ComfyUI V1 to V3 node migration - converting legacy nodes to the V3 API. Use when migrating existing custom nodes from V1 to V3, understanding differences between API versions, or modernizing node code.
Migrate existing V1 nodes to the modern V3 API. V3 uses classmethods, typed inputs/outputs, and ComfyExtension registration.
io.ComfyNodeINPUT_TYPES() with define_schema() returning io.Schemaexecute and make it a @classmethodio.NodeOutput(...)IS_CHANGED with fingerprint_inputsVALIDATE_INPUTS with validate_inputscheck_lazy_status to @classmethodNODE_CLASS_MAPPINGS with ComfyExtension + comfy_entrypoint()cls.hidden instead of kwargs__init__ methods (no instance state in V3)import torch
class ImageInvertV1:
CATEGORY = "image"
FUNCTION = "invert"
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
OUTPUT_TOOLTIPS = ("The inverted image",)
DESCRIPTION = "Inverts image colors"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"strength": ("FLOAT", {
"default": 1.0,
"min": 0.0,
"max": 1.0,
"step": 0.01,
}),
},
"optional": {
"mask": ("MASK",),
},
"hidden": {
"unique_id": "UNIQUE_ID",
},
}
@classmethod
def IS_CHANGED(s, image, strength, mask=None, unique_id=None):
return strength
@classmethod
def VALIDATE_INPUTS(s, image, strength, mask=None, unique_id=None):
if strength < 0:
return "Strength must be non-negative"
return True
def invert(self, image, strength, mask=None, unique_id=None):
inverted = 1.0 - image
result = image * (1 - strength) + inverted * strength
if mask is not None:
result = image * (1 - mask.unsqueeze(-1)) + result * mask.unsqueeze(-1)
return (result,)
NODE_CLASS_MAPPINGS = {"ImageInvertV1": ImageInvertV1}
NODE_DISPLAY_NAME_MAPPINGS = {"ImageInvertV1": "Invert Image"}
import torch
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
class ImageInvertV3(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="ImageInvertV3",
display_name="Invert Image",
description="Inverts image colors",
category="image",
inputs=[
io.Image.Input("image"),
io.Float.Input("strength", default=1.0, min=0.0, max=1.0, step=0.01),
io.Mask.Input("mask", optional=True),
],
outputs=[
io.Image.Output("IMAGE", tooltip="The inverted image"),
],
hidden=[io.Hidden.unique_id],
)
@classmethod
def fingerprint_inputs(cls, image, strength, mask=None):
return strength
@classmethod
def validate_inputs(cls, image, strength, mask=None):
if strength < 0:
return "Strength must be non-negative"
return True
@classmethod
def execute(cls, image, strength, mask=None):
node_id = cls.hidden.unique_id # access hidden via cls.hidden
inverted = 1.0 - image
result = image * (1 - strength) + inverted * strength
if mask is not None:
result = image * (1 - mask.unsqueeze(-1)) + result * mask.unsqueeze(-1)
return io.NodeOutput(result)
class MyExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [ImageInvertV3]
async def comfy_entrypoint() -> MyExtension:
return MyExtension()
| V1 Property | V3 Equivalent |
|---|---|
CATEGORY = "image" | io.Schema(category="image") |
FUNCTION = "my_func" | Always execute (fixed name) |
RETURN_TYPES = ("IMAGE",) | outputs=[io.Image.Output()] |
RETURN_NAMES = ("image",) | outputs=[io.Image.Output(display_name="image")] |
OUTPUT_TOOLTIPS = ("tip",) | outputs=[io.Image.Output(tooltip="tip")] |
OUTPUT_NODE = True | io.Schema(is_output_node=True) |
DEPRECATED = True | io.Schema(is_deprecated=True) |
EXPERIMENTAL = True | io.Schema(is_experimental=True) |
API_NODE = True | io.Schema(is_api_node=True) |
NOT_IDEMPOTENT = True | io.Schema(not_idempotent=True) |
DESCRIPTION = "..." | io.Schema(description="...") |
SEARCH_ALIASES = [...] | io.Schema(search_aliases=[...]) |
INPUT_IS_LIST = True | io.Schema(is_input_list=True) |
OUTPUT_IS_LIST = (True,) | io.Image.Output(is_output_list=True) |
DEV_ONLY = True | io.Schema(is_dev_only=True) |
ESSENTIALS_CATEGORY = "Basic" | io.Schema(essentials_category="Basic") |
| V1 Input | V3 Input |
|---|---|
("IMAGE",) | io.Image.Input("id") |
("MASK",) | io.Mask.Input("id") |
("LATENT",) | io.Latent.Input("id") |
("MODEL",) | io.Model.Input("id") |
("CLIP",) | io.Clip.Input("id") |
("VAE",) | io.Vae.Input("id") |
("CONDITIONING",) | io.Conditioning.Input("id") |
("INT", {"default": 0, ...}) | io.Int.Input("id", default=0, ...) |
("FLOAT", {"default": 1.0, ...}) | io.Float.Input("id", default=1.0, ...) |
("STRING", {"multiline": True}) | io.String.Input("id", multiline=True) |
("BOOLEAN", {"default": True}) | io.Boolean.Input("id", default=True) |
(["opt1", "opt2"],) | io.Combo.Input("id", options=["opt1", "opt2"]) |
("CONTROL_NET",) | io.ControlNet.Input("id") |
("CLIP_VISION",) | io.ClipVision.Input("id") |
("CLIP_VISION_OUTPUT",) | io.ClipVisionOutput.Input("id") |
("STYLE_MODEL",) | io.StyleModel.Input("id") |
("GLIGEN",) | io.Gligen.Input("id") |
("UPSCALE_MODEL",) | io.UpscaleModel.Input("id") |
("AUDIO",) | io.Audio.Input("id") |
("VIDEO",) | io.Video.Input("id") |
("SAMPLER",) | io.Sampler.Input("id") |
("SIGMAS",) | io.Sigmas.Input("id") |
("NOISE",) | io.Noise.Input("id") |
("GUIDER",) | io.Guider.Input("id") |
("HOOKS",) | io.Hooks.Input("id") |
("LORA_MODEL",) | io.LoraModel.Input("id") |
("MESH",) | io.Mesh.Input("id") |
("VOXEL",) | io.Voxel.Input("id") |
("FILE_3D",) | io.File3DAny.Input("id") |
("FILE_3D_GLB",) | io.File3DGLB.Input("id") |
("SVG",) | io.SVG.Input("id") |
("COLOR",) | io.Color.Input("id") |
("BOUNDING_BOX",) | io.BoundingBox.Input("id") |
("CURVE",) | io.Curve.Input("id") |
("LATENT_UPSCALE_MODEL",) | io.LatentUpscaleModel.Input("id") |
("MODEL_PATCH",) | io.ModelPatch.Input("id") |
("HOOK_KEYFRAMES",) | io.HookKeyframes.Input("id") |
("AUDIO_ENCODER",) | io.AudioEncoder.Input("id") |
("AUDIO_ENCODER_OUTPUT",) | io.AudioEncoderOutput.Input("id") |
("TRACKS",) | io.Tracks.Input("id") |
("LOSS_MAP",) | io.LossMap.Input("id") |
("TIMESTEPS_RANGE",) | io.TimestepsRange.Input("id") |
("LATENT_OPERATION",) | io.LatentOperation.Input("id") |
("WEBCAM",) | io.Webcam.Input("id") |
("PHOTOMAKER",) | io.Photomaker.Input("id") |
("WAN_CAMERA_EMBEDDING",) | io.WanCameraEmbedding.Input("id") |
("LOAD_3D",) | io.Load3D.Input("id") |
("LOAD_3D_ANIMATION",) | io.Load3DAnimation.Input("id") |
("LOAD3D_CAMERA",) | io.Load3DCamera.Input("id") |
("FILE_3D_GLTF",) | io.File3DGLTF.Input("id") |
("FILE_3D_FBX",) | io.File3DFBX.Input("id") |
("FILE_3D_OBJ",) | io.File3DOBJ.Input("id") |
("FILE_3D_STL",) | io.File3DSTL.Input("id") |
("FILE_3D_USDZ",) | io.File3DUSDZ.Input("id") |
("POINT",) | io.Point.Input("id") |
("FACE_ANALYSIS",) | io.FaceAnalysis.Input("id") |
("BBOX",) | io.BBOX.Input("id") |
("SEGS",) | io.SEGS.Input("id") |
("IMAGECOMPARE",) | io.ImageCompare.Input("id") |
("*",) | io.AnyType.Input("id") or io.MultiType.Input("id", types=[...]) |
# V1: instance method with custom name
class V1Node:
FUNCTION = "process"
def process(self, image, value):
return (result,)
# V3: classmethod named "execute", returns NodeOutput
class V3Node(io.ComfyNode):
@classmethod
def execute(cls, image, value):
return io.NodeOutput(result)
# V1
@classmethod
def IS_CHANGED(s, **kwargs):
return float("NaN") # always re-execute
# V3
@classmethod
def fingerprint_inputs(cls, **kwargs):
import time
return time.time() # always re-execute
# V1
@classmethod
def VALIDATE_INPUTS(s, input_types=None, **kwargs):
return True
# V3
@classmethod
def validate_inputs(cls, input_types=None, **kwargs):
return True
# V1: instance method
def check_lazy_status(self, **kwargs):
return ["input_name"]
# V3: classmethod
@classmethod
def check_lazy_status(cls, **kwargs):
return ["input_name"]
# V1: received as kwargs
def execute(self, image, unique_id=None, prompt=None):
node_id = unique_id
# V3: accessed via cls.hidden
@classmethod
def execute(cls, image):
node_id = cls.hidden.unique_id
prompt = cls.hidden.prompt
# V1
NODE_CLASS_MAPPINGS = {
"Node1": Node1Class,
"Node2": Node2Class,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"Node1": "Node One",
"Node2": "Node Two",
}
WEB_DIRECTORY = "./js"
# V3
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
class MyExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [Node1Class, Node2Class]
@override
async def on_load(self):
# Optional: initialization logic
pass
async def comfy_entrypoint() -> MyExtension:
return MyExtension()
# WEB_DIRECTORY still works the same way for JS extensions
WEB_DIRECTORY = "./js"
# V1
class V1SaveNode:
RETURN_TYPES = ()
OUTPUT_NODE = True
FUNCTION = "save"
def save(self, images, prefix):
# ... save logic ...
return {"ui": {"images": results}}
# V3
from comfy_api.latest import io, ui
class V3SaveNode(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="V3SaveNode",
display_name="Save",
category="image",
is_output_node=True,
inputs=[
io.Image.Input("images"),
io.String.Input("prefix", default="output"),
],
outputs=[],
hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
)
@classmethod
def execute(cls, images, prefix):
saved = ui.ImageSaveHelper.get_save_images_ui(images, prefix, cls=cls)
return io.NodeOutput(ui=saved)
self. Use external storage if needed.execute, never custom names.cls.hidden.prompt not function parameters.io.NodeOutput(val) not (val,).=None default in execute params, not separate "optional" dict.async def execute(cls, ...).comfyui-node-basics - V3 node fundamentalscomfyui-node-packaging - Project structurecomfyui-node-lifecycle - Execution lifecycle differences