Merge pull request #8951 from comfyanonymous/v3-definition-wip

V3 update - refactor names and node structure

comfy_api/internal/__init__.py

@@ -1,6 +1,10 @@
-class ComfyNodeInternal:
+from abc import ABC, abstractmethod
+
+class ComfyNodeInternal(ABC):
     """Class that all V3-based APIs inherit from for ComfyNode.
 
     This is intended to only be referenced within execution.py, as it has to handle all V3 APIs going forward."""
-    ...
-
+    @classmethod
+    @abstractmethod
+    def GET_NODE_INFO_V1(cls):
+        ...

comfy_api/v3/helpers.py

@@ -1,10 +1,16 @@
 from typing import Callable, Optional
 
 
-def first_real_override(cls: type, name: str, *, base: type) -> Optional[Callable]:
+def first_real_override(cls: type, name: str, *, base: type=None) -> Optional[Callable]:
     """Return the *callable* override of `name` visible on `cls`, or None if every
     implementation up to (and including) `base` is the placeholder defined on `base`.
+
+    If base is not provided, it will assume cls has a GET_BASE_CLASS
     """
+    if base is None:
+        if not hasattr(cls, "GET_BASE_CLASS"):
+            raise ValueError("base is required if cls does not have a GET_BASE_CLASS; is this a valid ComfyNode subclass?")
+        base = cls.GET_BASE_CLASS()
     base_attr = getattr(base, name, None)
     if base_attr is None:
         return None

comfy_api/v3/io.py

@@ -6,11 +6,12 @@ from collections import Counter
 from dataclasses import asdict, dataclass
 from enum import Enum
 from typing import Any, Callable, Literal, TypedDict, TypeVar
+from comfy_api.v3.helpers import first_real_override
 
 # used for type hinting
 import torch
 from spandrel import ImageModelDescriptor
-from typing_extensions import NotRequired
+from typing_extensions import NotRequired, final
 
 from comfy.clip_vision import ClipVisionModel
 from comfy.clip_vision import Output as ClipVisionOutput_
@@ -189,14 +190,15 @@ class InputV3(IO_V3):
         self.lazy = lazy
         self.extra_dict = extra_dict if extra_dict is not None else {}
 
-    def as_dict_V1(self):
+    def as_dict(self):
         return prune_dict({
             "display_name": self.display_name,
+            "optional": self.optional,
             "tooltip": self.tooltip,
             "lazy": self.lazy,
         }) | prune_dict(self.extra_dict)
 
-    def get_io_type_V1(self):
+    def get_io_type(self):
         return self.io_type
 
 class WidgetInputV3(InputV3):
@@ -205,23 +207,23 @@ class WidgetInputV3(InputV3):
     '''
     def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                  default: Any=None,
-                 socketless: bool=None, widgetType: str=None, force_input: bool=None, extra_dict=None):
+                 socketless: bool=None, widget_type: str=None, force_input: bool=None, extra_dict=None):
         super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
         self.default = default
         self.socketless = socketless
-        self.widgetType = widgetType
+        self.widget_type = widget_type
         self.force_input = force_input
 
-    def as_dict_V1(self):
-        return super().as_dict_V1() | prune_dict({
+    def as_dict(self):
+        return super().as_dict() | prune_dict({
             "default": self.default,
             "socketless": self.socketless,
-            "widgetType": self.widgetType,
+            "widgetType": self.widget_type,
             "forceInput": self.force_input,
         })
 
-    def get_io_type_V1(self):
-        return self.widgetType if self.widgetType is not None else super().get_io_type_V1()
+    def get_io_type(self):
+        return self.widget_type if self.widget_type is not None else super().get_io_type()
 
 
 class OutputV3(IO_V3):
@@ -232,13 +234,16 @@ class OutputV3(IO_V3):
         self.tooltip = tooltip
         self.is_output_list = is_output_list
 
-    def as_dict_V3(self):
+    def as_dict(self):
         return prune_dict({
             "display_name": self.display_name,
             "tooltip": self.tooltip,
             "is_output_list": self.is_output_list,
         })
 
+    def get_io_type(self):
+        return self.io_type
+
 
 class ComfyTypeI(ComfyType):
     '''ComfyType subclass that only has a default Input class - intended for types that only have Inputs.'''
@@ -326,8 +331,8 @@ class Boolean(ComfyTypeIO):
             self.label_off = label_off
             self.default: bool
 
-        def as_dict_V1(self):
-            return super().as_dict_V1() | prune_dict({
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
                 "label_on": self.label_on,
                 "label_off": self.label_off,
             })
@@ -349,13 +354,13 @@ class Int(ComfyTypeIO):
             self.display_mode = display_mode
             self.default: int
 
-        def as_dict_V1(self):
-            return super().as_dict_V1() | prune_dict({
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
                 "min": self.min,
                 "max": self.max,
                 "step": self.step,
                 "control_after_generate": self.control_after_generate,
-                "display": self.display_mode.value if self.display_name else None,
+                "display": self.display_mode.value if self.display_mode else None,
             })
 
 @comfytype(io_type="FLOAT")
@@ -375,8 +380,8 @@ class Float(ComfyTypeIO):
             self.display_mode = display_mode
             self.default: float
 
-        def as_dict_V1(self):
-            return super().as_dict_V1() | prune_dict({
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
                 "min": self.min,
                 "max": self.max,
                 "step": self.step,
@@ -399,8 +404,8 @@ class String(ComfyTypeIO):
             self.dynamic_prompts = dynamic_prompts
             self.default: str
 
-        def as_dict_V1(self):
-            return super().as_dict_V1() | prune_dict({
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
                 "multiline": self.multiline,
                 "placeholder": self.placeholder,
                 "dynamicPrompts": self.dynamic_prompts,
@@ -426,8 +431,8 @@ class Combo(ComfyTypeI):
             self.remote = remote
             self.default: str
 
-        def as_dict_V1(self):
-            return super().as_dict_V1() | prune_dict({
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
                 "multiselect": self.multiselect,
                 "options": self.options,
                 "control_after_generate": self.control_after_generate,
@@ -445,15 +450,15 @@ class MultiCombo(ComfyTypeI):
     class Input(Combo.Input):
         def __init__(self, id: str, options: list[str], display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
                     default: list[str]=None, placeholder: str=None, chip: bool=None, control_after_generate: bool=None,
-                    socketless: bool=None, widgetType: str=None):
-            super().__init__(id, options, display_name, optional, tooltip, lazy, default, control_after_generate, socketless, widgetType)
+                    socketless: bool=None):
+            super().__init__(id, options, display_name, optional, tooltip, lazy, default, control_after_generate, socketless=socketless)
             self.multiselect = True
             self.placeholder = placeholder
             self.chip = chip
             self.default: list[str]
 
-        def as_dict_V1(self):
-            to_return = super().as_dict_V1() | prune_dict({
+        def as_dict(self):
+            to_return = super().as_dict() | prune_dict({
                 "multi_select": self.multiselect,
                 "placeholder": self.placeholder,
                 "chip": self.chip,
@@ -768,9 +773,9 @@ class MultiType:
                 display_name = id.display_name if id.display_name is not None else display_name
                 lazy = id.lazy if id.lazy is not None else lazy
                 id = id.id
-                # if is a widget input, make sure widgetType is set appropriately
+                # if is a widget input, make sure widget_type is set appropriately
                 if isinstance(self.input_override, WidgetInputV3):
-                    self.input_override.widgetType = self.input_override.get_io_type_V1()
+                    self.input_override.widget_type = self.input_override.get_io_type()
             super().__init__(id, display_name, optional, tooltip, lazy, extra_dict)
             self._io_types = types
 
@@ -787,18 +792,18 @@ class MultiType:
                     io_types.append(x)
             return io_types
 
-        def get_io_type_V1(self):
+        def get_io_type(self):
             # ensure types are unique and order is preserved
             str_types = [x.io_type for x in self.io_types]
             if self.input_override is not None:
-                str_types.insert(0, self.input_override.get_io_type_V1())
+                str_types.insert(0, self.input_override.get_io_type())
             return ",".join(list(dict.fromkeys(str_types)))
 
-        def as_dict_V1(self):
+        def as_dict(self):
             if self.input_override is not None:
-                return self.input_override.as_dict_V1() | super().as_dict_V1()
+                return self.input_override.as_dict() | super().as_dict()
             else:
-                return super().as_dict_V1()
+                return super().as_dict()
 
 class DynamicInput(InputV3, ABC):
     '''
@@ -890,8 +895,8 @@ class MatchType(ComfyTypeIO):
         def get_dynamic(self) -> list[InputV3]:
             return [self]
 
-        def as_dict_V1(self):
-            return super().as_dict_V1() | prune_dict({
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
                 "template": self.template.as_dict(),
             })
 
@@ -904,8 +909,8 @@ class MatchType(ComfyTypeIO):
         def get_dynamic(self) -> list[OutputV3]:
             return [self]
 
-        def as_dict_V3(self):
-            return super().as_dict_V3() | prune_dict({
+        def as_dict(self):
+            return super().as_dict() | prune_dict({
                 "template": self.template.as_dict(),
             })
 
@@ -980,6 +985,19 @@ class NodeInfoV1:
     experimental: bool=None
     api_node: bool=None
 
+@dataclass
+class NodeInfoV3:
+    input: dict=None
+    output: dict=None
+    hidden: list[str]=None
+    name: str=None
+    display_name: str=None
+    description: str=None
+    category: str=None
+    output_node: bool=None
+    deprecated: bool=None
+    experimental: bool=None
+    api_node: bool=None
 
 def as_pruned_dict(dataclass_obj):
     '''Return dict of dataclass object with pruned None values.'''
@@ -1082,6 +1100,84 @@ class SchemaV3:
                 if output.id is None:
                     output.id = f"_{i}_{output.io_type}_"
 
+    def get_v1_info(self, cls) -> NodeInfoV1:
+        # get V1 inputs
+        input = {
+            "required": {}
+        }
+        if self.inputs:
+            for i in self.inputs:
+                if isinstance(i, DynamicInput):
+                    dynamic_inputs = i.get_dynamic()
+                    for d in dynamic_inputs:
+                        add_to_dict_v1(d, input)
+                else:
+                    add_to_dict_v1(i, input)
+        if self.hidden:
+            for hidden in self.hidden:
+                input.setdefault("hidden", {})[hidden.name] = (hidden.value,)
+        # create separate lists from output fields
+        output = []
+        output_is_list = []
+        output_name = []
+        output_tooltips = []
+        if self.outputs:
+            for o in self.outputs:
+                output.append(o.io_type)
+                output_is_list.append(o.is_output_list)
+                output_name.append(o.display_name if o.display_name else o.io_type)
+                output_tooltips.append(o.tooltip if o.tooltip else None)
+
+        info = NodeInfoV1(
+            input=input,
+            input_order={key: list(value.keys()) for (key, value) in input.items()},
+            output=output,
+            output_is_list=output_is_list,
+            output_name=output_name,
+            output_tooltips=output_tooltips,
+            name=self.node_id,
+            display_name=self.display_name,
+            category=self.category,
+            description=self.description,
+            output_node=self.is_output_node,
+            deprecated=self.is_deprecated,
+            experimental=self.is_experimental,
+            api_node=self.is_api_node,
+            python_module=getattr(cls, "RELATIVE_PYTHON_MODULE", "nodes")
+        )
+        return info
+
+
+    def get_v3_info(self, cls) -> NodeInfoV3:
+        input_dict = {}
+        output_dict = {}
+        hidden_list = []
+        # TODO: make sure dynamic types will be handled correctly
+        if self.inputs:
+            for input in self.inputs:
+                add_to_dict_v3(input, input_dict)
+        if self.outputs:
+            for output in self.outputs:
+                add_to_dict_v3(output, output_dict)
+        if self.hidden:
+            for hidden in self.hidden:
+                hidden_list.append(hidden.value)
+
+        info = NodeInfoV3(
+            input=input_dict,
+            output=output_dict,
+            hidden=hidden_list,
+            name=self.node_id,
+            display_name=self.display_name,
+            description=self.description,
+            category=self.category,
+            output_node=self.is_output_node,
+            deprecated=self.is_deprecated,
+            experimental=self.is_experimental,
+            api_node=self.is_api_node,
+            python_module=getattr(cls, "RELATIVE_PYTHON_MODULE", "nodes")
+        )
+        return info
 
 class Serializer:
     def __init_subclass__(cls, io_type: str, **kwargs):
@@ -1140,11 +1236,18 @@ def lock_class(cls):
 
 def add_to_dict_v1(i: InputV3, input: dict):
     key = "optional" if i.optional else "required"
-    input.setdefault(key, {})[i.id] = (i.get_io_type_V1(), i.as_dict_V1())
+    as_dict = i.as_dict()
+    # for v1, we don't want to include the optional key
+    as_dict.pop("optional", None)
+    input.setdefault(key, {})[i.id] = (i.get_io_type(), as_dict)
+
+def add_to_dict_v3(io: InputV3 | OutputV3, d: dict):
+    d[io.id] = (io.get_io_type(), io.as_dict())
 
 
-class ComfyNodeV3(ComfyNodeInternal):
-    """Common base class for all V3 nodes."""
+
+class _ComfyNodeBaseInternal(ComfyNodeInternal):
+    """Common base class for storing internal methods and properties; DO NOT USE for defining nodes."""
 
     RELATIVE_PYTHON_MODULE = None
     SCHEMA = None
@@ -1163,6 +1266,7 @@ class ComfyNodeV3(ComfyNodeInternal):
     @classmethod
     @abstractmethod
     def execute(cls, **kwargs) -> NodeOutput:
+        """Override this function with one that performs node's actions."""
         raise NotImplementedError
 
     @classmethod
@@ -1191,28 +1295,28 @@ class ComfyNodeV3(ComfyNodeInternal):
         """
         return [name for name in kwargs if kwargs[name] is None]
 
-    @classmethod
-    def GET_SERIALIZERS(cls) -> list[Serializer]:
-        return []
-
-    @classmethod
-    def GET_NODE_INFO_V3(cls) -> dict[str, Any]:
-        # schema = cls.GET_SCHEMA()
-        # TODO: finish
-        return None
-
     def __init__(self):
         self.local_state: NodeStateLocal = None
         self.local_resources: ResourcesLocal = None
         self.__class__.VALIDATE_CLASS()
 
+    @classmethod
+    def GET_SERIALIZERS(cls) -> list[Serializer]:
+        return []
+
+    @classmethod
+    def GET_BASE_CLASS(cls):
+        return _ComfyNodeBaseInternal
+
+    @final
     @classmethod
     def VALIDATE_CLASS(cls):
-        if not callable(cls.define_schema):
+        if first_real_override(cls, "define_schema") is None:
             raise Exception(f"No define_schema function was defined for node class {cls.__name__}.")
-        if not callable(cls.execute):
+        if first_real_override(cls, "execute") is None:
             raise Exception(f"No execute function was defined for node class {cls.__name__}.")
 
+    @final
     @classmethod
     def EXECUTE_NORMALIZED(cls, *args, **kwargs) -> NodeOutput:
         to_return = cls.execute(*args, **kwargs)
@@ -1229,6 +1333,7 @@ class ComfyNodeV3(ComfyNodeInternal):
         else:
             raise Exception(f"Invalid return type from node: {type(to_return)}")
 
+    @final
     @classmethod
     def PREPARE_CLASS_CLONE(cls, hidden_inputs: dict) -> type[ComfyNodeV3]:
         """Creates clone of real node class to prevent monkey-patching."""
@@ -1238,10 +1343,24 @@ class ComfyNodeV3(ComfyNodeInternal):
         type_clone.hidden = HiddenHolder.from_dict(hidden_inputs)
         return type_clone
 
+    @final
+    @classmethod
+    def GET_NODE_INFO_V3(cls) -> dict[str, Any]:
+        schema = cls.GET_SCHEMA()
+        info = schema.get_v3_info(cls)
+        return asdict(info)
     #############################################
     # V1 Backwards Compatibility code
     #--------------------------------------------
+    @final
+    @classmethod
+    def GET_NODE_INFO_V1(cls) -> dict[str, Any]:
+        schema = cls.GET_SCHEMA()
+        info = schema.get_v1_info(cls)
+        return asdict(info)
+
     _DESCRIPTION = None
+    @final
     @classproperty
     def DESCRIPTION(cls):  # noqa
         if cls._DESCRIPTION is None:
@@ -1249,6 +1368,7 @@ class ComfyNodeV3(ComfyNodeInternal):
         return cls._DESCRIPTION
 
     _CATEGORY = None
+    @final
     @classproperty
     def CATEGORY(cls):  # noqa
         if cls._CATEGORY is None:
@@ -1256,6 +1376,7 @@ class ComfyNodeV3(ComfyNodeInternal):
         return cls._CATEGORY
 
     _EXPERIMENTAL = None
+    @final
     @classproperty
     def EXPERIMENTAL(cls):  # noqa
         if cls._EXPERIMENTAL is None:
@@ -1263,6 +1384,7 @@ class ComfyNodeV3(ComfyNodeInternal):
         return cls._EXPERIMENTAL
 
     _DEPRECATED = None
+    @final
     @classproperty
     def DEPRECATED(cls):  # noqa
         if cls._DEPRECATED is None:
@@ -1270,6 +1392,7 @@ class ComfyNodeV3(ComfyNodeInternal):
         return cls._DEPRECATED
 
     _API_NODE = None
+    @final
     @classproperty
     def API_NODE(cls):  # noqa
         if cls._API_NODE is None:
@@ -1277,6 +1400,7 @@ class ComfyNodeV3(ComfyNodeInternal):
         return cls._API_NODE
 
     _OUTPUT_NODE = None
+    @final
     @classproperty
     def OUTPUT_NODE(cls):  # noqa
         if cls._OUTPUT_NODE is None:
@@ -1284,6 +1408,7 @@ class ComfyNodeV3(ComfyNodeInternal):
         return cls._OUTPUT_NODE
 
     _INPUT_IS_LIST = None
+    @final
     @classproperty
     def INPUT_IS_LIST(cls):  # noqa
         if cls._INPUT_IS_LIST is None:
@@ -1291,6 +1416,7 @@ class ComfyNodeV3(ComfyNodeInternal):
         return cls._INPUT_IS_LIST
     _OUTPUT_IS_LIST = None
 
+    @final
     @classproperty
     def OUTPUT_IS_LIST(cls):  # noqa
         if cls._OUTPUT_IS_LIST is None:
@@ -1298,6 +1424,7 @@ class ComfyNodeV3(ComfyNodeInternal):
         return cls._OUTPUT_IS_LIST
 
     _RETURN_TYPES = None
+    @final
     @classproperty
     def RETURN_TYPES(cls):  # noqa
         if cls._RETURN_TYPES is None:
@@ -1305,6 +1432,7 @@ class ComfyNodeV3(ComfyNodeInternal):
         return cls._RETURN_TYPES
 
     _RETURN_NAMES = None
+    @final
     @classproperty
     def RETURN_NAMES(cls):  # noqa
         if cls._RETURN_NAMES is None:
@@ -1312,6 +1440,7 @@ class ComfyNodeV3(ComfyNodeInternal):
         return cls._RETURN_NAMES
 
     _OUTPUT_TOOLTIPS = None
+    @final
     @classproperty
     def OUTPUT_TOOLTIPS(cls):  # noqa
         if cls._OUTPUT_TOOLTIPS is None:
@@ -1319,6 +1448,7 @@ class ComfyNodeV3(ComfyNodeInternal):
         return cls._OUTPUT_TOOLTIPS
 
     _NOT_IDEMPOTENT = None
+    @final
     @classproperty
     def NOT_IDEMPOTENT(cls):  # noqa
         if cls._NOT_IDEMPOTENT is None:
@@ -1327,28 +1457,19 @@ class ComfyNodeV3(ComfyNodeInternal):
 
     FUNCTION = "EXECUTE_NORMALIZED"
 
+    @final
     @classmethod
     def INPUT_TYPES(cls, include_hidden=True, return_schema=False) -> dict[str, dict] | tuple[dict[str, dict], SchemaV3]:
         schema = cls.FINALIZE_SCHEMA()
-        # for V1, make inputs be a dict with potential keys {required, optional, hidden}
-        input = {
-            "required": {}
-        }
-        if schema.inputs:
-            for i in schema.inputs:
-                if isinstance(i, DynamicInput):
-                    dynamic_inputs = i.get_dynamic()
-                    for d in dynamic_inputs:
-                        add_to_dict_v1(d, input)
-                else:
-                    add_to_dict_v1(i, input)
-        if schema.hidden and include_hidden:
-            for hidden in schema.hidden:
-                input.setdefault("hidden", {})[hidden.name] = (hidden.value,)
+        info = schema.get_v1_info(cls)
+        input = info.input
+        if not include_hidden:
+            input.pop("hidden", None)
         if return_schema:
             return input, schema
         return input
 
+    @final
     @classmethod
     def FINALIZE_SCHEMA(cls):
         """Call define_schema and finalize it."""
@@ -1356,6 +1477,7 @@ class ComfyNodeV3(ComfyNodeInternal):
         schema.finalize()
         return schema
 
+    @final
     @classmethod
     def GET_SCHEMA(cls) -> SchemaV3:
         """Validate node class, finalize schema, validate schema, and set expected class properties."""
@@ -1397,47 +1519,58 @@ class ComfyNodeV3(ComfyNodeInternal):
             cls._OUTPUT_TOOLTIPS = output_tooltips
         cls.SCHEMA = schema
         return schema
-
-    @classmethod
-    def GET_NODE_INFO_V1(cls) -> dict[str, Any]:
-        schema = cls.GET_SCHEMA()
-        # get V1 inputs
-        input = cls.INPUT_TYPES()
-
-        # create separate lists from output fields
-        output = []
-        output_is_list = []
-        output_name = []
-        output_tooltips = []
-        if schema.outputs:
-            for o in schema.outputs:
-                output.append(o.io_type)
-                output_is_list.append(o.is_output_list)
-                output_name.append(o.display_name if o.display_name else o.io_type)
-                output_tooltips.append(o.tooltip if o.tooltip else None)
-
-        info = NodeInfoV1(
-            input=input,
-            input_order={key: list(value.keys()) for (key, value) in input.items()},
-            output=output,
-            output_is_list=output_is_list,
-            output_name=output_name,
-            output_tooltips=output_tooltips,
-            name=schema.node_id,
-            display_name=schema.display_name,
-            category=schema.category,
-            description=schema.description,
-            output_node=schema.is_output_node,
-            deprecated=schema.is_deprecated,
-            experimental=schema.is_experimental,
-            api_node=schema.is_api_node,
-            python_module=getattr(cls, "RELATIVE_PYTHON_MODULE", "nodes")
-        )
-        return asdict(info)
     #--------------------------------------------
     #############################################
 
 
+class ComfyNodeV3(_ComfyNodeBaseInternal):
+    """Common base class for all V3 nodes."""
+
+    @classmethod
+    @abstractmethod
+    def define_schema(cls) -> SchemaV3:
+        """Override this function with one that returns a SchemaV3 instance."""
+        raise NotImplementedError
+
+    @classmethod
+    @abstractmethod
+    def execute(cls, **kwargs) -> NodeOutput:
+        """Override this function with one that performs node's actions."""
+        raise NotImplementedError
+
+    @classmethod
+    def validate_inputs(cls, **kwargs) -> bool:
+        """Optionally, define this function to validate inputs; equivalent to V1's VALIDATE_INPUTS."""
+        raise NotImplementedError
+
+    @classmethod
+    def fingerprint_inputs(cls, **kwargs) -> Any:
+        """Optionally, define this function to fingerprint inputs; equivalent to V1's IS_CHANGED."""
+        raise NotImplementedError
+
+    @classmethod
+    def check_lazy_status(cls, **kwargs) -> list[str]:
+        """Optionally, define this function to return a list of input names that should be evaluated.
+
+        This basic mixin impl. requires all inputs.
+
+        :kwargs: All node inputs will be included here.  If the input is ``None``, it should be assumed that it has not yet been evaluated.  \
+            When using ``INPUT_IS_LIST = True``, unevaluated will instead be ``(None,)``.
+
+        Params should match the nodes execution ``FUNCTION`` (self, and all inputs by name).
+        Will be executed repeatedly until it returns an empty list, or all requested items were already evaluated (and sent as params).
+
+        Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lazy_evaluation#defining-check-lazy-status
+        """
+        return [name for name in kwargs if kwargs[name] is None]
+
+    @final
+    @classmethod
+    def GET_BASE_CLASS(cls):
+        """DO NOT override this class. Will break things in execution.py."""
+        return ComfyNodeV3
+
+
 class NodeOutput:
     '''
     Standardized output of a node; can pass in any number of args and/or a UIOutput into 'ui' kwarg.
@@ -1479,57 +1612,4 @@ class _UIOutput(ABC):
 
     @abstractmethod
     def as_dict(self) -> dict:
-        ... # TODO: finish
-
-class TestNode(ComfyNodeV3):
-    @classmethod
-    def define_schema(cls):
-        return SchemaV3(
-        node_id="TestNode_v3",
-        display_name="Test Node (V3)",
-        category="v3_test",
-        inputs=[Int.Input("my_int"),
-                #AutoGrowDynamicInput("growing", Image.Input),
-                Mask.Input("thing"),
-                ],
-        outputs=[Image.Output("image_output")],
-        hidden=[Hidden.api_key_comfy_org, Hidden.auth_token_comfy_org, Hidden.unique_id]
-    )
-
-    @classmethod
-    def execute(cls, **kwargs):
-        pass
-
-# if __name__ == "__main__":
-#     print("hello there")
-#     inputs: list[InputV3] = [
-#         Int.Input("tessfes", widgetType=String.io_type),
-#         Int.Input("my_int"),
-#         Custom("XYZ").Input("xyz"),
-#         Custom("MODEL_M").Input("model1"),
-#         Image.Input("my_image"),
-#         Float.Input("my_float"),
-#         MultiType.Input("my_inputs", [String, Custom("MODEL_M"), Custom("XYZ")]),
-#     ]
-#     Custom("XYZ").Input()
-#     outputs: list[OutputV3] = [
-#         Image.Output("image"),
-#         Custom("XYZ").Output("xyz"),
-#     ]
-#
-#     for c in inputs:
-#         if isinstance(c, MultiType):
-#             print(f"{c}, {type(c)}, {type(c).io_type}, {c.id}, {[x.io_type for x in c.io_types]}")
-#             print(c.get_io_type_V1())
-#         else:
-#             print(f"{c}, {type(c)}, {type(c).io_type}, {c.id}")
-#
-#     for c in outputs:
-#         print(f"{c}, {type(c)}, {type(c).io_type}, {c.id}")
-#
-#     zz = TestNode()
-#     print(zz.GET_NODE_INFO_V1())
-#
-#     # aa = NodeInfoV1()
-#     # print(asdict(aa))
-#     # print(as_pruned_dict(aa))
+        ...

comfy_extras/v3/nodes_advanced_samplers.py

@@ -0,0 +1,128 @@
+import numpy as np
+import torch
+from tqdm.auto import trange
+
+import comfy.model_patcher
+import comfy.samplers
+import comfy.utils
+from comfy.k_diffusion.sampling import to_d
+from comfy_api.v3 import io
+
+
+@torch.no_grad()
+def sample_lcm_upscale(
+    model, x, sigmas, extra_args=None, callback=None, disable=None, total_upscale=2.0, upscale_method="bislerp", upscale_steps=None
+):
+    extra_args = {} if extra_args is None else extra_args
+
+    if upscale_steps is None:
+        upscale_steps = max(len(sigmas) // 2 + 1, 2)
+    else:
+        upscale_steps += 1
+        upscale_steps = min(upscale_steps, len(sigmas) + 1)
+
+    upscales = np.linspace(1.0, total_upscale, upscale_steps)[1:]
+
+    orig_shape = x.size()
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
+
+        x = denoised
+        if i < len(upscales):
+            x = comfy.utils.common_upscale(
+                x, round(orig_shape[-1] * upscales[i]), round(orig_shape[-2] * upscales[i]), upscale_method, "disabled"
+            )
+
+        if sigmas[i + 1] > 0:
+            x += sigmas[i + 1] * torch.randn_like(x)
+    return x
+
+
+class SamplerLCMUpscale(io.ComfyNodeV3):
+    UPSCALE_METHODS = ["bislerp", "nearest-exact", "bilinear", "area", "bicubic"]
+
+    @classmethod
+    def define_schema(cls) -> io.SchemaV3:
+        return io.SchemaV3(
+            node_id="SamplerLCMUpscale_V3",
+            category="sampling/custom_sampling/samplers",
+            inputs=[
+                io.Float.Input("scale_ratio", default=1.0, min=0.1, max=20.0, step=0.01),
+                io.Int.Input("scale_steps", default=-1, min=-1, max=1000, step=1),
+                io.Combo.Input("upscale_method", options=cls.UPSCALE_METHODS),
+            ],
+            outputs=[io.Sampler.Output()],
+        )
+
+    @classmethod
+    def execute(cls, scale_ratio, scale_steps, upscale_method) -> io.NodeOutput:
+        if scale_steps < 0:
+            scale_steps = None
+        sampler = comfy.samplers.KSAMPLER(
+            sample_lcm_upscale,
+            extra_options={
+                "total_upscale": scale_ratio,
+                "upscale_steps": scale_steps,
+                "upscale_method": upscale_method,
+            },
+        )
+        return io.NodeOutput(sampler)
+
+
+@torch.no_grad()
+def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    extra_args = {} if extra_args is None else extra_args
+
+    temp = [0]
+
+    def post_cfg_function(args):
+        temp[0] = args["uncond_denoised"]
+        return args["denoised"]
+
+    model_options = extra_args.get("model_options", {}).copy()
+    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(
+        model_options, post_cfg_function, disable_cfg1_optimization=True
+    )
+
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        sigma_hat = sigmas[i]
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x - denoised + temp[0], sigmas[i], denoised)
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigma_hat, "denoised": denoised})
+        dt = sigmas[i + 1] - sigma_hat
+        x = x + d * dt
+    return x
+
+
+class SamplerEulerCFGpp(io.ComfyNodeV3):
+    @classmethod
+    def define_schema(cls) -> io.SchemaV3:
+        return io.SchemaV3(
+            node_id="SamplerEulerCFGpp_V3",
+            display_name="SamplerEulerCFG++ _V3",
+            category="_for_testing",
+            inputs=[
+                io.Combo.Input("version", options=["regular", "alternative"]),
+            ],
+            outputs=[io.Sampler.Output()],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, version) -> io.NodeOutput:
+        if version == "alternative":
+            sampler = comfy.samplers.KSAMPLER(sample_euler_pp)
+        else:
+            sampler = comfy.samplers.ksampler("euler_cfg_pp")
+        return io.NodeOutput(sampler)
+
+
+NODES_LIST = [
+    SamplerLCMUpscale,
+    SamplerEulerCFGpp,
+]

comfy_extras/v3/nodes_align_your_steps.py

@@ -0,0 +1,83 @@
+# from: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
+
+import numpy as np
+import torch
+
+from comfy_api.v3 import io
+
+NOISE_LEVELS = {
+    "SD1": [
+        14.6146412293,
+        6.4745760956,
+        3.8636745985,
+        2.6946151520,
+        1.8841921177,
+        1.3943805092,
+        0.9642583904,
+        0.6523686016,
+        0.3977456272,
+        0.1515232662,
+        0.0291671582,
+    ],
+    "SDXL": [
+        14.6146412293,
+        6.3184485287,
+        3.7681790315,
+        2.1811480769,
+        1.3405244945,
+        0.8620721141,
+        0.5550693289,
+        0.3798540708,
+        0.2332364134,
+        0.1114188177,
+        0.0291671582,
+    ],
+    "SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002],
+}
+
+
+def loglinear_interp(t_steps, num_steps):
+    """Performs log-linear interpolation of a given array of decreasing numbers."""
+    xs = np.linspace(0, 1, len(t_steps))
+    ys = np.log(t_steps[::-1])
+
+    new_xs = np.linspace(0, 1, num_steps)
+    new_ys = np.interp(new_xs, xs, ys)
+
+    return np.exp(new_ys)[::-1].copy()
+
+
+class AlignYourStepsScheduler(io.ComfyNodeV3):
+    @classmethod
+    def define_schema(cls) -> io.SchemaV3:
+        return io.SchemaV3(
+            node_id="AlignYourStepsScheduler_V3",
+            category="sampling/custom_sampling/schedulers",
+            inputs=[
+                io.Combo.Input("model_type", options=["SD1", "SDXL", "SVD"]),
+                io.Int.Input("steps", default=10, min=1, max=10000),
+                io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[io.Sigmas.Output()],
+        )
+
+    @classmethod
+    def execute(cls, model_type, steps, denoise) -> io.NodeOutput:
+        total_steps = steps
+        if denoise < 1.0:
+            if denoise <= 0.0:
+                return io.NodeOutput(torch.FloatTensor([]))
+            total_steps = round(steps * denoise)
+
+        sigmas = NOISE_LEVELS[model_type][:]
+        if (steps + 1) != len(sigmas):
+            sigmas = loglinear_interp(sigmas, steps + 1)
+
+        sigmas = sigmas[-(total_steps + 1) :]
+        sigmas[-1] = 0
+        return io.NodeOutput(torch.FloatTensor(sigmas))
+
+
+NODES_LIST = [
+    AlignYourStepsScheduler,
+]

comfy_extras/v3/nodes_apg.py

@@ -0,0 +1,98 @@
+import torch
+
+from comfy_api.v3 import io
+
+
+def project(v0, v1):
+    v1 = torch.nn.functional.normalize(v1, dim=[-1, -2, -3])
+    v0_parallel = (v0 * v1).sum(dim=[-1, -2, -3], keepdim=True) * v1
+    v0_orthogonal = v0 - v0_parallel
+    return v0_parallel, v0_orthogonal
+
+
+class APG(io.ComfyNodeV3):
+    @classmethod
+    def define_schema(cls) -> io.SchemaV3:
+        return io.SchemaV3(
+            node_id="APG_V3",
+            display_name="Adaptive Projected Guidance _V3",
+            category="sampling/custom_sampling",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input(
+                    "eta",
+                    default=1.0,
+                    min=-10.0,
+                    max=10.0,
+                    step=0.01,
+                    tooltip="Controls the scale of the parallel guidance vector. Default CFG behavior at a setting of 1.",
+                ),
+                io.Float.Input(
+                    "norm_threshold",
+                    default=5.0,
+                    min=0.0,
+                    max=50.0,
+                    step=0.1,
+                    tooltip="Normalize guidance vector to this value, normalization disable at a setting of 0.",
+                ),
+                io.Float.Input(
+                    "momentum",
+                    default=0.0,
+                    min=-5.0,
+                    max=1.0,
+                    step=0.01,
+                    tooltip="Controls a running average of guidance during diffusion, disabled at a setting of 0.",
+                ),
+            ],
+            outputs=[io.Model.Output()],
+        )
+
+    @classmethod
+    def execute(cls, model, eta, norm_threshold, momentum) -> io.NodeOutput:
+        running_avg = 0
+        prev_sigma = None
+
+        def pre_cfg_function(args):
+            nonlocal running_avg, prev_sigma
+
+            if len(args["conds_out"]) == 1:
+                return args["conds_out"]
+
+            cond = args["conds_out"][0]
+            uncond = args["conds_out"][1]
+            sigma = args["sigma"][0]
+            cond_scale = args["cond_scale"]
+
+            if prev_sigma is not None and sigma > prev_sigma:
+                running_avg = 0
+            prev_sigma = sigma
+
+            guidance = cond - uncond
+
+            if momentum != 0:
+                if not torch.is_tensor(running_avg):
+                    running_avg = guidance
+                else:
+                    running_avg = momentum * running_avg + guidance
+                guidance = running_avg
+
+            if norm_threshold > 0:
+                guidance_norm = guidance.norm(p=2, dim=[-1, -2, -3], keepdim=True)
+                scale = torch.minimum(torch.ones_like(guidance_norm), norm_threshold / guidance_norm)
+                guidance = guidance * scale
+
+            guidance_parallel, guidance_orthogonal = project(guidance, cond)
+            modified_guidance = guidance_orthogonal + eta * guidance_parallel
+
+            modified_cond = (uncond + modified_guidance) + (cond - uncond) / cond_scale
+
+            return [modified_cond, uncond] + args["conds_out"][2:]
+
+        m = model.clone()
+        m.set_model_sampler_pre_cfg_function(pre_cfg_function)
+        return io.NodeOutput(m)
+
+
+NODES_LIST = [
+    APG,
+]

comfy_extras/v3/nodes_attention_multiply.py

@@ -0,0 +1,139 @@
+from comfy_api.v3 import io
+
+
+def attention_multiply(attn, model, q, k, v, out):
+    m = model.clone()
+    sd = model.model_state_dict()
+
+    for key in sd:
+        if key.endswith("{}.to_q.bias".format(attn)) or key.endswith("{}.to_q.weight".format(attn)):
+            m.add_patches({key: (None,)}, 0.0, q)
+        if key.endswith("{}.to_k.bias".format(attn)) or key.endswith("{}.to_k.weight".format(attn)):
+            m.add_patches({key: (None,)}, 0.0, k)
+        if key.endswith("{}.to_v.bias".format(attn)) or key.endswith("{}.to_v.weight".format(attn)):
+            m.add_patches({key: (None,)}, 0.0, v)
+        if key.endswith("{}.to_out.0.bias".format(attn)) or key.endswith("{}.to_out.0.weight".format(attn)):
+            m.add_patches({key: (None,)}, 0.0, out)
+    return m
+
+
+class UNetSelfAttentionMultiply(io.ComfyNodeV3):
+    @classmethod
+    def define_schema(cls) -> io.SchemaV3:
+        return io.SchemaV3(
+            node_id="UNetSelfAttentionMultiply_V3",
+            category="_for_testing/attention_experiments",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01),
+            ],
+            outputs=[io.Model.Output()],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, model, q, k, v, out) -> io.NodeOutput:
+        return io.NodeOutput(attention_multiply("attn1", model, q, k, v, out))
+
+
+class UNetCrossAttentionMultiply(io.ComfyNodeV3):
+    @classmethod
+    def define_schema(cls) -> io.SchemaV3:
+        return io.SchemaV3(
+            node_id="UNetCrossAttentionMultiply_V3",
+            category="_for_testing/attention_experiments",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01),
+            ],
+            outputs=[io.Model.Output()],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, model, q, k, v, out) -> io.NodeOutput:
+        return io.NodeOutput(attention_multiply("attn2", model, q, k, v, out))
+
+
+class CLIPAttentionMultiply(io.ComfyNodeV3):
+    @classmethod
+    def define_schema(cls) -> io.SchemaV3:
+        return io.SchemaV3(
+            node_id="CLIPAttentionMultiply_V3",
+            category="_for_testing/attention_experiments",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("k", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("v", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("out", default=1.0, min=0.0, max=10.0, step=0.01),
+            ],
+            outputs=[io.Clip.Output()],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, clip, q, k, v, out) -> io.NodeOutput:
+        m = clip.clone()
+        sd = m.patcher.model_state_dict()
+
+        for key in sd:
+            if key.endswith("self_attn.q_proj.weight") or key.endswith("self_attn.q_proj.bias"):
+                m.add_patches({key: (None,)}, 0.0, q)
+            if key.endswith("self_attn.k_proj.weight") or key.endswith("self_attn.k_proj.bias"):
+                m.add_patches({key: (None,)}, 0.0, k)
+            if key.endswith("self_attn.v_proj.weight") or key.endswith("self_attn.v_proj.bias"):
+                m.add_patches({key: (None,)}, 0.0, v)
+            if key.endswith("self_attn.out_proj.weight") or key.endswith("self_attn.out_proj.bias"):
+                m.add_patches({key: (None,)}, 0.0, out)
+        return io.NodeOutput(m)
+
+
+class UNetTemporalAttentionMultiply(io.ComfyNodeV3):
+    @classmethod
+    def define_schema(cls) -> io.SchemaV3:
+        return io.SchemaV3(
+            node_id="UNetTemporalAttentionMultiply_V3",
+            category="_for_testing/attention_experiments",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("self_structural", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("self_temporal", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("cross_structural", default=1.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("cross_temporal", default=1.0, min=0.0, max=10.0, step=0.01),
+            ],
+            outputs=[io.Model.Output()],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, model, self_structural, self_temporal, cross_structural, cross_temporal) -> io.NodeOutput:
+        m = model.clone()
+        sd = model.model_state_dict()
+
+        for k in sd:
+            if (k.endswith("attn1.to_out.0.bias") or k.endswith("attn1.to_out.0.weight")):
+                if '.time_stack.' in k:
+                    m.add_patches({k: (None,)}, 0.0, self_temporal)
+                else:
+                    m.add_patches({k: (None,)}, 0.0, self_structural)
+            elif (k.endswith("attn2.to_out.0.bias") or k.endswith("attn2.to_out.0.weight")):
+                if '.time_stack.' in k:
+                    m.add_patches({k: (None,)}, 0.0, cross_temporal)
+                else:
+                    m.add_patches({k: (None,)}, 0.0, cross_structural)
+        return io.NodeOutput(m)
+
+
+NODES_LIST = [
+    UNetSelfAttentionMultiply,
+    UNetCrossAttentionMultiply,
+    CLIPAttentionMultiply,
+    UNetTemporalAttentionMultiply,
+]

comfy_extras/v3/nodes_audio.py

@@ -16,7 +16,7 @@ from comfy.cli_args import args
 from comfy_api.v3 import io, ui
 
 
-class ConditioningStableAudio_V3(io.ComfyNodeV3):
+class ConditioningStableAudio(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -46,7 +46,7 @@ class ConditioningStableAudio_V3(io.ComfyNodeV3):
         )
 
 
-class EmptyLatentAudio_V3(io.ComfyNodeV3):
+class EmptyLatentAudio(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -68,7 +68,7 @@ class EmptyLatentAudio_V3(io.ComfyNodeV3):
         return io.NodeOutput({"samples": latent, "type": "audio"})
 
 
-class LoadAudio_V3(io.ComfyNodeV3):
+class LoadAudio(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -106,7 +106,7 @@ class LoadAudio_V3(io.ComfyNodeV3):
         return True
 
 
-class PreviewAudio_V3(io.ComfyNodeV3):
+class PreviewAudio(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -125,7 +125,7 @@ class PreviewAudio_V3(io.ComfyNodeV3):
         return io.NodeOutput(ui=ui.PreviewAudio(audio, cls=cls))
 
 
-class SaveAudioMP3_V3(io.ComfyNodeV3):
+class SaveAudioMP3(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -146,7 +146,7 @@ class SaveAudioMP3_V3(io.ComfyNodeV3):
         return _save_audio(self, audio, filename_prefix, format, quality)
 
 
-class SaveAudioOpus_V3(io.ComfyNodeV3):
+class SaveAudioOpus(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -167,7 +167,7 @@ class SaveAudioOpus_V3(io.ComfyNodeV3):
         return _save_audio(self, audio, filename_prefix, format, quality)
 
 
-class SaveAudio_V3(io.ComfyNodeV3):
+class SaveAudio(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -187,7 +187,7 @@ class SaveAudio_V3(io.ComfyNodeV3):
         return _save_audio(cls, audio, filename_prefix, format)
 
 
-class VAEDecodeAudio_V3(io.ComfyNodeV3):
+class VAEDecodeAudio(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -209,7 +209,7 @@ class VAEDecodeAudio_V3(io.ComfyNodeV3):
         return io.NodeOutput({"waveform": audio, "sample_rate": 44100})
 
 
-class VAEEncodeAudio_V3(io.ComfyNodeV3):
+class VAEEncodeAudio(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -335,13 +335,13 @@ def _save_audio(cls, audio, filename_prefix="ComfyUI", format="flac", quality="1
 
 
 NODES_LIST: list[type[io.ComfyNodeV3]] = [
-    ConditioningStableAudio_V3,
-    EmptyLatentAudio_V3,
-    LoadAudio_V3,
-    PreviewAudio_V3,
-    SaveAudioMP3_V3,
-    SaveAudioOpus_V3,
-    SaveAudio_V3,
-    VAEDecodeAudio_V3,
-    VAEEncodeAudio_V3,
+    ConditioningStableAudio,
+    EmptyLatentAudio,
+    LoadAudio,
+    PreviewAudio,
+    SaveAudioMP3,
+    SaveAudioOpus,
+    SaveAudio,
+    VAEDecodeAudio,
+    VAEEncodeAudio,
 ]

comfy_extras/v3/nodes_controlnet.py

@@ -3,7 +3,7 @@ from comfy.cldm.control_types import UNION_CONTROLNET_TYPES
 from comfy_api.v3 import io
 
 
-class ControlNetApplyAdvanced_V3(io.ComfyNodeV3):
+class ControlNetApplyAdvanced(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -60,7 +60,7 @@ class ControlNetApplyAdvanced_V3(io.ComfyNodeV3):
         return io.NodeOutput(out[0], out[1])
 
 
-class SetUnionControlNetType_V3(io.ComfyNodeV3):
+class SetUnionControlNetType(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -87,7 +87,7 @@ class SetUnionControlNetType_V3(io.ComfyNodeV3):
         return io.NodeOutput(control_net)
 
 
-class ControlNetInpaintingAliMamaApply_V3(ControlNetApplyAdvanced_V3):
+class ControlNetInpaintingAliMamaApply(ControlNetApplyAdvanced):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -135,7 +135,7 @@ class ControlNetInpaintingAliMamaApply_V3(ControlNetApplyAdvanced_V3):
 
 
 NODES_LIST: list[type[io.ComfyNodeV3]] = [
-    ControlNetApplyAdvanced_V3,
-    SetUnionControlNetType_V3,
-    ControlNetInpaintingAliMamaApply_V3,
+    ControlNetApplyAdvanced,
+    SetUnionControlNetType,
+    ControlNetInpaintingAliMamaApply,
 ]

comfy_extras/v3/nodes_primitive.py

@@ -5,7 +5,7 @@ import sys
 from comfy_api.v3 import io
 
 
-class String_V3(io.ComfyNodeV3):
+class String(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -23,7 +23,7 @@ class String_V3(io.ComfyNodeV3):
         return io.NodeOutput(value)
 
 
-class StringMultiline_V3(io.ComfyNodeV3):
+class StringMultiline(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -41,7 +41,7 @@ class StringMultiline_V3(io.ComfyNodeV3):
         return io.NodeOutput(value)
 
 
-class Int_V3(io.ComfyNodeV3):
+class Int(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -59,7 +59,7 @@ class Int_V3(io.ComfyNodeV3):
         return io.NodeOutput(value)
 
 
-class Float_V3(io.ComfyNodeV3):
+class Float(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -77,7 +77,7 @@ class Float_V3(io.ComfyNodeV3):
         return io.NodeOutput(value)
 
 
-class Boolean_V3(io.ComfyNodeV3):
+class Boolean(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -96,9 +96,9 @@ class Boolean_V3(io.ComfyNodeV3):
 
 
 NODES_LIST: list[type[io.ComfyNodeV3]] = [
-    String_V3,
-    StringMultiline_V3,
-    Int_V3,
-    Float_V3,
-    Boolean_V3,
+    String,
+    StringMultiline,
+    Int,
+    Float,
+    Boolean,
 ]

comfy_extras/v3/nodes_stable_cascade.py

@@ -23,7 +23,7 @@ import nodes
 from comfy_api.v3 import io
 
 
-class StableCascade_EmptyLatentImage_V3(io.ComfyNodeV3):
+class StableCascade_EmptyLatentImage(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -48,7 +48,7 @@ class StableCascade_EmptyLatentImage_V3(io.ComfyNodeV3):
         return io.NodeOutput({"samples": c_latent}, {"samples": b_latent})
 
 
-class StableCascade_StageC_VAEEncode_V3(io.ComfyNodeV3):
+class StableCascade_StageC_VAEEncode(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -79,7 +79,7 @@ class StableCascade_StageC_VAEEncode_V3(io.ComfyNodeV3):
         return io.NodeOutput({"samples": c_latent}, {"samples": b_latent})
 
 
-class StableCascade_StageB_Conditioning_V3(io.ComfyNodeV3):
+class StableCascade_StageB_Conditioning(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -105,7 +105,7 @@ class StableCascade_StageB_Conditioning_V3(io.ComfyNodeV3):
         return io.NodeOutput(c)
 
 
-class StableCascade_SuperResolutionControlnet_V3(io.ComfyNodeV3):
+class StableCascade_SuperResolutionControlnet(io.ComfyNodeV3):
     @classmethod
     def define_schema(cls):
         return io.SchemaV3(
@@ -136,8 +136,8 @@ class StableCascade_SuperResolutionControlnet_V3(io.ComfyNodeV3):
 
 
 NODES_LIST: list[type[io.ComfyNodeV3]] = [
-    StableCascade_EmptyLatentImage_V3,
-    StableCascade_StageB_Conditioning_V3,
-    StableCascade_StageC_VAEEncode_V3,
-    StableCascade_SuperResolutionControlnet_V3,
+    StableCascade_EmptyLatentImage,
+    StableCascade_StageB_Conditioning,
+    StableCascade_StageC_VAEEncode,
+    StableCascade_SuperResolutionControlnet,
 ]

execution.py

@@ -28,6 +28,7 @@ from comfy_execution.graph import (
 )
 from comfy_execution.graph_utils import GraphBuilder, is_link
 from comfy_execution.validation import validate_node_input
+from comfy_api.internal import ComfyNodeInternal
 from comfy_api.v3 import io, helpers
 
 
@@ -54,7 +55,7 @@ class IsChangedCache:
         class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
         has_is_changed = False
         is_changed_name = None
-        if issubclass(class_def, io.ComfyNodeV3) and helpers.first_real_override(class_def, "fingerprint_inputs", base=io.ComfyNodeV3) is not None:
+        if issubclass(class_def, ComfyNodeInternal) and helpers.first_real_override(class_def, "fingerprint_inputs") is not None:
             has_is_changed = True
             is_changed_name = "fingerprint_inputs"
         elif hasattr(class_def, "IS_CHANGED"):
@@ -127,7 +128,7 @@ class CacheSet:
         return result
 
 def get_input_data(inputs, class_def, unique_id, outputs=None, dynprompt=None, extra_data={}):
-    is_v3 = issubclass(class_def, io.ComfyNodeV3)
+    is_v3 = issubclass(class_def, ComfyNodeInternal)
     if is_v3:
         valid_inputs, schema = class_def.INPUT_TYPES(include_hidden=False, return_schema=True)
     else:
@@ -224,7 +225,7 @@ def _map_node_over_list(obj, input_data_all, func, allow_interrupt=False, execut
             if pre_execute_cb is not None and index is not None:
                 pre_execute_cb(index)
             # V3
-            if isinstance(obj, io.ComfyNodeV3) or (io.is_class(obj) and issubclass(obj, io.ComfyNodeV3)):
+            if isinstance(obj, ComfyNodeInternal) or (io.is_class(obj) and issubclass(obj, ComfyNodeInternal)):
                 # if is just a class, then assign no resources or state, just create clone
                 if io.is_class(obj):
                     type_obj = obj
@@ -411,8 +412,8 @@ def execute(server, dynprompt, caches, current_item, extra_data, executed, promp
                 obj = class_def()
                 caches.objects.set(unique_id, obj)
 
-            if issubclass(class_def, io.ComfyNodeV3):
-                lazy_status_present = helpers.first_real_override(class_def, "check_lazy_status", base=io.ComfyNodeV3) is not None
+            if issubclass(class_def, ComfyNodeInternal):
+                lazy_status_present = helpers.first_real_override(class_def, "check_lazy_status") is not None
             else:
                 lazy_status_present = getattr(obj, "check_lazy_status", None) is not None
             if lazy_status_present:
@@ -674,9 +675,9 @@ def validate_inputs(prompt, item, validated):
 
     validate_function_inputs = []
     validate_has_kwargs = False
-    if issubclass(obj_class, io.ComfyNodeV3):
+    if issubclass(obj_class, ComfyNodeInternal):
         validate_function_name = "validate_inputs"
-        validate_function = helpers.first_real_override(obj_class, validate_function_name, base=io.ComfyNodeV3)
+        validate_function = helpers.first_real_override(obj_class, validate_function_name)
     else:
         validate_function_name = "VALIDATE_INPUTS"
         validate_function = getattr(obj_class, validate_function_name, None)

nodes.py

@@ -2300,7 +2300,11 @@ def init_builtin_extra_nodes():
         "nodes_v3_test.py",
         "nodes_v1_test.py",
         "v3/nodes_ace.py",
+        "v3/nodes_advanced_samplers.py",
+        "v3/nodes_align_your_steps.py",
         "v3/nodes_audio.py",
+        "v3/nodes_apg.py",
+        "v3/nodes_attention_multiply.py",
         "v3/nodes_controlnet.py",
         "v3/nodes_images.py",
         "v3/nodes_mask.py",

pyproject.toml

@@ -24,7 +24,7 @@ lint.select = [
   "F",
 ]
 exclude = ["*.ipynb"]
-line-length = 120
+line-length = 144
 lint.pycodestyle.ignore-overlong-task-comments = true
 
 [tool.ruff.lint.per-file-ignores]

server.py

@@ -29,7 +29,7 @@ import comfy.model_management
 import node_helpers
 from comfyui_version import __version__
 from app.frontend_management import FrontendManager
-from comfy_api.v3.io import ComfyNodeV3
+from comfy_api.internal import ComfyNodeInternal
 
 from app.user_manager import UserManager
 from app.model_manager import ModelFileManager
@@ -555,7 +555,7 @@ class PromptServer():
 
         def node_info(node_class):
             obj_class = nodes.NODE_CLASS_MAPPINGS[node_class]
-            if issubclass(obj_class, ComfyNodeV3):
+            if issubclass(obj_class, ComfyNodeInternal):
                 return obj_class.GET_NODE_INFO_V1()
             info = {}
             info['input'] = obj_class.INPUT_TYPES()