Comfyui Node Lifecycle

ComfyUI Node Execution Lifecycle

Understanding the execution lifecycle helps build efficient, correct nodes.

Execution Flow Overview

1. Prompt received from frontend
2. Validation phase
   ├── Look up each node class
   ├── Call INPUT_TYPES() / define_schema() for input specs
   ├── Validate connections and types
   └── Call validate_inputs() for each node
3. Build execution order (topological sort from output nodes)
4. For each node in order:
   ├── Cache check (fingerprint_inputs)
   ├── Input resolution (get upstream values)
   ├── Lazy evaluation (check_lazy_status)
   ├── Execute function
   └── Store outputs in cache
5. Return results to frontend

Execution Order

ComfyUI executes from output nodes backward:

1. Identifies output nodes (is_output_node=True)
2. Builds dependency graph
3. Topological sort determines execution order
4. Only nodes connected to output nodes execute

Cache Control: fingerprintinputs (V3) / ISCHANGED (V1)

Controls when a node re-executes vs uses cached results.

class RandomNode(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="RandomNode",
            display_name="Random Value",
            category="utils",
            inputs=[
                io.Float.Input("min_val", default=0.0),
                io.Float.Input("max_val", default=1.0),
            ],
            outputs=[io.Float.Output("FLOAT")],
        )

    @classmethod
    def fingerprint_inputs(cls, min_val, max_val):
        """Return value compared to last run. Different value = re-execute."""
        # Return unique value each time to always re-execute
        import time
        return time.time()

    @classmethod
    def execute(cls, min_val, max_val):
        import random
        return io.NodeOutput(random.uniform(min_val, max_val))

How caching works:

• Before execution, fingerprint_inputs() is called with the same args as execute()
• Return value is compared to the previous run's return value
• If same → skip execution, use cached output
• If different → re-execute the node
• If fingerprint_inputs is not defined → cache based on input values

V1 equivalent (IS_CHANGED):

@classmethod
def IS_CHANGED(s, min_val, max_val):
    return time.time()  # always re-execute

not_idempotent Flag

For nodes that should never be cached:

io.Schema(
    node_id="AlwaysRunNode",
    not_idempotent=True,  # prevents all caching
    # ...
)

hasintermediateoutput Flag

For nodes with interactive UI that produce intermediate outputs (e.g., Image Crop, Painter). These behave like output nodes (UI results are cached and resent to the frontend on page refresh) but do NOT automatically get added to the execution list — they only execute if on the dependency path of a real output node.

io.Schema(
    node_id="InteractiveCropNode",
    has_intermediate_output=True,
    # ...
)

Input Validation: validateinputs (V3) / VALIDATEINPUTS (V1)

Validates inputs before execution. Runs during the validation phase.

class ValidatedNode(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="ValidatedNode",
            display_name="Validated Node",
            category="utils",
            inputs=[
                io.Int.Input("width", default=512, min=1, max=8192),
                io.Int.Input("height", default=512, min=1, max=8192),
            ],
            outputs=[io.Image.Output("IMAGE")],
        )

    @classmethod
    def validate_inputs(cls, width, height):
        """Return True if valid, or error string if invalid."""
        if width % 8 != 0 or height % 8 != 0:
            return "Width and height must be multiples of 8"
        if width * height > 4096 * 4096:
            return "Total pixels exceed maximum (4096x4096)"
        return True

    @classmethod
    def execute(cls, width, height):
        import torch
        return io.NodeOutput(torch.zeros(1, height, width, 3))

V1 equivalent:

@classmethod
def VALIDATE_INPUTS(s, width, height):
    if width % 8 != 0:
        return "Width must be a multiple of 8"
    return True

Skipping Type Validation

To accept any type (wildcard inputs), include input_types parameter:

@classmethod
def validate_inputs(cls, input_types: dict = None, **kwargs):
    # input_types contains the actual types of connected inputs
    # Returning True skips the default type checking
    return True

Lazy Evaluation: checklazystatus

Controls which lazy inputs actually need evaluation. See comfyui-node-inputs for full details.

@classmethod
def check_lazy_status(cls, condition, value_a=None, value_b=None):
    """Called before execute. Return names of inputs that need evaluation."""
    if condition and value_a is None:
        return ["value_a"]
    if not condition and value_b is None:
        return ["value_b"]
    return []

Key behaviors:

• Only called if the node has lazy inputs
• May be called multiple times as inputs become available
• Unevaluated lazy inputs are None
• Return empty list (or None) when ready to execute
• Evaluated inputs retain their value across calls

Output Nodes

Nodes with is_output_node=True are execution roots — ComfyUI traces backward from these:

class SaveMyData(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="SaveMyData",
            display_name="Save Data",
            category="output",
            is_output_node=True,  # marks as output node
            inputs=[
                io.String.Input("data"),
                io.String.Input("filename", default="output.txt"),
            ],
            outputs=[],  # output nodes may have no outputs
            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
        )

    @classmethod
    def execute(cls, data, filename):
        import folder_paths, os
        output_dir = folder_paths.get_output_directory()
        with open(os.path.join(output_dir, filename), 'w') as f:
            f.write(data)
        return io.NodeOutput()

List Processing

Receiving Lists

# V3: is_input_list=True in Schema (same as V1 INPUT_IS_LIST)
# All inputs arrive as lists — including widget values like batch_size
# Widget values: use widget_value[0] to get the scalar
# Shorter lists are padded by repeating the last value

# V1: INPUT_IS_LIST = True to receive full lists
class ListNode:
    INPUT_IS_LIST = True
    # Now execute() receives lists instead of individual items

Outputting Lists

# V3
io.Image.Output("IMAGE", is_output_list=True)

# V1
OUTPUT_IS_LIST = (True,)  # tuple matching RETURN_TYPES

Error Handling

@classmethod
def execute(cls, image, model):
    try:
        result = model.process(image)
    except RuntimeError as e:
        if "out of memory" in str(e):
            import torch
            torch.cuda.empty_cache()
            # Try with smaller batch
            result = process_in_chunks(image, model)
        else:
            raise
    return io.NodeOutput(result)

Server Communication

Send messages to the frontend during execution:

from server import PromptServer

@classmethod
def execute(cls, data):
    PromptServer.instance.send_sync(
        "my_extension.status",
        {"message": "Processing complete", "progress": 100}
    )
    return io.NodeOutput(data)

Complete Lifecycle Example

import time
import torch
from comfy_api.latest import ComfyExtension, io, ComfyAPISync

class FullLifecycleNode(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="FullLifecycleNode",
            display_name="Full Lifecycle Demo",
            category="example",
            inputs=[
                io.Image.Input("image"),
                io.Float.Input("threshold", default=0.5, min=0.0, max=1.0),
                io.Image.Input("optional_ref", optional=True, lazy=True),
            ],
            outputs=[
                io.Image.Output("IMAGE"),
                io.Mask.Output("MASK"),
            ],
            hidden=[io.Hidden.unique_id],
        )

    @classmethod
    def validate_inputs(cls, image, threshold, optional_ref=None):
        if threshold == 0.0:
            return "Threshold cannot be exactly 0"
        return True

    @classmethod
    def fingerprint_inputs(cls, image, threshold, optional_ref=None):
        # Re-execute if threshold changed; cache otherwise
        return threshold

    @classmethod
    def check_lazy_status(cls, image, threshold, optional_ref=None):
        # Only request optional_ref if threshold is high
        if threshold > 0.8 and optional_ref is None:
            return ["optional_ref"]
        return []

    @classmethod
    def execute(cls, image, threshold, optional_ref=None):
        node_id = cls.hidden.unique_id

        api = ComfyAPISync()  # use ComfyAPISync in sync execute; ComfyAPI in async
        api.execution.set_progress(0, 2)

        # Generate mask from threshold
        gray = image[:, :, :, 0] * 0.299 + image[:, :, :, 1] * 0.587 + image[:, :, :, 2] * 0.114
        mask = (gray > threshold).float()

        api.execution.set_progress(1, 2)

        # Apply mask
        result = image * mask.unsqueeze(-1)
        if optional_ref is not None:
            result = result + optional_ref * (1 - mask.unsqueeze(-1))

        api.execution.set_progress(2, 2)
        return io.NodeOutput(result, mask)

See Also

• comfyui-node-basics - Node structure fundamentals
• comfyui-node-inputs - Input types and lazy evaluation
• comfyui-node-advanced - Expansion, MatchType, DynamicCombo
• comfyui-node-outputs - UI outputs and previews

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: jtydhr88
• Source: jtydhr88/comfyui-custom-node-skills
• License: MIT

Install and usage instructions live in the source repository linked above.