Remove commented out UILatents class, will be added back in later when finished

V3 update - fixed ComfyExtension, removed v3/v1 test nodes from loading

comfy_api/latest/__init__.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+from abc import ABC, abstractmethod
 from typing import Type, TYPE_CHECKING
 from comfy_api.internal import ComfyAPIBase
 from comfy_api.internal.singleton import ProxiedSingleton
@@ -75,6 +76,19 @@ class ComfyAPI_latest(ComfyAPIBase):
 
     execution: Execution
 
+class ComfyExtension(ABC):
+    async def on_load(self) -> None:
+        """
+        Called when an extension is loaded.
+        This should be used to initialize any global resources neeeded by the extension.
+        """
+
+    @abstractmethod
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        """
+        Returns a list of nodes that this extension provides.
+        """
+
 class Input:
     Image = ImageInput
     Audio = AudioInput
@@ -106,4 +120,5 @@ __all__ = [
     "Input",
     "InputImpl",
     "Types",
+    "ComfyExtension",
 ]

comfy_api/latest/_io.py

@@ -6,26 +6,27 @@ from abc import ABC, abstractmethod
 from collections import Counter
 from dataclasses import asdict, dataclass
 from enum import Enum
-from typing import Any, Callable, Literal, TypedDict, TypeVar
+from typing import Any, Callable, Literal, TypedDict, TypeVar, TYPE_CHECKING
+from typing_extensions import NotRequired, final
 
 # used for type hinting
 import torch
-from spandrel import ImageModelDescriptor
-from typing_extensions import NotRequired, final
 
-from comfy.clip_vision import ClipVisionModel
-from comfy.clip_vision import Output as ClipVisionOutput_
-from comfy.controlnet import ControlNet
-from comfy.hooks import HookGroup, HookKeyframeGroup
-from comfy.model_patcher import ModelPatcher
-from comfy.samplers import CFGGuider, Sampler
-from comfy.sd import CLIP, VAE
-from comfy.sd import StyleModel as StyleModel_
-from comfy_api.input import VideoInput
+if TYPE_CHECKING:
+    from spandrel import ImageModelDescriptor
+    from comfy.clip_vision import ClipVisionModel
+    from comfy.clip_vision import Output as ClipVisionOutput_
+    from comfy.controlnet import ControlNet
+    from comfy.hooks import HookGroup, HookKeyframeGroup
+    from comfy.model_patcher import ModelPatcher
+    from comfy.samplers import CFGGuider, Sampler
+    from comfy.sd import CLIP, VAE
+    from comfy.sd import StyleModel as StyleModel_
+    from comfy_api.input import VideoInput
 from comfy_api.internal import (_ComfyNodeInternal, _NodeOutputInternal, classproperty, copy_class, first_real_override, is_class,
     prune_dict, shallow_clone_class)
 from comfy_api.latest._resources import Resources, ResourcesLocal
-from comfy_execution.graph import ExecutionBlocker
+from comfy_execution.graph_utils import ExecutionBlocker
 
 # from comfy_extras.nodes_images import SVG as SVG_ # NOTE: needs to be moved before can be imported due to circular reference
 
@@ -543,7 +544,8 @@ class Conditioning(ComfyTypeIO):
 
 @comfytype(io_type="SAMPLER")
 class Sampler(ComfyTypeIO):
-    Type = Sampler
+    if TYPE_CHECKING:
+        Type = Sampler
 
 @comfytype(io_type="SIGMAS")
 class Sigmas(ComfyTypeIO):
@@ -555,44 +557,54 @@ class Noise(ComfyTypeIO):
 
 @comfytype(io_type="GUIDER")
 class Guider(ComfyTypeIO):
-    Type = CFGGuider
+    if TYPE_CHECKING:
+        Type = CFGGuider
 
 @comfytype(io_type="CLIP")
 class Clip(ComfyTypeIO):
-    Type = CLIP
+    if TYPE_CHECKING:
+        Type = CLIP
 
 @comfytype(io_type="CONTROL_NET")
 class ControlNet(ComfyTypeIO):
-    Type = ControlNet
+    if TYPE_CHECKING:
+        Type = ControlNet
 
 @comfytype(io_type="VAE")
 class Vae(ComfyTypeIO):
-    Type = VAE
+    if TYPE_CHECKING:
+        Type = VAE
 
 @comfytype(io_type="MODEL")
 class Model(ComfyTypeIO):
-    Type = ModelPatcher
+    if TYPE_CHECKING:
+        Type = ModelPatcher
 
 @comfytype(io_type="CLIP_VISION")
 class ClipVision(ComfyTypeIO):
-    Type = ClipVisionModel
+    if TYPE_CHECKING:
+        Type = ClipVisionModel
 
 @comfytype(io_type="CLIP_VISION_OUTPUT")
 class ClipVisionOutput(ComfyTypeIO):
-    Type = ClipVisionOutput_
+    if TYPE_CHECKING:
+        Type = ClipVisionOutput_
 
 @comfytype(io_type="STYLE_MODEL")
 class StyleModel(ComfyTypeIO):
-    Type = StyleModel_
+    if TYPE_CHECKING:
+        Type = StyleModel_
 
 @comfytype(io_type="GLIGEN")
 class Gligen(ComfyTypeIO):
     '''ModelPatcher that wraps around a 'Gligen' model.'''
-    Type = ModelPatcher
+    if TYPE_CHECKING:
+        Type = ModelPatcher
 
 @comfytype(io_type="UPSCALE_MODEL")
 class UpscaleModel(ComfyTypeIO):
-    Type = ImageModelDescriptor
+    if TYPE_CHECKING:
+        Type = ImageModelDescriptor
 
 @comfytype(io_type="AUDIO")
 class Audio(ComfyTypeIO):
@@ -603,7 +615,8 @@ class Audio(ComfyTypeIO):
 
 @comfytype(io_type="VIDEO")
 class Video(ComfyTypeIO):
-    Type = VideoInput
+    if TYPE_CHECKING:
+        Type = VideoInput
 
 @comfytype(io_type="SVG")
 class SVG(ComfyTypeIO):
@@ -629,11 +642,13 @@ class Mesh(ComfyTypeIO):
 
 @comfytype(io_type="HOOKS")
 class Hooks(ComfyTypeIO):
-    Type = HookGroup
+    if TYPE_CHECKING:
+        Type = HookGroup
 
 @comfytype(io_type="HOOK_KEYFRAMES")
 class HookKeyframes(ComfyTypeIO):
-    Type = HookKeyframeGroup
+    if TYPE_CHECKING:
+        Type = HookKeyframeGroup
 
 @comfytype(io_type="TIMESTEPS_RANGE")
 class TimestepsRange(ComfyTypeIO):

comfy_api/latest/_ui.py

@@ -403,54 +403,6 @@ class PreviewMask(PreviewImage):
         super().__init__(preview, animated, cls, **kwargs)
 
 
-# class UILatent(_UIOutput):
-#     def __init__(self, values: list[SavedResult | dict], **kwargs):
-#         output_dir = folder_paths.get_temp_directory()
-#         type = "temp"
-#         prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
-#         compress_level = 1
-#         filename_prefix = "ComfyUI"
-
-
-#         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
-
-#         # support save metadata for latent sharing
-#         prompt_info = ""
-#         if prompt is not None:
-#             prompt_info = json.dumps(prompt)
-
-#         metadata = None
-#         if not args.disable_metadata:
-#             metadata = {"prompt": prompt_info}
-#             if extra_pnginfo is not None:
-#                 for x in extra_pnginfo:
-#                     metadata[x] = json.dumps(extra_pnginfo[x])
-
-#         file = f"{filename}_{counter:05}_.latent"
-
-#         results: list[FileLocator] = []
-#         results.append({
-#             "filename": file,
-#             "subfolder": subfolder,
-#             "type": "output"
-#         })
-
-#         file = os.path.join(full_output_folder, file)
-
-#         output = {}
-#         output["latent_tensor"] = samples["samples"].contiguous()
-#         output["latent_format_version_0"] = torch.tensor([])
-
-#         comfy.utils.save_torch_file(output, file, metadata=metadata)
-
-#         self.values = values
-
-#     def as_dict(self):
-#         return {
-#             "latents": self.values,
-#         }
-
-
 class PreviewAudio(_UIOutput):
     def __init__(self, audio: dict, cls: Type[ComfyNode] = None, **kwargs):
         self.values = AudioSaveHelper.save_audio(

comfy_api/v0_0_2/__init__.py

@@ -6,7 +6,7 @@ from comfy_api.latest import (
 )
 from typing import Type, TYPE_CHECKING
 from comfy_api.internal.async_to_sync import create_sync_class
-from comfy_api.latest import io, ui  #noqa: F401
+from comfy_api.latest import io, ui, ComfyExtension  #noqa: F401
 
 
 class ComfyAPIAdapter_v0_0_2(ComfyAPI_latest):
@@ -41,4 +41,5 @@ __all__ = [
     "Input",
     "InputImpl",
     "Types",
+    "ComfyExtension",
 ]

comfy_execution/graph.py

@@ -4,9 +4,12 @@ from typing import Type, Literal
 import nodes
 import asyncio
 import inspect
-from comfy_execution.graph_utils import is_link
+from comfy_execution.graph_utils import is_link, ExecutionBlocker
 from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, InputTypeOptions
 
+# NOTE: ExecutionBlocker code got moved to graph_utils.py to prevent torch being imported too soon during unit tests
+ExecutionBlocker = ExecutionBlocker
+
 class DependencyCycleError(Exception):
     pass
 
@@ -294,21 +297,3 @@ class ExecutionList(TopologicalSort):
                 del blocked_by[node_id]
             to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0]
         return list(blocked_by.keys())
-
-class ExecutionBlocker:
-    """
-    Return this from a node and any users will be blocked with the given error message.
-    If the message is None, execution will be blocked silently instead.
-    Generally, you should avoid using this functionality unless absolutely necessary. Whenever it's
-    possible, a lazy input will be more efficient and have a better user experience.
-    This functionality is useful in two cases:
-    1. You want to conditionally prevent an output node from executing. (Particularly a built-in node
-       like SaveImage. For your own output nodes, I would recommend just adding a BOOL input and using
-       lazy evaluation to let it conditionally disable itself.)
-    2. You have a node with multiple possible outputs, some of which are invalid and should not be used.
-       (I would recommend not making nodes like this in the future -- instead, make multiple nodes with
-       different outputs. Unfortunately, there are several popular existing nodes using this pattern.)
-    """
-    def __init__(self, message):
-        self.message = message
-

comfy_execution/graph_utils.py

@@ -137,3 +137,19 @@ def add_graph_prefix(graph, outputs, prefix):
 
     return new_graph, tuple(new_outputs)
 
+class ExecutionBlocker:
+    """
+    Return this from a node and any users will be blocked with the given error message.
+    If the message is None, execution will be blocked silently instead.
+    Generally, you should avoid using this functionality unless absolutely necessary. Whenever it's
+    possible, a lazy input will be more efficient and have a better user experience.
+    This functionality is useful in two cases:
+    1. You want to conditionally prevent an output node from executing. (Particularly a built-in node
+       like SaveImage. For your own output nodes, I would recommend just adding a BOOL input and using
+       lazy evaluation to let it conditionally disable itself.)
+    2. You have a node with multiple possible outputs, some of which are invalid and should not be used.
+       (I would recommend not making nodes like this in the future -- instead, make multiple nodes with
+       different outputs. Unfortunately, there are several popular existing nodes using this pattern.)
+    """
+    def __init__(self, message):
+        self.message = message

comfy_extras/nodes_v1_test.py

@@ -1,77 +0,0 @@
-import torch
-from comfy.comfy_types.node_typing import ComfyNodeABC, IO
-import asyncio
-from comfy.utils import ProgressBar
-import time
-
-
-class TestNode(ComfyNodeABC):
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "image": (IO.IMAGE,),
-                "some_int": (IO.INT, {"display_name": "new_name",
-                                      "min": 0, "max": 127, "default": 42,
-                                      "tooltip": "My tooltip 😎", "display": "slider"}),
-                "combo": (IO.COMBO, {"options": ["a", "b", "c"], "tooltip": "This is a combo input"}),
-                "combo2": (IO.COMBO, {"options": ["a", "b", "c"], "multi_select": True, "tooltip": "This is a combo input"}),
-            },
-            "optional": {
-                "xyz": ("XYZ",),
-                "mask": (IO.MASK,),
-            }
-        }
-
-    RETURN_TYPES = (IO.INT, IO.IMAGE)
-    RETURN_NAMES = ("INT", "img🖼️")
-    OUTPUT_TOOLTIPS = (None, "This is an image")
-    FUNCTION = "do_thing"
-
-    OUTPUT_NODE = True
-
-    CATEGORY = "v3 nodes"
-
-    def do_thing(self, image: torch.Tensor, some_int: int, combo: str, combo2: list[str], xyz=None, mask: torch.Tensor=None):
-        return (some_int, image)
-
-
-class TestSleep(ComfyNodeABC):
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "value": (IO.ANY, {}),
-                "seconds": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 9999.0, "step": 0.01, "tooltip": "The amount of seconds to sleep."}),
-            },
-            "hidden": {
-                "unique_id": "UNIQUE_ID",
-            },
-        }
-    RETURN_TYPES = (IO.ANY,)
-    FUNCTION = "sleep"
-
-    CATEGORY = "_for_testing"
-
-    async def sleep(self, value, seconds, unique_id):
-        pbar = ProgressBar(seconds, node_id=unique_id)
-        start = time.time()
-        expiration = start + seconds
-        now = start
-        while now < expiration:
-            now = time.time()
-            pbar.update_absolute(now - start)
-            await asyncio.sleep(0.02)
-        return (value,)
-
-
-NODE_CLASS_MAPPINGS = {
-    "V1TestNode1": TestNode,
-    "V1TestSleep": TestSleep,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "V1TestNode1": "V1 Test Node",
-    "V1TestSleep": "V1 Test Sleep",
-}

comfy_extras/nodes_v3_test.py

@@ -1,283 +0,0 @@
-import torch
-import time
-from comfy_api.latest import io, ui, _io
-import logging  # noqa
-import comfy.utils
-import asyncio
-
-@io.comfytype(io_type="XYZ")
-class XYZ(io.ComfyTypeIO):
-    Type = tuple[int,str]
-
-
-class V3TestNode(io.ComfyNode):
-    # NOTE: this is here just to test that state is not leaking
-    def __init__(self):
-        super().__init__()
-        self.hahajkunless = ";)"
-
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="V3_01_TestNode1",
-            display_name="V3 Test Node",
-            category="v3 nodes",
-            description="This is a funky V3 node test.",
-            inputs=[
-                io.Image.Input("image", display_name="new_image"),
-                XYZ.Input("xyz", optional=True),
-                io.Custom("JKL").Input("jkl", optional=True),
-                io.Mask.Input("mask", display_name="mask haha", optional=True),
-                io.Int.Input("some_int", display_name="new_name", min=0, max=127, default=42,
-                             tooltip="My tooltip 😎", display_mode=io.NumberDisplay.slider),
-                io.Combo.Input("combo", options=["a", "b", "c"], tooltip="This is a combo input"),
-                io.MultiCombo.Input("combo2", options=["a","b","c"]),
-                io.MultiType.Input(io.Int.Input("int_multitype", display_name="haha"), types=[io.Float]),
-                io.MultiType.Input("multitype", types=[io.Mask, io.Float, io.Int], optional=True),
-                # ComboInput("combo", image_upload=True, image_folder=FolderType.output,
-                #             remote=RemoteOptions(
-                #                 route="/internal/files/output",
-                #                 refresh_button=True,
-                #             ),
-                #             tooltip="This is a combo input"),
-                # IntegerInput("some_int", display_name="new_name", min=0, tooltip="My tooltip 😎", display=NumberDisplay.slider, ),
-                # ComboDynamicInput("mask", behavior=InputBehavior.optional),
-                # IntegerInput("some_int", display_name="new_name", min=0, tooltip="My tooltip 😎", display=NumberDisplay.slider,
-                #              dependent_inputs=[ComboDynamicInput("mask", behavior=InputBehavior.optional)],
-                #              dependent_values=[lambda my_value: IO.STRING if my_value < 5 else IO.NUMBER],
-                #              ),
-                # ["option1", "option2". "option3"]
-                # ComboDynamicInput["sdfgjhl", [ComboDynamicOptions("option1", [IntegerInput("some_int", display_name="new_name", min=0, tooltip="My tooltip 😎", display=NumberDisplay.slider, ImageInput(), MaskInput(), String()]),
-                #                              CombyDynamicOptons("option2", [])
-                #                                                   ]]
-            ],
-            outputs=[
-                io.Int.Output(),
-                io.Image.Output(display_name="img🖼️", tooltip="This is an image"),
-            ],
-            hidden=[
-                io.Hidden.prompt,
-                io.Hidden.auth_token_comfy_org,
-                io.Hidden.unique_id,
-            ],
-            is_output_node=True,
-        )
-
-    @classmethod
-    def validate_inputs(cls, image: io.Image.Type, some_int: int, combo: io.Combo.Type, combo2: io.MultiCombo.Type, xyz: XYZ.Type=None, mask: io.Mask.Type=None, **kwargs):
-        if some_int < 0:
-            raise Exception("some_int must be greater than 0")
-        if combo == "c":
-            raise Exception("combo must be a or b")
-        return True
-
-    @classmethod
-    def execute(cls, image: io.Image.Type, some_int: int, combo: io.Combo.Type, combo2: io.MultiCombo.Type, xyz: XYZ.Type=None, mask: io.Mask.Type=None, **kwargs):
-        if hasattr(cls, "hahajkunless"):
-            raise Exception("The 'cls' variable leaked instance state between runs!")
-        if hasattr(cls, "doohickey"):
-            raise Exception("The 'cls' variable leaked state on class properties between runs!")
-        try:
-            cls.doohickey = "LOLJK"
-        except AttributeError:
-            pass
-        return io.NodeOutput(some_int, image, ui=ui.PreviewImage(image, cls=cls))
-
-
-# class V3LoraLoader(io.ComfyNode):
-#     @classmethod
-#     def define_schema(cls):
-#         return io.Schema(
-#             node_id="V3_LoraLoader",
-#             display_name="V3 LoRA Loader",
-#             category="v3 nodes",
-#             description="LoRAs are used to modify diffusion and CLIP models, altering the way in which latents are denoised such as applying styles. Multiple LoRA nodes can be linked together.",
-#             inputs=[
-#                 io.Model.Input("model", tooltip="The diffusion model the LoRA will be applied to."),
-#                 io.Clip.Input("clip", tooltip="The CLIP model the LoRA will be applied to."),
-#                 io.Combo.Input(
-#                     "lora_name",
-#                     options=folder_paths.get_filename_list("loras"),
-#                     tooltip="The name of the LoRA."
-#                 ),
-#                 io.Float.Input(
-#                     "strength_model",
-#                     default=1.0,
-#                     min=-100.0,
-#                     max=100.0,
-#                     step=0.01,
-#                     tooltip="How strongly to modify the diffusion model. This value can be negative."
-#                 ),
-#                 io.Float.Input(
-#                     "strength_clip",
-#                     default=1.0,
-#                     min=-100.0,
-#                     max=100.0,
-#                     step=0.01,
-#                     tooltip="How strongly to modify the CLIP model. This value can be negative."
-#                 ),
-#             ],
-#             outputs=[
-#                 io.Model.Output(),
-#                 io.Clip.Output(),
-#             ],
-#         )
-
-#     @classmethod
-#     def execute(cls, model: io.Model.Type, clip: io.Clip.Type, lora_name: str, strength_model: float, strength_clip: float, **kwargs):
-#         if strength_model == 0 and strength_clip == 0:
-#             return io.NodeOutput(model, clip)
-
-#         lora = cls.resources.get(resources.TorchDictFolderFilename("loras", lora_name))
-
-#         model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
-#         return io.NodeOutput(model_lora, clip_lora)
-
-
-class NInputsTest(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="V3_NInputsTest",
-            display_name="V3 N Inputs Test",
-            inputs=[
-                _io.AutogrowDynamic.Input("nmock", template_input=io.Image.Input("image"), min=1, max=3),
-                _io.AutogrowDynamic.Input("nmock2", template_input=io.Int.Input("int"), optional=True, min=1, max=4),
-            ],
-            outputs=[
-                io.Image.Output(),
-            ],
-        )
-
-    @classmethod
-    def validate_inputs(cls, nmock, nmock2):
-        return True
-
-    @classmethod
-    def fingerprint_inputs(cls, nmock, nmock2):
-        return time.time()
-
-    @classmethod
-    def check_lazy_status(cls, **kwargs) -> list[str]:
-        need = [name for name in kwargs if kwargs[name] is None]
-        return need
-
-    @classmethod
-    def execute(cls, nmock, nmock2):
-        first_image = nmock[0]
-        all_images = []
-        for img in nmock:
-            if img.shape != first_image.shape:
-                img = img.movedim(-1,1)
-                img = comfy.utils.common_upscale(img, first_image.shape[2], first_image.shape[1], "lanczos", "center")
-                img = img.movedim(1,-1)
-            all_images.append(img)
-        combined_image = torch.cat(all_images, dim=0)
-        return io.NodeOutput(combined_image)
-
-
-class V3TestSleep(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="V3_TestSleep",
-            display_name="V3 Test Sleep",
-            category="_for_testing",
-            description="Test async sleep functionality.",
-            inputs=[
-                io.AnyType.Input("value", display_name="Value"),
-                io.Float.Input("seconds", display_name="Seconds", default=1.0, min=0.0, max=9999.0, step=0.01, tooltip="The amount of seconds to sleep."),
-            ],
-            outputs=[
-                io.AnyType.Output(),
-            ],
-            hidden=[
-                io.Hidden.unique_id,
-            ],
-            is_experimental=True,
-        )
-
-    @classmethod
-    async def execute(cls, value: io.AnyType.Type, seconds: io.Float.Type, **kwargs):
-        logging.info(f"V3TestSleep: {cls.hidden.unique_id}")
-        pbar = comfy.utils.ProgressBar(seconds, node_id=cls.hidden.unique_id)
-        start = time.time()
-        expiration = start + seconds
-        now = start
-        while now < expiration:
-            now = time.time()
-            pbar.update_absolute(now - start)
-            await asyncio.sleep(0.02)
-        return io.NodeOutput(value)
-
-
-class V3DummyStart(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="V3_DummyStart",
-            display_name="V3 Dummy Start",
-            category="v3 nodes",
-            description="This is a dummy start node.",
-            inputs=[],
-            outputs=[
-                io.Custom("XYZ").Output(),
-            ],
-        )
-
-    @classmethod
-    def execute(cls):
-        return io.NodeOutput(None)
-
-
-class V3DummyEnd(io.ComfyNode):
-    COOL_VALUE = 123
-
-    @classmethod
-    def define_schema(cls):
-        return io.Schema(
-            node_id="V3_DummyEnd",
-            display_name="V3 Dummy End",
-            category="v3 nodes",
-            description="This is a dummy end node.",
-            inputs=[
-                io.Custom("XYZ").Input("xyz"),
-            ],
-            outputs=[],
-            is_output_node=True,
-        )
-
-    @classmethod
-    def custom_action(cls):
-        return 456
-
-    @classmethod
-    def execute(cls, xyz: io.Custom("XYZ").Type):
-        logging.info(f"V3DummyEnd: {cls.COOL_VALUE}")
-        logging.info(f"V3DummyEnd: {cls.custom_action()}")
-        return
-
-
-class V3DummyEndInherit(V3DummyEnd):
-    @classmethod
-    def define_schema(cls):
-        schema = super().define_schema()
-        schema.node_id = "V3_DummyEndInherit"
-        schema.display_name = "V3 Dummy End Inherit"
-        return schema
-
-    @classmethod
-    def execute(cls, xyz: io.Custom("XYZ").Type):
-        logging.info(f"V3DummyEndInherit: {cls.COOL_VALUE}")
-        return super().execute(xyz)
-
-
-NODES_LIST: list[type[io.ComfyNode]] = [
-    V3TestNode,
-    # V3LoraLoader,
-    NInputsTest,
-    V3TestSleep,
-    V3DummyStart,
-    V3DummyEnd,
-    V3DummyEndInherit,
-]

nodes.py

@@ -6,6 +6,7 @@ import os
 import sys
 import json
 import hashlib
+import inspect
 import traceback
 import math
 import time
@@ -29,7 +30,7 @@ import comfy.controlnet
 from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict, FileLocator
 from comfy_api.internal import register_versions, ComfyAPIWithVersion
 from comfy_api.version_list import supported_versions
-from comfy_api.latest import io
+from comfy_api.latest import io, ComfyExtension
 
 import comfy.clip_vision
 
@@ -2162,17 +2163,36 @@ async def load_custom_node(module_path: str, ignore=set(), module_parent="custom
             if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None:
                 NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS)
             return True
-        # V3 node definition
-        elif getattr(module, "NODES_LIST", None) is not None:
-            for node_cls in module.NODES_LIST:
-                node_cls: io.ComfyNode
-                schema = node_cls.GET_SCHEMA()
-                if schema.node_id not in ignore:
-                    NODE_CLASS_MAPPINGS[schema.node_id] = node_cls
-                    node_cls.RELATIVE_PYTHON_MODULE = "{}.{}".format(module_parent, get_module_name(module_path))
-                if schema.display_name is not None:
-                    NODE_DISPLAY_NAME_MAPPINGS[schema.node_id] = schema.display_name
-            return True
+        # V3 Extension Definition
+        elif hasattr(module, "comfy_entrypoint"):
+            entrypoint = getattr(module, "comfy_entrypoint")
+            if not callable(entrypoint):
+                logging.warning(f"comfy_entrypoint in {module_path} is not callable, skipping.")
+                return False
+            try:
+                if inspect.iscoroutinefunction(entrypoint):
+                    extension = await entrypoint()
+                else:
+                    extension = entrypoint()
+                if not isinstance(extension, ComfyExtension):
+                    logging.warning(f"comfy_entrypoint in {module_path} did not return a ComfyExtension, skipping.")
+                    return False
+                node_list = await extension.get_node_list()
+                if not isinstance(node_list, list):
+                    logging.warning(f"comfy_entrypoint in {module_path} did not return a list of nodes, skipping.")
+                    return False
+                for node_cls in node_list:
+                    node_cls: io.ComfyNode
+                    schema = node_cls.GET_SCHEMA()
+                    if schema.node_id not in ignore:
+                        NODE_CLASS_MAPPINGS[schema.node_id] = node_cls
+                        node_cls.RELATIVE_PYTHON_MODULE = "{}.{}".format(module_parent, get_module_name(module_path))
+                    if schema.display_name is not None:
+                        NODE_DISPLAY_NAME_MAPPINGS[schema.node_id] = schema.display_name
+                return True
+            except Exception as e:
+                logging.warning(f"Error while calling comfy_entrypoint in {module_path}: {e}")
+                return False
         else:
             logging.warning(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS or NODES_LIST (need one).")
             return False
@@ -2300,8 +2320,6 @@ async def init_builtin_extra_nodes():
         "nodes_camera_trajectory.py",
         "nodes_edit_model.py",
         "nodes_tcfg.py",
-        "nodes_v3_test.py", # TODO: remove
-        "nodes_v1_test.py", # TODO: remove
     ]
 
     import_failed = []

pyproject.toml

@@ -12,8 +12,6 @@ documentation = "https://docs.comfy.org/"
 
 [tool.ruff]
 lint.select = [
-  "E",    # pycodestyle errors
-  "I",    # isort
   "N805", # invalid-first-argument-name-for-method
   "S307", # suspicious-eval-usage
   "S102", # exec
@@ -23,8 +21,4 @@ lint.select = [
   # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f
   "F",
 ]
-lint.ignore = ["E501"]  # disable line-length checking
 exclude = ["*.ipynb", "**/generated/*.pyi"]
-
-[tool.ruff.lint.per-file-ignores]
-"!comfy_extras/v3/*" = ["E", "I"]  # enable these rules only for V3 nodes