/comfyui-node-advanced

name: comfyui-node-advanced
description: ComfyUI advanced node patterns - MatchType, Autogrow, DynamicCombo, node expansion, MultiType, wildcard inputs, loops. Use when building complex nodes with dynamic inputs, type matching, or node expansion.

ComfyUI Advanced Node Patterns (V3)

V3 provides advanced input patterns for dynamic, type-safe, and flexible node designs.

MatchType - Generic Type Connections

`MatchType` ensures that inputs and outputs sharing a template have the same type at connection time. Like generics in typed languages.

class PassThrough(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        # Template(template_id, allowed_types=AnyType) - optional type constraint
        template = io.MatchType.Template("T")
        return io.Schema(
            node_id="PassThrough",
            display_name="Pass Through",
            category="utils",
            inputs=[
                io.MatchType.Input("value", template=template),
            ],
            outputs=[
                io.MatchType.Output(template=template, display_name="output"),
            ],
        )

    @classmethod
    def execute(cls, value):
        return io.NodeOutput(value)

When the user connects an IMAGE to the input, the output automatically becomes IMAGE type.

Switch Node Pattern

Core Switch (since v0.35) makes both branches optional so either may be left disconnected. A disconnected optional input is **absent** from kwargs; a connected-but-unevaluated lazy input is `None`. Use a sentinel to tell them apart:

MISSING = object()

class Switch(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        template = io.MatchType.Template("switch")
        return io.Schema(
            node_id="Switch",
            display_name="Switch",
            category="logic",
            inputs=[
                io.Boolean.Input("switch"),
                io.MatchType.Input("on_false", template=template, lazy=True, optional=True),
                io.MatchType.Input("on_true", template=template, lazy=True, optional=True),
            ],
            outputs=[
                io.MatchType.Output(template=template, display_name="output"),
            ],
        )

    @classmethod
    def check_lazy_status(cls, switch, on_false=MISSING, on_true=MISSING):
        if switch and on_true is None:      # connected, not yet evaluated
            return ["on_true"]
        if not switch and on_false is None:
            return ["on_false"]

    @classmethod
    def execute(cls, switch, on_true=MISSING, on_false=MISSING):
        selected = on_true if switch else on_false
        return io.NodeOutput(None if selected is MISSING else selected)

MultiType - Accept Multiple Types

A single input that accepts several different types:

io.MultiType.Input("data",
    types=[io.Image, io.Mask, io.Latent],
    optional=True,
)

Autogrow - Dynamic Growing Inputs

Inputs that automatically add more slots as the user connects to them. Two template modes:

TemplatePrefix (numbered slots)

class ConcatImages(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="ConcatImages",
            display_name="Concat Images",
            category="image",
            inputs=[
                io.Autogrow.Input("images",
                    template=io.Autogrow.TemplatePrefix(
                        input=io.Image.Input("img"),  # template for each slot
                        prefix="image_",              # slot names: image_0, image_1, ...
                        min=2,                        # minimum visible slots (default 1)
                        max=16,                       # maximum slots (default 10, hard limit 100)
                    ),
                ),
            ],
            outputs=[io.Image.Output("IMAGE")],
        )

    @classmethod
    def execute(cls, images: io.Autogrow.Type):
        # images is a dict: {"image_0": tensor, "image_1": tensor, ...}
        tensors = [v for v in images.values() if v is not None]
        return io.NodeOutput(torch.cat(tensors, dim=0))

TemplateNames (named slots)

io.Autogrow.Input("inputs",
    template=io.Autogrow.TemplateNames(
        input=io.Float.Input("val"),
        names=["red", "green", "blue", "alpha"],  # specific slot names
        min=3,  # first 3 are required
    ),
)
# Creates slots: "red" (required), "green" (required), "blue" (required), "alpha" (optional)

**Key behaviors**:

• Widget inputs in template are forced to connection-only (`force_input=True`)
• Slots below `min` are required; above `min` are optional
• Maximum 100 names total

DynamicCombo - Conditional Inputs

A combo dropdown where each option reveals different sub-inputs:

class ProcessNode(io.ComfyNode):
    @classmethod
    def define_schema(cls):
        return io.Schema(
            node_id="ProcessNode",
            display_name="Process Node",
            category="processing",
            is_output_node=True,
            inputs=[
                io.DynamicCombo.Input("mode", options=[
                    io.DynamicCombo.Option("resize", [
                        io.Int.Input("width", default=512, min=1, max=8192),
                        io.Int.Input("height", default=512, min=1, max=8192),
                    ]),
                    io.DynamicCombo.Option("blur", [
                        io.Float.Input("radius", default=5.0, min=0.1, max=100.0),
                    ]),
                    io.DynamicCombo.Option("sharpen", [
                        io.Float.Input("amount", default=1.0, min=0.0, max=10.0),
                    ]),
                ]),
                io.Image.Input("image"),
            ],
            outputs=[io.Image.Output("IMAGE")],
        )

    @classmethod
    def execute(cls, mode: io.DynamicCombo.Type, image, **kwargs):
        # mode is a dict with the combo value + sub-inputs