master/comfy_extras
• Example Custom Nodes: https://github.com/comfyanonymous/ComfyUI_examples
• ComfyUI Registry: https://registry.comfy.org