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: fingerprint_inputs (V3) / IS_CHANGED (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