Migrate all nodes to the V3 API

Remove obsolete frontend extensions

__init__.py

@@ -1,11 +1,10 @@
-from .adv_control.nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+from .adv_control.nodes import AdvancedControlNetExtension
-from .adv_control import documentation
 from .adv_control.dinklink import init_dinklink
 from .adv_control.sampling import prepare_dinklink_acn_wrapper
 
-WEB_DIRECTORY = "./web"
-__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', "WEB_DIRECTORY"]
-documentation.format_descriptions(NODE_CLASS_MAPPINGS)
-
 init_dinklink()
 prepare_dinklink_acn_wrapper()
+
+
+async def comfy_entrypoint() -> AdvancedControlNetExtension:
+    return AdvancedControlNetExtension()

adv_control/documentation.py

@@ -1,47 +0,0 @@
-from .logger import logger
-
-def image(src):
-    return f'<img src={src} style="width: 0px; min-width: 100%">'
-def video(src):
-    return f'<video src={src} autoplay muted loop controls controlslist="nodownload noremoteplayback noplaybackrate" style="width: 0px; min-width: 100%" class="VHS_loopedvideo">'
-def short_desc(desc):
-    return  f'<div id=VHS_shortdesc style="font-size: .8em">{desc}</div>'
-
-descriptions = {
-}
-
-sizes = ['1.4','1.2','1']
-def as_html(entry, depth=0):
-    if isinstance(entry, dict):
-        size = 0.8 if depth < 2 else 1
-        html = ''
-        for k in entry:
-            if k == "collapsed":
-                continue
-            collapse_single = k.endswith("_collapsed")
-            if collapse_single:
-                name = k[:-len("_collapsed")]
-            else:
-                name = k
-            collapse_flag = ' VHS_precollapse' if entry.get("collapsed", False) or collapse_single else ''
-            html += f'<div vhs_title=\"{name}\" style=\"display: flex; font-size: {size}em\" class=\"VHS_collapse{collapse_flag}\"><div style=\"color: #AAA; height: 1.5em;\">[<span style=\"font-family: monospace\">-</span>]</div><div style=\"width: 100%\">{name}: {as_html(entry[k], depth=depth+1)}</div></div>'
-        return html
-    if isinstance(entry, list):
-        html = ''
-        for i in entry:
-            html += f'<div>{as_html(i, depth=depth)}</div>'
-        return html
-    return str(entry)
-
-def format_descriptions(nodes):
-    for k in descriptions:
-        if k.endswith("_collapsed"):
-            k = k[:-len("_collapsed")]
-        nodes[k].DESCRIPTION = as_html(descriptions[k])
-    # undocumented_nodes = []
-    # for k in nodes:
-    #     if not hasattr(nodes[k], "DESCRIPTION"):
-    #         undocumented_nodes.append(k)
-    # if len(undocumented_nodes) > 0:
-    #     logger.info(f"Undocumented nodes: {undocumented_nodes}")
-

adv_control/nodes.py

@@ -1,4 +1,4 @@
-import comfy.sample
+from comfy_api.latest import ComfyExtension, io
 
 from .nodes_main import (ControlNetLoaderAdvanced, DiffControlNetLoaderAdvanced, AnimaLLLiteLoaderAdvanced,
                          AdvancedControlNetApply, AdvancedControlNetApplySingle)
@@ -12,132 +12,62 @@ from .nodes_sparsectrl import SparseCtrlMergedLoaderAdvanced, SparseCtrlLoaderAd
 from .nodes_reference import ReferenceControlNetNode, ReferenceControlFinetune, ReferencePreprocessorNode
 from .nodes_plusplus import PlusPlusLoaderAdvanced, PlusPlusLoaderSingle, PlusPlusInputNode
 from .nodes_ctrlora import CtrLoRALoader
-from .nodes_loosecontrol import ControlNetLoaderWithLoraAdvanced
 from .nodes_deprecated import (LoadImagesFromDirectory, ScaledSoftUniversalWeightsDeprecated,
                                SoftControlNetWeightsDeprecated, CustomControlNetWeightsDeprecated, 
                                SoftT2IAdapterWeightsDeprecated, CustomT2IAdapterWeightsDeprecated,
                                AdvancedControlNetApplyDEPR, AdvancedControlNetApplySingleDEPR,
                                ControlNetLoaderAdvancedDEPR, DiffControlNetLoaderAdvancedDEPR)
-from .logger import logger
 
 
-# NODE MAPPING
-NODE_CLASS_MAPPINGS = {
-    # Keyframes
-    "TimestepKeyframe": TimestepKeyframeNode,
-    "ACN_TimestepKeyframeInterpolation": TimestepKeyframeInterpolationNode,
-    "ACN_TimestepKeyframeFromStrengthList": TimestepKeyframeFromStrengthListNode,
-    "LatentKeyframe": LatentKeyframeNode,
-    "LatentKeyframeTiming": LatentKeyframeInterpolationNode,
-    "LatentKeyframeBatchedGroup": LatentKeyframeBatchedGroupNode,
-    "LatentKeyframeGroup": LatentKeyframeGroupNode,
-    # Conditioning
-    "ACN_AdvancedControlNetApply_v2": AdvancedControlNetApply,
-    "ACN_AdvancedControlNetApplySingle_v2": AdvancedControlNetApplySingle,
-    # Loaders
-    "ACN_ControlNetLoaderAdvanced": ControlNetLoaderAdvanced,
-    "ACN_DiffControlNetLoaderAdvanced": DiffControlNetLoaderAdvanced,
-    "ACN_AnimaLLLiteLoaderAdvanced": AnimaLLLiteLoaderAdvanced,
-    # Weights
-    "ACN_ScaledSoftControlNetWeights": ScaledSoftUniversalWeights,
-    "ScaledSoftMaskedUniversalWeights": ScaledSoftMaskedUniversalWeights,
-    "ACN_SoftControlNetWeightsSD15": SoftControlNetWeightsSD15,
-    "ACN_CustomControlNetWeightsSD15": CustomControlNetWeightsSD15,
-    "ACN_CustomControlNetWeightsFlux": CustomControlNetWeightsFlux,
-    "ACN_CustomControlNetWeightsAnima": CustomControlNetWeightsAnima,
-    "ACN_SoftT2IAdapterWeights": SoftT2IAdapterWeights,
-    "ACN_CustomT2IAdapterWeights": CustomT2IAdapterWeights,
-    "ACN_DefaultUniversalWeights": DefaultWeights,
-    "ACN_ExtrasMiddleMult": ExtrasMiddleMultNode,
-    "ACN_AnimaLLLiteExtras": AnimaLLLiteExtras,
-    # SparseCtrl
-    "ACN_SparseCtrlRGBPreprocessor": RgbSparseCtrlPreprocessor,
-    "ACN_SparseCtrlLoaderAdvanced": SparseCtrlLoaderAdvanced,
-    "ACN_SparseCtrlMergedLoaderAdvanced": SparseCtrlMergedLoaderAdvanced,
-    "ACN_SparseCtrlIndexMethodNode": SparseIndexMethodNode,
-    "ACN_SparseCtrlSpreadMethodNode": SparseSpreadMethodNode,
-    "ACN_SparseCtrlWeightExtras": SparseWeightExtras,
-    # ControlNet++
-    "ACN_ControlNet++LoaderSingle": PlusPlusLoaderSingle,
-    "ACN_ControlNet++LoaderAdvanced": PlusPlusLoaderAdvanced,
-    "ACN_ControlNet++InputNode": PlusPlusInputNode,
-    # CtrLoRA
-    "ACN_CtrLoRALoader": CtrLoRALoader,
-    # Reference
-    "ACN_ReferencePreprocessor": ReferencePreprocessorNode,
-    "ACN_ReferenceControlNet": ReferenceControlNetNode,
-    "ACN_ReferenceControlNetFinetune": ReferenceControlFinetune,
-    # LOOSEControl
-    #"ACN_ControlNetLoaderWithLoraAdvanced": ControlNetLoaderWithLoraAdvanced,
-    # Deprecated
-    "LoadImagesFromDirectory": LoadImagesFromDirectory,
-    "ScaledSoftControlNetWeights": ScaledSoftUniversalWeightsDeprecated,
-    "SoftControlNetWeights": SoftControlNetWeightsDeprecated,
-    "CustomControlNetWeights": CustomControlNetWeightsDeprecated,
-    "SoftT2IAdapterWeights": SoftT2IAdapterWeightsDeprecated,
-    "CustomT2IAdapterWeights": CustomT2IAdapterWeightsDeprecated,
-    "ACN_AdvancedControlNetApply": AdvancedControlNetApplyDEPR,
-    "ACN_AdvancedControlNetApplySingle": AdvancedControlNetApplySingleDEPR,
-    "ControlNetLoaderAdvanced": ControlNetLoaderAdvancedDEPR,
-    "DiffControlNetLoaderAdvanced": DiffControlNetLoaderAdvancedDEPR,
-}
 
-NODE_DISPLAY_NAME_MAPPINGS = {
+
-    # Keyframes
+class AdvancedControlNetExtension(ComfyExtension):
-    "TimestepKeyframe": "Timestep Keyframe 🛂🅐🅒🅝",
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
-    "ACN_TimestepKeyframeInterpolation": "Timestep Keyframe Interp. 🛂🅐🅒🅝",
+        return [
-    "ACN_TimestepKeyframeFromStrengthList": "Timestep Keyframe From List 🛂🅐🅒🅝",
+            TimestepKeyframeNode,
-    "LatentKeyframe": "Latent Keyframe 🛂🅐🅒🅝",
+            TimestepKeyframeInterpolationNode,
-    "LatentKeyframeTiming": "Latent Keyframe Interp. 🛂🅐🅒🅝",
+            TimestepKeyframeFromStrengthListNode,
-    "LatentKeyframeBatchedGroup": "Latent Keyframe From List 🛂🅐🅒🅝",
+            LatentKeyframeNode,
-    "LatentKeyframeGroup": "Latent Keyframe Group 🛂🅐🅒🅝",
+            LatentKeyframeInterpolationNode,
-    # Conditioning
+            LatentKeyframeBatchedGroupNode,
-    "ACN_AdvancedControlNetApply_v2": "Apply Advanced ControlNet 🛂🅐🅒🅝",
+            LatentKeyframeGroupNode,
-    "ACN_AdvancedControlNetApplySingle_v2": "Apply Advanced ControlNet(1) 🛂🅐🅒🅝",
+            AdvancedControlNetApply,
-    # Loaders
+            AdvancedControlNetApplySingle,
-    "ACN_ControlNetLoaderAdvanced": "Load Advanced ControlNet Model 🛂🅐🅒🅝",
+            ControlNetLoaderAdvanced,
-    "ACN_DiffControlNetLoaderAdvanced": "Load Advanced ControlNet Model (diff) 🛂🅐🅒🅝",
+            DiffControlNetLoaderAdvanced,
-    "ACN_AnimaLLLiteLoaderAdvanced": "Load Anima LLLite Model 🛂🅐🅒🅝",
+            AnimaLLLiteLoaderAdvanced,
-    # Weights
+            ScaledSoftUniversalWeights,
-    "ACN_ScaledSoftControlNetWeights": "Scaled Soft Weights 🛂🅐🅒🅝",
+            ScaledSoftMaskedUniversalWeights,
-    "ScaledSoftMaskedUniversalWeights": "Scaled Soft Masked Weights 🛂🅐🅒🅝",
+            SoftControlNetWeightsSD15,
-    "ACN_SoftControlNetWeightsSD15": "ControlNet Soft Weights [SD1.5] 🛂🅐🅒🅝",
+            CustomControlNetWeightsSD15,
-    "ACN_CustomControlNetWeightsSD15": "ControlNet Custom Weights [SD1.5] 🛂🅐🅒🅝",
+            CustomControlNetWeightsFlux,
-    "ACN_CustomControlNetWeightsFlux": "ControlNet Custom Weights [Flux] 🛂🅐🅒🅝",
+            CustomControlNetWeightsAnima,
-    "ACN_CustomControlNetWeightsAnima": "ControlNet Custom Weights [Anima] 🛂🅐🅒🅝",
+            SoftT2IAdapterWeights,
-    "ACN_SoftT2IAdapterWeights": "T2IAdapter Soft Weights 🛂🅐🅒🅝",
+            CustomT2IAdapterWeights,
-    "ACN_CustomT2IAdapterWeights": "T2IAdapter Custom Weights 🛂🅐🅒🅝",
+            DefaultWeights,
-    "ACN_DefaultUniversalWeights": "Default Weights 🛂🅐🅒🅝",
+            ExtrasMiddleMultNode,
-    "ACN_ExtrasMiddleMult": "Middle Weight Extras 🛂🅐🅒🅝",
+            AnimaLLLiteExtras,
-    "ACN_AnimaLLLiteExtras": "Anima LLLite Extras 🛂🅐🅒🅝",
+            RgbSparseCtrlPreprocessor,
-    # SparseCtrl
+            SparseCtrlLoaderAdvanced,
-    "ACN_SparseCtrlRGBPreprocessor": "RGB SparseCtrl 🛂🅐🅒🅝",
+            SparseCtrlMergedLoaderAdvanced,
-    "ACN_SparseCtrlLoaderAdvanced": "Load SparseCtrl Model 🛂🅐🅒🅝",
+            SparseIndexMethodNode,
-    "ACN_SparseCtrlMergedLoaderAdvanced": "🧪Load Merged SparseCtrl Model 🛂🅐🅒🅝",
+            SparseSpreadMethodNode,
-    "ACN_SparseCtrlIndexMethodNode": "SparseCtrl Index Method 🛂🅐🅒🅝",
+            SparseWeightExtras,
-    "ACN_SparseCtrlSpreadMethodNode": "SparseCtrl Spread Method 🛂🅐🅒🅝",
+            PlusPlusLoaderSingle,
-    "ACN_SparseCtrlWeightExtras": "SparseCtrl Weight Extras 🛂🅐🅒🅝",
+            PlusPlusLoaderAdvanced,
-    # ControlNet++
+            PlusPlusInputNode,
-    "ACN_ControlNet++LoaderSingle": "Load ControlNet++ Model (Single) 🛂🅐🅒🅝",
+            CtrLoRALoader,
-    "ACN_ControlNet++LoaderAdvanced": "Load ControlNet++ Model (Multi) 🛂🅐🅒🅝",
+            ReferencePreprocessorNode,
-    "ACN_ControlNet++InputNode": "ControlNet++ Input 🛂🅐🅒🅝",
+            ReferenceControlNetNode,
-    # CtrLoRA
+            ReferenceControlFinetune,
-    "ACN_CtrLoRALoader": "Load CtrLoRA Model 🛂🅐🅒🅝",
+            LoadImagesFromDirectory,
-    # Reference
+            ScaledSoftUniversalWeightsDeprecated,
-    "ACN_ReferencePreprocessor": "Reference Preproccessor 🛂🅐🅒🅝",
+            SoftControlNetWeightsDeprecated,
-    "ACN_ReferenceControlNet": "Reference ControlNet 🛂🅐🅒🅝",
+            CustomControlNetWeightsDeprecated,
-    "ACN_ReferenceControlNetFinetune": "Reference ControlNet (Finetune) 🛂🅐🅒🅝",
+            SoftT2IAdapterWeightsDeprecated,
-    # LOOSEControl
+            CustomT2IAdapterWeightsDeprecated,
-    #"ACN_ControlNetLoaderWithLoraAdvanced": "Load Adv. ControlNet Model w/ LoRA 🛂🅐🅒🅝",
+            AdvancedControlNetApplyDEPR,
-    # Deprecated
+            AdvancedControlNetApplySingleDEPR,
-    "LoadImagesFromDirectory": "🚫Load Images [DEPRECATED] 🛂🅐🅒🅝",
+            ControlNetLoaderAdvancedDEPR,
-    "ScaledSoftControlNetWeights": "Scaled Soft Weights 🛂🅐🅒🅝",
+            DiffControlNetLoaderAdvancedDEPR
-    "SoftControlNetWeights": "ControlNet Soft Weights 🛂🅐🅒🅝",
+        ]
-    "CustomControlNetWeights": "ControlNet Custom Weights 🛂🅐🅒🅝",
-    "SoftT2IAdapterWeights": "T2IAdapter Soft Weights 🛂🅐🅒🅝",
-    "CustomT2IAdapterWeights": "T2IAdapter Custom Weights 🛂🅐🅒🅝",
-    "ACN_AdvancedControlNetApply": "Apply Advanced ControlNet 🛂🅐🅒🅝",
-    "ACN_AdvancedControlNetApplySingle": "Apply Advanced ControlNet(1) 🛂🅐🅒🅝",
-    "ControlNetLoaderAdvanced": "Load Advanced ControlNet Model 🛂🅐🅒🅝",
-    "DiffControlNetLoaderAdvanced": "Load Advanced ControlNet Model (diff) 🛂🅐🅒🅝",
-}

adv_control/nodes_ctrlora.py

@@ -1,25 +1,28 @@
+from comfy_api.latest import io
 import folder_paths
 
 from .control_ctrlora import load_ctrlora
 
-
+class CtrLoRALoader(io.ComfyNode):
-class CtrLoRALoader:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_CtrLoRALoader',
-                "base": (folder_paths.get_filename_list("controlnet"), ),
+            display_name='Load CtrLoRA Model 🛂🅐🅒🅝',
-                "lora": (folder_paths.get_filename_list("controlnet"), ),
+            category='Adv-ControlNet 🛂🅐🅒🅝/CtrLoRA',
-            }
+            inputs=[
-        }
+                io.Combo.Input('base', options=folder_paths.get_filename_list("controlnet")),
+                io.Combo.Input('lora', options=folder_paths.get_filename_list("controlnet"))
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET",)
-    FUNCTION = "load_controlnet_plusplus"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/CtrLoRA"
+    @classmethod
-
+    def execute(cls, base: str, lora: str):
-    def load_controlnet_plusplus(self, base: str, lora: str):
         base_path = folder_paths.get_full_path("controlnet", base)
         lora_path = folder_paths.get_full_path("controlnet", lora)
         controlnet = load_ctrlora(base_path, lora_path)
-        return (controlnet,)
+        return io.NodeOutput(controlnet,)

adv_control/nodes_deprecated.py

@@ -1,3 +1,4 @@
+from comfy_api.latest import io
 import os
 
 import torch
@@ -7,29 +8,31 @@ import numpy as np
 from PIL import Image, ImageOps
 from .control import load_controlnet, is_advanced_controlnet
 from .nodes_main import AdvancedControlNetApply
-from .utils import BIGMAX, ControlWeights, TimestepKeyframeGroup, TimestepKeyframe, get_properly_arranged_t2i_weights
+from .utils import ControlWeights, TimestepKeyframeGroup, TimestepKeyframe, get_properly_arranged_t2i_weights
-from .logger import logger
 
-
+class LoadImagesFromDirectory(io.ComfyNode):
-class LoadImagesFromDirectory:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='LoadImagesFromDirectory',
-                "directory": ("STRING", {"default": ""}),
+            display_name='🚫Load Images [DEPRECATED] 🛂🅐🅒🅝',
-            },
+            category='',
-            "optional": {
+            inputs=[
-                "image_load_cap": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                io.String.Input('directory', default=''),
-                "start_index": ("INT", {"default": 0, "min": 0, "max": BIGMAX, "step": 1}),
+                io.Int.Input('image_load_cap', optional=True, default=0, max=9007199254740991, min=0, step=1),
-            }
+                io.Int.Input('start_index', optional=True, default=0, max=9007199254740991, min=0, step=1)
-        }
+            ],
+            outputs=[
+                io.Image.Output('IMAGE', is_output_list=False),
+                io.Mask.Output('MASK', is_output_list=False),
+                io.Int.Output('INT', is_output_list=False)
+            ],
+            is_deprecated=True
+        )
     
-    RETURN_TYPES = ("IMAGE", "MASK", "INT")
-    FUNCTION = "load_images"
 
-    CATEGORY = ""
+    @classmethod
-
+    def execute(cls, directory: str, image_load_cap: int = 0, start_index: int = 0):
-    def load_images(self, directory: str, image_load_cap: int = 0, start_index: int = 0):
         if not os.path.isdir(directory):
             raise FileNotFoundError(f"Directory '{directory} cannot be found.'")
         dir_files = os.listdir(directory)
@@ -71,306 +74,283 @@ class LoadImagesFromDirectory:
         if len(images) == 0:
             raise FileNotFoundError(f"No images could be loaded from directory '{directory}'.")
 
-        return (torch.cat(images, dim=0), torch.stack(masks, dim=0), image_count)
+        return io.NodeOutput(torch.cat(images, dim=0), torch.stack(masks, dim=0), image_count)
 
-
+class ScaledSoftUniversalWeightsDeprecated(io.ComfyNode):
-class ScaledSoftUniversalWeightsDeprecated:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ScaledSoftControlNetWeights',
-                "base_multiplier": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 1.0, "step": 0.001}, ),
+            display_name='Scaled Soft Weights 🛂🅐🅒🅝',
-                "flip_weights": ("BOOLEAN", {"default": False}),
+            category='',
-            },
+            inputs=[
-            "optional": {
+                io.Float.Input('base_multiplier', default=0.825, max=1.0, min=0.0, step=0.001),
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
+                io.Boolean.Input('flip_weights', default=False),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
-            },
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
-        }
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
-    FUNCTION = "load_weights"
 
-    CATEGORY = ""
+    @classmethod
-
+    def execute(cls, base_multiplier, flip_weights, uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
-    def load_weights(self, base_multiplier, flip_weights, uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         weights = ControlWeights.universal(base_multiplier=base_multiplier, uncond_multiplier=uncond_multiplier, extras=cn_extras)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
+class SoftControlNetWeightsDeprecated(io.ComfyNode):
-class SoftControlNetWeightsDeprecated:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='SoftControlNetWeights',
-                "weight_00": ("FLOAT", {"default": 0.09941396206337118, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            display_name='ControlNet Soft Weights 🛂🅐🅒🅝',
-                "weight_01": ("FLOAT", {"default": 0.12050177219802567, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            category='',
-                "weight_02": ("FLOAT", {"default": 0.14606275417942507, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            inputs=[
-                "weight_03": ("FLOAT", {"default": 0.17704576264172736, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_00', default=0.09941396206337118, max=10.0, min=0.0, step=0.001),
-                "weight_04": ("FLOAT", {"default": 0.214600924414215, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_01', default=0.12050177219802567, max=10.0, min=0.0, step=0.001),
-                "weight_05": ("FLOAT", {"default": 0.26012233262329093, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_02', default=0.14606275417942507, max=10.0, min=0.0, step=0.001),
-                "weight_06": ("FLOAT", {"default": 0.3152997971191405, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_03', default=0.17704576264172736, max=10.0, min=0.0, step=0.001),
-                "weight_07": ("FLOAT", {"default": 0.3821815722656249, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_04', default=0.214600924414215, max=10.0, min=0.0, step=0.001),
-                "weight_08": ("FLOAT", {"default": 0.4632503906249999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_05', default=0.26012233262329093, max=10.0, min=0.0, step=0.001),
-                "weight_09": ("FLOAT", {"default": 0.561515625, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_06', default=0.3152997971191405, max=10.0, min=0.0, step=0.001),
-                "weight_10": ("FLOAT", {"default": 0.6806249999999999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_07', default=0.3821815722656249, max=10.0, min=0.0, step=0.001),
-                "weight_11": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_08', default=0.4632503906249999, max=10.0, min=0.0, step=0.001),
-                "weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_09', default=0.561515625, max=10.0, min=0.0, step=0.001),
-                "flip_weights": ("BOOLEAN", {"default": False}),
+                io.Float.Input('weight_10', default=0.6806249999999999, max=10.0, min=0.0, step=0.001),
-            },
+                io.Float.Input('weight_11', default=0.825, max=10.0, min=0.0, step=0.001),
-            "optional": {
+                io.Float.Input('weight_12', default=1.0, max=10.0, min=0.0, step=0.001),
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
+                io.Boolean.Input('flip_weights', default=False),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
-            },
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
-        }
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ],
+            is_deprecated=True
+        )
     
-    DEPRECATED = True
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
-    FUNCTION = "load_weights"
 
-    CATEGORY = ""
+    @classmethod
-
+    def execute(cls, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
-    def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, 
                      weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         weights_output = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, 
                    weight_07, weight_08, weight_09, weight_10, weight_11]
         weights_middle = [weight_12]
         weights = ControlWeights.controlnet(weights_output=weights_output, weights_middle=weights_middle, uncond_multiplier=uncond_multiplier, extras=cn_extras)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
+class CustomControlNetWeightsDeprecated(io.ComfyNode):
-class CustomControlNetWeightsDeprecated:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='CustomControlNetWeights',
-                "weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            display_name='ControlNet Custom Weights 🛂🅐🅒🅝',
-                "weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            category='',
-                "weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            inputs=[
-                "weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_00', default=1.0, max=10.0, min=0.0, step=0.001),
-                "weight_04": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_01', default=1.0, max=10.0, min=0.0, step=0.001),
-                "weight_05": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_02', default=1.0, max=10.0, min=0.0, step=0.001),
-                "weight_06": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_03', default=1.0, max=10.0, min=0.0, step=0.001),
-                "weight_07": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_04', default=1.0, max=10.0, min=0.0, step=0.001),
-                "weight_08": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_05', default=1.0, max=10.0, min=0.0, step=0.001),
-                "weight_09": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_06', default=1.0, max=10.0, min=0.0, step=0.001),
-                "weight_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_07', default=1.0, max=10.0, min=0.0, step=0.001),
-                "weight_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_08', default=1.0, max=10.0, min=0.0, step=0.001),
-                "weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_09', default=1.0, max=10.0, min=0.0, step=0.001),
-                "flip_weights": ("BOOLEAN", {"default": False}),
+                io.Float.Input('weight_10', default=1.0, max=10.0, min=0.0, step=0.001),
-            },
+                io.Float.Input('weight_11', default=1.0, max=10.0, min=0.0, step=0.001),
-            "optional": {
+                io.Float.Input('weight_12', default=1.0, max=10.0, min=0.0, step=0.001),
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
+                io.Boolean.Input('flip_weights', default=False),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
-            },
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
-        }
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ],
+            is_deprecated=True
+        )
     
-    DEPRECATED = True
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
-    FUNCTION = "load_weights"
 
-    CATEGORY = ""
+    @classmethod
-
+    def execute(cls, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06,
-    def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, 
                      weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         weights_output = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, 
                    weight_07, weight_08, weight_09, weight_10, weight_11]
         weights_middle = [weight_12]
         weights = ControlWeights.controlnet(weights_output=weights_output, weights_middle=weights_middle, uncond_multiplier=uncond_multiplier, extras=cn_extras)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
+class SoftT2IAdapterWeightsDeprecated(io.ComfyNode):
-class SoftT2IAdapterWeightsDeprecated:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='SoftT2IAdapterWeights',
-                "weight_00": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            display_name='T2IAdapter Soft Weights 🛂🅐🅒🅝',
-                "weight_01": ("FLOAT", {"default": 0.62, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            category='',
-                "weight_02": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            inputs=[
-                "weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_00', default=0.25, max=10.0, min=0.0, step=0.001),
-                "flip_weights": ("BOOLEAN", {"default": False}),
+                io.Float.Input('weight_01', default=0.62, max=10.0, min=0.0, step=0.001),
-            },
+                io.Float.Input('weight_02', default=0.825, max=10.0, min=0.0, step=0.001),
-            "optional": {
+                io.Float.Input('weight_03', default=1.0, max=10.0, min=0.0, step=0.001),
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
+                io.Boolean.Input('flip_weights', default=False),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
-            },
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
-        }
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ],
+            is_deprecated=True
+        )
     
-    DEPRECATED = True
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
-    FUNCTION = "load_weights"
 
-    CATEGORY = ""
+    @classmethod
-
+    def execute(cls, weight_00, weight_01, weight_02, weight_03, flip_weights,
-    def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         weights = [weight_00, weight_01, weight_02, weight_03]
         weights = get_properly_arranged_t2i_weights(weights)
         weights = ControlWeights.t2iadapter(weights_input=weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
+class CustomT2IAdapterWeightsDeprecated(io.ComfyNode):
-class CustomT2IAdapterWeightsDeprecated:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='CustomT2IAdapterWeights',
-                "weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            display_name='T2IAdapter Custom Weights 🛂🅐🅒🅝',
-                "weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            category='',
-                "weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            inputs=[
-                "weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('weight_00', default=1.0, max=10.0, min=0.0, step=0.001),
-                "flip_weights": ("BOOLEAN", {"default": False}),
+                io.Float.Input('weight_01', default=1.0, max=10.0, min=0.0, step=0.001),
-            },
+                io.Float.Input('weight_02', default=1.0, max=10.0, min=0.0, step=0.001),
-            "optional": {
+                io.Float.Input('weight_03', default=1.0, max=10.0, min=0.0, step=0.001),
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
+                io.Boolean.Input('flip_weights', default=False),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
-            },
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
-        }
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ],
+            is_deprecated=True
+        )
     
-    DEPRECATED = True
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
-    FUNCTION = "load_weights"
 
-    CATEGORY = ""
+    @classmethod
-
+    def execute(cls, weight_00, weight_01, weight_02, weight_03, flip_weights,
-    def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         weights = [weight_00, weight_01, weight_02, weight_03]
         weights = get_properly_arranged_t2i_weights(weights)
         weights = ControlWeights.t2iadapter(weights_input=weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
+class AdvancedControlNetApplyDEPR(io.ComfyNode):
-class AdvancedControlNetApplyDEPR:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_AdvancedControlNetApply',
-                "positive": ("CONDITIONING", ),
+            display_name='Apply Advanced ControlNet 🛂🅐🅒🅝',
-                "negative": ("CONDITIONING", ),
+            category='',
-                "control_net": ("CONTROL_NET", ),
+            inputs=[
-                "image": ("IMAGE", ),
+                io.Conditioning.Input('positive'),
-                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                io.Conditioning.Input('negative'),
-                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                io.ControlNet.Input('control_net'),
-                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
+                io.Image.Input('image'),
-            },
+                io.Float.Input('strength', default=1.0, max=10.0, min=0.0, step=0.01),
-            "optional": {
+                io.Float.Input('start_percent', default=0.0, max=1.0, min=0.0, step=0.001),
-                "mask_optional": ("MASK", ),
+                io.Float.Input('end_percent', default=1.0, max=1.0, min=0.0, step=0.001),
-                "timestep_kf": ("TIMESTEP_KEYFRAME", ),
+                io.Mask.Input('mask_optional', optional=True),
-                "latent_kf_override": ("LATENT_KEYFRAME", ),
+                io.Custom('TIMESTEP_KEYFRAME').Input('timestep_kf', optional=True),
-                "weights_override": ("CONTROL_NET_WEIGHTS", ),
+                io.Custom('LATENT_KEYFRAME').Input('latent_kf_override', optional=True),
-                "model_optional": ("MODEL",),
+                io.Custom('CONTROL_NET_WEIGHTS').Input('weights_override', optional=True),
-                "vae_optional": ("VAE",),
+                io.Model.Input('model_optional', optional=True),
-            },
+                io.Vae.Input('vae_optional', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Conditioning.Output('positive', is_output_list=False),
-        }
+                io.Conditioning.Output('negative', is_output_list=False),
+                io.Model.Output('model_opt', is_output_list=False)
+            ],
+            is_deprecated=True
+        )
 
-    DEPRECATED = True
+    @classmethod
-    RETURN_TYPES = ("CONDITIONING","CONDITIONING","MODEL",)
+    def execute(cls, positive, negative, control_net, image, strength, start_percent, end_percent,
-    RETURN_NAMES = ("positive", "negative", "model_opt")
-    FUNCTION = "apply_controlnet"
-
-    CATEGORY = ""
-
-    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent,
                          mask_optional=None, model_optional=None, vae_optional=None,
                          timestep_kf: TimestepKeyframeGroup=None, latent_kf_override=None,
                          weights_override: ControlWeights=None, control_apply_to_uncond=False):
-        new_positive, new_negative = AdvancedControlNetApply.apply_controlnet(self, positive=positive, negative=negative, control_net=control_net, image=image,
+        new_positive, new_negative = AdvancedControlNetApply.execute(positive=positive, negative=negative, control_net=control_net, image=image,
                                                           strength=strength, start_percent=start_percent, end_percent=end_percent,
                                                           mask_optional=mask_optional, vae_optional=vae_optional,
-                                                          timestep_kf=timestep_kf, latent_kf_override=latent_kf_override, weights_override=weights_override,)
+                                                          timestep_kf=timestep_kf, latent_kf_override=latent_kf_override, weights_override=weights_override,).args
-        return (new_positive, new_negative, model_optional)
+        return io.NodeOutput(new_positive, new_negative, model_optional)
 
-
+class AdvancedControlNetApplySingleDEPR(io.ComfyNode):
-class AdvancedControlNetApplySingleDEPR:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_AdvancedControlNetApplySingle',
-                "conditioning": ("CONDITIONING", ),
+            display_name='Apply Advanced ControlNet(1) 🛂🅐🅒🅝',
-                "control_net": ("CONTROL_NET", ),
+            category='',
-                "image": ("IMAGE", ),
+            inputs=[
-                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                io.Conditioning.Input('conditioning'),
-                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                io.ControlNet.Input('control_net'),
-                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
+                io.Image.Input('image'),
-            },
+                io.Float.Input('strength', default=1.0, max=10.0, min=0.0, step=0.01),
-            "optional": {
+                io.Float.Input('start_percent', default=0.0, max=1.0, min=0.0, step=0.001),
-                "mask_optional": ("MASK", ),
+                io.Float.Input('end_percent', default=1.0, max=1.0, min=0.0, step=0.001),
-                "timestep_kf": ("TIMESTEP_KEYFRAME", ),
+                io.Mask.Input('mask_optional', optional=True),
-                "latent_kf_override": ("LATENT_KEYFRAME", ),
+                io.Custom('TIMESTEP_KEYFRAME').Input('timestep_kf', optional=True),
-                "weights_override": ("CONTROL_NET_WEIGHTS", ),
+                io.Custom('LATENT_KEYFRAME').Input('latent_kf_override', optional=True),
-                "model_optional": ("MODEL",),
+                io.Custom('CONTROL_NET_WEIGHTS').Input('weights_override', optional=True),
-                "vae_optional": ("VAE",),
+                io.Model.Input('model_optional', optional=True),
-            },
+                io.Vae.Input('vae_optional', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Conditioning.Output('CONDITIONING', is_output_list=False),
-        }
+                io.Model.Output('model_opt', is_output_list=False)
+            ],
+            is_deprecated=True
+        )
 
-    DEPRECATED = True
+    @classmethod
-    RETURN_TYPES = ("CONDITIONING","MODEL",)
+    def execute(cls, conditioning, control_net, image, strength, start_percent, end_percent,
-    RETURN_NAMES = ("CONDITIONING", "model_opt")
-    FUNCTION = "apply_controlnet"
-
-    CATEGORY = ""
-
-    def apply_controlnet(self, conditioning, control_net, image, strength, start_percent, end_percent,
                          mask_optional=None, model_optional=None, vae_optional=None,
                          timestep_kf: TimestepKeyframeGroup=None, latent_kf_override=None,
                          weights_override: ControlWeights=None):
-        values = AdvancedControlNetApply.apply_controlnet(self, positive=conditioning, negative=None, control_net=control_net, image=image,
+        values = AdvancedControlNetApply.execute(positive=conditioning, negative=None, control_net=control_net, image=image,
                                                           strength=strength, start_percent=start_percent, end_percent=end_percent,
                                                           mask_optional=mask_optional, vae_optional=vae_optional,
                                                           timestep_kf=timestep_kf, latent_kf_override=latent_kf_override, weights_override=weights_override,
                                                           control_apply_to_uncond=True)
-        return (values[0], model_optional)
+        return io.NodeOutput(values.args[0], model_optional)
 
-
+class ControlNetLoaderAdvancedDEPR(io.ComfyNode):
-class ControlNetLoaderAdvancedDEPR:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ControlNetLoaderAdvanced',
-                "control_net_name": (folder_paths.get_filename_list("controlnet"), ),
+            display_name='Load Advanced ControlNet Model 🛂🅐🅒🅝',
-            },
+            category='',
-            "optional": {
+            inputs=[
-                "tk_optional": ("TIMESTEP_KEYFRAME", ),
+                io.Combo.Input('control_net_name', options=folder_paths.get_filename_list("controlnet")),
-            }
+                io.Custom('TIMESTEP_KEYFRAME').Input('tk_optional', optional=True)
-        }
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ],
+            is_deprecated=True
+        )
 
-    DEPRECATED = True
+    @classmethod
-    RETURN_TYPES = ("CONTROL_NET", )
+    def execute(cls, control_net_name,
-    FUNCTION = "load_controlnet"
-
-    CATEGORY = ""
-
-    def load_controlnet(self, control_net_name,
                         tk_optional: TimestepKeyframeGroup=None,
                         timestep_keyframe: TimestepKeyframeGroup=None,
                         ):
@@ -378,32 +358,30 @@ class ControlNetLoaderAdvancedDEPR:
             tk_optional = timestep_keyframe
         controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
         controlnet = load_controlnet(controlnet_path, tk_optional)
-        return (controlnet,)
+        return io.NodeOutput(controlnet,)
     
 
-class DiffControlNetLoaderAdvancedDEPR:
+class DiffControlNetLoaderAdvancedDEPR(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='DiffControlNetLoaderAdvanced',
-                "model": ("MODEL",),
+            display_name='Load Advanced ControlNet Model (diff) 🛂🅐🅒🅝',
-                "control_net_name": (folder_paths.get_filename_list("controlnet"), )
+            category='',
-            },
+            inputs=[
-            "optional": {
+                io.Model.Input('model'),
-                "tk_optional": ("TIMESTEP_KEYFRAME", ),
+                io.Combo.Input('control_net_name', options=folder_paths.get_filename_list("controlnet")),
-            },
+                io.Custom('TIMESTEP_KEYFRAME').Input('tk_optional', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
-        }
+            ],
+            is_deprecated=True
+        )
     
-    DEPRECATED = True
-    RETURN_TYPES = ("CONTROL_NET", )
-    FUNCTION = "load_controlnet"
 
-    CATEGORY = ""
+    @classmethod
-
+    def execute(cls, control_net_name, model,
-    def load_controlnet(self, control_net_name, model,
                         tk_optional: TimestepKeyframeGroup=None,
                         timestep_keyframe: TimestepKeyframeGroup=None
                         ):
@@ -413,4 +391,4 @@ class DiffControlNetLoaderAdvancedDEPR:
         controlnet = load_controlnet(controlnet_path, tk_optional, model)
         if is_advanced_controlnet(controlnet):
             controlnet.verify_all_weights()
-        return (controlnet,)
+        return io.NodeOutput(controlnet,)

adv_control/nodes_keyframes.py

@@ -1,43 +1,40 @@
+from comfy_api.latest import io
 from typing import Union
 import numpy as np
 from collections.abc import Iterable
 
-from .utils import ControlWeights, TimestepKeyframe, TimestepKeyframeGroup, LatentKeyframe, LatentKeyframeGroup, BIGMIN, BIGMAX
+from .utils import ControlWeights, TimestepKeyframe, TimestepKeyframeGroup, LatentKeyframe, LatentKeyframeGroup
 from .utils import StrengthInterpolation as SI
 from .logger import logger
 
-
+class TimestepKeyframeNode(io.ComfyNode):
-class TimestepKeyframeNode:
     OUTDATED_DUMMY = -39
 
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='TimestepKeyframe',
-                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
+            display_name='Timestep Keyframe 🛂🅐🅒🅝',
-            },
+            category='Adv-ControlNet 🛂🅐🅒🅝/keyframes',
-            "optional": {
+            inputs=[
-                "prev_timestep_kf": ("TIMESTEP_KEYFRAME", ),
+                io.Float.Input('start_percent', default=0.0, max=1.0, min=0.0, step=0.001),
-                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Custom('TIMESTEP_KEYFRAME').Input('prev_timestep_kf', optional=True),
-                "cn_weights": ("CONTROL_NET_WEIGHTS", ),
+                io.Float.Input('strength', optional=True, default=1.0, max=10.0, min=0.0, step=0.001),
-                "latent_keyframe": ("LATENT_KEYFRAME", ),
+                io.Custom('CONTROL_NET_WEIGHTS').Input('cn_weights', optional=True),
-                "null_latent_kf_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Custom('LATENT_KEYFRAME').Input('latent_keyframe', optional=True),
-                "inherit_missing": ("BOOLEAN", {"default": True}, ),
+                io.Float.Input('null_latent_kf_strength', optional=True, default=0.0, max=10.0, min=0.0, step=0.001),
-                "guarantee_steps": ("INT", {"default": 1, "min": 0, "max": BIGMAX}),
+                io.Boolean.Input('inherit_missing', optional=True, default=True),
-                "mask_optional": ("MASK", ),
+                io.Int.Input('guarantee_steps', optional=True, default=1, max=9007199254740991, min=0),
-            },
+                io.Mask.Input('mask_optional', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('TIMESTEP_KEYFRAME').Output('TIMESTEP_KF', is_output_list=False)
-        }
+            ]
+        )
     
-    RETURN_NAMES = ("TIMESTEP_KF", )
-    RETURN_TYPES = ("TIMESTEP_KEYFRAME", )
-    FUNCTION = "load_keyframe"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
+    @classmethod
-
+    def execute(cls,
-    def load_keyframe(self,
                       start_percent: float,
                       strength: float=1.0,
                       cn_weights: ControlWeights=None, control_net_weights: ControlWeights=None, # old name
@@ -49,7 +46,7 @@ class TimestepKeyframeNode:
                       guarantee_usage=True, # old input
                       mask_optional=None,):
         # if using outdated dummy value, means node on workflow is outdated and should appropriately convert behavior
-        if guarantee_steps == self.OUTDATED_DUMMY:
+        if guarantee_steps == cls.OUTDATED_DUMMY:
             guarantee_steps = int(guarantee_usage)
         control_net_weights = control_net_weights if control_net_weights else cn_weights
         prev_timestep_keyframe = prev_timestep_keyframe if prev_timestep_keyframe else prev_timestep_kf
@@ -61,42 +58,39 @@ class TimestepKeyframeNode:
                                     control_weights=control_net_weights, latent_keyframes=latent_keyframe, inherit_missing=inherit_missing,
                                     guarantee_steps=guarantee_steps, mask_hint_orig=mask_optional)
         prev_timestep_keyframe.add(keyframe)
-        return (prev_timestep_keyframe,)
+        return io.NodeOutput(prev_timestep_keyframe,)
     
 
-class TimestepKeyframeInterpolationNode:
+class TimestepKeyframeInterpolationNode(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_TimestepKeyframeInterpolation',
-                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001},),
+            display_name='Timestep Keyframe Interp. 🛂🅐🅒🅝',
-                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+            category='Adv-ControlNet 🛂🅐🅒🅝/keyframes',
-                "strength_start": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001},),
+            inputs=[
-                "strength_end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001},),
+                io.Float.Input('start_percent', default=0.0, max=1.0, min=0.0, step=0.001),
-                "interpolation": (SI._LIST, ),
+                io.Float.Input('end_percent', default=1.0, max=1.0, min=0.0, step=0.001),
-                "intervals": ("INT", {"default": 50, "min": 2, "max": 100, "step": 1}),
+                io.Float.Input('strength_start', default=1.0, max=10.0, min=0.0, step=0.001),
-            },
+                io.Float.Input('strength_end', default=1.0, max=10.0, min=0.0, step=0.001),
-            "optional": {
+                io.Combo.Input('interpolation', options=['linear', 'ease-in', 'ease-out', 'ease-in-out']),
-                "prev_timestep_kf": ("TIMESTEP_KEYFRAME", ),
+                io.Int.Input('intervals', default=50, max=100, min=2, step=1),
-                "cn_weights": ("CONTROL_NET_WEIGHTS", ),
+                io.Custom('TIMESTEP_KEYFRAME').Input('prev_timestep_kf', optional=True),
-                "latent_keyframe": ("LATENT_KEYFRAME", ),
+                io.Custom('CONTROL_NET_WEIGHTS').Input('cn_weights', optional=True),
-                "null_latent_kf_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001},),
+                io.Custom('LATENT_KEYFRAME').Input('latent_keyframe', optional=True),
-                "inherit_missing": ("BOOLEAN", {"default": True},),
+                io.Float.Input('null_latent_kf_strength', optional=True, default=0.0, max=10.0, min=0.0, step=0.001),
-                "mask_optional": ("MASK", ),
+                io.Boolean.Input('inherit_missing', optional=True, default=True),
-                "print_keyframes": ("BOOLEAN", {"default": False}),
+                io.Mask.Input('mask_optional', optional=True),
-            },
+                io.Boolean.Input('print_keyframes', optional=True, default=False)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('TIMESTEP_KEYFRAME').Output('TIMESTEP_KF', is_output_list=False)
-        }
+            ]
+        )
     
-    RETURN_NAMES = ("TIMESTEP_KF", )
-    RETURN_TYPES = ("TIMESTEP_KEYFRAME", )
-    FUNCTION = "load_keyframe"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
+    @classmethod
-
+    def execute(cls,
-    def load_keyframe(self,
                       start_percent: float, end_percent: float,
                       strength_start: float, strength_end: float, interpolation: str, intervals: int,
                       cn_weights: ControlWeights=None,
@@ -125,39 +119,35 @@ class TimestepKeyframeInterpolationNode:
                                     guarantee_steps=guarantee_steps, mask_hint_orig=mask_optional))
             if print_keyframes:
                 logger.info(f"TimestepKeyframe - start_percent:{percent} = {strength}")
-        return (prev_timestep_kf,)
+        return io.NodeOutput(prev_timestep_kf,)
 
-
+class TimestepKeyframeFromStrengthListNode(io.ComfyNode):
-class TimestepKeyframeFromStrengthListNode:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_TimestepKeyframeFromStrengthList',
-                "float_strengths": ("FLOAT", {"default": -1, "min": -1, "step": 0.001, "forceInput": True}),
+            display_name='Timestep Keyframe From List 🛂🅐🅒🅝',
-                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001},),
+            category='Adv-ControlNet 🛂🅐🅒🅝/keyframes',
-                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+            inputs=[
-            },
+                io.Float.Input('float_strengths', default=-1, force_input=True, min=-1, step=0.001),
-            "optional": {
+                io.Float.Input('start_percent', default=0.0, max=1.0, min=0.0, step=0.001),
-                "prev_timestep_kf": ("TIMESTEP_KEYFRAME", ),
+                io.Float.Input('end_percent', default=1.0, max=1.0, min=0.0, step=0.001),
-                "cn_weights": ("CONTROL_NET_WEIGHTS", ),
+                io.Custom('TIMESTEP_KEYFRAME').Input('prev_timestep_kf', optional=True),
-                "latent_keyframe": ("LATENT_KEYFRAME", ),
+                io.Custom('CONTROL_NET_WEIGHTS').Input('cn_weights', optional=True),
-                "null_latent_kf_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.001},),
+                io.Custom('LATENT_KEYFRAME').Input('latent_keyframe', optional=True),
-                "inherit_missing": ("BOOLEAN", {"default": True},),
+                io.Float.Input('null_latent_kf_strength', optional=True, default=0.0, max=10.0, min=0.0, step=0.001),
-                "mask_optional": ("MASK", ),
+                io.Boolean.Input('inherit_missing', optional=True, default=True),
-                "print_keyframes": ("BOOLEAN", {"default": False}),
+                io.Mask.Input('mask_optional', optional=True),
-            },
+                io.Boolean.Input('print_keyframes', optional=True, default=False)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('TIMESTEP_KEYFRAME').Output('TIMESTEP_KF', is_output_list=False)
-        }
+            ]
+        )
     
-    RETURN_NAMES = ("TIMESTEP_KF", )
-    RETURN_TYPES = ("TIMESTEP_KEYFRAME", )
-    FUNCTION = "load_keyframe"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
+    @classmethod
-
+    def execute(cls,
-    def load_keyframe(self,
                       start_percent: float, end_percent: float,
                       float_strengths: float,
                       cn_weights: ControlWeights=None,
@@ -191,32 +181,27 @@ class TimestepKeyframeFromStrengthListNode:
                                     guarantee_steps=guarantee_steps, mask_hint_orig=mask_optional))
             if print_keyframes:
                 logger.info(f"TimestepKeyframe - start_percent:{percent} = {strength}")
-        return (prev_timestep_kf,)
+        return io.NodeOutput(prev_timestep_kf,)
 
-
+class LatentKeyframeNode(io.ComfyNode):
-class LatentKeyframeNode:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='LatentKeyframe',
-                "batch_index": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX, "step": 1}),
+            display_name='Latent Keyframe 🛂🅐🅒🅝',
-                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            category='Adv-ControlNet 🛂🅐🅒🅝/keyframes',
-            },
+            inputs=[
-            "optional": {
+                io.Int.Input('batch_index', default=0, max=9007199254740991, min=-9007199254740991, step=1),
-                "prev_latent_kf": ("LATENT_KEYFRAME", ),
+                io.Float.Input('strength', default=1.0, max=10.0, min=0.0, step=0.001),
-            },
+                io.Custom('LATENT_KEYFRAME').Input('prev_latent_kf', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('LATENT_KEYFRAME').Output('LATENT_KF', is_output_list=False)
-        }
+            ]
+        )
 
-    RETURN_NAMES = ("LATENT_KF", )
+    @classmethod
-    RETURN_TYPES = ("LATENT_KEYFRAME", )
+    def execute(cls,
-    FUNCTION = "load_keyframe"
-
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
-
-    def load_keyframe(self,
                       batch_index: int,
                       strength: float,
                       prev_latent_kf: LatentKeyframeGroup=None,
@@ -229,33 +214,29 @@ class LatentKeyframeNode:
             prev_latent_keyframe = prev_latent_keyframe.clone()
         keyframe = LatentKeyframe(batch_index, strength)
         prev_latent_keyframe.add(keyframe)
-        return (prev_latent_keyframe,)
+        return io.NodeOutput(prev_latent_keyframe,)
 
-
+class LatentKeyframeGroupNode(io.ComfyNode):
-class LatentKeyframeGroupNode:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='LatentKeyframeGroup',
-                "index_strengths": ("STRING", {"multiline": True, "default": ""}),
+            display_name='Latent Keyframe Group 🛂🅐🅒🅝',
-            },
+            category='Adv-ControlNet 🛂🅐🅒🅝/keyframes',
-            "optional": {
+            inputs=[
-                "prev_latent_kf": ("LATENT_KEYFRAME", ),
+                io.String.Input('index_strengths', default='', multiline=True),
-                "latent_optional": ("LATENT", ),
+                io.Custom('LATENT_KEYFRAME').Input('prev_latent_kf', optional=True),
-                "print_keyframes": ("BOOLEAN", {"default": False}),
+                io.Latent.Input('latent_optional', optional=True),
-            },
+                io.Boolean.Input('print_keyframes', optional=True, default=False)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('LATENT_KEYFRAME').Output('LATENT_KF', is_output_list=False)
-        }
+            ]
+        )
     
-    RETURN_NAMES = ("LATENT_KF", )
-    RETURN_TYPES = ("LATENT_KEYFRAME", )
-    FUNCTION = "load_keyframes"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
+    @staticmethod
-
+    def validate_index(index: int, latent_count: int = 0, is_range: bool = False, allow_negative = False) -> int:
-    def validate_index(self, index: int, latent_count: int = 0, is_range: bool = False, allow_negative = False) -> int:
         # if part of range, do nothing
         if is_range:
             return index
@@ -273,13 +254,15 @@ class LatentKeyframeGroupNode:
             index = conv_index
         return index
 
-    def convert_to_index_int(self, raw_index: str, latent_count: int = 0, is_range: bool = False, allow_negative = False) -> int:
+    @classmethod
+    def convert_to_index_int(cls, raw_index: str, latent_count: int = 0, is_range: bool = False, allow_negative = False) -> int:
         try:
-            return self.validate_index(int(raw_index), latent_count=latent_count, is_range=is_range, allow_negative=allow_negative)
+            return cls.validate_index(int(raw_index), latent_count=latent_count, is_range=is_range, allow_negative=allow_negative)
         except ValueError as e:
             raise ValueError(f"index '{raw_index}' must be an integer.", e)
 
-    def convert_to_latent_keyframes(self, latent_indeces: str, latent_count: int) -> set[LatentKeyframe]:
+    @classmethod
+    def convert_to_latent_keyframes(cls, latent_indeces: str, latent_count: int) -> set[LatentKeyframe]:
         if not latent_indeces:
             return set()
         int_latent_indeces = [i for i in range(0, latent_count)]
@@ -304,8 +287,8 @@ class LatentKeyframeGroupNode:
             if ':' in g:
                 index_range = g.split(":", 1)
                 index_range = [r.strip() for r in index_range]
-                start_index = self.convert_to_index_int(index_range[0], latent_count=latent_count, is_range=True, allow_negative=allow_negative)
+                start_index = cls.convert_to_index_int(index_range[0], latent_count=latent_count, is_range=True, allow_negative=allow_negative)
-                end_index = self.convert_to_index_int(index_range[1], latent_count=latent_count, is_range=True, allow_negative=allow_negative)
+                end_index = cls.convert_to_index_int(index_range[1], latent_count=latent_count, is_range=True, allow_negative=allow_negative)
                 # if latents were passed in, base indeces on known latent count
                 if len(int_latent_indeces) > 0:
                     for i in int_latent_indeces[start_index:end_index]:
@@ -316,14 +299,16 @@ class LatentKeyframeGroupNode:
                         chosen_indeces.add(LatentKeyframe(i, strength))
             # parse individual indeces
             else:
-                chosen_indeces.add(LatentKeyframe(self.convert_to_index_int(g, latent_count=latent_count, allow_negative=allow_negative), strength))
+                chosen_indeces.add(LatentKeyframe(cls.convert_to_index_int(g, latent_count=latent_count, allow_negative=allow_negative), strength))
         return chosen_indeces
 
-    def load_keyframes(self,
+    @classmethod
+    def execute(cls,
                        index_strengths: str,
                        prev_latent_kf: LatentKeyframeGroup=None,
                        prev_latent_keyframe: LatentKeyframeGroup=None, # old name
-                       latent_image_opt=None,
+                       latent_optional=None,
+                       latent_image_opt=None, # old name
                        print_keyframes=False):
         prev_latent_keyframe = prev_latent_keyframe if prev_latent_keyframe else prev_latent_kf
         if not prev_latent_keyframe:
@@ -332,10 +317,11 @@ class LatentKeyframeGroupNode:
             prev_latent_keyframe = prev_latent_keyframe.clone()
         curr_latent_keyframe = LatentKeyframeGroup()
 
+        latent_image_opt = latent_image_opt if latent_image_opt is not None else latent_optional
         latent_count = -1
         if latent_image_opt:
             latent_count = latent_image_opt['samples'].size()[0]
-        latent_keyframes = self.convert_to_latent_keyframes(index_strengths, latent_count=latent_count)
+        latent_keyframes = cls.convert_to_latent_keyframes(index_strengths, latent_count=latent_count)
 
         for latent_keyframe in latent_keyframes:
             curr_latent_keyframe.add(latent_keyframe)
@@ -348,35 +334,32 @@ class LatentKeyframeGroupNode:
         for latent_keyframe in prev_latent_keyframe.keyframes:
             curr_latent_keyframe.add(latent_keyframe)
 
-        return (curr_latent_keyframe,)
+        return io.NodeOutput(curr_latent_keyframe,)
 
         
-class LatentKeyframeInterpolationNode:
+class LatentKeyframeInterpolationNode(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='LatentKeyframeTiming',
-                "batch_index_from": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX, "step": 1}),
+            display_name='Latent Keyframe Interp. 🛂🅐🅒🅝',
-                "batch_index_to_excl": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX, "step": 1}),
+            category='Adv-ControlNet 🛂🅐🅒🅝/keyframes',
-                "strength_from": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            inputs=[
-                "strength_to": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Int.Input('batch_index_from', default=0, max=9007199254740991, min=-9007199254740991, step=1),
-                "interpolation": (SI._LIST, ),
+                io.Int.Input('batch_index_to_excl', default=0, max=9007199254740991, min=-9007199254740991, step=1),
-            },
+                io.Float.Input('strength_from', default=1.0, max=10.0, min=0.0, step=0.001),
-            "optional": {
+                io.Float.Input('strength_to', default=1.0, max=10.0, min=0.0, step=0.001),
-                "prev_latent_kf": ("LATENT_KEYFRAME", ),
+                io.Combo.Input('interpolation', options=['linear', 'ease-in', 'ease-out', 'ease-in-out']),
-                "print_keyframes": ("BOOLEAN", {"default": False}),
+                io.Custom('LATENT_KEYFRAME').Input('prev_latent_kf', optional=True),
-            },
+                io.Boolean.Input('print_keyframes', optional=True, default=False)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('LATENT_KEYFRAME').Output('LATENT_KF', is_output_list=False)
-        }
+            ]
+        )
 
-    RETURN_NAMES = ("LATENT_KF", )
+    @classmethod
-    RETURN_TYPES = ("LATENT_KEYFRAME", )
+    def execute(cls,
-    FUNCTION = "load_keyframe"
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
-
-    def load_keyframe(self,
                         batch_index_from: int,
                         strength_from: float,
                         batch_index_to_excl: int,
@@ -425,31 +408,27 @@ class LatentKeyframeInterpolationNode:
         for latent_keyframe in prev_latent_keyframe.keyframes:
             curr_latent_keyframe.add(latent_keyframe)
 
-        return (curr_latent_keyframe,)
+        return io.NodeOutput(curr_latent_keyframe,)
 
-
+class LatentKeyframeBatchedGroupNode(io.ComfyNode):
-class LatentKeyframeBatchedGroupNode:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='LatentKeyframeBatchedGroup',
-                "float_strengths": ("FLOAT", {"default": -1, "min": -1, "step": 0.001, "forceInput": True}),
+            display_name='Latent Keyframe From List 🛂🅐🅒🅝',
-            },
+            category='Adv-ControlNet 🛂🅐🅒🅝/keyframes',
-            "optional": {
+            inputs=[
-                "prev_latent_kf": ("LATENT_KEYFRAME", ),
+                io.Float.Input('float_strengths', default=-1, force_input=True, min=-1, step=0.001),
-                "print_keyframes": ("BOOLEAN", {"default": False}),
+                io.Custom('LATENT_KEYFRAME').Input('prev_latent_kf', optional=True),
-            },
+                io.Boolean.Input('print_keyframes', optional=True, default=False)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('LATENT_KEYFRAME').Output('LATENT_KF', is_output_list=False)
-        }
+            ]
+        )
 
-    RETURN_NAMES = ("LATENT_KF", )
+    @classmethod
-    RETURN_TYPES = ("LATENT_KEYFRAME", )
+    def execute(cls, float_strengths: Union[float, list[float]],
-    FUNCTION = "load_keyframe"
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/keyframes"
-
-    def load_keyframe(self, float_strengths: Union[float, list[float]],
                       prev_latent_kf: LatentKeyframeGroup=None,
                       prev_latent_keyframe: LatentKeyframeGroup=None, # old name
                       print_keyframes=False):
@@ -479,4 +458,4 @@ class LatentKeyframeBatchedGroupNode:
         for latent_keyframe in prev_latent_keyframe.keyframes:
             curr_latent_keyframe.add(latent_keyframe)
 
-        return (curr_latent_keyframe,)
+        return io.NodeOutput(curr_latent_keyframe,)

adv_control/nodes_main.py

@@ -1,129 +1,120 @@
+from comfy_api.latest import io
 from torch import Tensor
 
 import folder_paths
-from comfy.model_patcher import ModelPatcher
 
 from .control import load_controlnet, convert_to_advanced, is_advanced_controlnet, is_sd3_advanced_controlnet
 from .control_lllite import load_anima_lllite
-from .utils import ControlWeights, LatentKeyframeGroup, TimestepKeyframeGroup, AbstractPreprocWrapper, BIGMAX
+from .utils import ControlWeights, LatentKeyframeGroup, TimestepKeyframeGroup, AbstractPreprocWrapper
 
-from .logger import logger
+class ControlNetLoaderAdvanced(io.ComfyNode):
-
-
-class ControlNetLoaderAdvanced:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_ControlNetLoaderAdvanced',
-                "cnet": (folder_paths.get_filename_list("controlnet"), ),
+            display_name='Load Advanced ControlNet Model 🛂🅐🅒🅝',
-            },
+            category='Adv-ControlNet 🛂🅐🅒🅝',
-            "optional": {
+            inputs=[
-                "_tk_opt": ("TIMESTEP_KEYFRAME", ),
+                io.Combo.Input('cnet', options=folder_paths.get_filename_list("controlnet")),
-            }
+                io.Custom('TIMESTEP_KEYFRAME').Input('_tk_opt', optional=True)
-        }
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ]
+        )
 
-    RETURN_TYPES = ("CONTROL_NET", )
+    @classmethod
-    FUNCTION = "load_controlnet"
+    def execute(cls, cnet,
-
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
-
-    def load_controlnet(self, cnet,
                         _tk_opt: TimestepKeyframeGroup=None,
                         ):
         controlnet_path = folder_paths.get_full_path("controlnet", cnet)
         controlnet = load_controlnet(controlnet_path, _tk_opt)
-        return (controlnet,)
+        return io.NodeOutput(controlnet,)
     
 
-class DiffControlNetLoaderAdvanced:
+class DiffControlNetLoaderAdvanced(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_DiffControlNetLoaderAdvanced',
-                "model": ("MODEL",),
+            display_name='Load Advanced ControlNet Model (diff) 🛂🅐🅒🅝',
-                "cnet": (folder_paths.get_filename_list("controlnet"), )
+            category='Adv-ControlNet 🛂🅐🅒🅝',
-            },
+            inputs=[
-            "optional": {
+                io.Model.Input('model'),
-                "_tk_opt": ("TIMESTEP_KEYFRAME", ),
+                io.Combo.Input('cnet', options=folder_paths.get_filename_list("controlnet")),
-            },
+                io.Custom('TIMESTEP_KEYFRAME').Input('_tk_opt', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
-        }
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET", )
-    FUNCTION = "load_controlnet"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
+    @classmethod
-
+    def execute(cls, cnet, model,
-    def load_controlnet(self, cnet, model,
                         _tk_opt: TimestepKeyframeGroup=None,
                         ):
         controlnet_path = folder_paths.get_full_path("controlnet", cnet)
         controlnet = load_controlnet(controlnet_path, _tk_opt, model)
         if is_advanced_controlnet(controlnet):
             controlnet.verify_all_weights()
-        return (controlnet,)
+        return io.NodeOutput(controlnet,)
 
-
+class AnimaLLLiteLoaderAdvanced(io.ComfyNode):
-class AnimaLLLiteLoaderAdvanced:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_AnimaLLLiteLoaderAdvanced',
-                "model_patch": (folder_paths.get_filename_list("model_patches"), ),
+            display_name='Load Anima LLLite Model 🛂🅐🅒🅝',
-            },
+            category='Adv-ControlNet 🛂🅐🅒🅝/loaders',
-            "optional": {
+            inputs=[
-                "timestep_kf": ("TIMESTEP_KEYFRAME", ),
+                io.Combo.Input('model_patch', options=folder_paths.get_filename_list("model_patches")),
-            },
+                io.Custom('TIMESTEP_KEYFRAME').Input('timestep_kf', optional=True)
-        }
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ]
+        )
 
-    RETURN_TYPES = ("CONTROL_NET", )
+    @classmethod
-    FUNCTION = "load_controlnet"
+    def execute(cls, model_patch, timestep_kf: TimestepKeyframeGroup=None):
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/loaders"
-
-    def load_controlnet(self, model_patch, timestep_kf: TimestepKeyframeGroup=None):
         model_patch_path = folder_paths.get_full_path_or_raise("model_patches", model_patch)
-        return (load_anima_lllite(model_patch_path, timestep_keyframe=timestep_kf),)
+        return io.NodeOutput(load_anima_lllite(model_patch_path, timestep_keyframe=timestep_kf),)
 
-
+class AdvancedControlNetApply(io.ComfyNode):
-class AdvancedControlNetApply:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_AdvancedControlNetApply_v2',
-                "positive": ("CONDITIONING", ),
+            display_name='Apply Advanced ControlNet 🛂🅐🅒🅝',
-                "negative": ("CONDITIONING", ),
+            category='Adv-ControlNet 🛂🅐🅒🅝',
-                "control_net": ("CONTROL_NET", ),
+            inputs=[
-                "image": ("IMAGE", ),
+                io.Conditioning.Input('positive'),
-                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                io.Conditioning.Input('negative'),
-                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                io.ControlNet.Input('control_net'),
-                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
+                io.Image.Input('image'),
-            },
+                io.Float.Input('strength', default=1.0, max=10.0, min=0.0, step=0.01),
-            "optional": {
+                io.Float.Input('start_percent', default=0.0, max=1.0, min=0.0, step=0.001),
-                "mask_optional": ("MASK", ),
+                io.Float.Input('end_percent', default=1.0, max=1.0, min=0.0, step=0.001),
-                "timestep_kf": ("TIMESTEP_KEYFRAME", ),
+                io.Mask.Input('mask_optional', optional=True),
-                "latent_kf_override": ("LATENT_KEYFRAME", ),
+                io.Custom('TIMESTEP_KEYFRAME').Input('timestep_kf', optional=True),
-                "weights_override": ("CONTROL_NET_WEIGHTS", ),
+                io.Custom('LATENT_KEYFRAME').Input('latent_kf_override', optional=True),
-                "vae_optional": ("VAE",),
+                io.Custom('CONTROL_NET_WEIGHTS').Input('weights_override', optional=True),
-            },
+                io.Vae.Input('vae_optional', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Conditioning.Output('positive', is_output_list=False),
-        }
+                io.Conditioning.Output('negative', is_output_list=False)
+            ]
+        )
 
-    RETURN_TYPES = ("CONDITIONING","CONDITIONING",)
+    @classmethod
-    RETURN_NAMES = ("positive", "negative")
+    def execute(cls, positive, negative, control_net, image, strength, start_percent, end_percent,
-    FUNCTION = "apply_controlnet"
-
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
-
-    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent,
                          mask_optional: Tensor=None, vae_optional=None,
                          timestep_kf: TimestepKeyframeGroup=None, latent_kf_override: LatentKeyframeGroup=None,
                          weights_override: ControlWeights=None, control_apply_to_uncond=False):
         if strength == 0:
-            return (positive, negative)
+            return io.NodeOutput(positive, negative)
 
         control_hint = image.movedim(-1,1)
         cnets = {}
@@ -189,46 +180,43 @@ class AdvancedControlNetApply:
                     n = [t[0], d]
                     c.append(n)
             out.append(c)
-        return (out[0], out[1])
+        return io.NodeOutput(out[0], out[1])
     
 
-class AdvancedControlNetApplySingle:
+class AdvancedControlNetApplySingle(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_AdvancedControlNetApplySingle_v2',
-                "conditioning": ("CONDITIONING", ),
+            display_name='Apply Advanced ControlNet(1) 🛂🅐🅒🅝',
-                "control_net": ("CONTROL_NET", ),
+            category='Adv-ControlNet 🛂🅐🅒🅝',
-                "image": ("IMAGE", ),
+            inputs=[
-                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                io.Conditioning.Input('conditioning'),
-                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                io.ControlNet.Input('control_net'),
-                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
+                io.Image.Input('image'),
-            },
+                io.Float.Input('strength', default=1.0, max=10.0, min=0.0, step=0.01),
-            "optional": {
+                io.Float.Input('start_percent', default=0.0, max=1.0, min=0.0, step=0.001),
-                "mask_optional": ("MASK", ),
+                io.Float.Input('end_percent', default=1.0, max=1.0, min=0.0, step=0.001),
-                "timestep_kf": ("TIMESTEP_KEYFRAME", ),
+                io.Mask.Input('mask_optional', optional=True),
-                "latent_kf_override": ("LATENT_KEYFRAME", ),
+                io.Custom('TIMESTEP_KEYFRAME').Input('timestep_kf', optional=True),
-                "weights_override": ("CONTROL_NET_WEIGHTS", ),
+                io.Custom('LATENT_KEYFRAME').Input('latent_kf_override', optional=True),
-                "vae_optional": ("VAE",),
+                io.Custom('CONTROL_NET_WEIGHTS').Input('weights_override', optional=True),
-            },
+                io.Vae.Input('vae_optional', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Conditioning.Output('CONDITIONING', is_output_list=False),
-        }
+                io.Model.Output('model_opt', is_output_list=False)
+            ]
+        )
 
-    RETURN_TYPES = ("CONDITIONING","MODEL",)
+    @classmethod
-    RETURN_NAMES = ("CONDITIONING", "model_opt")
+    def execute(cls, conditioning, control_net, image, strength, start_percent, end_percent,
-    FUNCTION = "apply_controlnet"
-
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
-
-    def apply_controlnet(self, conditioning, control_net, image, strength, start_percent, end_percent,
                          mask_optional: Tensor=None, vae_optional=None,
                          timestep_kf: TimestepKeyframeGroup=None, latent_kf_override: LatentKeyframeGroup=None,
                          weights_override: ControlWeights=None):
-        values = AdvancedControlNetApply.apply_controlnet(self, positive=conditioning, negative=None, control_net=control_net, image=image,
+        values = AdvancedControlNetApply.execute(positive=conditioning, negative=None, control_net=control_net, image=image,
                                                           strength=strength, start_percent=start_percent, end_percent=end_percent,
                                                           mask_optional=mask_optional, vae_optional=vae_optional,
                                                           timestep_kf=timestep_kf, latent_kf_override=latent_kf_override, weights_override=weights_override,
                                                           control_apply_to_uncond=True)
-        return (values[0],)
+        return io.NodeOutput(values.args[0], None)

adv_control/nodes_plusplus.py

@@ -1,81 +1,82 @@
+from comfy_api.latest import io
 from torch import Tensor
 import math
 
 import folder_paths
 
-from .control_plusplus import load_controlnetplusplus, PlusPlusType, PlusPlusInput, PlusPlusInputGroup, PlusPlusImageWrapper
+from .control_plusplus import load_controlnetplusplus, PlusPlusInput, PlusPlusInputGroup, PlusPlusImageWrapper
-from .utils import BIGMAX
 
-
+class PlusPlusLoaderAdvanced(io.ComfyNode):
-class PlusPlusLoaderAdvanced:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_ControlNet++LoaderAdvanced',
-                "plus_input": ("PLUS_INPUT", ),
+            display_name='Load ControlNet++ Model (Multi) 🛂🅐🅒🅝',
-                "name": (folder_paths.get_filename_list("controlnet"), ),
+            category='Adv-ControlNet 🛂🅐🅒🅝/ControlNet++',
-            }
+            inputs=[
-        }
+                io.Custom('PLUS_INPUT').Input('plus_input'),
+                io.Combo.Input('name', options=folder_paths.get_filename_list("controlnet"))
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False),
+                io.Image.Output('IMAGE', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET", "IMAGE",)
-    FUNCTION = "load_controlnet_plusplus"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/ControlNet++"
+    @classmethod
-
+    def execute(cls, plus_input: PlusPlusInputGroup, name: str):
-    def load_controlnet_plusplus(self, plus_input: PlusPlusInputGroup, name: str):
         controlnet_path = folder_paths.get_full_path("controlnet", name)
         controlnet = load_controlnetplusplus(controlnet_path)
         controlnet.verify_control_type(name, plus_input)
         controlnet.allow_condhint_latents = True
-        return (controlnet, PlusPlusImageWrapper(plus_input),)
+        return io.NodeOutput(controlnet, PlusPlusImageWrapper(plus_input),)
 
-
+class PlusPlusLoaderSingle(io.ComfyNode):
-class PlusPlusLoaderSingle:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_ControlNet++LoaderSingle',
-                "name": (folder_paths.get_filename_list("controlnet"), ),
+            display_name='Load ControlNet++ Model (Single) 🛂🅐🅒🅝',
-                "control_type": (PlusPlusType._LIST_WITH_NONE, {"default": PlusPlusType.NONE}, ),
+            category='Adv-ControlNet 🛂🅐🅒🅝/ControlNet++',
-            }
+            inputs=[
-        }
+                io.Combo.Input('name', options=folder_paths.get_filename_list("controlnet")),
+                io.Combo.Input('control_type', options=['openpose', 'depth', 'hed/pidi/scribble/ted', 'canny/lineart/mlsd', 'normal', 'segment', 'tile', 'inpaint/outpaint', 'none'], default='none')
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET",)
-    FUNCTION = "load_controlnet_plusplus"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/ControlNet++"
+    @classmethod
-
+    def execute(cls, name: str, control_type: str):
-    def load_controlnet_plusplus(self, name: str, control_type: str):
         controlnet_path = folder_paths.get_full_path("controlnet", name)
         controlnet = load_controlnetplusplus(controlnet_path)
         controlnet.single_control_type = control_type
         controlnet.verify_control_type(name)
-        return (controlnet,)
+        return io.NodeOutput(controlnet,)
 
-
+class PlusPlusInputNode(io.ComfyNode):
-class PlusPlusInputNode:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_ControlNet++InputNode',
-                "image": ("IMAGE",),
+            display_name='ControlNet++ Input 🛂🅐🅒🅝',
-                "control_type": (PlusPlusType._LIST,),
+            category='Adv-ControlNet 🛂🅐🅒🅝/ControlNet++',
-            },
+            inputs=[
-            "optional": {
+                io.Image.Input('image'),
-                "prev_plus_input": ("PLUS_INPUT",),
+                io.Combo.Input('control_type', options=['openpose', 'depth', 'hed/pidi/scribble/ted', 'canny/lineart/mlsd', 'normal', 'segment', 'tile', 'inpaint/outpaint']),
-                #"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": BIGMAX, "step": 0.01}),
+                io.Custom('PLUS_INPUT').Input('prev_plus_input', optional=True)
-            },
+            ],
-            "hidden": {
+            outputs=[
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+                io.Custom('PLUS_INPUT').Output('PLUS_INPUT', is_output_list=False)
-            }
+            ]
-        }
+        )
     
-    RETURN_TYPES = ("PLUS_INPUT", )
-    FUNCTION = "wrap_images"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/ControlNet++"
+    @classmethod
-
+    def execute(cls, image: Tensor, control_type: str, strength=1.0, prev_plus_input: PlusPlusInputGroup=None):
-    def wrap_images(self, image: Tensor, control_type: str, strength=1.0, prev_plus_input: PlusPlusInputGroup=None):
         if prev_plus_input is None:
             prev_plus_input = PlusPlusInputGroup()
         prev_plus_input = prev_plus_input.clone()
@@ -85,4 +86,4 @@ class PlusPlusInputNode:
         pp_input = PlusPlusInput(image, control_type, strength)
         prev_plus_input.add(pp_input)
 
-        return (prev_plus_input,)
+        return io.NodeOutput(prev_plus_input,)

adv_control/nodes_reference.py

@@ -1,3 +1,4 @@
+from comfy_api.latest import io
 from torch import Tensor
 
 from nodes import VAEEncode
@@ -6,77 +7,81 @@ from comfy.sd import VAE
 
 from .control_reference import ReferenceAdvanced, ReferenceOptions, ReferenceType, ReferencePreprocWrapper
 
-
 # node for ReferenceCN
-class ReferenceControlNetNode:
+class ReferenceControlNetNode(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_ReferenceControlNet',
-                "reference_type": (ReferenceType._LIST,),
+            display_name='Reference ControlNet 🛂🅐🅒🅝',
-                "style_fidelity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+            category='Adv-ControlNet 🛂🅐🅒🅝/Reference',
-                "ref_weight": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+            inputs=[
-            },
+                io.Combo.Input('reference_type', options=['reference_attn', 'reference_adain', 'reference_attn+adain']),
-        }
+                io.Float.Input('style_fidelity', default=0.5, max=1.0, min=0.0, step=0.01),
+                io.Float.Input('ref_weight', default=1.0, max=1.0, min=0.0, step=0.01)
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET", )
-    FUNCTION = "load_controlnet"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/Reference"
+    @classmethod
-
+    def execute(cls, reference_type: str, style_fidelity: float, ref_weight: float):
-    def load_controlnet(self, reference_type: str, style_fidelity: float, ref_weight: float):
         ref_opts = ReferenceOptions.create_combo(reference_type=reference_type, style_fidelity=style_fidelity, ref_weight=ref_weight)
         controlnet = ReferenceAdvanced(ref_opts=ref_opts, timestep_keyframes=None)
-        return (controlnet,)
+        return io.NodeOutput(controlnet,)
 
-
+class ReferenceControlFinetune(io.ComfyNode):
-class ReferenceControlFinetune:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_ReferenceControlNetFinetune',
-                "attn_style_fidelity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+            display_name='Reference ControlNet (Finetune) 🛂🅐🅒🅝',
-                "attn_ref_weight": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+            category='Adv-ControlNet 🛂🅐🅒🅝/Reference',
-                "attn_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+            inputs=[
-                "adain_style_fidelity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                io.Float.Input('attn_style_fidelity', default=0.5, max=1.0, min=0.0, step=0.01),
-                "adain_ref_weight": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                io.Float.Input('attn_ref_weight', default=1.0, max=1.0, min=0.0, step=0.01),
-                "adain_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                io.Float.Input('attn_strength', default=1.0, max=1.0, min=0.0, step=0.01),
-            },
+                io.Float.Input('adain_style_fidelity', default=0.5, max=1.0, min=0.0, step=0.01),
-        }
+                io.Float.Input('adain_ref_weight', default=1.0, max=1.0, min=0.0, step=0.01),
+                io.Float.Input('adain_strength', default=1.0, max=1.0, min=0.0, step=0.01)
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET", )
-    FUNCTION = "load_controlnet"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/Reference"
+    @classmethod
-
+    def execute(cls,
-    def load_controlnet(self,
                         attn_style_fidelity: float, attn_ref_weight: float, attn_strength: float,
                         adain_style_fidelity: float, adain_ref_weight: float, adain_strength: float):
         ref_opts = ReferenceOptions(reference_type=ReferenceType.ATTN_ADAIN,
                                     attn_style_fidelity=attn_style_fidelity, attn_ref_weight=attn_ref_weight, attn_strength=attn_strength,
                                     adain_style_fidelity=adain_style_fidelity, adain_ref_weight=adain_ref_weight, adain_strength=adain_strength)
         controlnet = ReferenceAdvanced(ref_opts=ref_opts, timestep_keyframes=None)
-        return (controlnet,)
+        return io.NodeOutput(controlnet,)
 
-
+class ReferencePreprocessorNode(io.ComfyNode):
-class ReferencePreprocessorNode:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_ReferencePreprocessor',
-                "image": ("IMAGE", ),
+            display_name='Reference Preproccessor 🛂🅐🅒🅝',
-                "vae": ("VAE", ),
+            category='Adv-ControlNet 🛂🅐🅒🅝/Reference/preprocess',
-                "latent_size": ("LATENT", ),
+            inputs=[
-            }
+                io.Image.Input('image'),
-        }
+                io.Vae.Input('vae'),
+                io.Latent.Input('latent_size')
+            ],
+            outputs=[
+                io.Image.Output('proc_IMAGE', is_output_list=False)
+            ]
+        )
 
-    RETURN_TYPES = ("IMAGE",)
+    @classmethod
-    RETURN_NAMES = ("proc_IMAGE",)
+    def execute(cls, vae: VAE, image: Tensor, latent_size: Tensor):
-    FUNCTION = "preprocess_images"
-
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/Reference/preprocess"
-
-    def preprocess_images(self, vae: VAE, image: Tensor, latent_size: Tensor):
         # first, resize image to match latents
         image = image.movedim(-1,1)
         image = comfy.utils.common_upscale(image, latent_size["samples"].shape[3] * 8, latent_size["samples"].shape[2] * 8, 'nearest-exact', "center")
@@ -87,4 +92,4 @@ class ReferencePreprocessorNode:
         except Exception:
             image = VAEEncode.vae_encode_crop_pixels(image)
         encoded = vae.encode(image[:,:,:,:3])
-        return (ReferencePreprocWrapper(condhint=encoded),)
+        return io.NodeOutput(ReferencePreprocWrapper(condhint=encoded),)

adv_control/nodes_sparsectrl.py

@@ -1,3 +1,4 @@
+from comfy_api.latest import io
 from torch import Tensor
 
 import folder_paths
@@ -7,68 +8,68 @@ from comfy.sd import VAE
 
 from .utils import TimestepKeyframeGroup
 from .control_sparsectrl import SparseMethod, SparseIndexMethod, SparseSettings, SparseSpreadMethod, PreprocSparseRGBWrapper, SparseConst, SparseContextAware, get_idx_list_from_str
-from .control import load_sparsectrl, load_controlnet, ControlNetAdvanced, SparseCtrlAdvanced
+from .control import load_sparsectrl, load_controlnet, ControlNetAdvanced
-
 
 # node for SparseCtrl loading
-class SparseCtrlLoaderAdvanced:
+class SparseCtrlLoaderAdvanced(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_SparseCtrlLoaderAdvanced',
-                "sparsectrl_name": (folder_paths.get_filename_list("controlnet"), ),
+            display_name='Load SparseCtrl Model 🛂🅐🅒🅝',
-                "use_motion": ("BOOLEAN", {"default": True}, ),
+            category='Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl',
-                "motion_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            inputs=[
-                "motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Combo.Input('sparsectrl_name', options=folder_paths.get_filename_list("controlnet")),
-            },
+                io.Boolean.Input('use_motion', default=True),
-            "optional": {
+                io.Float.Input('motion_strength', default=1.0, max=10.0, min=0.0, step=0.001),
-                "sparse_method": ("SPARSE_METHOD", ),
+                io.Float.Input('motion_scale', default=1.0, max=10.0, min=0.0, step=0.001),
-                "tk_optional": ("TIMESTEP_KEYFRAME", ),
+                io.Custom('SPARSE_METHOD').Input('sparse_method', optional=True),
-                "context_aware": (SparseContextAware.LIST, ),
+                io.Custom('TIMESTEP_KEYFRAME').Input('tk_optional', optional=True),
-                "sparse_hint_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Combo.Input('context_aware', optional=True, options=['nearest_hint', 'off']),
-                "sparse_nonhint_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('sparse_hint_mult', optional=True, default=1.0, max=10.0, min=0.0, step=0.001),
-                "sparse_mask_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('sparse_nonhint_mult', optional=True, default=1.0, max=10.0, min=0.0, step=0.001),
-            }
+                io.Float.Input('sparse_mask_mult', optional=True, default=1.0, max=10.0, min=0.0, step=0.001)
-        }
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET", )
-    FUNCTION = "load_controlnet"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl"
+    @classmethod
-
+    def execute(cls, sparsectrl_name: str, use_motion: bool, motion_strength: float, motion_scale: float, sparse_method: SparseMethod=SparseSpreadMethod(), tk_optional: TimestepKeyframeGroup=None,
-    def load_controlnet(self, sparsectrl_name: str, use_motion: bool, motion_strength: float, motion_scale: float, sparse_method: SparseMethod=SparseSpreadMethod(), tk_optional: TimestepKeyframeGroup=None,
                         context_aware=SparseContextAware.NEAREST_HINT, sparse_hint_mult=1.0, sparse_nonhint_mult=1.0, sparse_mask_mult=1.0):
         sparsectrl_path = folder_paths.get_full_path("controlnet", sparsectrl_name)
         sparse_settings = SparseSettings(sparse_method=sparse_method, use_motion=use_motion, motion_strength=motion_strength, motion_scale=motion_scale,
                                          context_aware=context_aware,
                                          sparse_mask_mult=sparse_mask_mult, sparse_hint_mult=sparse_hint_mult, sparse_nonhint_mult=sparse_nonhint_mult)
         sparsectrl = load_sparsectrl(sparsectrl_path, timestep_keyframe=tk_optional, sparse_settings=sparse_settings)
-        return (sparsectrl,)
+        return io.NodeOutput(sparsectrl,)
 
-
+class SparseCtrlMergedLoaderAdvanced(io.ComfyNode):
-class SparseCtrlMergedLoaderAdvanced:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_SparseCtrlMergedLoaderAdvanced',
-                "sparsectrl_name": (folder_paths.get_filename_list("controlnet"), ),
+            display_name='🧪Load Merged SparseCtrl Model 🛂🅐🅒🅝',
-                "control_net_name": (folder_paths.get_filename_list("controlnet"), ),
+            category='Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/experimental',
-                "use_motion": ("BOOLEAN", {"default": True}, ),
+            inputs=[
-                "motion_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Combo.Input('sparsectrl_name', options=folder_paths.get_filename_list("controlnet")),
-                "motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Combo.Input('control_net_name', options=folder_paths.get_filename_list("controlnet")),
-            },
+                io.Boolean.Input('use_motion', default=True),
-            "optional": {
+                io.Float.Input('motion_strength', default=1.0, max=10.0, min=0.0, step=0.001),
-                "sparse_method": ("SPARSE_METHOD", ),
+                io.Float.Input('motion_scale', default=1.0, max=10.0, min=0.0, step=0.001),
-                "tk_optional": ("TIMESTEP_KEYFRAME", ),
+                io.Custom('SPARSE_METHOD').Input('sparse_method', optional=True),
-            }
+                io.Custom('TIMESTEP_KEYFRAME').Input('tk_optional', optional=True)
-        }
+            ],
+            outputs=[
+                io.ControlNet.Output('CONTROL_NET', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET", )
-    FUNCTION = "load_controlnet"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/experimental"
+    @classmethod
-
+    def execute(cls, sparsectrl_name: str, control_net_name: str, use_motion: bool, motion_strength: float, motion_scale: float, sparse_method: SparseMethod=SparseSpreadMethod(), tk_optional: TimestepKeyframeGroup=None):
-    def load_controlnet(self, sparsectrl_name: str, control_net_name: str, use_motion: bool, motion_strength: float, motion_scale: float, sparse_method: SparseMethod=SparseSpreadMethod(), tk_optional: TimestepKeyframeGroup=None):
         sparsectrl_path = folder_paths.get_full_path("controlnet", sparsectrl_name)
         controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
         sparse_settings = SparseSettings(sparse_method=sparse_method, use_motion=use_motion, motion_strength=motion_strength, motion_scale=motion_scale, merged=True)
@@ -85,67 +86,68 @@ class SparseCtrlMergedLoaderAdvanced:
             new_state_dict[key] = value
         # now, reload sparsectrl with real settings
         sparsectrl = load_sparsectrl(sparsectrl_path, controlnet_data=new_state_dict, timestep_keyframe=tk_optional, sparse_settings=sparse_settings)
-        return (sparsectrl,)
+        return io.NodeOutput(sparsectrl,)
 
-
+class SparseIndexMethodNode(io.ComfyNode):
-class SparseIndexMethodNode:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_SparseCtrlIndexMethodNode',
-                "indexes": ("STRING", {"default": "0"}),
+            display_name='SparseCtrl Index Method 🛂🅐🅒🅝',
-            }
+            category='Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl',
-        }
+            inputs=[
+                io.String.Input('indexes', default='0')
+            ],
+            outputs=[
+                io.Custom('SPARSE_METHOD').Output('SPARSE_METHOD', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("SPARSE_METHOD",)
-    FUNCTION = "get_method"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl"
+    @classmethod
-
+    def execute(cls, indexes: str):
-    def get_method(self, indexes: str):
         idxs = get_idx_list_from_str(indexes)
-        return (SparseIndexMethod(idxs),)
+        return io.NodeOutput(SparseIndexMethod(idxs),)
 
-
+class SparseSpreadMethodNode(io.ComfyNode):
-class SparseSpreadMethodNode:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_SparseCtrlSpreadMethodNode',
-                "spread": (SparseSpreadMethod.LIST,),
+            display_name='SparseCtrl Spread Method 🛂🅐🅒🅝',
-            }
+            category='Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl',
-        }
+            inputs=[
+                io.Combo.Input('spread', options=['uniform', 'starting', 'ending', 'center'])
+            ],
+            outputs=[
+                io.Custom('SPARSE_METHOD').Output('SPARSE_METHOD', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("SPARSE_METHOD",)
-    FUNCTION = "get_method"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl"
-
-    def get_method(self, spread: str):
-        return (SparseSpreadMethod(spread=spread),)
-
-
-class RgbSparseCtrlPreprocessor:
     @classmethod
-    def INPUT_TYPES(s):
+    def execute(cls, spread: str):
-        return {
+        return io.NodeOutput(SparseSpreadMethod(spread=spread),)
-            "required": {
-                "image": ("IMAGE", ),
-                "vae": ("VAE", ),
-                "latent_size": ("LATENT", ),
-            },
-            "hidden": {
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
-            }
-        }
 
-    RETURN_TYPES = ("IMAGE",)
+class RgbSparseCtrlPreprocessor(io.ComfyNode):
-    RETURN_NAMES = ("proc_IMAGE",)
+    @classmethod
-    FUNCTION = "preprocess_images"
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id='ACN_SparseCtrlRGBPreprocessor',
+            display_name='RGB SparseCtrl 🛂🅐🅒🅝',
+            category='Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/preprocess',
+            inputs=[
+                io.Image.Input('image'),
+                io.Vae.Input('vae'),
+                io.Latent.Input('latent_size')
+            ],
+            outputs=[
+                io.Image.Output('proc_IMAGE', is_output_list=False)
+            ]
+        )
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/preprocess"
+    @classmethod
-
+    def execute(cls, vae: VAE, image: Tensor, latent_size: Tensor):
-    def preprocess_images(self, vae: VAE, image: Tensor, latent_size: Tensor):
         # first, resize image to match latents
         image = image.movedim(-1,1)
         image = comfy.utils.common_upscale(image, latent_size["samples"].shape[3] * 8, latent_size["samples"].shape[2] * 8, 'nearest-exact', "center")
@@ -156,33 +158,31 @@ class RgbSparseCtrlPreprocessor:
         except Exception:
             image = VAEEncode.vae_encode_crop_pixels(image)
         encoded = vae.encode(image[:,:,:,:3])
-        return (PreprocSparseRGBWrapper(condhint=encoded),)
+        return io.NodeOutput(PreprocSparseRGBWrapper(condhint=encoded),)
 
-
+class SparseWeightExtras(io.ComfyNode):
-class SparseWeightExtras:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "optional": {
+            node_id='ACN_SparseCtrlWeightExtras',
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+            display_name='SparseCtrl Weight Extras 🛂🅐🅒🅝',
-                "sparse_hint_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            category='Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/extras',
-                "sparse_nonhint_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            inputs=[
-                "sparse_mask_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True),
-            },
+                io.Float.Input('sparse_hint_mult', optional=True, default=1.0, max=10.0, min=0.0, step=0.001),
-            "hidden": {
+                io.Float.Input('sparse_nonhint_mult', optional=True, default=1.0, max=10.0, min=0.0, step=0.001),
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+                io.Float.Input('sparse_mask_mult', optional=True, default=1.0, max=10.0, min=0.0, step=0.001)
-            }
+            ],
-        }
+            outputs=[
+                io.Custom('CN_WEIGHTS_EXTRAS').Output('cn_extras', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CN_WEIGHTS_EXTRAS", )
-    RETURN_NAMES = ("cn_extras", )
-    FUNCTION = "create_weight_extras"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/extras"
+    @classmethod
-
+    def execute(cls, cn_extras: dict[str]={}, sparse_hint_mult=1.0, sparse_nonhint_mult=1.0, sparse_mask_mult=1.0):
-    def create_weight_extras(self, cn_extras: dict[str]={}, sparse_hint_mult=1.0, sparse_nonhint_mult=1.0, sparse_mask_mult=1.0):
         cn_extras = cn_extras.copy()
         cn_extras[SparseConst.HINT_MULT] = sparse_hint_mult
         cn_extras[SparseConst.NONHINT_MULT] = sparse_nonhint_mult
         cn_extras[SparseConst.MASK_MULT] = sparse_mask_mult
-        return (cn_extras, )
+        return io.NodeOutput(cn_extras, )

adv_control/nodes_weight.py

@@ -1,63 +1,56 @@
+from comfy_api.latest import io
 from torch import Tensor
 import torch
 from .utils import TimestepKeyframe, TimestepKeyframeGroup, ControlWeights, Extras, get_properly_arranged_t2i_weights, linear_conversion
 from .control_lllite import AnimaLLLiteConst
-from .logger import logger
-
 
 WEIGHTS_RETURN_NAMES = ("CN_WEIGHTS", "TK_SHORTCUT")
 
-
+class DefaultWeights(io.ComfyNode):
-class DefaultWeights:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "optional": {
+            node_id='ACN_DefaultUniversalWeights',
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+            display_name='Default Weights 🛂🅐🅒🅝',
-            },
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights',
-            "hidden": {
+            inputs=[
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
-            }
+            ],
-        }
+            outputs=[
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = WEIGHTS_RETURN_NAMES
-    FUNCTION = "load_weights"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
+    @classmethod
-
+    def execute(cls, cn_extras: dict[str]={}):
-    def load_weights(self, cn_extras: dict[str]={}):
         weights = ControlWeights.default(extras=cn_extras)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) 
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
+class ScaledSoftMaskedUniversalWeights(io.ComfyNode):
-class ScaledSoftMaskedUniversalWeights:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ScaledSoftMaskedUniversalWeights',
-                "mask": ("MASK", ),
+            display_name='Scaled Soft Masked Weights 🛂🅐🅒🅝',
-                "min_base_multiplier": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights',
-                "max_base_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}, ),
+            inputs=[
-                #"lock_min": ("BOOLEAN", {"default": False}, ),
+                io.Mask.Input('mask'),
-                #"lock_max": ("BOOLEAN", {"default": False}, ),
+                io.Float.Input('min_base_multiplier', default=0.0, max=1.0, min=0.0, step=0.001),
-            },
+                io.Float.Input('max_base_multiplier', default=1.0, max=1.0, min=0.0, step=0.001),
-            "optional": {
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+            ],
-            },
+            outputs=[
-            "hidden": {
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
-            }
+            ]
-        }
+        )
     
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = WEIGHTS_RETURN_NAMES
-    FUNCTION = "load_weights"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
+    @classmethod
-
+    def execute(cls, mask: Tensor, min_base_multiplier: float, max_base_multiplier: float, lock_min=False, lock_max=False,
-    def load_weights(self, mask: Tensor, min_base_multiplier: float, max_base_multiplier: float, lock_min=False, lock_max=False,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         # normalize mask
         mask = mask.clone()
@@ -68,116 +61,107 @@ class ScaledSoftMaskedUniversalWeights:
         else:
             mask = linear_conversion(mask, x_min, x_max, min_base_multiplier, max_base_multiplier)
         weights = ControlWeights.universal_mask(weight_mask=mask, uncond_multiplier=uncond_multiplier, extras=cn_extras)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
+class ScaledSoftUniversalWeights(io.ComfyNode):
-class ScaledSoftUniversalWeights:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_ScaledSoftControlNetWeights',
-                "base_multiplier": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 1.0, "step": 0.001}, ),
+            display_name='Scaled Soft Weights 🛂🅐🅒🅝',
-            },
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights',
-            "optional": {
+            inputs=[
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
+                io.Float.Input('base_multiplier', default=0.825, max=1.0, min=0.0, step=0.001),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
-            },
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
-        }
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = WEIGHTS_RETURN_NAMES
-    FUNCTION = "load_weights"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
+    @classmethod
-
+    def execute(cls, base_multiplier, uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
-    def load_weights(self, base_multiplier, uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         weights = ControlWeights.universal(base_multiplier=base_multiplier, uncond_multiplier=uncond_multiplier, extras=cn_extras)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
+class SoftControlNetWeightsSD15(io.ComfyNode):
-class SoftControlNetWeightsSD15:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_SoftControlNetWeightsSD15',
-                "output_0": ("FLOAT", {"default": 0.09941396206337118, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            display_name='ControlNet Soft Weights [SD1.5] 🛂🅐🅒🅝',
-                "output_1": ("FLOAT", {"default": 0.12050177219802567, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet',
-                "output_2": ("FLOAT", {"default": 0.14606275417942507, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            inputs=[
-                "output_3": ("FLOAT", {"default": 0.17704576264172736, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_0', default=0.09941396206337118, max=10.0, min=0.0, step=0.001),
-                "output_4": ("FLOAT", {"default": 0.214600924414215, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_1', default=0.12050177219802567, max=10.0, min=0.0, step=0.001),
-                "output_5": ("FLOAT", {"default": 0.26012233262329093, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_2', default=0.14606275417942507, max=10.0, min=0.0, step=0.001),
-                "output_6": ("FLOAT", {"default": 0.3152997971191405, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_3', default=0.17704576264172736, max=10.0, min=0.0, step=0.001),
-                "output_7": ("FLOAT", {"default": 0.3821815722656249, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_4', default=0.214600924414215, max=10.0, min=0.0, step=0.001),
-                "output_8": ("FLOAT", {"default": 0.4632503906249999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_5', default=0.26012233262329093, max=10.0, min=0.0, step=0.001),
-                "output_9": ("FLOAT", {"default": 0.561515625, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_6', default=0.3152997971191405, max=10.0, min=0.0, step=0.001),
-                "output_10": ("FLOAT", {"default": 0.6806249999999999, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_7', default=0.3821815722656249, max=10.0, min=0.0, step=0.001),
-                "output_11": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_8', default=0.4632503906249999, max=10.0, min=0.0, step=0.001),
-                "middle_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_9', default=0.561515625, max=10.0, min=0.0, step=0.001),
-            },
+                io.Float.Input('output_10', default=0.6806249999999999, max=10.0, min=0.0, step=0.001),
-            "optional": {
+                io.Float.Input('output_11', default=0.825, max=10.0, min=0.0, step=0.001),
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
+                io.Float.Input('middle_0', default=1.0, max=10.0, min=0.0, step=0.001),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
-            },
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
-        }
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = WEIGHTS_RETURN_NAMES
-    FUNCTION = "load_weights"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
+    @classmethod
-
+    def execute(cls, output_0, output_1, output_2, output_3, output_4, output_5, output_6,
-    def load_weights(self, output_0, output_1, output_2, output_3, output_4, output_5, output_6, 
                      output_7, output_8, output_9, output_10, output_11, middle_0,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
-        return CustomControlNetWeightsSD15.load_weights(self,
+        return CustomControlNetWeightsSD15.execute(
                                                         output_0=output_0, output_1=output_1, output_2=output_2, output_3=output_3,
                                                         output_4=output_4, output_5=output_5, output_6=output_6, output_7=output_7,
                                                         output_8=output_8, output_9=output_9, output_10=output_10, output_11=output_11,
                                                         middle_0=middle_0,
                                                         uncond_multiplier=uncond_multiplier, cn_extras=cn_extras)
 
-
+class CustomControlNetWeightsSD15(io.ComfyNode):
-class CustomControlNetWeightsSD15:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_CustomControlNetWeightsSD15',
-                "output_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            display_name='ControlNet Custom Weights [SD1.5] 🛂🅐🅒🅝',
-                "output_1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet',
-                "output_2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            inputs=[
-                "output_3": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_0', default=1.0, max=10.0, min=0.0, step=0.001),
-                "output_4": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_1', default=1.0, max=10.0, min=0.0, step=0.001),
-                "output_5": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_2', default=1.0, max=10.0, min=0.0, step=0.001),
-                "output_6": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_3', default=1.0, max=10.0, min=0.0, step=0.001),
-                "output_7": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_4', default=1.0, max=10.0, min=0.0, step=0.001),
-                "output_8": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_5', default=1.0, max=10.0, min=0.0, step=0.001),
-                "output_9": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_6', default=1.0, max=10.0, min=0.0, step=0.001),
-                "output_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_7', default=1.0, max=10.0, min=0.0, step=0.001),
-                "output_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_8', default=1.0, max=10.0, min=0.0, step=0.001),
-                "middle_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('output_9', default=1.0, max=10.0, min=0.0, step=0.001),
-            },
+                io.Float.Input('output_10', default=1.0, max=10.0, min=0.0, step=0.001),
-            "optional": {
+                io.Float.Input('output_11', default=1.0, max=10.0, min=0.0, step=0.001),
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
+                io.Float.Input('middle_0', default=1.0, max=10.0, min=0.0, step=0.001),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
-            },
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
-        }
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = WEIGHTS_RETURN_NAMES
-    FUNCTION = "load_weights"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
+    @classmethod
-
+    def execute(cls, output_0, output_1, output_2, output_3, output_4, output_5, output_6,
-    def load_weights(self, output_0, output_1, output_2, output_3, output_4, output_5, output_6, 
                      output_7, output_8, output_9, output_10, output_11, middle_0,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         weights_output = [output_0, output_1, output_2, output_3, output_4, output_5, output_6,
@@ -185,50 +169,47 @@ class CustomControlNetWeightsSD15:
         weights_middle = [middle_0]
         weights = ControlWeights.controlnet(weights_output=weights_output, weights_middle=weights_middle, uncond_multiplier=uncond_multiplier,
                                             extras=cn_extras, disable_applied_to=True)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
+class CustomControlNetWeightsFlux(io.ComfyNode):
-class CustomControlNetWeightsFlux:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_CustomControlNetWeightsFlux',
-                "input_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            display_name='ControlNet Custom Weights [Flux] 🛂🅐🅒🅝',
-                "input_1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet',
-                "input_2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            inputs=[
-                "input_3": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('input_0', default=1.0, max=10.0, min=0.0, step=0.001),
-                "input_4": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('input_1', default=1.0, max=10.0, min=0.0, step=0.001),
-                "input_5": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('input_2', default=1.0, max=10.0, min=0.0, step=0.001),
-                "input_6": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('input_3', default=1.0, max=10.0, min=0.0, step=0.001),
-                "input_7": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('input_4', default=1.0, max=10.0, min=0.0, step=0.001),
-                "input_8": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('input_5', default=1.0, max=10.0, min=0.0, step=0.001),
-                "input_9": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('input_6', default=1.0, max=10.0, min=0.0, step=0.001),
-                "input_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('input_7', default=1.0, max=10.0, min=0.0, step=0.001),
-                "input_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('input_8', default=1.0, max=10.0, min=0.0, step=0.001),
-                "input_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('input_9', default=1.0, max=10.0, min=0.0, step=0.001),
-                "input_13": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('input_10', default=1.0, max=10.0, min=0.0, step=0.001),
-                "input_14": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('input_11', default=1.0, max=10.0, min=0.0, step=0.001),
-                "input_15": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('input_12', default=1.0, max=10.0, min=0.0, step=0.001),
-                "input_16": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('input_13', default=1.0, max=10.0, min=0.0, step=0.001),
-                "input_17": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('input_14', default=1.0, max=10.0, min=0.0, step=0.001),
-                "input_18": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('input_15', default=1.0, max=10.0, min=0.0, step=0.001),
-            },
+                io.Float.Input('input_16', default=1.0, max=10.0, min=0.0, step=0.001),
-            "optional": {
+                io.Float.Input('input_17', default=1.0, max=10.0, min=0.0, step=0.001),
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
+                io.Float.Input('input_18', default=1.0, max=10.0, min=0.0, step=0.001),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
-            },
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
-        }
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = WEIGHTS_RETURN_NAMES
-    FUNCTION = "load_weights"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
+    @classmethod
-
+    def execute(cls, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
-    def load_weights(self, input_0, input_1, input_2, input_3, input_4, input_5, input_6, 
                      input_7, input_8, input_9, input_10, input_11, input_12, input_13,
                      input_14, input_15, input_16, input_17, input_18,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
@@ -236,154 +217,162 @@ class CustomControlNetWeightsFlux:
                          input_6, input_7, input_8, input_9, input_10, input_11,
                          input_12, input_13, input_14, input_15, input_16, input_17, input_18]
         weights = ControlWeights.controlnet(weights_input=weights_input, uncond_multiplier=uncond_multiplier, extras=cn_extras, disable_applied_to=True)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
+class CustomControlNetWeightsAnima(io.ComfyNode):
-class CustomControlNetWeightsAnima:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        required = {
+        return io.Schema(
-            f"block_{index}": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001})
+            node_id='ACN_CustomControlNetWeightsAnima',
-            for index in range(28)
+            display_name='ControlNet Custom Weights [Anima] 🛂🅐🅒🅝',
-        }
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet',
-        return {
+            inputs=[
-            "required": required,
+                io.Float.Input('block_0', default=1.0, max=10.0, min=0.0, step=0.001),
-            "optional": {
+                io.Float.Input('block_1', default=1.0, max=10.0, min=0.0, step=0.001),
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                io.Float.Input('block_2', default=1.0, max=10.0, min=0.0, step=0.001),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+                io.Float.Input('block_3', default=1.0, max=10.0, min=0.0, step=0.001),
-            },
+                io.Float.Input('block_4', default=1.0, max=10.0, min=0.0, step=0.001),
-            "hidden": {
+                io.Float.Input('block_5', default=1.0, max=10.0, min=0.0, step=0.001),
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+                io.Float.Input('block_6', default=1.0, max=10.0, min=0.0, step=0.001),
-            },
+                io.Float.Input('block_7', default=1.0, max=10.0, min=0.0, step=0.001),
-        }
+                io.Float.Input('block_8', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_9', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_10', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_11', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_12', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_13', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_14', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_15', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_16', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_17', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_18', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_19', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_20', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_21', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_22', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_23', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_24', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_25', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_26', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('block_27', default=1.0, max=10.0, min=0.0, step=0.001),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
+            ],
+            outputs=[
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
 
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
+    @classmethod
-    RETURN_NAMES = WEIGHTS_RETURN_NAMES
+    def execute(cls, uncond_multiplier: float=1.0, cn_extras: dict[str]={}, **kwargs):
-    FUNCTION = "load_weights"
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
-
-    def load_weights(self, uncond_multiplier: float=1.0, cn_extras: dict[str]={}, **kwargs):
         weights = [kwargs[f"block_{index}"] for index in range(28)]
         control_weights = ControlWeights.controllllite(
             weights_input=weights,
             uncond_multiplier=uncond_multiplier,
             extras=cn_extras,
         )
-        return (control_weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=control_weights)))
+        return io.NodeOutput(control_weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=control_weights)))
 
-
+class SoftT2IAdapterWeights(io.ComfyNode):
-class SoftT2IAdapterWeights:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_SoftT2IAdapterWeights',
-                "input_0": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            display_name='T2IAdapter Soft Weights 🛂🅐🅒🅝',
-                "input_1": ("FLOAT", {"default": 0.62, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter',
-                "input_2": ("FLOAT", {"default": 0.825, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            inputs=[
-                "input_3": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('input_0', default=0.25, max=10.0, min=0.0, step=0.001),
-            },
+                io.Float.Input('input_1', default=0.62, max=10.0, min=0.0, step=0.001),
-            "optional": {
+                io.Float.Input('input_2', default=0.825, max=10.0, min=0.0, step=0.001),
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
+                io.Float.Input('input_3', default=1.0, max=10.0, min=0.0, step=0.001),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
-            },
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
-        }
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = WEIGHTS_RETURN_NAMES
-    FUNCTION = "load_weights"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"
+    @classmethod
-
+    def execute(cls, input_0, input_1, input_2, input_3,
-    def load_weights(self, input_0, input_1, input_2, input_3,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
-        return CustomT2IAdapterWeights.load_weights(self, input_0=input_0, input_1=input_1, input_2=input_2, input_3=input_3,
+        return CustomT2IAdapterWeights.execute(input_0=input_0, input_1=input_1, input_2=input_2, input_3=input_3,
                                                     uncond_multiplier=uncond_multiplier, cn_extras=cn_extras)
 
-
+class CustomT2IAdapterWeights(io.ComfyNode):
-class CustomT2IAdapterWeights:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_CustomT2IAdapterWeights',
-                "input_0": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            display_name='T2IAdapter Custom Weights 🛂🅐🅒🅝',
-                "input_1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter',
-                "input_2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+            inputs=[
-                "input_3": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
+                io.Float.Input('input_0', default=1.0, max=10.0, min=0.0, step=0.001),
-            },
+                io.Float.Input('input_1', default=1.0, max=10.0, min=0.0, step=0.001),
-            "optional": {
+                io.Float.Input('input_2', default=1.0, max=10.0, min=0.0, step=0.001),
-                "uncond_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}, ),
+                io.Float.Input('input_3', default=1.0, max=10.0, min=0.0, step=0.001),
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+                io.Float.Input('uncond_multiplier', optional=True, default=1.0, max=1.0, min=0.0, step=0.01),
-            },
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('CONTROL_NET_WEIGHTS').Output('CN_WEIGHTS', is_output_list=False),
-        }
+                io.Custom('TIMESTEP_KEYFRAME').Output('TK_SHORTCUT', is_output_list=False)
+            ]
+        )
     
-    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
-    RETURN_NAMES = WEIGHTS_RETURN_NAMES
-    FUNCTION = "load_weights"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"
+    @classmethod
-
+    def execute(cls, input_0, input_1, input_2, input_3,
-    def load_weights(self, input_0, input_1, input_2, input_3,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         weights = [input_0, input_1, input_2, input_3]
         weights = get_properly_arranged_t2i_weights(weights)
         weights = ControlWeights.t2iadapter(weights_input=weights, uncond_multiplier=uncond_multiplier, extras=cn_extras, disable_applied_to=True)
-        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
+        return io.NodeOutput(weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
-
+class ExtrasMiddleMultNode(io.ComfyNode):
-class ExtrasMiddleMultNode:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_ExtrasMiddleMult',
-                "middle_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}),
+            display_name='Middle Weight Extras 🛂🅐🅒🅝',
-            },
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights/extras',
-            "optional": {
+            inputs=[
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+                io.Float.Input('middle_mult', default=1.0, max=10.0, min=0.0, step=0.001),
-            },
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            }
+                io.Custom('CN_WEIGHTS_EXTRAS').Output('cn_extras', is_output_list=False)
-        }
+            ]
+        )
     
-    RETURN_TYPES = ("CN_WEIGHTS_EXTRAS",)
-    RETURN_NAMES = ("cn_extras",)
-    FUNCTION = "create_extras"
 
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/extras"
+    @classmethod
-
+    def execute(cls, middle_mult: float, cn_extras: dict[str]={}):
-    def create_extras(self, middle_mult: float, cn_extras: dict[str]={}):
         cn_extras = cn_extras.copy()
         cn_extras[Extras.MIDDLE_MULT] = middle_mult
-        return (cn_extras,)
+        return io.NodeOutput(cn_extras,)
 
-
+class AnimaLLLiteExtras(io.ComfyNode):
-class AnimaLLLiteExtras:
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
-        return {
+        return io.Schema(
-            "required": {
+            node_id='ACN_AnimaLLLiteExtras',
-                "inpaint_mask": ("MASK",),
+            display_name='Anima LLLite Extras 🛂🅐🅒🅝',
-            },
+            category='Adv-ControlNet 🛂🅐🅒🅝/weights/extras',
-            "optional": {
+            inputs=[
-                "cn_extras": ("CN_WEIGHTS_EXTRAS",),
+                io.Mask.Input('inpaint_mask'),
-            },
+                io.Custom('CN_WEIGHTS_EXTRAS').Input('cn_extras', optional=True)
-            "hidden": {
+            ],
-                "autosize": ("ACNAUTOSIZE", {"padding": 0}),
+            outputs=[
-            },
+                io.Custom('CN_WEIGHTS_EXTRAS').Output('cn_extras', is_output_list=False)
-        }
+            ]
+        )
 
-    RETURN_TYPES = ("CN_WEIGHTS_EXTRAS",)
+    @classmethod
-    RETURN_NAMES = ("cn_extras",)
+    def execute(cls, inpaint_mask: Tensor, cn_extras: dict[str]={}):
-    FUNCTION = "create_extras"
-    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/extras"
-
-    def create_extras(self, inpaint_mask: Tensor, cn_extras: dict[str]={}):
         cn_extras = cn_extras.copy()
         cn_extras[AnimaLLLiteConst.INPAINT_MASK] = inpaint_mask.clone()
-        return (cn_extras,)
+        return io.NodeOutput(cn_extras,)

web/js/autosize.js

@@ -1,53 +0,0 @@
-import { app } from '../../../scripts/app.js'
-
-function addResizeHook(node, padding, useOldMin=false) {
-    let origOnCreated = node.onNodeCreated
-    node.onNodeCreated = function() {
-        let r = origOnCreated?.apply(this, arguments)
-        let size = this.computeSize();
-        size[0] += padding || 0;
-        if (useOldMin) {
-            //equal to LiteGraph.NODE_WIDTH*1.5*1.5
-            size[0] = Math.max(size[0], 315)
-        }
-        this.setSize(size);
-        return r
-    }
-}
-
-app.registerExtension({
-    name: "AdvancedControlNet.autosize",
-    async beforeRegisterNodeDef(nodeType, nodeData, app) {
-        //since python_module is based off folder path,
-        //it could be changed by users and should only be used as fallback
-        if (nodeData?.name?.startsWith("ACN_")
-            || nodeData.python_module == 'custom_nodes.ComfyUI-Advanced-ControlNet') {
-            if (nodeData?.input?.hidden?.autosize) {
-                addResizeHook(nodeType.prototype, nodeData.input.hidden.autosize[1]?.padding)
-            } else if (!nodeData?.input?.optional?.autosize) {
-                addResizeHook(nodeType.prototype, 0, true)
-            }
-        }
-    },
-    async getCustomWidgets() {
-        return {
-            ACNAUTOSIZE(node, inputName, inputData) {
-                let w = {
-                    name : inputName,
-                    type : "ACN.AUTOSIZE",
-                    value : "",
-                    options : {"serialize": false},
-                    computeSize : function(width) {
-                        return [0, -4];
-                    }
-                }
-                if (!node.widgets) {
-                    node.widgets = []
-                }
-                node.widgets.push(w)
-                addResizeHook(node, inputData[1].padding);
-                return w;
-            }
-        }
-    }
-});

web/js/documentation.js

@@ -1,293 +0,0 @@
-import { app } from '../../../scripts/app.js'
-
-function chainCallback(object, property, callback) {
-    if (object == undefined) {
-        //This should not happen.
-        console.error("Tried to add callback to non-existant object")
-        return;
-    }
-    if (property in object && object[property]) {
-        const callback_orig = object[property]
-        object[property] = function () {
-            const r = callback_orig.apply(this, arguments);
-            callback.apply(this, arguments);
-            return r
-        };
-    } else {
-        object[property] = callback;
-    }
-}
-var helpDOM;
-function initHelpDOM() {
-    let parentDOM = document.createElement("div");
-    document.body.appendChild(parentDOM)
-    parentDOM.appendChild(helpDOM)
-    helpDOM.className = "litegraph";
-    let scrollbarStyle = document.createElement('style');
-    scrollbarStyle.innerHTML = `
-    <style id="scroll-properties">
-            * {
-                scrollbar-width: 6px;
-                scrollbar-color: #0003  #0000;
-            }
-            ::-webkit-scrollbar {
-                background: transparent;
-                width: 6px;
-            }
-            ::-webkit-scrollbar-thumb {
-                background: #0005;
-                border-radius: 20px
-            }
-            ::-webkit-scrollbar-button {
-                display: none;
-            }
-            .VHS_loopedvideo::-webkit-media-controls-mute-button {
-                display:none;
-            }
-            .VHS_loopedvideo::-webkit-media-controls-fullscreen-button {
-                display:none;
-            }
-        </style>
-    `
-    parentDOM.appendChild(scrollbarStyle)
-    chainCallback(app.canvas, "onDrawForeground", function (ctx, visible_rect){
-        let n = helpDOM.node
-        if (!n || !n?.graph) {
-            parentDOM.style['left'] = '-5000px'
-            return
-        }
-        //draw : function(ctx, node, widgetWidth, widgetY, height) {
-        //update widget position, even if off screen
-        const transform = ctx.getTransform();
-        const scale = app.canvas.ds.scale;//gets the litegraph zoom
-        //calculate coordinates with account for browser zoom
-        const bcr = app.canvas.canvas.getBoundingClientRect()
-        const x = transform.e*scale/transform.a + bcr.x;
-        const y = transform.f*scale/transform.a + bcr.y;
-        //TODO: text reflows at low zoom. investigate alternatives
-        Object.assign(parentDOM.style, {
-            left: (x+(n.pos[0] + n.size[0]+15)*scale) + "px",
-            top: (y+(n.pos[1]-LiteGraph.NODE_TITLE_HEIGHT)*scale) + "px",
-            width: "400px",
-            minHeight: "100px",
-            maxHeight: "600px",
-            overflowY: 'scroll',
-            transformOrigin: '0 0',
-            transform: 'scale(' + scale + ',' + scale +')',
-            fontSize: '18px',
-            backgroundColor: LiteGraph.NODE_DEFAULT_BGCOLOR,
-            boxShadow: '0 0 10px black',
-            borderRadius: '4px',
-            padding: '3px',
-            zIndex: 3,
-            position: "absolute",
-            display: 'inline',
-        });
-    });
-    function setCollapse(el, doCollapse) {
-        if (doCollapse) {
-            el.children[0].children[0].innerHTML = '+'
-            Object.assign(el.children[1].style, {
-                color: '#CCC',
-                overflowX: 'hidden',
-                width: '0px',
-                minWidth: 'calc(100% - 20px)',
-                textOverflow: 'ellipsis',
-                whiteSpace: 'nowrap',
-            })
-            for (let child of el.children[1].children) {
-                if (child.style.display != 'none'){
-                    child.origDisplay = child.style.display
-                }
-                child.style.display = 'none'
-            }
-        } else {
-            el.children[0].children[0].innerHTML = '-'
-            Object.assign(el.children[1].style, {
-                color: '',
-                overflowX: '',
-                width: '100%',
-                minWidth: '',
-                textOverflow: '',
-                whiteSpace: '',
-            })
-            for (let child of el.children[1].children) {
-                child.style.display = child.origDisplay
-            }
-        }
-    }
-    helpDOM.collapseOnClick = function() {
-        let doCollapse = this.children[0].innerHTML == '-'
-        setCollapse(this.parentElement, doCollapse)
-    }
-    helpDOM.selectHelp = function(name, value) {
-        //attempt to navigate to name in help
-        function collapseUnlessMatch(items,t) {
-            var match = items.querySelector('[vhs_title="' + t + '"]')
-            if (!match) {
-                for (let i of items.children) {
-                    if (i.innerHTML.slice(0,t.length+5).includes(t)) {
-                        match = i
-                        break
-                    }
-                }
-            }
-            if (!match) {
-                return null
-            }
-            //For longer documentation items with fewer collapsable elements,
-            //scroll to make sure the entirety of the selected item is visible
-            //This has the unfortunate side effect of trying to scroll the main
-            //window if the documentation windows is forcibly offscreen,
-            //but it's easy to simply scroll the main window back and seems to
-            //have no visual side effects
-            match.scrollIntoView(false)
-            window.scrollTo(0,0)
-            for (let i of items.querySelectorAll('.VHS_collapse')) {
-                if (i.contains(match)) {
-                    setCollapse(i, false)
-                } else {
-                    setCollapse(i, true)
-                }
-            }
-            return match
-        }
-        let target = collapseUnlessMatch(helpDOM, name)
-        if (target && value) {
-            collapseUnlessMatch(target, value)
-        }
-    }
-
-    helpDOM.addHelp = function(node, nodeType, description) {
-        if (!description) {
-            return
-        }
-        //Pad computed size for the clickable question mark
-        let originalComputeSize = node.computeSize
-        node.computeSize = function() {
-            let size = originalComputeSize.apply(this, arguments)
-            if (!this.title) {
-                return size
-            }
-            let title_width = this.title.length * 0.6 * LiteGraph.NODE_TEXT_SIZE
-            size[0] = Math.max(size[0], title_width + LiteGraph.NODE_TITLE_HEIGHT)
-            return size
-        }
-
-        node.description = description
-        chainCallback(node, "onDrawForeground", function (ctx) {
-            //draw question mark
-            ctx.save()
-            ctx.font = 'bold 20px Arial'
-            ctx.fillText("?", this.size[0]-17, -8)
-            ctx.restore()
-        })
-        chainCallback(node, "onMouseDown", function (e, pos, canvas) {
-            //On click would be preferred, but this'll be good enough
-            if (pos[1] < 0 && pos[0] + LiteGraph.NODE_TITLE_HEIGHT > this.size[0]) {
-                //corner question mark clicked
-                if (helpDOM.node == this) {
-                    helpDOM.node = undefined
-                } else {
-                    helpDOM.node = this;
-                    helpDOM.innerHTML = this.description || "no help provided ".repeat(20)
-                    for (let e of helpDOM.querySelectorAll('.VHS_collapse')) {
-                        e.children[0].onclick = helpDOM.collapseOnClick
-                        e.children[0].style.cursor = 'pointer'
-                    }
-                    for (let e of helpDOM.querySelectorAll('.VHS_precollapse')) {
-                        setCollapse(e, true)
-                    }
-                }
-                return true
-            }
-        })
-        let timeout = null
-        chainCallback(node, "onMouseMove", function (e, pos, canvas) {
-            if (timeout) {
-                clearTimeout(timeout)
-                timeout = null
-            }
-            if (helpDOM.node != this) {
-                return
-            }
-            timeout = setTimeout(() => {
-                let n = this
-                if (pos[0] > 0 && pos[0] < n.size[0]
-                    && pos[1] > 0 && pos[1] < n.size[1]) {
-                    //TODO: provide help specific to element clicked
-                    let inputRows = Math.max(n.inputs.length, n.outputs.length)
-                    if (pos[1] < LiteGraph.NODE_SLOT_HEIGHT * inputRows) {
-                        let row = Math.floor((pos[1] - 7) / LiteGraph.NODE_SLOT_HEIGHT)
-                        if (pos[0] < n.size[0]/2) {
-                            if (row < n.inputs.length) {
-                                helpDOM.selectHelp(n.inputs[row].name)
-                            }
-                        } else {
-                            if (row < n.outputs.length) {
-                                helpDOM.selectHelp(n.outputs[row].name)
-                            }
-                        }
-                    } else {
-                        //probably widget, but widgets have variable height.
-                        let basey = LiteGraph.NODE_SLOT_HEIGHT * inputRows + 6
-                        for (let w of n.widgets) {
-                            if (w.y) {
-                                basey = w.y
-                            }
-                            let wheight = LiteGraph.NODE_WIDGET_HEIGHT+4
-                            if (w.computeSize) {
-                                wheight = w.computeSize(n.size[0])[1]
-                            }
-                            if (pos[1] < basey + wheight) {
-                                helpDOM.selectHelp(w.name, w.value)
-                                break
-                            }
-                            basey += wheight
-                        }
-                    }
-                }
-            }, 500)
-        })
-        chainCallback(node, "onMouseLeave", function (e, pos, canvas) {
-            if (timeout) {
-                clearTimeout(timeout)
-                timeout = null
-            }
-        });
-    }
-}
-
-
-
-app.registerExtension({
-    name: "AdvancedControlNet.documentation",
-    async init() {
-        if (app.VHSHelp) {
-            helpDOM = app.VHSHelp
-        } else {
-            helpDOM = document.createElement("div");
-            initHelpDOM()
-            app.VHSHelp = helpDOM
-        }
-    },
-    async beforeRegisterNodeDef(nodeType, nodeData, app) {
-        // NOTE: May need manual adjusting for the few non-namespaced nodes
-        if(nodeData?.name?.startsWith("ACN_") && nodeData.description) {
-            let description = nodeData.description
-            let el = document.createElement("div")
-            el.innerHTML = description
-            if (!el.children.length) {
-                //Is plaintext. Do minor convenience formatting
-                let chunks = description.split('\n')
-                nodeData.description = chunks[0]
-                description = chunks.join('<br>')
-            } else {
-                nodeData.description = el.querySelector('#VHS_shortdesc')?.innerHTML || el.children[1]?.firstChild?.innerHTML
-            }
-            chainCallback(nodeType.prototype, "onNodeCreated", function () {
-                helpDOM.addHelp(this, nodeType, description)
-            })
-        }
-    },
-});