try removing kjnodes from repo

.gitignore

@@ -20,3 +20,4 @@ venv/
 *.log
 web_custom_versions/
 .DS_Store
+custom_nodes/ComfyUI-KJNodes

app.py

@@ -8,7 +8,8 @@ from typing import Any, Mapping, Sequence, Union
 import gradio as gr
 import spaces
 import torch
-from huggingface_hub import hf_hub_download
+from huggingface_hub import snapshot_download, hf_hub_download
+
 
 from nodes import NODE_CLASS_MAPPINGS
 
@@ -27,7 +28,12 @@ hf_hub_download(
     filename="codeformer-v0.1.0.pth",
     local_dir="models/facerestore_models",
 )
-
+snapshot_download(
+    repo_id="multimodalart/flux-style-shaping",
+    repo_type="space",                        # It's a Space repository [oai_citation_attribution:3‡github.com](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/_snapshot_download.py#:~:text=repo_type%20%28%60str%60%2C%20)
+    local_dir=".",                            # Download to current working directory (Space runtime)
+    allow_patterns="custom_nodes/ComfyUI-KJNodes/*",  # Only files in this folder [oai_citation_attribution:4‡huggingface.co](https://huggingface.co/docs/huggingface_hub/en/guides/download#:~:text=,nlp%22%2C%20allow_patterns%3D%22%2A.json)
+)
 
 def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
     """Returns the value at the given index of a sequence or mapping.

custom_nodes/ComfyUI-KJNodes-main/.gitignore

@@ -1,11 +0,0 @@
-__pycache__
-/venv
-*.code-workspace
-.history
-.vscode
-*.ckpt
-*.pth
-types
-models
-jsconfig.json
-custom_dimensions.json

custom_nodes/ComfyUI-KJNodes-main/LICENSE

@@ -1,674 +0,0 @@
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-section 13, concerning interaction through a network will apply to the
-combination as such.
-
-  14. Revised Versions of this License.
-
-  The Free Software Foundation may publish revised and/or new versions of
-the GNU General Public License from time to time.  Such new versions will
-be similar in spirit to the present version, but may differ in detail to
-address new problems or concerns.
-
-  Each version is given a distinguishing version number.  If the
-Program specifies that a certain numbered version of the GNU General
-Public License "or any later version" applies to it, you have the
-option of following the terms and conditions either of that numbered
-version or of any later version published by the Free Software
-Foundation.  If the Program does not specify a version number of the
-GNU General Public License, you may choose any version ever published
-by the Free Software Foundation.
-
-  If the Program specifies that a proxy can decide which future
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-to choose that version for the Program.
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-
-  15. Disclaimer of Warranty.
-
-  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
-APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
-HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
-OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
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-ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
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-  16. Limitation of Liability.
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-  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
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-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
-  17. Interpretation of Sections 15 and 16.
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-  If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
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-    Copyright (C) <year>  <name of author>
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-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
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-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
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-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
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-parts of the General Public License.  Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
-  You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<https://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.  But first, please read
-<https://www.gnu.org/licenses/why-not-lgpl.html>.

custom_nodes/ComfyUI-KJNodes-main/README.md

@@ -1,65 +0,0 @@
-# KJNodes for ComfyUI
-
-Various quality of life and masking related -nodes and scripts made by combining functionality of existing nodes for ComfyUI.
-
-I know I'm bad at documentation, especially this project that has grown from random practice nodes to... too many lines in one file.
-I have however started to add descriptions to the nodes themselves, there's a small ? you can click for info what the node does.
-This is still work in progress, like everything else.
-
-# Installation
-1. Clone this repo into `custom_nodes` folder.
-2. Install dependencies: `pip install -r requirements.txt`
-   or if you use the portable install, run this in ComfyUI_windows_portable -folder:
-
-  `python_embeded\python.exe -m pip install -r ComfyUI\custom_nodes\ComfyUI-KJNodes\requirements.txt`
-   
-
-## Javascript
-
-### browserstatus.js
-Sets the favicon to green circle when not processing anything, sets it to red when processing and shows progress percentage and the length of your queue. 
-Default off, needs to be enabled from options, overrides Custom-Scripts favicon when enabled.
-
-## Nodes:
-
-### Set/Get
-
-Javascript nodes to set and get constants to reduce unnecessary lines. Takes in and returns anything, purely visual nodes.
-On the right click menu of these nodes there's now an options to visualize the paths, as well as option to jump to the corresponding node on the other end.
-
-**Known limitations**:
-  - Will not work with any node that dynamically sets it's outpute, such as reroute or other Set/Get node
-  - Will not work when directly connected to a bypassed node
-  - Other possible conflicts with javascript based nodes.
-
-### ColorToMask
-
-RBG color value to mask, works with batches and AnimateDiff.
-
-### ConditioningMultiCombine
-
-Combine any number of conditions, saves space.
-
-### ConditioningSetMaskAndCombine
-
-Mask and combine two sets of conditions, saves space.
-
-### GrowMaskWithBlur
-
-Grows or shrinks (with negative values) mask, option to invert input, returns mask and inverted mask. Additionally Blurs the mask, this is a slow operation especially with big batches.
-
-### RoundMask
-
-![image](https://github.com/kijai/ComfyUI-KJNodes/assets/40791699/52c85202-f74e-4b96-9dac-c8bda5ddcc40)
-
-### WidgetToString
-Outputs the value of a widget on any node as a string
-![example of use](docs/images/2024-04-03_20_49_29-ComfyUI.png)
-
-Enable node id display from Manager menu, to get the ID of the node you want to read a widget from:
-![enable node id display](docs/images/319121636-706b5081-9120-4a29-bd76-901691ada688.png)
-
-Use the node id of the target node, and add the name of the widget to read from
-![use node id and widget name](docs/images/319121566-05f66385-7568-4b1f-8bbc-11053660b02f.png)
-
-Recreating or reloading the target node will change its id, and the WidgetToString node will no longer be able to find it until you update the node id value with the new id.

custom_nodes/ComfyUI-KJNodes-main/__init__.py

@@ -1,227 +0,0 @@
-from .nodes.nodes import *
-from .nodes.curve_nodes import *
-from .nodes.batchcrop_nodes import *
-from .nodes.audioscheduler_nodes import *
-from .nodes.image_nodes import *
-from .nodes.intrinsic_lora_nodes import *
-from .nodes.mask_nodes import *
-from .nodes.model_optimization_nodes import *
-NODE_CONFIG = {
-    #constants
-    "BOOLConstant": {"class": BOOLConstant, "name": "BOOL Constant"},
-    "INTConstant": {"class": INTConstant, "name": "INT Constant"},
-    "FloatConstant": {"class": FloatConstant, "name": "Float Constant"},
-    "StringConstant": {"class": StringConstant, "name": "String Constant"},
-    "StringConstantMultiline": {"class": StringConstantMultiline, "name": "String Constant Multiline"},
-    #conditioning
-    "ConditioningMultiCombine": {"class": ConditioningMultiCombine, "name": "Conditioning Multi Combine"},
-    "ConditioningSetMaskAndCombine": {"class": ConditioningSetMaskAndCombine, "name": "ConditioningSetMaskAndCombine"},
-    "ConditioningSetMaskAndCombine3": {"class": ConditioningSetMaskAndCombine3, "name": "ConditioningSetMaskAndCombine3"},
-    "ConditioningSetMaskAndCombine4": {"class": ConditioningSetMaskAndCombine4, "name": "ConditioningSetMaskAndCombine4"},
-    "ConditioningSetMaskAndCombine5": {"class": ConditioningSetMaskAndCombine5, "name": "ConditioningSetMaskAndCombine5"},
-    "CondPassThrough": {"class": CondPassThrough},
-    #masking
-    "DownloadAndLoadCLIPSeg": {"class": DownloadAndLoadCLIPSeg, "name": "(Down)load CLIPSeg"},
-    "BatchCLIPSeg": {"class": BatchCLIPSeg, "name": "Batch CLIPSeg"},
-    "ColorToMask": {"class": ColorToMask, "name": "Color To Mask"},
-    "CreateGradientMask": {"class": CreateGradientMask, "name": "Create Gradient Mask"},
-    "CreateTextMask": {"class": CreateTextMask, "name": "Create Text Mask"},
-    "CreateAudioMask": {"class": CreateAudioMask, "name": "Create Audio Mask"},
-    "CreateFadeMask": {"class": CreateFadeMask, "name": "Create Fade Mask"},
-    "CreateFadeMaskAdvanced": {"class": CreateFadeMaskAdvanced, "name": "Create Fade Mask Advanced"},
-    "CreateFluidMask": {"class": CreateFluidMask, "name": "Create Fluid Mask"},
-    "CreateShapeMask": {"class": CreateShapeMask, "name": "Create Shape Mask"},
-    "CreateVoronoiMask": {"class": CreateVoronoiMask, "name": "Create Voronoi Mask"},
-    "CreateMagicMask": {"class": CreateMagicMask, "name": "Create Magic Mask"},
-    "GetMaskSizeAndCount": {"class": GetMaskSizeAndCount, "name": "Get Mask Size & Count"},
-    "GrowMaskWithBlur": {"class": GrowMaskWithBlur, "name": "Grow Mask With Blur"},
-    "MaskBatchMulti": {"class": MaskBatchMulti, "name": "Mask Batch Multi"},
-    "OffsetMask": {"class": OffsetMask, "name": "Offset Mask"},
-    "RemapMaskRange": {"class": RemapMaskRange, "name": "Remap Mask Range"},
-    "ResizeMask": {"class": ResizeMask, "name": "Resize Mask"},
-    "RoundMask": {"class": RoundMask, "name": "Round Mask"},
-    "SeparateMasks": {"class": SeparateMasks, "name": "Separate Masks"},
-    #images
-    "AddLabel": {"class": AddLabel, "name": "Add Label"},
-    "ColorMatch": {"class": ColorMatch, "name": "Color Match"},
-    "ImageTensorList": {"class": ImageTensorList, "name": "Image Tensor List"},
-    "CrossFadeImages": {"class": CrossFadeImages, "name": "Cross Fade Images"},
-    "CrossFadeImagesMulti": {"class": CrossFadeImagesMulti, "name": "Cross Fade Images Multi"},
-    "GetImagesFromBatchIndexed": {"class": GetImagesFromBatchIndexed, "name": "Get Images From Batch Indexed"},
-    "GetImageRangeFromBatch": {"class": GetImageRangeFromBatch, "name": "Get Image or Mask Range From Batch"},
-    "GetLatentRangeFromBatch": {"class": GetLatentRangeFromBatch, "name": "Get Latent Range From Batch"},
-    "GetImageSizeAndCount": {"class": GetImageSizeAndCount, "name": "Get Image Size & Count"},
-    "FastPreview": {"class": FastPreview, "name": "Fast Preview"},
-    "ImageAndMaskPreview": {"class": ImageAndMaskPreview},
-    "ImageAddMulti": {"class": ImageAddMulti, "name": "Image Add Multi"},
-    "ImageBatchMulti": {"class": ImageBatchMulti, "name": "Image Batch Multi"},
-    "ImageBatchRepeatInterleaving": {"class": ImageBatchRepeatInterleaving},
-    "ImageBatchTestPattern": {"class": ImageBatchTestPattern, "name": "Image Batch Test Pattern"},
-    "ImageConcanate": {"class": ImageConcanate, "name": "Image Concatenate"},
-    "ImageConcatFromBatch": {"class": ImageConcatFromBatch, "name": "Image Concatenate From Batch"},
-    "ImageConcatMulti": {"class": ImageConcatMulti, "name": "Image Concatenate Multi"},
-    "ImageCropByMask": {"class": ImageCropByMask, "name": "Image Crop By Mask"},
-    "ImageCropByMaskAndResize": {"class": ImageCropByMaskAndResize, "name": "Image Crop By Mask And Resize"},
-    "ImageCropByMaskBatch": {"class": ImageCropByMaskBatch, "name": "Image Crop By Mask Batch"},
-    "ImageUncropByMask": {"class": ImageUncropByMask, "name": "Image Uncrop By Mask"},
-    "ImageGrabPIL": {"class": ImageGrabPIL, "name": "Image Grab PIL"},
-    "ImageGridComposite2x2": {"class": ImageGridComposite2x2, "name": "Image Grid Composite 2x2"},
-    "ImageGridComposite3x3": {"class": ImageGridComposite3x3, "name": "Image Grid Composite 3x3"},
-    "ImageGridtoBatch": {"class": ImageGridtoBatch, "name": "Image Grid To Batch"},
-    "ImageNoiseAugmentation": {"class": ImageNoiseAugmentation, "name": "Image Noise Augmentation"},
-    "ImageNormalize_Neg1_To_1": {"class": ImageNormalize_Neg1_To_1, "name": "Image Normalize -1 to 1"},
-    "ImagePass": {"class": ImagePass},
-    "ImagePadKJ": {"class": ImagePadKJ, "name": "ImagePad KJ"},
-    "ImagePadForOutpaintMasked": {"class": ImagePadForOutpaintMasked, "name": "Image Pad For Outpaint Masked"},
-    "ImagePadForOutpaintTargetSize": {"class": ImagePadForOutpaintTargetSize, "name": "Image Pad For Outpaint Target Size"},
-    "ImagePrepForICLora": {"class": ImagePrepForICLora, "name": "Image Prep For ICLora"},
-    "ImageResizeKJ": {"class": ImageResizeKJ, "name": "Resize Image"},
-    "ImageUpscaleWithModelBatched": {"class": ImageUpscaleWithModelBatched, "name": "Image Upscale With Model Batched"},
-    "InsertImagesToBatchIndexed": {"class": InsertImagesToBatchIndexed, "name": "Insert Images To Batch Indexed"},
-    "InsertLatentToIndexed": {"class": InsertLatentToIndex, "name": "Insert Latent To Index"},
-    "LoadAndResizeImage": {"class": LoadAndResizeImage, "name": "Load & Resize Image"},
-    "LoadImagesFromFolderKJ": {"class": LoadImagesFromFolderKJ, "name": "Load Images From Folder (KJ)"},
-    "MergeImageChannels": {"class": MergeImageChannels, "name": "Merge Image Channels"},
-    "PreviewAnimation": {"class": PreviewAnimation, "name": "Preview Animation"},
-    "RemapImageRange": {"class": RemapImageRange, "name": "Remap Image Range"},
-    "ReverseImageBatch": {"class": ReverseImageBatch, "name": "Reverse Image Batch"},
-    "ReplaceImagesInBatch": {"class": ReplaceImagesInBatch, "name": "Replace Images In Batch"},
-    "SaveImageWithAlpha": {"class": SaveImageWithAlpha, "name": "Save Image With Alpha"},
-    "SaveImageKJ": {"class": SaveImageKJ, "name": "Save Image KJ"},
-    "ShuffleImageBatch": {"class": ShuffleImageBatch, "name": "Shuffle Image Batch"},
-    "SplitImageChannels": {"class": SplitImageChannels, "name": "Split Image Channels"},
-    "TransitionImagesMulti": {"class": TransitionImagesMulti, "name": "Transition Images Multi"},
-    "TransitionImagesInBatch": {"class": TransitionImagesInBatch, "name": "Transition Images In Batch"},
-    #batch cropping
-    "BatchCropFromMask": {"class": BatchCropFromMask, "name": "Batch Crop From Mask"},
-    "BatchCropFromMaskAdvanced": {"class": BatchCropFromMaskAdvanced, "name": "Batch Crop From Mask Advanced"},
-    "FilterZeroMasksAndCorrespondingImages": {"class": FilterZeroMasksAndCorrespondingImages},
-    "InsertImageBatchByIndexes": {"class": InsertImageBatchByIndexes, "name": "Insert Image Batch By Indexes"},
-    "BatchUncrop": {"class": BatchUncrop, "name": "Batch Uncrop"},
-    "BatchUncropAdvanced": {"class": BatchUncropAdvanced, "name": "Batch Uncrop Advanced"},
-    "SplitBboxes": {"class": SplitBboxes, "name": "Split Bboxes"},
-    "BboxToInt": {"class": BboxToInt, "name": "Bbox To Int"},
-    "BboxVisualize": {"class": BboxVisualize, "name": "Bbox Visualize"},
-    #noise
-    "GenerateNoise": {"class": GenerateNoise, "name": "Generate Noise"},
-    "FlipSigmasAdjusted": {"class": FlipSigmasAdjusted, "name": "Flip Sigmas Adjusted"},
-    "InjectNoiseToLatent": {"class": InjectNoiseToLatent, "name": "Inject Noise To Latent"},
-    "CustomSigmas": {"class": CustomSigmas, "name": "Custom Sigmas"},
-    #utility
-    "StringToFloatList": {"class": StringToFloatList, "name": "String to Float List"},
-    "WidgetToString": {"class": WidgetToString, "name": "Widget To String"},
-    "SaveStringKJ": {"class": SaveStringKJ, "name": "Save String KJ"},
-    "DummyOut": {"class": DummyOut, "name": "Dummy Out"},
-    "GetLatentsFromBatchIndexed": {"class": GetLatentsFromBatchIndexed, "name": "Get Latents From Batch Indexed"},
-    "ScaleBatchPromptSchedule": {"class": ScaleBatchPromptSchedule, "name": "Scale Batch Prompt Schedule"},
-    "CameraPoseVisualizer": {"class": CameraPoseVisualizer, "name": "Camera Pose Visualizer"},
-    "AppendStringsToList": {"class": AppendStringsToList, "name": "Append Strings To List"},
-    "JoinStrings": {"class": JoinStrings, "name": "Join Strings"},
-    "JoinStringMulti": {"class": JoinStringMulti, "name": "Join String Multi"},
-    "SomethingToString": {"class": SomethingToString, "name": "Something To String"},
-    "Sleep": {"class": Sleep, "name": "Sleep"},
-    "VRAM_Debug": {"class": VRAM_Debug, "name": "VRAM Debug"},
-    "SomethingToString": {"class": SomethingToString, "name": "Something To String"},
-    "EmptyLatentImagePresets": {"class": EmptyLatentImagePresets, "name": "Empty Latent Image Presets"},
-    "EmptyLatentImageCustomPresets": {"class": EmptyLatentImageCustomPresets, "name": "Empty Latent Image Custom Presets"},
-    "ModelPassThrough": {"class": ModelPassThrough, "name": "ModelPass"},
-    "ModelSaveKJ": {"class": ModelSaveKJ, "name": "Model Save KJ"},
-    "SetShakkerLabsUnionControlNetType": {"class": SetShakkerLabsUnionControlNetType, "name": "Set Shakker Labs Union ControlNet Type"},
-    "StyleModelApplyAdvanced": {"class": StyleModelApplyAdvanced, "name": "Style Model Apply Advanced"},
-    #audioscheduler stuff
-    "NormalizedAmplitudeToMask": {"class": NormalizedAmplitudeToMask},
-    "NormalizedAmplitudeToFloatList": {"class": NormalizedAmplitudeToFloatList},
-    "OffsetMaskByNormalizedAmplitude": {"class": OffsetMaskByNormalizedAmplitude},
-    "ImageTransformByNormalizedAmplitude": {"class": ImageTransformByNormalizedAmplitude},
-    "AudioConcatenate": {"class": AudioConcatenate},
-    #curve nodes
-    "SplineEditor": {"class": SplineEditor, "name": "Spline Editor"},
-    "CreateShapeImageOnPath": {"class": CreateShapeImageOnPath, "name": "Create Shape Image On Path"},
-    "CreateShapeMaskOnPath": {"class": CreateShapeMaskOnPath, "name": "Create Shape Mask On Path"},
-    "CreateTextOnPath": {"class": CreateTextOnPath, "name": "Create Text On Path"},
-    "CreateGradientFromCoords": {"class": CreateGradientFromCoords, "name": "Create Gradient From Coords"},
-    "CutAndDragOnPath": {"class": CutAndDragOnPath, "name": "Cut And Drag On Path"},
-    "GradientToFloat": {"class": GradientToFloat, "name": "Gradient To Float"},
-    "WeightScheduleExtend": {"class": WeightScheduleExtend, "name": "Weight Schedule Extend"},
-    "MaskOrImageToWeight": {"class": MaskOrImageToWeight, "name": "Mask Or Image To Weight"},
-    "WeightScheduleConvert": {"class": WeightScheduleConvert, "name": "Weight Schedule Convert"},
-    "FloatToMask": {"class": FloatToMask, "name": "Float To Mask"},
-    "FloatToSigmas": {"class": FloatToSigmas, "name": "Float To Sigmas"},
-    "SigmasToFloat": {"class": SigmasToFloat, "name": "Sigmas To Float"},
-    "PlotCoordinates": {"class": PlotCoordinates, "name": "Plot Coordinates"},
-    "InterpolateCoords": {"class": InterpolateCoords, "name": "Interpolate Coords"},
-    "PointsEditor": {"class": PointsEditor, "name": "Points Editor"},
-    #experimental
-    "StabilityAPI_SD3": {"class": StabilityAPI_SD3, "name": "Stability API SD3"},
-    "SoundReactive": {"class": SoundReactive, "name": "Sound Reactive"},
-    "StableZero123_BatchSchedule": {"class": StableZero123_BatchSchedule, "name": "Stable Zero123 Batch Schedule"},
-    "SV3D_BatchSchedule": {"class": SV3D_BatchSchedule, "name": "SV3D Batch Schedule"},
-    "LoadResAdapterNormalization": {"class": LoadResAdapterNormalization},
-    "Superprompt": {"class": Superprompt, "name": "Superprompt"},
-    "GLIGENTextBoxApplyBatchCoords": {"class": GLIGENTextBoxApplyBatchCoords},
-    "Intrinsic_lora_sampling": {"class": Intrinsic_lora_sampling, "name": "Intrinsic Lora Sampling"},
-    "CheckpointPerturbWeights": {"class": CheckpointPerturbWeights, "name": "CheckpointPerturbWeights"},
-    "Screencap_mss": {"class": Screencap_mss, "name": "Screencap mss"},
-    "WebcamCaptureCV2": {"class": WebcamCaptureCV2, "name": "Webcam Capture CV2"},
-    "DifferentialDiffusionAdvanced": {"class": DifferentialDiffusionAdvanced, "name": "Differential Diffusion Advanced"},
-    "FluxBlockLoraLoader": {"class": FluxBlockLoraLoader, "name": "Flux Block Lora Loader"},
-    "FluxBlockLoraSelect": {"class": FluxBlockLoraSelect, "name": "Flux Block Lora Select"},
-    "HunyuanVideoBlockLoraSelect": {"class": HunyuanVideoBlockLoraSelect, "name": "Hunyuan Video Block Lora Select"},
-    "CustomControlNetWeightsFluxFromList": {"class": CustomControlNetWeightsFluxFromList, "name": "Custom ControlNet Weights Flux From List"},
-    "CheckpointLoaderKJ": {"class": CheckpointLoaderKJ, "name": "CheckpointLoaderKJ"},
-    "DiffusionModelLoaderKJ": {"class": DiffusionModelLoaderKJ, "name": "Diffusion Model Loader KJ"},
-    "TorchCompileModelFluxAdvanced": {"class": TorchCompileModelFluxAdvanced, "name": "TorchCompileModelFluxAdvanced"},
-    "TorchCompileModelHyVideo": {"class": TorchCompileModelHyVideo, "name": "TorchCompileModelHyVideo"},
-    "TorchCompileVAE": {"class": TorchCompileVAE, "name": "TorchCompileVAE"},
-    "TorchCompileControlNet": {"class": TorchCompileControlNet, "name": "TorchCompileControlNet"},
-    "PatchModelPatcherOrder": {"class": PatchModelPatcherOrder, "name": "Patch Model Patcher Order"},
-    "TorchCompileLTXModel": {"class": TorchCompileLTXModel, "name": "TorchCompileLTXModel"},
-    "TorchCompileCosmosModel": {"class": TorchCompileCosmosModel, "name": "TorchCompileCosmosModel"},
-    "TorchCompileModelWanVideo": {"class": TorchCompileModelWanVideo, "name": "TorchCompileModelWanVideo"},
-    "PathchSageAttentionKJ": {"class": PathchSageAttentionKJ, "name": "Patch Sage Attention KJ"},
-    "LeapfusionHunyuanI2VPatcher": {"class": LeapfusionHunyuanI2V, "name": "Leapfusion Hunyuan I2V Patcher"},
-    "VAELoaderKJ": {"class": VAELoaderKJ, "name": "VAELoader KJ"},
-    "ScheduledCFGGuidance": {"class": ScheduledCFGGuidance, "name": "Scheduled CFG Guidance"},
-    "ApplyRifleXRoPE_HunuyanVideo": {"class": ApplyRifleXRoPE_HunuyanVideo, "name": "Apply RifleXRoPE HunuyanVideo"},
-    "ApplyRifleXRoPE_WanVideo": {"class": ApplyRifleXRoPE_WanVideo, "name": "Apply RifleXRoPE WanVideo"},
-    "WanVideoTeaCacheKJ": {"class": WanVideoTeaCacheKJ, "name": "WanVideo Tea Cache (native)"},
-    "WanVideoEnhanceAVideoKJ": {"class": WanVideoEnhanceAVideoKJ, "name": "WanVideo Enhance A Video (native)"},
-    "SkipLayerGuidanceWanVideo": {"class": SkipLayerGuidanceWanVideo, "name": "Skip Layer Guidance WanVideo"},
-    "TimerNodeKJ": {"class": TimerNodeKJ, "name": "Timer Node KJ"},
-    "HunyuanVideoEncodeKeyframesToCond": {"class": HunyuanVideoEncodeKeyframesToCond, "name": "HunyuanVideo Encode Keyframes To Cond"},
-
-    #instance diffusion
-    "CreateInstanceDiffusionTracking": {"class": CreateInstanceDiffusionTracking},
-    "AppendInstanceDiffusionTracking": {"class": AppendInstanceDiffusionTracking},
-    "DrawInstanceDiffusionTracking": {"class": DrawInstanceDiffusionTracking},
-}
-
-def generate_node_mappings(node_config):
-    node_class_mappings = {}
-    node_display_name_mappings = {}
-
-    for node_name, node_info in node_config.items():
-        node_class_mappings[node_name] = node_info["class"]
-        node_display_name_mappings[node_name] = node_info.get("name", node_info["class"].__name__)
-
-    return node_class_mappings, node_display_name_mappings
-
-NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS = generate_node_mappings(NODE_CONFIG)
-
-__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
-
-WEB_DIRECTORY = "./web"
-
-from aiohttp import web
-from server import PromptServer
-from pathlib import Path
-
-if hasattr(PromptServer, "instance"):
-    try:
-        # NOTE: we add an extra static path to avoid comfy mechanism
-        # that loads every script in web.
-        PromptServer.instance.app.add_routes(
-            [web.static("/kjweb_async", (Path(__file__).parent.absolute() / "kjweb_async").as_posix())]
-        )
-    except:
-        pass

custom_nodes/ComfyUI-KJNodes-main/config.json

@@ -1,3 +0,0 @@
-{
-    "sai_api_key": "your_api_key_here"
-}

custom_nodes/ComfyUI-KJNodes-main/custom_dimensions_example.json

@@ -1,22 +0,0 @@
-[
-    {
-        "label": "SD",
-        "value": "512x512"
-    },
-    {
-        "label": "HD",
-        "value": "768x768"
-    },
-    {
-        "label": "Full HD",
-        "value": "1024x1024"
-    },
-    {
-        "label": "4k",
-        "value": "2048x2048"
-    },
-    {
-        "label": "SVD",
-        "value": "1024x576"
-    }
-]

custom_nodes/ComfyUI-KJNodes-main/example_workflows/leapfusion_hunyuuanvideo_i2v_native_testing.json

@@ -1,1188 +0,0 @@
-{
-  "last_node_id": 86,
-  "last_link_id": 144,
-  "nodes": [
-    {
-      "id": 62,
-      "type": "FluxGuidance",
-      "pos": [
-        -630,
-        -170
-      ],
-      "size": [
-        317.4000244140625,
-        58
-      ],
-      "flags": {},
-      "order": 13,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "conditioning",
-          "type": "CONDITIONING",
-          "link": 82
-        }
-      ],
-      "outputs": [
-        {
-          "name": "CONDITIONING",
-          "type": "CONDITIONING",
-          "links": [
-            83
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "FluxGuidance"
-      },
-      "widgets_values": [
-        6
-      ]
-    },
-    {
-      "id": 51,
-      "type": "KSamplerSelect",
-      "pos": [
-        -610,
-        -480
-      ],
-      "size": [
-        315,
-        58
-      ],
-      "flags": {},
-      "order": 0,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "SAMPLER",
-          "type": "SAMPLER",
-          "links": [
-            61
-          ]
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "KSamplerSelect"
-      },
-      "widgets_values": [
-        "euler"
-      ]
-    },
-    {
-      "id": 57,
-      "type": "VAEDecodeTiled",
-      "pos": [
-        -200,
-        90
-      ],
-      "size": [
-        315,
-        150
-      ],
-      "flags": {},
-      "order": 20,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "samples",
-          "type": "LATENT",
-          "link": 142
-        },
-        {
-          "name": "vae",
-          "type": "VAE",
-          "link": 74
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            105
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VAEDecodeTiled"
-      },
-      "widgets_values": [
-        128,
-        64,
-        64,
-        8
-      ]
-    },
-    {
-      "id": 65,
-      "type": "LoadImage",
-      "pos": [
-        -2212.498779296875,
-        -632.4085083007812
-      ],
-      "size": [
-        315,
-        314
-      ],
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            86
-          ],
-          "slot_index": 0
-        },
-        {
-          "name": "MASK",
-          "type": "MASK",
-          "links": null
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "LoadImage"
-      },
-      "widgets_values": [
-        "Mona-Lisa-oil-wood-panel-Leonardo-da.webp",
-        "image"
-      ]
-    },
-    {
-      "id": 64,
-      "type": "VAEEncode",
-      "pos": [
-        -1336.7884521484375,
-        -492.5806884765625
-      ],
-      "size": [
-        210,
-        46
-      ],
-      "flags": {},
-      "order": 14,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "pixels",
-          "type": "IMAGE",
-          "link": 144
-        },
-        {
-          "name": "vae",
-          "type": "VAE",
-          "link": 88
-        }
-      ],
-      "outputs": [
-        {
-          "name": "LATENT",
-          "type": "LATENT",
-          "links": [
-            137
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VAEEncode"
-      },
-      "widgets_values": []
-    },
-    {
-      "id": 44,
-      "type": "UNETLoader",
-      "pos": [
-        -2373.55029296875,
-        -193.91510009765625
-      ],
-      "size": [
-        459.56060791015625,
-        82
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-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "MODEL",
-          "type": "MODEL",
-          "links": [
-            135
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "UNETLoader"
-      },
-      "widgets_values": [
-        "hyvideo\\hunyuan_video_720_fp8_e4m3fn.safetensors",
-        "fp8_e4m3fn_fast"
-      ]
-    },
-    {
-      "id": 49,
-      "type": "VAELoader",
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-        -35.19633865356445
-      ],
-      "size": [
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-      ],
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "VAE",
-          "type": "VAE",
-          "links": [
-            74,
-            88
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VAELoader"
-      },
-      "widgets_values": [
-        "hyvid\\hunyuan_video_vae_bf16.safetensors"
-      ]
-    },
-    {
-      "id": 47,
-      "type": "DualCLIPLoader",
-      "pos": [
-        -2284.893798828125,
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-      ],
-      "size": [
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-      "order": 4,
-      "mode": 0,
-      "inputs": [],
-      "outputs": [
-        {
-          "name": "CLIP",
-          "type": "CLIP",
-          "links": [
-            56
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "DualCLIPLoader"
-      },
-      "widgets_values": [
-        "clip_l.safetensors",
-        "llava_llama3_fp16.safetensors",
-        "hunyuan_video",
-        "default"
-      ]
-    },
-    {
-      "id": 45,
-      "type": "CLIPTextEncode",
-      "pos": [
-        -1839.1649169921875,
-        143.5203094482422
-      ],
-      "size": [
-        400,
-        200
-      ],
-      "flags": {},
-      "order": 8,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "clip",
-          "type": "CLIP",
-          "link": 56
-        }
-      ],
-      "outputs": [
-        {
-          "name": "CONDITIONING",
-          "type": "CONDITIONING",
-          "links": [
-            69,
-            82
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "CLIPTextEncode"
-      },
-      "widgets_values": [
-        "woman puts on sunglasses"
-      ]
-    },
-    {
-      "id": 53,
-      "type": "EmptyHunyuanLatentVideo",
-      "pos": [
-        -1120,
-        90
-      ],
-      "size": [
-        315,
-        130
-      ],
-      "flags": {},
-      "order": 10,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "width",
-          "type": "INT",
-          "link": 89,
-          "widget": {
-            "name": "width"
-          }
-        },
-        {
-          "name": "height",
-          "type": "INT",
-          "link": 90,
-          "widget": {
-            "name": "height"
-          }
-        }
-      ],
-      "outputs": [
-        {
-          "name": "LATENT",
-          "type": "LATENT",
-          "links": [
-            119
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "EmptyHunyuanLatentVideo"
-      },
-      "widgets_values": [
-        960,
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-        65,
-        1
-      ]
-    },
-    {
-      "id": 55,
-      "type": "ConditioningZeroOut",
-      "pos": [
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-      ],
-      "size": [
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-      "mode": 0,
-      "inputs": [
-        {
-          "name": "conditioning",
-          "type": "CONDITIONING",
-          "link": 69
-        }
-      ],
-      "outputs": [
-        {
-          "name": "CONDITIONING",
-          "type": "CONDITIONING",
-          "links": [
-            70
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "ConditioningZeroOut"
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-    {
-      "id": 52,
-      "type": "BasicScheduler",
-      "pos": [
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-      "flags": {},
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-      "mode": 0,
-      "inputs": [
-        {
-          "name": "model",
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-          "link": 78
-        }
-      ],
-      "outputs": [
-        {
-          "name": "SIGMAS",
-          "type": "SIGMAS",
-          "links": [
-            62
-          ],
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "BasicScheduler"
-      },
-      "widgets_values": [
-        "simple",
-        20,
-        1
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-    },
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-      "id": 42,
-      "type": "SamplerCustom",
-      "pos": [
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-        10
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-      "size": [
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-        467.4666748046875
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-          "link": 77
-        },
-        {
-          "name": "positive",
-          "type": "CONDITIONING",
-          "link": 83
-        },
-        {
-          "name": "negative",
-          "type": "CONDITIONING",
-          "link": 70
-        },
-        {
-          "name": "sampler",
-          "type": "SAMPLER",
-          "link": 61
-        },
-        {
-          "name": "sigmas",
-          "type": "SIGMAS",
-          "link": 62
-        },
-        {
-          "name": "latent_image",
-          "type": "LATENT",
-          "link": 119
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-      "outputs": [
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-          "name": "output",
-          "type": "LATENT",
-          "links": null
-        },
-        {
-          "name": "denoised_output",
-          "type": "LATENT",
-          "links": [
-            141
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-          "slot_index": 1
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-      ],
-      "properties": {
-        "Node name for S&R": "SamplerCustom"
-      },
-      "widgets_values": [
-        true,
-        6,
-        "fixed",
-        1,
-        null
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-    {
-      "id": 84,
-      "type": "GetLatentRangeFromBatch",
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-          "type": "LATENT",
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-        }
-      ],
-      "outputs": [
-        {
-          "name": "LATENT",
-          "type": "LATENT",
-          "links": [
-            142
-          ],
-          "slot_index": 0
-        }
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-      "properties": {
-        "Node name for S&R": "GetLatentRangeFromBatch"
-      },
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-        },
-        {
-          "name": "audio",
-          "type": "AUDIO",
-          "link": null,
-          "shape": 7
-        },
-        {
-          "name": "meta_batch",
-          "type": "VHS_BatchManager",
-          "link": null,
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-        },
-        {
-          "name": "vae",
-          "type": "VAE",
-          "link": null,
-          "shape": 7
-        }
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-      "outputs": [
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-          "name": "Filenames",
-          "type": "VHS_FILENAMES",
-          "links": null
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "VHS_VideoCombine"
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-      "widgets_values": {
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-        "filename_prefix": "hyvidcomfy",
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-        "save_metadata": true,
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-        "pingpong": false,
-        "save_output": false,
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*(?:\\n{2,}|\\s*$))").replace("comment",S).replace(/tag/g,"(?!(?:a|em|strong|small|s|cite|q|dfn|abbr|data|time|code|var|samp|kbd|sub|sup|i|b|u|mark|ruby|rt|rp|bdi|bdo|span|br|wbr|ins|del|img)\\b)\\w+(?!:|[^\\w\\s@]*@)\\b").getRegex(),def:/^ *\[([^\]]+)\]: *<?([^\s>]+)>?(?: +(["(][^\n]+[")]))? *(?:\n+|$)/,heading:/^(#{1,6})(.*)(?:\n+|$)/,fences:f,lheading:/^(.+?)\n {0,3}(=+|-+) *(?:\n+|$)/,paragraph:k(z).replace("hr",m).replace("heading"," *#{1,6} *[^\n]").replace("lheading",$).replace("|table","").replace("blockquote"," {0,3}>").replace("|fences","").replace("|list","").replace("|html","").replace("|tag","").getRegex()},Q=/^\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/,v=/^( {2,}|\\)\n(?!\s*$)/,B="\\p{P}\\p{S}",C=k(/^((?![*_])[\spunctuation])/,"u").replace(/punctuation/g,B).getRegex(),M=k(/^(?:\*+(?:((?!\*)[punct])|[^\s*]))|^_+(?:((?!_)[punct])|([^\s_]))/,"u").replace(/punct/g,B).getRegex(),O=k("^[^_*]*?__[^_*]*?\\*[^_*]*?(?=__)|[^*]+(?=[^*])|(?!\\*)[punct](\\*+)(?=[\\s]|$)|[^punct\\s](\\*+)(?!\\*)(?=[punct\\s]|$)|(?!\\*)[punct\\s](\\*+)(?=[^punct\\s])|[\\s](\\*+)(?!\\*)(?=[punct])|(?!\\*)[punct](\\*+)(?!\\*)(?=[punct])|[^punct\\s](\\*+)(?=[^punct\\s])","gu").replace(/punct/g,B).getRegex(),D=k("^[^_*]*?\\*\\*[^_*]*?_[^_*]*?(?=\\*\\*)|[^_]+(?=[^_])|(?!_)[punct](_+)(?=[\\s]|$)|[^punct\\s](_+)(?!_)(?=[punct\\s]|$)|(?!_)[punct\\s](_+)(?=[^punct\\s])|[\\s](_+)(?!_)(?=[punct])|(?!_)[punct](_+)(?!_)(?=[punct])","gu").replace(/punct/g,B).getRegex(),j=k(/\\([punct])/,"gu").replace(/punct/g,B).getRegex(),H=k(/^<(scheme:[^\s\x00-\x1f<>]*|email)>/).replace("scheme",/[a-zA-Z][a-zA-Z0-9+.-]{1,31}/).replace("email",/[a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+(@)[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)+(?![-_])/).getRegex(),U=k(S).replace("(?:--\x3e|$)","--\x3e").getRegex(),X=k("^comment|^</[a-zA-Z][\\w:-]*\\s*>|^<[a-zA-Z][\\w-]*(?:attribute)*?\\s*/?>|^<\\?[\\s\\S]*?\\?>|^<![a-zA-Z]+\\s[\\s\\S]*?>|^<!\\[CDATA\\[[\\s\\S]*?\\]\\]>").replace("comment",U).replace("attribute",/\s+[a-zA-Z:_][\w.:-]*(?:\s*=\s*"[^"]*"|\s*=\s*'[^']*'|\s*=\s*[^\s"'=<>`]+)?/).getRegex(),F=/(?:\[(?:\\.|[^\[\]\\])*\]|\\.|`[^`]*`|[^\[\]\\`])*?/,N=k(/^!?\[(label)\]\(\s*(href)(?:\s+(title))?\s*\)/).replace("label",F).replace("href",/<(?:\\.|[^\n<>\\])+>|[^\s\x00-\x1f]*/).replace("title",/"(?:\\"?|[^"\\])*"|'(?:\\'?|[^'\\])*'|\((?:\\\)?|[^)\\])*\)/).getRegex(),G=k(/^!?\[(label)\]\[(ref)\]/).replace("label",F).replace("ref",T).getRegex(),J=k(/^!?\[(ref)\](?:\[\])?/).replace("ref",T).getRegex(),K={_backpedal:f,anyPunctuation:j,autolink:H,blockSkip:/\[[^[\]]*?\]\([^\(\)]*?\)|`[^`]*?`|<[^<>]*?>/g,br:v,code:/^(`+)([^`]|[^`][\s\S]*?[^`])\1(?!`)/,del:f,emStrongLDelim:M,emStrongRDelimAst:O,emStrongRDelimUnd:D,escape:Q,link:N,nolink:J,punctuation:C,reflink:G,reflinkSearch:k("reflink|nolink(?!\\()","g").replace("reflink",G).replace("nolink",J).getRegex(),tag:X,text:/^(`+|[^`])(?:(?= {2,}\n)|[\s\S]*?(?:(?=[\\<!\[`*_]|\b_|$)|[^ ](?= {2,}\n)))/,url:f},V={...K,link:k(/^!?\[(label)\]\((.*?)\)/).replace("label",F).getRegex(),reflink:k(/^!?\[(label)\]\s*\[([^\]]*)\]/).replace("label",F).getRegex()},W={...K,escape:k(Q).replace("])","~|])").getRegex(),url:k(/^((?:ftp|https?):\/\/|www\.)(?:[a-zA-Z0-9\-]+\.?)+[^\s<]*|^email/,"i").replace("email",/[A-Za-z0-9._+-]+(@)[a-zA-Z0-9-_]+(?:\.[a-zA-Z0-9-_]*[a-zA-Z0-9])+(?![-_])/).getRegex(),_backpedal:/(?:[^?!.,:;*_'"~()&]+|\([^)]*\)|&(?![a-zA-Z0-9]+;$)|[?!.,:;*_'"~)]+(?!$))+/,del:/^(~~?)(?=[^\s~])([\s\S]*?[^\s~])\1(?=[^~]|$)/,text:/^([`~]+|[^`~])(?:(?= {2,}\n)|(?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)|[\s\S]*?(?:(?=[\\<!\[`*~_]|\b_|https?:\/\/|ftp:\/\/|www\.|$)|[^ ](?= {2,}\n)|[^a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-](?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)))/},Y={...W,br:k(v).replace("{2,}","*").getRegex(),text:k(W.text).replace("\\b_","\\b_| {2,}\\n").replace(/\{2,\}/g,"*").getRegex()},ee={normal:q,gfm:L,pedantic:P},te={normal:K,gfm:W,breaks:Y,pedantic:V};class ne{tokens;options;state;tokenizer;inlineQueue;constructor(t){this.tokens=[],this.tokens.links=Object.create(null),this.options=t||e.defaults,this.options.tokenizer=this.options.tokenizer||new w,this.tokenizer=this.options.tokenizer,this.tokenizer.options=this.options,this.tokenizer.lexer=this,this.inlineQueue=[],this.state={inLink:!1,inRawBlock:!1,top:!0};const n={block:ee.normal,inline:te.normal};this.options.pedantic?(n.block=ee.pedantic,n.inline=te.pedantic):this.options.gfm&&(n.block=ee.gfm,this.options.breaks?n.inline=te.breaks:n.inline=te.gfm),this.tokenizer.rules=n}static get rules(){return{block:ee,inline:te}}static lex(e,t){return new ne(t).lex(e)}static lexInline(e,t){return new ne(t).inlineTokens(e)}lex(e){e=e.replace(/\r\n|\r/g,"\n"),this.blockTokens(e,this.tokens);for(let e=0;e<this.inlineQueue.length;e++){const t=this.inlineQueue[e];this.inlineTokens(t.src,t.tokens)}return this.inlineQueue=[],this.tokens}blockTokens(e,t=[]){let n,s,r,i;for(e=this.options.pedantic?e.replace(/\t/g,"    ").replace(/^ +$/gm,""):e.replace(/^( *)(\t+)/gm,((e,t,n)=>t+"    ".repeat(n.length)));e;)if(!(this.options.extensions&&this.options.extensions.block&&this.options.extensions.block.some((s=>!!(n=s.call({lexer:this},e,t))&&(e=e.substring(n.raw.length),t.push(n),!0)))))if(n=this.tokenizer.space(e))e=e.substring(n.raw.length),1===n.raw.length&&t.length>0?t[t.length-1].raw+="\n":t.push(n);else if(n=this.tokenizer.code(e))e=e.substring(n.raw.length),s=t[t.length-1],!s||"paragraph"!==s.type&&"text"!==s.type?t.push(n):(s.raw+="\n"+n.raw,s.text+="\n"+n.text,this.inlineQueue[this.inlineQueue.length-1].src=s.text);else if(n=this.tokenizer.fences(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.heading(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.hr(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.blockquote(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.list(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.html(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.def(e))e=e.substring(n.raw.length),s=t[t.length-1],!s||"paragraph"!==s.type&&"text"!==s.type?this.tokens.links[n.tag]||(this.tokens.links[n.tag]={href:n.href,title:n.title}):(s.raw+="\n"+n.raw,s.text+="\n"+n.raw,this.inlineQueue[this.inlineQueue.length-1].src=s.text);else if(n=this.tokenizer.table(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.lheading(e))e=e.substring(n.raw.length),t.push(n);else{if(r=e,this.options.extensions&&this.options.extensions.startBlock){let t=1/0;const n=e.slice(1);let s;this.options.extensions.startBlock.forEach((e=>{s=e.call({lexer:this},n),"number"==typeof s&&s>=0&&(t=Math.min(t,s))})),t<1/0&&t>=0&&(r=e.substring(0,t+1))}if(this.state.top&&(n=this.tokenizer.paragraph(r)))s=t[t.length-1],i&&"paragraph"===s.type?(s.raw+="\n"+n.raw,s.text+="\n"+n.text,this.inlineQueue.pop(),this.inlineQueue[this.inlineQueue.length-1].src=s.text):t.push(n),i=r.length!==e.length,e=e.substring(n.raw.length);else if(n=this.tokenizer.text(e))e=e.substring(n.raw.length),s=t[t.length-1],s&&"text"===s.type?(s.raw+="\n"+n.raw,s.text+="\n"+n.text,this.inlineQueue.pop(),this.inlineQueue[this.inlineQueue.length-1].src=s.text):t.push(n);else if(e){const t="Infinite loop on byte: "+e.charCodeAt(0);if(this.options.silent){console.error(t);break}throw new Error(t)}}return this.state.top=!0,t}inline(e,t=[]){return this.inlineQueue.push({src:e,tokens:t}),t}inlineTokens(e,t=[]){let n,s,r,i,l,o,a=e;if(this.tokens.links){const e=Object.keys(this.tokens.links);if(e.length>0)for(;null!=(i=this.tokenizer.rules.inline.reflinkSearch.exec(a));)e.includes(i[0].slice(i[0].lastIndexOf("[")+1,-1))&&(a=a.slice(0,i.index)+"["+"a".repeat(i[0].length-2)+"]"+a.slice(this.tokenizer.rules.inline.reflinkSearch.lastIndex))}for(;null!=(i=this.tokenizer.rules.inline.blockSkip.exec(a));)a=a.slice(0,i.index)+"["+"a".repeat(i[0].length-2)+"]"+a.slice(this.tokenizer.rules.inline.blockSkip.lastIndex);for(;null!=(i=this.tokenizer.rules.inline.anyPunctuation.exec(a));)a=a.slice(0,i.index)+"++"+a.slice(this.tokenizer.rules.inline.anyPunctuation.lastIndex);for(;e;)if(l||(o=""),l=!1,!(this.options.extensions&&this.options.extensions.inline&&this.options.extensions.inline.some((s=>!!(n=s.call({lexer:this},e,t))&&(e=e.substring(n.raw.length),t.push(n),!0)))))if(n=this.tokenizer.escape(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.tag(e))e=e.substring(n.raw.length),s=t[t.length-1],s&&"text"===n.type&&"text"===s.type?(s.raw+=n.raw,s.text+=n.text):t.push(n);else if(n=this.tokenizer.link(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.reflink(e,this.tokens.links))e=e.substring(n.raw.length),s=t[t.length-1],s&&"text"===n.type&&"text"===s.type?(s.raw+=n.raw,s.text+=n.text):t.push(n);else if(n=this.tokenizer.emStrong(e,a,o))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.codespan(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.br(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.del(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.autolink(e))e=e.substring(n.raw.length),t.push(n);else if(this.state.inLink||!(n=this.tokenizer.url(e))){if(r=e,this.options.extensions&&this.options.extensions.startInline){let t=1/0;const n=e.slice(1);let s;this.options.extensions.startInline.forEach((e=>{s=e.call({lexer:this},n),"number"==typeof s&&s>=0&&(t=Math.min(t,s))})),t<1/0&&t>=0&&(r=e.substring(0,t+1))}if(n=this.tokenizer.inlineText(r))e=e.substring(n.raw.length),"_"!==n.raw.slice(-1)&&(o=n.raw.slice(-1)),l=!0,s=t[t.length-1],s&&"text"===s.type?(s.raw+=n.raw,s.text+=n.text):t.push(n);else if(e){const t="Infinite loop on byte: "+e.charCodeAt(0);if(this.options.silent){console.error(t);break}throw new Error(t)}}else e=e.substring(n.raw.length),t.push(n);return t}}class se{options;constructor(t){this.options=t||e.defaults}code(e,t,n){const s=(t||"").match(/^\S*/)?.[0];return e=e.replace(/\n$/,"")+"\n",s?'<pre><code class="language-'+c(s)+'">'+(n?e:c(e,!0))+"</code></pre>\n":"<pre><code>"+(n?e:c(e,!0))+"</code></pre>\n"}blockquote(e){return`<blockquote>\n${e}</blockquote>\n`}html(e,t){return e}heading(e,t,n){return`<h${t}>${e}</h${t}>\n`}hr(){return"<hr>\n"}list(e,t,n){const s=t?"ol":"ul";return"<"+s+(t&&1!==n?' 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e}link(e,t,n){return""+n}image(e,t,n){return""+n}br(){return""}}class ie{options;renderer;textRenderer;constructor(t){this.options=t||e.defaults,this.options.renderer=this.options.renderer||new se,this.renderer=this.options.renderer,this.renderer.options=this.options,this.textRenderer=new re}static parse(e,t){return new ie(t).parse(e)}static parseInline(e,t){return new ie(t).parseInline(e)}parse(e,t=!0){let n="";for(let s=0;s<e.length;s++){const r=e[s];if(this.options.extensions&&this.options.extensions.renderers&&this.options.extensions.renderers[r.type]){const e=r,t=this.options.extensions.renderers[e.type].call({parser:this},e);if(!1!==t||!["space","hr","heading","code","table","blockquote","list","html","paragraph","text"].includes(e.type)){n+=t||"";continue}}switch(r.type){case"space":continue;case"hr":n+=this.renderer.hr();continue;case"heading":{const 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custom_nodes/ComfyUI-KJNodes-main/nodes/audioscheduler_nodes.py

@@ -1,251 +0,0 @@
-# to be used with https://github.com/a1lazydog/ComfyUI-AudioScheduler 
-import torch
-from torchvision.transforms import functional as TF
-from PIL import Image, ImageDraw
-import numpy as np
-from ..utility.utility import pil2tensor
-from nodes import MAX_RESOLUTION
-
-class NormalizedAmplitudeToMask:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-                    "normalized_amp": ("NORMALIZED_AMPLITUDE",),
-                    "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                    "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                    "frame_offset": ("INT", {"default": 0,"min": -255, "max": 255, "step": 1}),
-                    "location_x": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}),
-                    "location_y": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}),
-                    "size": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}),
-                    "shape": (
-                        [   
-                            'none',
-                            'circle',
-                            'square',
-                            'triangle',
-                        ],
-                        {
-                        "default": 'none'
-                        }),
-                    "color": (
-                        [   
-                            'white',
-                            'amplitude',
-                        ],
-                        {
-                        "default": 'amplitude'
-                        }),
-                     },}
-
-    CATEGORY = "KJNodes/audio"
-    RETURN_TYPES = ("MASK",)
-    FUNCTION = "convert"
-    DESCRIPTION = """
-Works as a bridge to the AudioScheduler -nodes:  
-https://github.com/a1lazydog/ComfyUI-AudioScheduler  
-Creates masks based on the normalized amplitude.
-"""
-
-    def convert(self, normalized_amp, width, height, frame_offset, shape, location_x, location_y, size, color):
-        # Ensure normalized_amp is an array and within the range [0, 1]
-        normalized_amp = np.clip(normalized_amp, 0.0, 1.0)
-
-        # Offset the amplitude values by rolling the array
-        normalized_amp = np.roll(normalized_amp, frame_offset)
-        
-        # Initialize an empty list to hold the image tensors
-        out = []
-        # Iterate over each amplitude value to create an image
-        for amp in normalized_amp:
-            # Scale the amplitude value to cover the full range of grayscale values
-            if color == 'amplitude':
-                grayscale_value = int(amp * 255)
-            elif color == 'white':
-                grayscale_value = 255
-            # Convert the grayscale value to an RGB format
-            gray_color = (grayscale_value, grayscale_value, grayscale_value)
-            finalsize = size * amp
-            
-            if shape == 'none':
-                shapeimage = Image.new("RGB", (width, height), gray_color)
-            else:
-                shapeimage = Image.new("RGB", (width, height), "black")
-
-            draw = ImageDraw.Draw(shapeimage)
-            if shape == 'circle' or shape == 'square':
-                # Define the bounding box for the shape
-                left_up_point = (location_x - finalsize, location_y - finalsize)
-                right_down_point = (location_x + finalsize,location_y + finalsize)
-                two_points = [left_up_point, right_down_point]
-
-                if shape == 'circle':
-                    draw.ellipse(two_points, fill=gray_color)
-                elif shape == 'square':
-                    draw.rectangle(two_points, fill=gray_color)
-                    
-            elif shape == 'triangle':
-                # Define the points for the triangle
-                left_up_point = (location_x - finalsize, location_y + finalsize) # bottom left
-                right_down_point = (location_x + finalsize, location_y + finalsize) # bottom right
-                top_point = (location_x, location_y) # top point
-                draw.polygon([top_point, left_up_point, right_down_point], fill=gray_color)
-            
-            shapeimage = pil2tensor(shapeimage)
-            mask = shapeimage[:, :, :, 0]
-            out.append(mask)
-        
-        return (torch.cat(out, dim=0),)
-    
-class NormalizedAmplitudeToFloatList:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-                    "normalized_amp": ("NORMALIZED_AMPLITUDE",),
-                     },}
-
-    CATEGORY = "KJNodes/audio"
-    RETURN_TYPES = ("FLOAT",)
-    FUNCTION = "convert"
-    DESCRIPTION = """
-Works as a bridge to the AudioScheduler -nodes:  
-https://github.com/a1lazydog/ComfyUI-AudioScheduler  
-Creates a list of floats from the normalized amplitude.
-"""
-
-    def convert(self, normalized_amp):
-        # Ensure normalized_amp is an array and within the range [0, 1]
-        normalized_amp = np.clip(normalized_amp, 0.0, 1.0)
-        return (normalized_amp.tolist(),)
-
-class OffsetMaskByNormalizedAmplitude:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "normalized_amp": ("NORMALIZED_AMPLITUDE",),
-                "mask": ("MASK",),
-                "x": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
-                "y": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
-                "rotate": ("BOOLEAN", { "default": False }),
-                "angle_multiplier": ("FLOAT", { "default": 0.0, "min": -1.0, "max": 1.0, "step": 0.001, "display": "number" }),
-            }
-        }
-
-    RETURN_TYPES = ("MASK",)
-    RETURN_NAMES = ("mask",)
-    FUNCTION = "offset"
-    CATEGORY = "KJNodes/audio"
-    DESCRIPTION = """
-Works as a bridge to the AudioScheduler -nodes:  
-https://github.com/a1lazydog/ComfyUI-AudioScheduler  
-Offsets masks based on the normalized amplitude.
-"""
-
-    def offset(self, mask, x, y, angle_multiplier, rotate, normalized_amp):
-
-         # Ensure normalized_amp is an array and within the range [0, 1]
-        offsetmask = mask.clone()
-        normalized_amp = np.clip(normalized_amp, 0.0, 1.0)
-       
-        batch_size, height, width = mask.shape
-
-        if rotate:
-            for i in range(batch_size):
-                rotation_amp = int(normalized_amp[i] * (360 * angle_multiplier))
-                rotation_angle = rotation_amp
-                offsetmask[i] = TF.rotate(offsetmask[i].unsqueeze(0), rotation_angle).squeeze(0)
-        if x != 0 or y != 0:
-            for i in range(batch_size):
-                offset_amp = normalized_amp[i] * 10
-                shift_x = min(x*offset_amp, width-1)
-                shift_y = min(y*offset_amp, height-1)
-                if shift_x != 0:
-                    offsetmask[i] = torch.roll(offsetmask[i], shifts=int(shift_x), dims=1)
-                if shift_y != 0:
-                    offsetmask[i] = torch.roll(offsetmask[i], shifts=int(shift_y), dims=0)
-        
-        return offsetmask,
-
-class ImageTransformByNormalizedAmplitude:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "normalized_amp": ("NORMALIZED_AMPLITUDE",),
-            "zoom_scale": ("FLOAT", { "default": 0.0, "min": -1.0, "max": 1.0, "step": 0.001, "display": "number" }),
-            "x_offset": ("INT", { "default": 0, "min": (1 -MAX_RESOLUTION), "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
-            "y_offset": ("INT", { "default": 0, "min": (1 -MAX_RESOLUTION), "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
-            "cumulative": ("BOOLEAN", { "default": False }),
-            "image": ("IMAGE",),
-        }}
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "amptransform"
-    CATEGORY = "KJNodes/audio"
-    DESCRIPTION = """
-Works as a bridge to the AudioScheduler -nodes:  
-https://github.com/a1lazydog/ComfyUI-AudioScheduler  
-Transforms image based on the normalized amplitude.
-"""
-
-    def amptransform(self, image, normalized_amp, zoom_scale, cumulative, x_offset, y_offset):
-        # Ensure normalized_amp is an array and within the range [0, 1]
-        normalized_amp = np.clip(normalized_amp, 0.0, 1.0)
-        transformed_images = []
-
-        # Initialize the cumulative zoom factor
-        prev_amp = 0.0
-
-        for i in range(image.shape[0]):
-            img = image[i]  # Get the i-th image in the batch
-            amp = normalized_amp[i]  # Get the corresponding amplitude value
-
-            # Incrementally increase the cumulative zoom factor
-            if cumulative:
-                prev_amp += amp
-                amp += prev_amp
-
-            # Convert the image tensor from BxHxWxC to CxHxW format expected by torchvision
-            img = img.permute(2, 0, 1)
-            
-            # Convert PyTorch tensor to PIL Image for processing
-            pil_img = TF.to_pil_image(img)
-            
-            # Calculate the crop size based on the amplitude
-            width, height = pil_img.size
-            crop_size = int(min(width, height) * (1 - amp * zoom_scale))
-            crop_size = max(crop_size, 1)
-            
-            # Calculate the crop box coordinates (centered crop)
-            left = (width - crop_size) // 2
-            top = (height - crop_size) // 2
-            right = (width + crop_size) // 2
-            bottom = (height + crop_size) // 2
-            
-            # Crop and resize back to original size
-            cropped_img = TF.crop(pil_img, top, left, crop_size, crop_size)
-            resized_img = TF.resize(cropped_img, (height, width))
-            
-            # Convert back to tensor in CxHxW format
-            tensor_img = TF.to_tensor(resized_img)
-            
-            # Convert the tensor back to BxHxWxC format
-            tensor_img = tensor_img.permute(1, 2, 0)
-            
-            # Offset the image based on the amplitude
-            offset_amp = amp * 10  # Calculate the offset magnitude based on the amplitude
-            shift_x = min(x_offset * offset_amp, img.shape[1] - 1)  # Calculate the shift in x direction
-            shift_y = min(y_offset * offset_amp, img.shape[0] - 1)  # Calculate the shift in y direction
-
-            # Apply the offset to the image tensor
-            if shift_x != 0:
-                tensor_img = torch.roll(tensor_img, shifts=int(shift_x), dims=1)
-            if shift_y != 0:
-                tensor_img = torch.roll(tensor_img, shifts=int(shift_y), dims=0)
-
-            # Add to the list
-            transformed_images.append(tensor_img)
-        
-        # Stack all transformed images into a batch
-        transformed_batch = torch.stack(transformed_images)
-        
-        return (transformed_batch,)

custom_nodes/ComfyUI-KJNodes-main/nodes/batchcrop_nodes.py

@@ -1,757 +0,0 @@
-from ..utility.utility import tensor2pil, pil2tensor
-from PIL import Image, ImageDraw, ImageFilter
-import numpy as np
-import torch
-from torchvision.transforms import Resize, CenterCrop, InterpolationMode
-import math
-
-#based on nodes from mtb https://github.com/melMass/comfy_mtb
-
-def bbox_to_region(bbox, target_size=None):
-    bbox = bbox_check(bbox, target_size)
-    return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3])
-
-def bbox_check(bbox, target_size=None):
-    if not target_size:
-        return bbox
-
-    new_bbox = (
-        bbox[0],
-        bbox[1],
-        min(target_size[0] - bbox[0], bbox[2]),
-        min(target_size[1] - bbox[1], bbox[3]),
-    )
-    return new_bbox
-
-class BatchCropFromMask:
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "original_images": ("IMAGE",),
-                "masks": ("MASK",),
-                "crop_size_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}),
-                "bbox_smooth_alpha": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
-            },
-        }
-
-    RETURN_TYPES = (
-        "IMAGE",
-        "IMAGE",
-        "BBOX",
-        "INT",
-        "INT",
-    )
-    RETURN_NAMES = (
-        "original_images",
-        "cropped_images",
-        "bboxes",
-        "width",
-        "height",
-    )
-    FUNCTION = "crop"
-    CATEGORY = "KJNodes/masking"
-
-    def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha):
-        if alpha == 0:
-            return prev_bbox_size
-        return round(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size)
-
-    def smooth_center(self, prev_center, curr_center, alpha=0.5):
-        if alpha == 0:
-            return prev_center
-        return (
-            round(alpha * curr_center[0] + (1 - alpha) * prev_center[0]),
-            round(alpha * curr_center[1] + (1 - alpha) * prev_center[1])
-        )
-
-    def crop(self, masks, original_images, crop_size_mult, bbox_smooth_alpha):
- 
-        bounding_boxes = []
-        cropped_images = []
-
-        self.max_bbox_width = 0
-        self.max_bbox_height = 0
-
-        # First, calculate the maximum bounding box size across all masks
-        curr_max_bbox_width = 0
-        curr_max_bbox_height = 0
-        for mask in masks:
-            _mask = tensor2pil(mask)[0]
-            non_zero_indices = np.nonzero(np.array(_mask))
-            min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
-            min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
-            width = max_x - min_x
-            height = max_y - min_y
-            curr_max_bbox_width = max(curr_max_bbox_width, width)
-            curr_max_bbox_height = max(curr_max_bbox_height, height)
-
-        # Smooth the changes in the bounding box size
-        self.max_bbox_width = self.smooth_bbox_size(self.max_bbox_width, curr_max_bbox_width, bbox_smooth_alpha)
-        self.max_bbox_height = self.smooth_bbox_size(self.max_bbox_height, curr_max_bbox_height, bbox_smooth_alpha)
-
-        # Apply the crop size multiplier
-        self.max_bbox_width = round(self.max_bbox_width * crop_size_mult)
-        self.max_bbox_height = round(self.max_bbox_height * crop_size_mult)
-        bbox_aspect_ratio = self.max_bbox_width / self.max_bbox_height
-
-        # Then, for each mask and corresponding image...
-        for i, (mask, img) in enumerate(zip(masks, original_images)):
-            _mask = tensor2pil(mask)[0]
-            non_zero_indices = np.nonzero(np.array(_mask))
-            min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
-            min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
-            
-            # Calculate center of bounding box
-            center_x = np.mean(non_zero_indices[1])
-            center_y = np.mean(non_zero_indices[0])
-            curr_center = (round(center_x), round(center_y))
-
-            # If this is the first frame, initialize prev_center with curr_center
-            if not hasattr(self, 'prev_center'):
-                self.prev_center = curr_center
-
-            # Smooth the changes in the center coordinates from the second frame onwards
-            if i > 0:
-                center = self.smooth_center(self.prev_center, curr_center, bbox_smooth_alpha)
-            else:
-                center = curr_center
-
-            # Update prev_center for the next frame
-            self.prev_center = center
-
-            # Create bounding box using max_bbox_width and max_bbox_height
-            half_box_width = round(self.max_bbox_width / 2)
-            half_box_height = round(self.max_bbox_height / 2)
-            min_x = max(0, center[0] - half_box_width)
-            max_x = min(img.shape[1], center[0] + half_box_width)
-            min_y = max(0, center[1] - half_box_height)
-            max_y = min(img.shape[0], center[1] + half_box_height)
-
-            # Append bounding box coordinates
-            bounding_boxes.append((min_x, min_y, max_x - min_x, max_y - min_y))
-
-            # Crop the image from the bounding box
-            cropped_img = img[min_y:max_y, min_x:max_x, :]
-            
-            # Calculate the new dimensions while maintaining the aspect ratio
-            new_height = min(cropped_img.shape[0], self.max_bbox_height)
-            new_width = round(new_height * bbox_aspect_ratio)
-
-            # Resize the image
-            resize_transform = Resize((new_height, new_width))
-            resized_img = resize_transform(cropped_img.permute(2, 0, 1))
-
-            # Perform the center crop to the desired size
-            crop_transform = CenterCrop((self.max_bbox_height, self.max_bbox_width)) # swap the order here if necessary
-            cropped_resized_img = crop_transform(resized_img)
-
-            cropped_images.append(cropped_resized_img.permute(1, 2, 0))
-
-        cropped_out = torch.stack(cropped_images, dim=0)
-        
-        return (original_images, cropped_out, bounding_boxes, self.max_bbox_width, self.max_bbox_height, )
-
-class BatchUncrop:
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "original_images": ("IMAGE",),
-                "cropped_images": ("IMAGE",),
-                "bboxes": ("BBOX",),
-                "border_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}, ),
-                "crop_rescale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
-                "border_top": ("BOOLEAN", {"default": True}),
-                "border_bottom": ("BOOLEAN", {"default": True}),
-                "border_left": ("BOOLEAN", {"default": True}),
-                "border_right": ("BOOLEAN", {"default": True}),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "uncrop"
-
-    CATEGORY = "KJNodes/masking"
-
-    def uncrop(self, original_images, cropped_images, bboxes, border_blending, crop_rescale, border_top, border_bottom, border_left, border_right):
-        def inset_border(image, border_width, border_color, border_top, border_bottom, border_left, border_right):
-            draw = ImageDraw.Draw(image)
-            width, height = image.size
-            if border_top:
-                draw.rectangle((0, 0, width, border_width), fill=border_color)
-            if border_bottom:
-                draw.rectangle((0, height - border_width, width, height), fill=border_color)
-            if border_left:
-                draw.rectangle((0, 0, border_width, height), fill=border_color)
-            if border_right:
-                draw.rectangle((width - border_width, 0, width, height), fill=border_color)
-            return image
-
-        if len(original_images) != len(cropped_images):
-            raise ValueError(f"The number of original_images ({len(original_images)}) and cropped_images ({len(cropped_images)}) should be the same")
-
-        # Ensure there are enough bboxes, but drop the excess if there are more bboxes than images
-        if len(bboxes) > len(original_images):
-            print(f"Warning: Dropping excess bounding boxes. Expected {len(original_images)}, but got {len(bboxes)}")
-            bboxes = bboxes[:len(original_images)]
-        elif len(bboxes) < len(original_images):
-            raise ValueError("There should be at least as many bboxes as there are original and cropped images")
-
-        input_images = tensor2pil(original_images)
-        crop_imgs = tensor2pil(cropped_images)
-        
-        out_images = []
-        for i in range(len(input_images)):
-            img = input_images[i]
-            crop = crop_imgs[i]
-            bbox = bboxes[i]
-            
-            # uncrop the image based on the bounding box
-            bb_x, bb_y, bb_width, bb_height = bbox
-
-            paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size)
-            
-            # scale factors
-            scale_x = crop_rescale
-            scale_y = crop_rescale
-
-            # scaled paste_region
-            paste_region = (round(paste_region[0]*scale_x), round(paste_region[1]*scale_y), round(paste_region[2]*scale_x), round(paste_region[3]*scale_y))
-
-            # rescale the crop image to fit the paste_region
-            crop = crop.resize((round(paste_region[2]-paste_region[0]), round(paste_region[3]-paste_region[1])))
-            crop_img = crop.convert("RGB")
-   
-            if border_blending > 1.0:
-                border_blending = 1.0
-            elif border_blending < 0.0:
-                border_blending = 0.0
-
-            blend_ratio = (max(crop_img.size) / 2) * float(border_blending)
-
-            blend = img.convert("RGBA")
-            mask = Image.new("L", img.size, 0)
-
-            mask_block = Image.new("L", (paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]), 255)
-            mask_block = inset_border(mask_block, round(blend_ratio / 2), (0), border_top, border_bottom, border_left, border_right)
-                      
-            mask.paste(mask_block, paste_region)
-            blend.paste(crop_img, paste_region)
-
-            mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio / 4))
-            mask = mask.filter(ImageFilter.GaussianBlur(radius=blend_ratio / 4))
-
-            blend.putalpha(mask)
-            img = Image.alpha_composite(img.convert("RGBA"), blend)
-            out_images.append(img.convert("RGB"))
-
-        return (pil2tensor(out_images),)
-
-class BatchCropFromMaskAdvanced:
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "original_images": ("IMAGE",),
-                "masks": ("MASK",),
-                "crop_size_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
-                "bbox_smooth_alpha": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
-            },
-        }
-
-    RETURN_TYPES = (
-        "IMAGE",
-        "IMAGE",
-        "MASK",
-        "IMAGE",
-        "MASK",
-        "BBOX",
-        "BBOX",
-        "INT",
-        "INT",
-    )
-    RETURN_NAMES = (
-        "original_images",
-        "cropped_images",
-        "cropped_masks",
-        "combined_crop_image",
-        "combined_crop_masks",
-        "bboxes",
-        "combined_bounding_box",
-        "bbox_width",
-        "bbox_height",
-    )
-    FUNCTION = "crop"
-    CATEGORY = "KJNodes/masking"
-
-    def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha):
-          return round(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size)
-
-    def smooth_center(self, prev_center, curr_center, alpha=0.5):
-        return (round(alpha * curr_center[0] + (1 - alpha) * prev_center[0]),
-                round(alpha * curr_center[1] + (1 - alpha) * prev_center[1]))
-
-    def crop(self, masks, original_images, crop_size_mult, bbox_smooth_alpha):
-        bounding_boxes = []
-        combined_bounding_box = []
-        cropped_images = []
-        cropped_masks = []
-        cropped_masks_out = []
-        combined_crop_out = []
-        combined_cropped_images = []
-        combined_cropped_masks = []
-        
-        def calculate_bbox(mask):
-            non_zero_indices = np.nonzero(np.array(mask))
-
-            # handle empty masks
-            min_x, max_x, min_y, max_y = 0, 0, 0, 0
-            if len(non_zero_indices[1]) > 0 and len(non_zero_indices[0]) > 0:
-                min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
-                min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
-
-            width = max_x - min_x
-            height = max_y - min_y
-            bbox_size = max(width, height)
-            return min_x, max_x, min_y, max_y, bbox_size
-
-        combined_mask = torch.max(masks, dim=0)[0]
-        _mask = tensor2pil(combined_mask)[0]
-        new_min_x, new_max_x, new_min_y, new_max_y, combined_bbox_size = calculate_bbox(_mask)
-        center_x = (new_min_x + new_max_x) / 2
-        center_y = (new_min_y + new_max_y) / 2
-        half_box_size = round(combined_bbox_size // 2)
-        new_min_x = max(0, round(center_x - half_box_size))
-        new_max_x = min(original_images[0].shape[1], round(center_x + half_box_size))
-        new_min_y = max(0, round(center_y - half_box_size))
-        new_max_y = min(original_images[0].shape[0], round(center_y + half_box_size))
-        
-        combined_bounding_box.append((new_min_x, new_min_y, new_max_x - new_min_x, new_max_y - new_min_y))   
-        
-        self.max_bbox_size = 0
-        
-        # First, calculate the maximum bounding box size across all masks
-        curr_max_bbox_size = max(calculate_bbox(tensor2pil(mask)[0])[-1] for mask in masks)
-        # Smooth the changes in the bounding box size
-        self.max_bbox_size = self.smooth_bbox_size(self.max_bbox_size, curr_max_bbox_size, bbox_smooth_alpha)
-        # Apply the crop size multiplier
-        self.max_bbox_size = round(self.max_bbox_size * crop_size_mult)
-        # Make sure max_bbox_size is divisible by 16, if not, round it upwards so it is
-        self.max_bbox_size = math.ceil(self.max_bbox_size / 16) * 16
-
-        if self.max_bbox_size > original_images[0].shape[0] or self.max_bbox_size > original_images[0].shape[1]:
-            # max_bbox_size can only be as big as our input's width or height, and it has to be even
-            self.max_bbox_size = math.floor(min(original_images[0].shape[0], original_images[0].shape[1]) / 2) * 2
-
-        # Then, for each mask and corresponding image...
-        for i, (mask, img) in enumerate(zip(masks, original_images)):
-            _mask = tensor2pil(mask)[0]
-            non_zero_indices = np.nonzero(np.array(_mask))
-
-            # check for empty masks
-            if len(non_zero_indices[0]) > 0 and len(non_zero_indices[1]) > 0:
-                min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
-                min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
-
-                # Calculate center of bounding box
-                center_x = np.mean(non_zero_indices[1])
-                center_y = np.mean(non_zero_indices[0])
-                curr_center = (round(center_x), round(center_y))
-
-                # If this is the first frame, initialize prev_center with curr_center
-                if not hasattr(self, 'prev_center'):
-                    self.prev_center = curr_center
-
-                # Smooth the changes in the center coordinates from the second frame onwards
-                if i > 0:
-                    center = self.smooth_center(self.prev_center, curr_center, bbox_smooth_alpha)
-                else:
-                    center = curr_center
-
-                # Update prev_center for the next frame
-                self.prev_center = center
-
-                # Create bounding box using max_bbox_size
-                half_box_size = self.max_bbox_size // 2
-                min_x = max(0, center[0] - half_box_size)
-                max_x = min(img.shape[1], center[0] + half_box_size)
-                min_y = max(0, center[1] - half_box_size)
-                max_y = min(img.shape[0], center[1] + half_box_size)
-
-                # Append bounding box coordinates
-                bounding_boxes.append((min_x, min_y, max_x - min_x, max_y - min_y))
-
-                # Crop the image from the bounding box
-                cropped_img = img[min_y:max_y, min_x:max_x, :]
-                cropped_mask = mask[min_y:max_y, min_x:max_x]
-
-                # Resize the cropped image to a fixed size
-                new_size = max(cropped_img.shape[0], cropped_img.shape[1])
-                resize_transform = Resize(new_size, interpolation=InterpolationMode.NEAREST, max_size=max(img.shape[0], img.shape[1]))
-                resized_mask = resize_transform(cropped_mask.unsqueeze(0).unsqueeze(0)).squeeze(0).squeeze(0)
-                resized_img = resize_transform(cropped_img.permute(2, 0, 1))
-                # Perform the center crop to the desired size
-                # Constrain the crop to the smaller of our bbox or our image so we don't expand past the image dimensions.
-                crop_transform = CenterCrop((min(self.max_bbox_size, resized_img.shape[1]), min(self.max_bbox_size, resized_img.shape[2])))
-
-                cropped_resized_img = crop_transform(resized_img)
-                cropped_images.append(cropped_resized_img.permute(1, 2, 0))
-
-                cropped_resized_mask = crop_transform(resized_mask)
-                cropped_masks.append(cropped_resized_mask)
-
-                combined_cropped_img = original_images[i][new_min_y:new_max_y, new_min_x:new_max_x, :]
-                combined_cropped_images.append(combined_cropped_img)
-
-                combined_cropped_mask = masks[i][new_min_y:new_max_y, new_min_x:new_max_x]
-                combined_cropped_masks.append(combined_cropped_mask)
-            else:
-                bounding_boxes.append((0, 0, img.shape[1], img.shape[0]))
-                cropped_images.append(img)
-                cropped_masks.append(mask)
-                combined_cropped_images.append(img)
-                combined_cropped_masks.append(mask)
-
-        cropped_out = torch.stack(cropped_images, dim=0)
-        combined_crop_out = torch.stack(combined_cropped_images, dim=0)
-        cropped_masks_out = torch.stack(cropped_masks, dim=0)
-        combined_crop_mask_out = torch.stack(combined_cropped_masks, dim=0)
-
-        return (original_images, cropped_out, cropped_masks_out, combined_crop_out, combined_crop_mask_out, bounding_boxes, combined_bounding_box, self.max_bbox_size, self.max_bbox_size)
-
-class FilterZeroMasksAndCorrespondingImages:
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "masks": ("MASK",),
-            },
-            "optional": {
-                "original_images": ("IMAGE",), 
-            },
-        }
-
-    RETURN_TYPES = ("MASK", "IMAGE", "IMAGE", "INDEXES",)
-    RETURN_NAMES = ("non_zero_masks_out", "non_zero_mask_images_out", "zero_mask_images_out", "zero_mask_images_out_indexes",)
-    FUNCTION = "filter"
-    CATEGORY = "KJNodes/masking"
-    DESCRIPTION = """
-Filter out all the empty (i.e. all zero) mask in masks  
-Also filter out all the corresponding images in original_images by indexes if provide  
-  
-original_images (optional): If provided, need have same length as masks.
-"""
-    
-    def filter(self, masks, original_images=None):
-        non_zero_masks = []
-        non_zero_mask_images = []
-        zero_mask_images = []
-        zero_mask_images_indexes = []
-        
-        masks_num = len(masks)
-        also_process_images = False
-        if original_images is not None:
-            imgs_num = len(original_images)
-            if len(original_images) == masks_num:
-                also_process_images = True
-            else:
-                print(f"[WARNING] ignore input: original_images, due to number of original_images ({imgs_num}) is not equal to number of masks ({masks_num})")
-        
-        for i in range(masks_num):
-            non_zero_num = np.count_nonzero(np.array(masks[i]))
-            if non_zero_num > 0:
-                non_zero_masks.append(masks[i])
-                if also_process_images:
-                    non_zero_mask_images.append(original_images[i])
-            else:
-                zero_mask_images.append(original_images[i])
-                zero_mask_images_indexes.append(i)
-
-        non_zero_masks_out = torch.stack(non_zero_masks, dim=0)
-        non_zero_mask_images_out = zero_mask_images_out = zero_mask_images_out_indexes = None
-        
-        if also_process_images:
-            non_zero_mask_images_out = torch.stack(non_zero_mask_images, dim=0)
-            if len(zero_mask_images) > 0:
-                zero_mask_images_out = torch.stack(zero_mask_images, dim=0)
-                zero_mask_images_out_indexes = zero_mask_images_indexes
-
-        return (non_zero_masks_out, non_zero_mask_images_out, zero_mask_images_out, zero_mask_images_out_indexes)
-
-class InsertImageBatchByIndexes:
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "images": ("IMAGE",), 
-                "images_to_insert": ("IMAGE",), 
-                "insert_indexes": ("INDEXES",),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE", )
-    RETURN_NAMES = ("images_after_insert", )
-    FUNCTION = "insert"
-    CATEGORY = "KJNodes/image"
-    DESCRIPTION = """
-This node is designed to be use with node FilterZeroMasksAndCorrespondingImages
-It inserts the images_to_insert into images according to insert_indexes
-
-Returns:
-    images_after_insert: updated original images with origonal sequence order
-"""
-    
-    def insert(self, images, images_to_insert, insert_indexes):        
-        images_after_insert = images
-        
-        if images_to_insert is not None and insert_indexes is not None:
-            images_to_insert_num = len(images_to_insert)
-            insert_indexes_num = len(insert_indexes)
-            if images_to_insert_num == insert_indexes_num:
-                images_after_insert = []
-
-                i_images = 0
-                for i in range(len(images) + images_to_insert_num):
-                    if i in insert_indexes:
-                        images_after_insert.append(images_to_insert[insert_indexes.index(i)])
-                    else:
-                        images_after_insert.append(images[i_images])
-                        i_images += 1
-                        
-                images_after_insert = torch.stack(images_after_insert, dim=0)
-                
-            else:
-                print(f"[WARNING] skip this node, due to number of images_to_insert ({images_to_insert_num}) is not equal to number of insert_indexes ({insert_indexes_num})")
-
-
-        return (images_after_insert, )
-
-class BatchUncropAdvanced:
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "original_images": ("IMAGE",),
-                "cropped_images": ("IMAGE",), 
-                "cropped_masks": ("MASK",),
-                "combined_crop_mask": ("MASK",),
-                "bboxes": ("BBOX",),
-                "border_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}, ),
-                "crop_rescale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
-                "use_combined_mask": ("BOOLEAN", {"default": False}),
-                "use_square_mask": ("BOOLEAN", {"default": True}),
-            },
-            "optional": {
-                "combined_bounding_box": ("BBOX", {"default": None}),  
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "uncrop"
-    CATEGORY = "KJNodes/masking"
-
-
-    def uncrop(self, original_images, cropped_images, cropped_masks, combined_crop_mask, bboxes, border_blending, crop_rescale, use_combined_mask, use_square_mask, combined_bounding_box = None):
-        
-        def inset_border(image, border_width=20, border_color=(0)):
-            width, height = image.size
-            bordered_image = Image.new(image.mode, (width, height), border_color)
-            bordered_image.paste(image, (0, 0))
-            draw = ImageDraw.Draw(bordered_image)
-            draw.rectangle((0, 0, width - 1, height - 1), outline=border_color, width=border_width)
-            return bordered_image
-
-        if len(original_images) != len(cropped_images):
-            raise ValueError(f"The number of original_images ({len(original_images)}) and cropped_images ({len(cropped_images)}) should be the same")
-
-        # Ensure there are enough bboxes, but drop the excess if there are more bboxes than images
-        if len(bboxes) > len(original_images):
-            print(f"Warning: Dropping excess bounding boxes. Expected {len(original_images)}, but got {len(bboxes)}")
-            bboxes = bboxes[:len(original_images)]
-        elif len(bboxes) < len(original_images):
-            raise ValueError("There should be at least as many bboxes as there are original and cropped images")
-
-        crop_imgs = tensor2pil(cropped_images)
-        input_images = tensor2pil(original_images)
-        out_images = []
-
-        for i in range(len(input_images)):
-            img = input_images[i]
-            crop = crop_imgs[i]
-            bbox = bboxes[i]
-            
-            if use_combined_mask:
-                bb_x, bb_y, bb_width, bb_height = combined_bounding_box[0]
-                paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size)
-                mask = combined_crop_mask[i]
-            else:
-                bb_x, bb_y, bb_width, bb_height = bbox
-                paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size)
-                mask = cropped_masks[i]
-            
-            # scale paste_region
-            scale_x = scale_y = crop_rescale
-            paste_region = (round(paste_region[0]*scale_x), round(paste_region[1]*scale_y), round(paste_region[2]*scale_x), round(paste_region[3]*scale_y))
-
-            # rescale the crop image to fit the paste_region
-            crop = crop.resize((round(paste_region[2]-paste_region[0]), round(paste_region[3]-paste_region[1])))
-            crop_img = crop.convert("RGB")
-
-            #border blending
-            if border_blending > 1.0:
-                border_blending = 1.0
-            elif border_blending < 0.0:
-                border_blending = 0.0
-
-            blend_ratio = (max(crop_img.size) / 2) * float(border_blending)
-            blend = img.convert("RGBA")
-
-            if use_square_mask:
-                mask = Image.new("L", img.size, 0)
-                mask_block = Image.new("L", (paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]), 255)
-                mask_block = inset_border(mask_block, round(blend_ratio / 2), (0))
-                mask.paste(mask_block, paste_region)
-            else:
-                original_mask = tensor2pil(mask)[0]
-                original_mask = original_mask.resize((paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]))
-                mask = Image.new("L", img.size, 0)
-                mask.paste(original_mask, paste_region)
-
-            mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio / 4))
-            mask = mask.filter(ImageFilter.GaussianBlur(radius=blend_ratio / 4))
-
-            blend.paste(crop_img, paste_region) 
-            blend.putalpha(mask)
-            
-            img = Image.alpha_composite(img.convert("RGBA"), blend)
-            out_images.append(img.convert("RGB"))
-
-        return (pil2tensor(out_images),)
-
-class SplitBboxes:
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "bboxes": ("BBOX",),
-                "index": ("INT", {"default": 0,"min": 0, "max": 99999999, "step": 1}),
-            },
-        }
-
-    RETURN_TYPES = ("BBOX","BBOX",)
-    RETURN_NAMES = ("bboxes_a","bboxes_b",)
-    FUNCTION = "splitbbox"
-    CATEGORY = "KJNodes/masking"
-    DESCRIPTION = """
-Splits the specified bbox list at the given index into two lists.
-"""
-
-    def splitbbox(self, bboxes, index):
-        bboxes_a = bboxes[:index]  # Sub-list from the start of bboxes up to (but not including) the index
-        bboxes_b = bboxes[index:]  # Sub-list from the index to the end of bboxes
-
-        return (bboxes_a, bboxes_b,)
-    
-class BboxToInt:
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "bboxes": ("BBOX",),
-                "index": ("INT", {"default": 0,"min": 0, "max": 99999999, "step": 1}),
-            },
-        }
-
-    RETURN_TYPES = ("INT","INT","INT","INT","INT","INT",)
-    RETURN_NAMES = ("x_min","y_min","width","height", "center_x","center_y",)
-    FUNCTION = "bboxtoint"
-    CATEGORY = "KJNodes/masking"
-    DESCRIPTION = """
-Returns selected index from bounding box list as integers.
-"""
-    def bboxtoint(self, bboxes, index):
-        x_min, y_min, width, height = bboxes[index]
-        center_x = int(x_min + width / 2)
-        center_y = int(y_min + height / 2)
-        
-        return (x_min, y_min, width, height, center_x, center_y,)
-
-class BboxVisualize:
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "images": ("IMAGE",),
-                "bboxes": ("BBOX",),
-                "line_width": ("INT", {"default": 1,"min": 1, "max": 10, "step": 1}),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("images",)
-    FUNCTION = "visualizebbox"
-    DESCRIPTION = """
-Visualizes the specified bbox on the image.
-"""
-
-    CATEGORY = "KJNodes/masking"
-
-    def visualizebbox(self, bboxes, images, line_width):
-        image_list = []
-        for image, bbox in zip(images, bboxes):
-            x_min, y_min, width, height = bbox
-            
-            # Ensure bbox coordinates are integers
-            x_min = int(x_min)
-            y_min = int(y_min)
-            width = int(width)
-            height = int(height)
-            
-            # Permute the image dimensions
-            image = image.permute(2, 0, 1)
-
-            # Clone the image to draw bounding boxes
-            img_with_bbox = image.clone()
-            
-            # Define the color for the bbox, e.g., red
-            color = torch.tensor([1, 0, 0], dtype=torch.float32)
-            
-            # Ensure color tensor matches the image channels
-            if color.shape[0] != img_with_bbox.shape[0]:
-                color = color.unsqueeze(1).expand(-1, line_width)
-
-            # Draw lines for each side of the bbox with the specified line width
-            for lw in range(line_width):
-                # Top horizontal line
-                if y_min + lw < img_with_bbox.shape[1]:
-                    img_with_bbox[:, y_min + lw, x_min:x_min + width] = color[:, None]
-                
-                # Bottom horizontal line
-                if y_min + height - lw < img_with_bbox.shape[1]:
-                    img_with_bbox[:, y_min + height - lw, x_min:x_min + width] = color[:, None]
-                
-                # Left vertical line
-                if x_min + lw < img_with_bbox.shape[2]:
-                    img_with_bbox[:, y_min:y_min + height, x_min + lw] = color[:, None]
-                
-                # Right vertical line
-                if x_min + width - lw < img_with_bbox.shape[2]:
-                    img_with_bbox[:, y_min:y_min + height, x_min + width - lw] = color[:, None]
-        
-            # Permute the image dimensions back
-            img_with_bbox = img_with_bbox.permute(1, 2, 0).unsqueeze(0)
-            image_list.append(img_with_bbox)
-
-        return (torch.cat(image_list, dim=0),)
-
-        return (torch.cat(image_list, dim=0),)

custom_nodes/ComfyUI-KJNodes-main/nodes/curve_nodes.py

@@ -1,1561 +0,0 @@
-import torch
-from torchvision import transforms
-import json
-from PIL import Image, ImageDraw, ImageFont, ImageColor, ImageFilter, ImageChops
-import numpy as np
-from ..utility.utility import pil2tensor, tensor2pil
-import folder_paths
-import io
-import base64
-        
-from comfy.utils import common_upscale
-
-def plot_coordinates_to_tensor(coordinates, height, width, bbox_height, bbox_width, size_multiplier, prompt):
-        import matplotlib
-        matplotlib.use('Agg')
-        from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
-        text_color = '#999999'
-        bg_color = '#353535'
-        matplotlib.pyplot.rcParams['text.color'] = text_color
-        fig, ax = matplotlib.pyplot.subplots(figsize=(width/100, height/100), dpi=100)
-        fig.patch.set_facecolor(bg_color)
-        ax.set_facecolor(bg_color)
-        ax.grid(color=text_color, linestyle='-', linewidth=0.5)
-        ax.set_xlabel('x', color=text_color)
-        ax.set_ylabel('y', color=text_color)
-        for text in ax.get_xticklabels() + ax.get_yticklabels():
-            text.set_color(text_color)
-        ax.set_title('position for: ' + prompt)
-        ax.set_xlabel('X Coordinate')
-        ax.set_ylabel('Y Coordinate')
-        #ax.legend().remove()
-        ax.set_xlim(0, width) # Set the x-axis to match the input latent width
-        ax.set_ylim(height, 0) # Set the y-axis to match the input latent height, with (0,0) at top-left
-        # Adjust the margins of the subplot
-        matplotlib.pyplot.subplots_adjust(left=0.08, right=0.95, bottom=0.05, top=0.95, wspace=0.2, hspace=0.2)
-
-        cmap = matplotlib.pyplot.get_cmap('rainbow')
-        image_batch = []
-        canvas = FigureCanvas(fig)
-        width, height = fig.get_size_inches() * fig.get_dpi()
-        # Draw a box at each coordinate
-        for i, ((x, y), size) in enumerate(zip(coordinates, size_multiplier)):
-            color_index = i / (len(coordinates) - 1)
-            color = cmap(color_index)
-            draw_height = bbox_height * size
-            draw_width = bbox_width * size
-            rect = matplotlib.patches.Rectangle((x - draw_width/2, y - draw_height/2), draw_width, draw_height,
-                                            linewidth=1, edgecolor=color, facecolor='none', alpha=0.5)
-            ax.add_patch(rect)
-
-            # Check if there is a next coordinate to draw an arrow to
-            if i < len(coordinates) - 1:
-                x1, y1 = coordinates[i]
-                x2, y2 = coordinates[i + 1]
-                ax.annotate("", xy=(x2, y2), xytext=(x1, y1),
-                            arrowprops=dict(arrowstyle="->",
-                                            linestyle="-",
-                                            lw=1,
-                                            color=color,
-                                            mutation_scale=20))
-            canvas.draw()
-            image_np = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3).copy()
-            image_tensor = torch.from_numpy(image_np).float() / 255.0
-            image_tensor = image_tensor.unsqueeze(0)
-            image_batch.append(image_tensor)
-            
-        matplotlib.pyplot.close(fig)
-        image_batch_tensor = torch.cat(image_batch, dim=0)
-
-        return image_batch_tensor
-
-class PlotCoordinates:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-                              "coordinates": ("STRING", {"forceInput": True}),
-                              "text": ("STRING", {"default": 'title', "multiline": False}),
-                              "width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
-                              "height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
-                              "bbox_width": ("INT", {"default": 128, "min": 8, "max": 4096, "step": 8}),
-                              "bbox_height": ("INT", {"default": 128, "min": 8, "max": 4096, "step": 8}),
-                            },
-                "optional": {"size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True})},
-                }
-    RETURN_TYPES = ("IMAGE", "INT", "INT", "INT", "INT",)
-    RETURN_NAMES = ("images", "width", "height", "bbox_width", "bbox_height",)
-    FUNCTION = "append"
-    CATEGORY = "KJNodes/experimental"
-    DESCRIPTION = """
-Plots coordinates to sequence of images using Matplotlib.  
-
-"""
-
-    def append(self, coordinates, text, width, height, bbox_width, bbox_height, size_multiplier=[1.0]):
-        coordinates = json.loads(coordinates.replace("'", '"'))
-        coordinates = [(coord['x'], coord['y']) for coord in coordinates]
-        batch_size = len(coordinates)
-        if not size_multiplier or len(size_multiplier) != batch_size:
-            size_multiplier = [0] * batch_size
-        else:
-            size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)]
-
-        plot_image_tensor = plot_coordinates_to_tensor(coordinates, height, width, bbox_height, bbox_width, size_multiplier, text)
-        
-        return (plot_image_tensor, width, height, bbox_width, bbox_height)
-    
-class SplineEditor:
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "points_store": ("STRING", {"multiline": False}),
-                "coordinates": ("STRING", {"multiline": False}),
-                "mask_width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
-                "mask_height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
-                "points_to_sample": ("INT", {"default": 16, "min": 2, "max": 1000, "step": 1}),
-                "sampling_method": (
-                [   
-                    'path',
-                    'time',
-                    'controlpoints'
-                ],
-                {
-                    "default": 'time'
-                }),
-                "interpolation": (
-                [   
-                    'cardinal',
-                    'monotone',
-                    'basis',
-                    'linear',
-                    'step-before',
-                    'step-after',
-                    'polar',
-                    'polar-reverse',
-                ],
-                {
-                "default": 'cardinal'
-                    }),
-                "tension": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
-                "repeat_output": ("INT", {"default": 1, "min": 1, "max": 4096, "step": 1}),
-                "float_output_type": (
-                [   
-                    'list',
-                    'pandas series',
-                    'tensor',
-                ],
-                {
-                    "default": 'list'
-                }),
-            },
-            "optional": {
-                "min_value": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
-                "max_value": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
-                "bg_image": ("IMAGE", ),
-            }
-        }
-
-    RETURN_TYPES = ("MASK", "STRING", "FLOAT", "INT", "STRING",)
-    RETURN_NAMES = ("mask", "coord_str", "float", "count", "normalized_str",)
-    FUNCTION = "splinedata"
-    CATEGORY = "KJNodes/weights"
-    DESCRIPTION = """
-# WORK IN PROGRESS  
-Do not count on this as part of your workflow yet,  
-probably contains lots of bugs and stability is not  
-guaranteed!!  
-  
-## Graphical editor to create values for various   
-## schedules and/or mask batches.  
-
-**Shift + click** to add control point at end.
-**Ctrl + click** to add control point (subdivide) between two points.  
-**Right click on a point** to delete it.    
-Note that you can't delete from start/end.  
-  
-Right click on canvas for context menu:  
-These are purely visual options, doesn't affect the output:  
- - Toggle handles visibility
- - Display sample points: display the points to be returned.  
-
-**points_to_sample** value sets the number of samples  
-returned from the **drawn spline itself**, this is independent from the  
-actual control points, so the interpolation type matters.  
-sampling_method: 
- - time: samples along the time axis, used for schedules  
- - path: samples along the path itself, useful for coordinates  
-
-output types:
- - mask batch  
-        example compatible nodes: anything that takes masks  
- - list of floats
-        example compatible nodes: IPAdapter weights  
- - pandas series
-        example compatible nodes: anything that takes Fizz'  
-        nodes Batch Value Schedule  
- - torch tensor  
-        example compatible nodes: unknown
-"""
-
-    def splinedata(self, mask_width, mask_height, coordinates, float_output_type, interpolation, 
-                   points_to_sample, sampling_method, points_store, tension, repeat_output, 
-                   min_value=0.0, max_value=1.0, bg_image=None):
-        
-        coordinates = json.loads(coordinates)
-        normalized = []
-        normalized_y_values = []
-        for coord in coordinates:
-            coord['x'] = int(round(coord['x']))
-            coord['y'] = int(round(coord['y']))
-            norm_x = (1.0 - (coord['x'] / mask_height) - 0.0) * (max_value - min_value) + min_value
-            norm_y = (1.0 - (coord['y'] / mask_height) - 0.0) * (max_value - min_value) + min_value
-            normalized_y_values.append(norm_y)
-            normalized.append({'x':norm_x, 'y':norm_y})
-        if float_output_type == 'list':
-            out_floats = normalized_y_values * repeat_output
-        elif float_output_type == 'pandas series':
-            try:
-                import pandas as pd
-            except:
-                raise Exception("MaskOrImageToWeight: pandas is not installed. Please install pandas to use this output_type")
-            out_floats = pd.Series(normalized_y_values * repeat_output),
-        elif float_output_type == 'tensor':
-            out_floats = torch.tensor(normalized_y_values * repeat_output, dtype=torch.float32)
-        # Create a color map for grayscale intensities
-        color_map = lambda y: torch.full((mask_height, mask_width, 3), y, dtype=torch.float32)
-
-        # Create image tensors for each normalized y value
-        mask_tensors = [color_map(y) for y in normalized_y_values]
-        masks_out = torch.stack(mask_tensors)
-        masks_out = masks_out.repeat(repeat_output, 1, 1, 1)
-        masks_out = masks_out.mean(dim=-1)
-        if bg_image is None:
-            return (masks_out, json.dumps(coordinates), out_floats, len(out_floats) , json.dumps(normalized))
-        else:
-            transform = transforms.ToPILImage()
-            image = transform(bg_image[0].permute(2, 0, 1))
-            buffered = io.BytesIO()
-            image.save(buffered, format="JPEG", quality=75)
-
-            # Step 3: Encode the image bytes to a Base64 string
-            img_bytes = buffered.getvalue()
-            img_base64 = base64.b64encode(img_bytes).decode('utf-8')
-        return {
-                "ui": {"bg_image": [img_base64]},
-                "result":(masks_out, json.dumps(coordinates), out_floats, len(out_floats) , json.dumps(normalized))
-                }
-     
-
-class CreateShapeMaskOnPath:
-    
-    RETURN_TYPES = ("MASK", "MASK",)
-    RETURN_NAMES = ("mask", "mask_inverted",)
-    FUNCTION = "createshapemask"
-    CATEGORY = "KJNodes/masking/generate"
-    DESCRIPTION = """
-Creates a mask or batch of masks with the specified shape.  
-Locations are center locations.  
-"""
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "shape": (
-            [   'circle',
-                'square',
-                'triangle',
-            ],
-            {
-            "default": 'circle'
-             }),
-                "coordinates": ("STRING", {"forceInput": True}),
-                "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                "shape_width": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}),
-                "shape_height": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}),
-        },
-        "optional": {
-            "size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True}),
-        }
-    } 
-
-    def createshapemask(self, coordinates, frame_width, frame_height, shape_width, shape_height, shape, size_multiplier=[1.0]):
-        # Define the number of images in the batch
-        coordinates = coordinates.replace("'", '"')
-        coordinates = json.loads(coordinates)
-
-        batch_size = len(coordinates)
-        out = []
-        color = "white"
-        if not size_multiplier or len(size_multiplier) != batch_size:
-            size_multiplier = [0] * batch_size
-        else:
-            size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)]
-        for i, coord in enumerate(coordinates):
-            image = Image.new("RGB", (frame_width, frame_height), "black")
-            draw = ImageDraw.Draw(image)
-
-            # Calculate the size for this frame and ensure it's not less than 0
-            current_width = max(0, shape_width + i * size_multiplier[i])
-            current_height = max(0, shape_height + i * size_multiplier[i])
-
-            location_x = coord['x']
-            location_y = coord['y']
-
-            if shape == 'circle' or shape == 'square':
-                # Define the bounding box for the shape
-                left_up_point = (location_x - current_width // 2, location_y - current_height // 2)
-                right_down_point = (location_x + current_width // 2, location_y + current_height // 2)
-                two_points = [left_up_point, right_down_point]
-
-                if shape == 'circle':
-                    draw.ellipse(two_points, fill=color)
-                elif shape == 'square':
-                    draw.rectangle(two_points, fill=color)
-                    
-            elif shape == 'triangle':
-                # Define the points for the triangle
-                left_up_point = (location_x - current_width // 2, location_y + current_height // 2) # bottom left
-                right_down_point = (location_x + current_width // 2, location_y + current_height // 2) # bottom right
-                top_point = (location_x, location_y - current_height // 2) # top point
-                draw.polygon([top_point, left_up_point, right_down_point], fill=color)
-
-            image = pil2tensor(image)
-            mask = image[:, :, :, 0]
-            out.append(mask)
-        outstack = torch.cat(out, dim=0)
-        return (outstack, 1.0 - outstack,)
-
-class CreateShapeImageOnPath:
-    
-    RETURN_TYPES = ("IMAGE", "MASK",)
-    RETURN_NAMES = ("image","mask", )
-    FUNCTION = "createshapemask"
-    CATEGORY = "KJNodes/image"
-    DESCRIPTION = """
-Creates an image or batch of images with the specified shape.  
-Locations are center locations.  
-"""
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "shape": (
-            [   'circle',
-                'square',
-                'triangle',
-            ],
-            {
-            "default": 'circle'
-             }),
-                "coordinates": ("STRING", {"forceInput": True}),
-                "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                "shape_width": ("INT", {"default": 128,"min": 2, "max": 4096, "step": 1}),
-                "shape_height": ("INT", {"default": 128,"min": 2, "max": 4096, "step": 1}),
-                "shape_color": ("STRING", {"default": 'white'}),
-                "bg_color": ("STRING", {"default": 'black'}),
-                "blur_radius": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100, "step": 0.1}),
-                "intensity": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 100.0, "step": 0.01}),
-        },
-        "optional": {
-            "size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True}),
-            "trailing": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
-        }
-    } 
-
-    def createshapemask(self, coordinates, frame_width, frame_height, shape_width, shape_height, shape_color, 
-                        bg_color, blur_radius, shape, intensity, size_multiplier=[1.0], accumulate=False, trailing=1.0):
-        # Define the number of images in the batch
-        if len(coordinates) < 10:
-            coords_list = []
-            for coords in coordinates:
-                coords = json.loads(coords.replace("'", '"'))
-                coords_list.append(coords)
-        else:
-            coords = json.loads(coordinates.replace("'", '"'))
-            coords_list = [coords]
-
-        batch_size = len(coords_list[0])
-        images_list = []
-        masks_list = []
-
-        if not size_multiplier or len(size_multiplier) != batch_size:
-            size_multiplier = [0] * batch_size
-        else:
-            size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)]
-
-        previous_output = None
-
-        for i in range(batch_size):
-            image = Image.new("RGB", (frame_width, frame_height), bg_color)
-            draw = ImageDraw.Draw(image)
-
-            # Calculate the size for this frame and ensure it's not less than 0
-            current_width = max(0, shape_width + i * size_multiplier[i])
-            current_height = max(0, shape_height + i * size_multiplier[i])
-            
-            for coords in coords_list:
-                location_x = coords[i]['x']
-                location_y = coords[i]['y']
-            
-                if shape == 'circle' or shape == 'square':
-                    # Define the bounding box for the shape
-                    left_up_point = (location_x - current_width // 2, location_y - current_height // 2)
-                    right_down_point = (location_x + current_width // 2, location_y + current_height // 2)
-                    two_points = [left_up_point, right_down_point]
-
-                    if shape == 'circle':
-                        draw.ellipse(two_points, fill=shape_color)
-                    elif shape == 'square':
-                        draw.rectangle(two_points, fill=shape_color)
-                        
-                elif shape == 'triangle':
-                    # Define the points for the triangle
-                    left_up_point = (location_x - current_width // 2, location_y + current_height // 2) # bottom left
-                    right_down_point = (location_x + current_width // 2, location_y + current_height // 2) # bottom right
-                    top_point = (location_x, location_y - current_height // 2) # top point
-                    draw.polygon([top_point, left_up_point, right_down_point], fill=shape_color)
-
-            if blur_radius != 0:
-                    image = image.filter(ImageFilter.GaussianBlur(blur_radius))
-            # Blend the current image with the accumulated image
-            
-            image = pil2tensor(image)
-            if trailing != 1.0 and previous_output is not None:
-                # Add the decayed previous output to the current frame
-                image += trailing * previous_output
-                image = image / image.max()
-            previous_output = image
-            image = image * intensity
-            mask = image[:, :, :, 0]
-            masks_list.append(mask)
-            images_list.append(image)
-        out_images = torch.cat(images_list, dim=0).cpu().float()
-        out_masks = torch.cat(masks_list, dim=0)
-        return (out_images, out_masks)
-    
-class CreateTextOnPath:
-    
-    RETURN_TYPES = ("IMAGE", "MASK", "MASK",)
-    RETURN_NAMES = ("image", "mask", "mask_inverted",)
-    FUNCTION = "createtextmask"
-    CATEGORY = "KJNodes/masking/generate"
-    DESCRIPTION = """
-Creates a mask or batch of masks with the specified text.  
-Locations are center locations.  
-"""
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "coordinates": ("STRING", {"forceInput": True}),
-                "text": ("STRING", {"default": 'text', "multiline": True}),
-                "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                "font": (folder_paths.get_filename_list("kjnodes_fonts"), ),
-                "font_size": ("INT", {"default": 42}),
-                 "alignment": (
-                [   'left',
-                    'center',
-                    'right'
-                ],
-                {"default": 'center'}
-                ),
-                "text_color": ("STRING", {"default": 'white'}),
-        },
-        "optional": {
-            "size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True}),
-        }
-    } 
-
-    def createtextmask(self, coordinates, frame_width, frame_height, font, font_size, text, text_color, alignment, size_multiplier=[1.0]):
-        coordinates = coordinates.replace("'", '"')
-        coordinates = json.loads(coordinates)
-
-        batch_size = len(coordinates)
-        mask_list = []
-        image_list = []
-        color = text_color
-        font_path = folder_paths.get_full_path("kjnodes_fonts", font)
-
-        if len(size_multiplier) != batch_size:
-            size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)]
-        
-        for i, coord in enumerate(coordinates):
-            image = Image.new("RGB", (frame_width, frame_height), "black")
-            draw = ImageDraw.Draw(image)
-            lines = text.split('\n')  # Split the text into lines
-            # Apply the size multiplier to the font size for this iteration
-            current_font_size = int(font_size * size_multiplier[i])
-            current_font = ImageFont.truetype(font_path, current_font_size)
-            line_heights = [current_font.getbbox(line)[3] for line in lines]  # List of line heights
-            total_text_height = sum(line_heights)  # Total height of text block
-
-            # Calculate the starting Y position to center the block of text
-            start_y = coord['y'] - total_text_height // 2
-            for j, line in enumerate(lines):
-                text_width, text_height = current_font.getbbox(line)[2], line_heights[j]
-                if alignment == 'left':
-                    location_x = coord['x']
-                elif alignment == 'center':
-                    location_x = int(coord['x'] - text_width // 2)
-                elif alignment == 'right':
-                    location_x = int(coord['x'] - text_width)
-                
-                location_y = int(start_y + sum(line_heights[:j]))
-                text_position = (location_x, location_y)
-                # Draw the text
-                try:
-                    draw.text(text_position, line, fill=color, font=current_font, features=['-liga'])
-                except:
-                    draw.text(text_position, line, fill=color, font=current_font)
-            
-            image = pil2tensor(image)
-            non_black_pixels = (image > 0).any(dim=-1)
-            mask = non_black_pixels.to(image.dtype)
-            mask_list.append(mask)
-            image_list.append(image)
-
-        out_images = torch.cat(image_list, dim=0).cpu().float()
-        out_masks = torch.cat(mask_list, dim=0)
-        return (out_images, out_masks, 1.0 - out_masks,)
-
-class CreateGradientFromCoords:
-    
-    RETURN_TYPES = ("IMAGE", )
-    RETURN_NAMES = ("image", )
-    FUNCTION = "generate"
-    CATEGORY = "KJNodes/image"
-    DESCRIPTION = """
-Creates a gradient image from coordinates.    
-"""
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "coordinates": ("STRING", {"forceInput": True}),
-                "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                "start_color": ("STRING", {"default": 'white'}),
-                "end_color": ("STRING", {"default": 'black'}),
-                "multiplier": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 100.0, "step": 0.01}),
-        },
-    } 
-    
-    def generate(self, coordinates, frame_width, frame_height, start_color, end_color, multiplier):
-        # Parse the coordinates
-        coordinates = json.loads(coordinates.replace("'", '"'))
-
-        # Create an image
-        image = Image.new("RGB", (frame_width, frame_height))
-        draw = ImageDraw.Draw(image)
-
-        # Extract start and end points for the gradient
-        start_coord = coordinates[0]
-        end_coord = coordinates[1]
-
-        start_color = ImageColor.getrgb(start_color)
-        end_color = ImageColor.getrgb(end_color)
-
-        # Calculate the gradient direction (vector)
-        gradient_direction = (end_coord['x'] - start_coord['x'], end_coord['y'] - start_coord['y'])
-        gradient_length = (gradient_direction[0] ** 2 + gradient_direction[1] ** 2) ** 0.5
-
-        # Iterate over each pixel in the image
-        for y in range(frame_height):
-            for x in range(frame_width):
-                # Calculate the projection of the point on the gradient line
-                point_vector = (x - start_coord['x'], y - start_coord['y'])
-                projection = (point_vector[0] * gradient_direction[0] + point_vector[1] * gradient_direction[1]) / gradient_length
-                projection = max(min(projection, gradient_length), 0)  # Clamp the projection value
-
-                # Calculate the blend factor for the current pixel
-                blend = projection * multiplier / gradient_length 
-
-                # Determine the color of the current pixel
-                color = (
-                    int(start_color[0] + (end_color[0] - start_color[0]) * blend),
-                    int(start_color[1] + (end_color[1] - start_color[1]) * blend),
-                    int(start_color[2] + (end_color[2] - start_color[2]) * blend)
-                )
-
-                # Set the pixel color
-                draw.point((x, y), fill=color)
-
-        # Convert the PIL image to a tensor (assuming such a function exists in your context)
-        image_tensor = pil2tensor(image)
-
-        return (image_tensor,)
-
-class GradientToFloat:
-    
-    RETURN_TYPES = ("FLOAT", "FLOAT",)
-    RETURN_NAMES = ("float_x", "float_y", )
-    FUNCTION = "sample"
-    CATEGORY = "KJNodes/image"
-    DESCRIPTION = """
-Calculates list of floats from image.    
-"""
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE", ),
-                "steps": ("INT", {"default": 10, "min": 2, "max": 10000, "step": 1}),
-        },
-    } 
-    
-    def sample(self, image, steps):
-        # Assuming image is a tensor with shape [B, H, W, C]
-        B, H, W, C = image.shape
-
-        # Sample along the width axis (W)
-        w_intervals = torch.linspace(0, W - 1, steps=steps, dtype=torch.int64)
-        # Assuming we're sampling from the first batch and the first channel
-        w_sampled = image[0, :, w_intervals, 0]
-
-        # Sample along the height axis (H)
-        h_intervals = torch.linspace(0, H - 1, steps=steps, dtype=torch.int64)
-        # Assuming we're sampling from the first batch and the first channel
-        h_sampled = image[0, h_intervals, :, 0]
-
-        # Taking the mean across the height for width sampling, and across the width for height sampling
-        w_values = w_sampled.mean(dim=0).tolist()
-        h_values = h_sampled.mean(dim=1).tolist()
-
-        return (w_values, h_values)
-    
-class MaskOrImageToWeight:
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "output_type": (
-                [   
-                    'list',
-                    'pandas series',
-                    'tensor',
-                    'string'
-                ],
-                {
-                "default": 'list'
-                    }),
-             },
-            "optional": {
-                "images": ("IMAGE",),
-                "masks": ("MASK",),                
-            },
-
-        }
-    RETURN_TYPES = ("FLOAT", "STRING",)
-    FUNCTION = "execute"
-    CATEGORY = "KJNodes/weights"
-    DESCRIPTION = """
-Gets the mean values from mask or image batch  
-and returns that as the selected output type.   
-"""
-
-    def execute(self, output_type, images=None, masks=None):
-        mean_values = []
-        if masks is not None and images is None:
-            for mask in masks:
-                mean_values.append(mask.mean().item())
-        elif masks is None and images is not None:
-            for image in images:
-                mean_values.append(image.mean().item())
-        elif masks is not None and images is not None:
-            raise Exception("MaskOrImageToWeight: Use either mask or image input only.")
-                  
-        # Convert mean_values to the specified output_type
-        if output_type == 'list':
-            out = mean_values
-        elif output_type == 'pandas series':
-            try:
-                import pandas as pd
-            except:
-                raise Exception("MaskOrImageToWeight: pandas is not installed. Please install pandas to use this output_type")
-            out = pd.Series(mean_values),
-        elif output_type == 'tensor':
-            out = torch.tensor(mean_values, dtype=torch.float32),
-        return (out, [str(value) for value in mean_values],)
-    
-class WeightScheduleConvert:
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "input_values": ("FLOAT", {"default": 0.0, "forceInput": True}),
-                "output_type": (
-                [   
-                    'match_input',
-                    'list',
-                    'pandas series',
-                    'tensor',
-                ],
-                {
-                "default": 'list'
-                    }),
-                "invert": ("BOOLEAN", {"default": False}),
-                "repeat": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}),
-             },
-             "optional": {
-                "remap_to_frames": ("INT", {"default": 0}),
-                "interpolation_curve": ("FLOAT", {"forceInput": True}),
-                "remap_values": ("BOOLEAN", {"default": False}),
-                "remap_min": ("FLOAT", {"default": 0.0, "min": -100000, "max": 100000.0, "step": 0.01}),
-                "remap_max": ("FLOAT", {"default": 1.0, "min": -100000, "max": 100000.0, "step": 0.01}),
-             },
-             
-        }
-    RETURN_TYPES = ("FLOAT", "STRING", "INT",)
-    FUNCTION = "execute"
-    CATEGORY = "KJNodes/weights"
-    DESCRIPTION = """
-Converts different value lists/series to another type.  
-"""
-
-    def detect_input_type(self, input_values):
-        import pandas as pd
-        if isinstance(input_values, list):
-            return 'list'
-        elif isinstance(input_values, pd.Series):
-            return 'pandas series'
-        elif isinstance(input_values, torch.Tensor):
-            return 'tensor'
-        else:
-            raise ValueError("Unsupported input type")
-
-    def execute(self, input_values, output_type, invert, repeat, remap_to_frames=0, interpolation_curve=None, remap_min=0.0, remap_max=1.0, remap_values=False):
-        import pandas as pd
-        input_type = self.detect_input_type(input_values)
-
-        if input_type == 'pandas series':
-            float_values = input_values.tolist()
-        elif input_type == 'tensor':
-            float_values = input_values
-        else:
-            float_values = input_values
-
-        if invert:
-            float_values = [1 - value for value in float_values]
-
-        if interpolation_curve is not None:
-            interpolated_pattern = []
-            orig_float_values = float_values
-            for value in interpolation_curve:
-                min_val = min(orig_float_values)
-                max_val = max(orig_float_values)
-                # Normalize the values to [0, 1]
-                normalized_values = [(value - min_val) / (max_val - min_val) for value in orig_float_values]
-                # Interpolate the normalized values to the new frame count
-                remapped_float_values = np.interp(np.linspace(0, 1, int(remap_to_frames * value)), np.linspace(0, 1, len(normalized_values)), normalized_values).tolist()
-                interpolated_pattern.extend(remapped_float_values)
-            float_values = interpolated_pattern
-        else:
-            # Remap float_values to match target_frame_amount
-            if remap_to_frames > 0 and remap_to_frames != len(float_values):
-                min_val = min(float_values)
-                max_val = max(float_values)
-                # Normalize the values to [0, 1]
-                normalized_values = [(value - min_val) / (max_val - min_val) for value in float_values]
-                # Interpolate the normalized values to the new frame count
-                float_values = np.interp(np.linspace(0, 1, remap_to_frames), np.linspace(0, 1, len(normalized_values)), normalized_values).tolist()
-       
-            float_values = float_values * repeat
-            if remap_values:
-                float_values = self.remap_values(float_values, remap_min, remap_max)
-
-        if output_type == 'list':
-            out = float_values,
-        elif output_type == 'pandas series':
-            out = pd.Series(float_values),
-        elif output_type == 'tensor':
-            if input_type == 'pandas series':
-                out = torch.tensor(float_values.values, dtype=torch.float32),
-            else:   
-                out = torch.tensor(float_values, dtype=torch.float32),
-        elif output_type == 'match_input':
-            out = float_values,
-        return (out, [str(value) for value in float_values], [int(value) for value in float_values])
-    
-    def remap_values(self, values, target_min, target_max):
-        # Determine the current range
-        current_min = min(values)
-        current_max = max(values)
-        current_range = current_max - current_min
-        
-        # Determine the target range
-        target_range = target_max - target_min
-        
-        # Perform the linear interpolation for each value
-        remapped_values = [(value - current_min) / current_range * target_range + target_min for value in values]
-        
-        return remapped_values
-        
-
-class FloatToMask:
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "input_values": ("FLOAT", {"forceInput": True, "default": 0}),
-                "width": ("INT", {"default": 100, "min": 1}),
-                "height": ("INT", {"default": 100, "min": 1}),
-            },
-        }
-    RETURN_TYPES = ("MASK",)
-    FUNCTION = "execute"
-    CATEGORY = "KJNodes/masking/generate"
-    DESCRIPTION = """
-Generates a batch of masks based on the input float values.
-The batch size is determined by the length of the input float values.
-Each mask is generated with the specified width and height.
-"""
-
-    def execute(self, input_values, width, height):
-        import pandas as pd
-        # Ensure input_values is a list
-        if isinstance(input_values, (float, int)):
-            input_values = [input_values]
-        elif isinstance(input_values, pd.Series):
-            input_values = input_values.tolist()
-        elif isinstance(input_values, list) and all(isinstance(item, list) for item in input_values):
-            input_values = [item for sublist in input_values for item in sublist]
-
-        # Generate a batch of masks based on the input_values
-        masks = []
-        for value in input_values:
-            # Assuming value is a float between 0 and 1 representing the mask's intensity
-            mask = torch.ones((height, width), dtype=torch.float32) * value
-            masks.append(mask)
-        masks_out = torch.stack(masks, dim=0)
-    
-        return(masks_out,)
-class WeightScheduleExtend:
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "input_values_1": ("FLOAT", {"default": 0.0, "forceInput": True}),
-                "input_values_2": ("FLOAT", {"default": 0.0, "forceInput": True}),
-                "output_type": (
-                [   
-                    'match_input',
-                    'list',
-                    'pandas series',
-                    'tensor',
-                ],
-                {
-                "default": 'match_input'
-                    }),
-             },
-             
-        }
-    RETURN_TYPES = ("FLOAT",)
-    FUNCTION = "execute"
-    CATEGORY = "KJNodes/weights"
-    DESCRIPTION = """
-Extends, and converts if needed, different value lists/series  
-"""
-
-    def detect_input_type(self, input_values):
-        import pandas as pd
-        if isinstance(input_values, list):
-            return 'list'
-        elif isinstance(input_values, pd.Series):
-            return 'pandas series'
-        elif isinstance(input_values, torch.Tensor):
-            return 'tensor'
-        else:
-            raise ValueError("Unsupported input type")
-
-    def execute(self, input_values_1, input_values_2, output_type):
-        import pandas as pd
-        input_type_1 = self.detect_input_type(input_values_1)
-        input_type_2 = self.detect_input_type(input_values_2)
-        # Convert input_values_2 to the same format as input_values_1 if they do not match
-        if not input_type_1 == input_type_2:
-            print("Converting input_values_2 to the same format as input_values_1")
-            if input_type_1 == 'pandas series':
-                # Convert input_values_2 to a pandas Series
-                float_values_2 = pd.Series(input_values_2)
-            elif input_type_1 == 'tensor':
-                # Convert input_values_2 to a tensor
-                float_values_2 = torch.tensor(input_values_2, dtype=torch.float32)
-        else:
-            print("Input types match, no conversion needed")
-            # If the types match, no conversion is needed
-            float_values_2 = input_values_2
-     
-        float_values = input_values_1 + float_values_2
- 
-        if output_type == 'list':
-            return float_values,
-        elif output_type == 'pandas series':
-            return pd.Series(float_values),
-        elif output_type == 'tensor':
-            if input_type_1 == 'pandas series':
-                return torch.tensor(float_values.values, dtype=torch.float32),
-            else:
-                return torch.tensor(float_values, dtype=torch.float32),
-        elif output_type == 'match_input':
-            return float_values,
-        else:
-            raise ValueError(f"Unsupported output_type: {output_type}")
-        
-class FloatToSigmas:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                    {
-                     "float_list": ("FLOAT", {"default": 0.0, "forceInput": True}),
-                     }
-                }
-    RETURN_TYPES = ("SIGMAS",)
-    RETURN_NAMES = ("SIGMAS",)
-    CATEGORY = "KJNodes/noise"
-    FUNCTION = "customsigmas"
-    DESCRIPTION = """
-Creates a sigmas tensor from list of float values.  
-
-"""
-    def customsigmas(self, float_list):
-        return torch.tensor(float_list, dtype=torch.float32),
-
-class SigmasToFloat:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                    {
-                     "sigmas": ("SIGMAS",),
-                     }
-                }
-    RETURN_TYPES = ("FLOAT",)
-    RETURN_NAMES = ("float",)
-    CATEGORY = "KJNodes/noise"
-    FUNCTION = "customsigmas"
-    DESCRIPTION = """
-Creates a float list from sigmas tensors.  
-
-"""
-    def customsigmas(self, sigmas):
-        return sigmas.tolist(),
-
-class GLIGENTextBoxApplyBatchCoords:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"conditioning_to": ("CONDITIONING", ),
-                              "latents": ("LATENT", ),
-                              "clip": ("CLIP", ),
-                              "gligen_textbox_model": ("GLIGEN", ),
-                              "coordinates": ("STRING", {"forceInput": True}),
-                              "text": ("STRING", {"multiline": True}),
-                              "width": ("INT", {"default": 128, "min": 8, "max": 4096, "step": 8}),
-                              "height": ("INT", {"default": 128, "min": 8, "max": 4096, "step": 8}),
-                            },
-                "optional": {"size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True})},
-                }
-    RETURN_TYPES = ("CONDITIONING", "IMAGE", )
-    RETURN_NAMES = ("conditioning", "coord_preview", )
-    FUNCTION = "append"
-    CATEGORY = "KJNodes/experimental"
-    DESCRIPTION = """
-This node allows scheduling GLIGEN text box positions in a batch,  
-to be used with AnimateDiff-Evolved. Intended to pair with the  
-Spline Editor -node.  
-
-GLIGEN model can be downloaded through the Manage's "Install Models" menu.  
-Or directly from here:  
-https://huggingface.co/comfyanonymous/GLIGEN_pruned_safetensors/tree/main  
-  
-Inputs:  
-- **latents** input is used to calculate batch size  
-- **clip** is your standard text encoder, use same as for the main prompt  
-- **gligen_textbox_model** connects to GLIGEN Loader  
-- **coordinates** takes a json string of points, directly compatible  
-with the spline editor node.
-- **text** is the part of the prompt to set position for  
-- **width** and **height** are the size of the GLIGEN bounding box  
-  
-Outputs:
-- **conditioning** goes between to clip text encode and the sampler  
-- **coord_preview** is an optional preview of the coordinates and  
-bounding boxes.
-
-"""
-
-    def append(self, latents, coordinates, conditioning_to, clip, gligen_textbox_model, text, width, height, size_multiplier=[1.0]):
-        coordinates = json.loads(coordinates.replace("'", '"'))
-        coordinates = [(coord['x'], coord['y']) for coord in coordinates]
-
-        batch_size = sum(tensor.size(0) for tensor in latents.values())
-        if len(coordinates) != batch_size:
-            print("GLIGENTextBoxApplyBatchCoords WARNING: The number of coordinates does not match the number of latents")
-
-        c = []
-        _, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True)
-
-        for t in conditioning_to:
-            n = [t[0], t[1].copy()]
-            
-            position_params_batch = [[] for _ in range(batch_size)]  # Initialize a list of empty lists for each batch item
-            if len(size_multiplier) != batch_size:
-                size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)]
-
-            for i in range(batch_size):
-                x_position, y_position = coordinates[i]
-                position_param = (cond_pooled, int((height // 8) * size_multiplier[i]), int((width // 8) * size_multiplier[i]), (y_position - height // 2) // 8, (x_position - width // 2) // 8)
-                position_params_batch[i].append(position_param)  # Append position_param to the correct sublist
-
-            prev = []
-            if "gligen" in n[1]:
-                prev = n[1]['gligen'][2]
-            else:
-                prev = [[] for _ in range(batch_size)]
-            # Concatenate prev and position_params_batch, ensuring both are lists of lists
-            # and each sublist corresponds to a batch item
-            combined_position_params = [prev_item + batch_item for prev_item, batch_item in zip(prev, position_params_batch)]
-            n[1]['gligen'] = ("position_batched", gligen_textbox_model, combined_position_params)
-            c.append(n)
-
-        image_height = latents['samples'].shape[-2] * 8
-        image_width = latents['samples'].shape[-1] * 8
-        plot_image_tensor = plot_coordinates_to_tensor(coordinates, image_height, image_width, height, width, size_multiplier, text)
-        
-        return (c, plot_image_tensor,)
-    
-class CreateInstanceDiffusionTracking:
-    
-    RETURN_TYPES = ("TRACKING", "STRING", "INT", "INT", "INT", "INT",)
-    RETURN_NAMES = ("tracking", "prompt", "width", "height", "bbox_width", "bbox_height",)
-    FUNCTION = "tracking"
-    CATEGORY = "KJNodes/InstanceDiffusion"
-    DESCRIPTION = """
-Creates tracking data to be used with InstanceDiffusion:  
-https://github.com/logtd/ComfyUI-InstanceDiffusion  
-  
-InstanceDiffusion prompt format:  
-"class_id.class_name": "prompt",  
-for example:  
-"1.head": "((head))",  
-"""
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "coordinates": ("STRING", {"forceInput": True}),
-                "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                "bbox_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                "bbox_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                "class_name": ("STRING", {"default": "class_name"}),
-                "class_id": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
-                "prompt": ("STRING", {"default": "prompt", "multiline": True}),
-        },
-        "optional": {
-            "size_multiplier": ("FLOAT", {"default": [1.0], "forceInput": True}),
-            "fit_in_frame": ("BOOLEAN", {"default": True}),
-        }
-    } 
-
-    def tracking(self, coordinates, class_name, class_id, width, height, bbox_width, bbox_height, prompt, size_multiplier=[1.0], fit_in_frame=True):
-        # Define the number of images in the batch
-        coordinates = coordinates.replace("'", '"')
-        coordinates = json.loads(coordinates)
-
-        tracked = {}
-        tracked[class_name] = {}
-        batch_size = len(coordinates)
-        # Initialize a list to hold the coordinates for the current ID
-        id_coordinates = []
-        if not size_multiplier or len(size_multiplier) != batch_size:
-            size_multiplier = [0] * batch_size
-        else:
-            size_multiplier = size_multiplier * (batch_size // len(size_multiplier)) + size_multiplier[:batch_size % len(size_multiplier)]
-        for i, coord in enumerate(coordinates):
-            x = coord['x']
-            y = coord['y']
-            adjusted_bbox_width = bbox_width * size_multiplier[i]
-            adjusted_bbox_height = bbox_height * size_multiplier[i]
-            # Calculate the top left and bottom right coordinates
-            top_left_x = x - adjusted_bbox_width // 2
-            top_left_y = y - adjusted_bbox_height // 2
-            bottom_right_x = x + adjusted_bbox_width // 2
-            bottom_right_y = y + adjusted_bbox_height // 2
-
-            if fit_in_frame:
-                # Clip the coordinates to the frame boundaries
-                top_left_x = max(0, top_left_x)
-                top_left_y = max(0, top_left_y)
-                bottom_right_x = min(width, bottom_right_x)
-                bottom_right_y = min(height, bottom_right_y)
-                # Ensure width and height are positive
-                adjusted_bbox_width = max(1, bottom_right_x - top_left_x)
-                adjusted_bbox_height = max(1, bottom_right_y - top_left_y)
-
-                # Update the coordinates with the new width and height
-                bottom_right_x = top_left_x + adjusted_bbox_width
-                bottom_right_y = top_left_y + adjusted_bbox_height
-
-            # Append the top left and bottom right coordinates to the list for the current ID
-            id_coordinates.append([top_left_x, top_left_y, bottom_right_x, bottom_right_y, width, height])
-        
-        class_id = int(class_id)
-        # Assign the list of coordinates to the specified ID within the class_id dictionary
-        tracked[class_name][class_id] = id_coordinates
-
-        prompt_string = ""
-        for class_name, class_data in tracked.items():
-            for class_id in class_data.keys():
-                class_id_str = str(class_id)
-                # Use the incoming prompt for each class name and ID
-                prompt_string += f'"{class_id_str}.{class_name}": "({prompt})",\n'
-
-        # Remove the last comma and newline
-        prompt_string = prompt_string.rstrip(",\n")
-
-        return (tracked, prompt_string, width, height, bbox_width, bbox_height)
-
-class AppendInstanceDiffusionTracking:
-    
-    RETURN_TYPES = ("TRACKING", "STRING",)
-    RETURN_NAMES = ("tracking", "prompt",)
-    FUNCTION = "append"
-    CATEGORY = "KJNodes/InstanceDiffusion"
-    DESCRIPTION = """
-Appends tracking data to be used with InstanceDiffusion:  
-https://github.com/logtd/ComfyUI-InstanceDiffusion  
-
-"""
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "tracking_1": ("TRACKING", {"forceInput": True}),
-                "tracking_2": ("TRACKING", {"forceInput": True}),
-        },
-        "optional": {
-            "prompt_1": ("STRING", {"default": "", "forceInput": True}),
-            "prompt_2": ("STRING", {"default": "", "forceInput": True}),
-        }
-    } 
-
-    def append(self, tracking_1, tracking_2, prompt_1="", prompt_2=""):
-        tracking_copy = tracking_1.copy()
-        # Check for existing class names and class IDs, and raise an error if they exist
-        for class_name, class_data in tracking_2.items():
-            if class_name not in tracking_copy:
-                tracking_copy[class_name] = class_data
-            else:
-                # If the class name exists, merge the class data from tracking_2 into tracking_copy
-                # This will add new class IDs under the same class name without raising an error
-                tracking_copy[class_name].update(class_data)
-        prompt_string = prompt_1 + "," + prompt_2
-        return (tracking_copy, prompt_string)
-        
-class InterpolateCoords:
-    
-    RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("coordinates",)
-    FUNCTION = "interpolate"
-    CATEGORY = "KJNodes/experimental"
-    DESCRIPTION = """
-Interpolates coordinates based on a curve.   
-"""
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "coordinates": ("STRING", {"forceInput": True}),
-                "interpolation_curve": ("FLOAT", {"forceInput": True}),
-                
-        },
-    } 
-
-    def interpolate(self, coordinates, interpolation_curve):
-        # Parse the JSON string to get the list of coordinates
-        coordinates = json.loads(coordinates.replace("'", '"'))
-
-        # Convert the list of dictionaries to a list of (x, y) tuples for easier processing
-        coordinates = [(coord['x'], coord['y']) for coord in coordinates]
-
-        # Calculate the total length of the original path
-        path_length = sum(np.linalg.norm(np.array(coordinates[i]) - np.array(coordinates[i-1])) 
-                        for i in range(1, len(coordinates)))
-
-        # Initialize variables for interpolation
-        interpolated_coords = []
-        current_length = 0
-        current_index = 0
-
-        # Iterate over the normalized curve
-        for normalized_length in interpolation_curve:
-            target_length = normalized_length * path_length # Convert to the original scale
-            while current_index < len(coordinates) - 1:
-                segment_start, segment_end = np.array(coordinates[current_index]), np.array(coordinates[current_index + 1])
-                segment_length = np.linalg.norm(segment_end - segment_start)
-                if current_length + segment_length >= target_length:
-                    break
-                current_length += segment_length
-                current_index += 1
-
-            # Interpolate between the last two points
-            if current_index < len(coordinates) - 1:
-                p1, p2 = np.array(coordinates[current_index]), np.array(coordinates[current_index + 1])
-                segment_length = np.linalg.norm(p2 - p1)
-                if segment_length > 0:
-                    t = (target_length - current_length) / segment_length
-                    interpolated_point = p1 + t * (p2 - p1)
-                    interpolated_coords.append(interpolated_point.tolist())
-                else:
-                    interpolated_coords.append(p1.tolist())
-            else:
-                # If the target_length is at or beyond the end of the path, add the last coordinate
-                interpolated_coords.append(coordinates[-1])
-
-        # Convert back to string format if necessary
-        interpolated_coords_str = "[" + ", ".join([f"{{'x': {round(coord[0])}, 'y': {round(coord[1])}}}" for coord in interpolated_coords]) + "]"
-        print(interpolated_coords_str)
-
-        return (interpolated_coords_str,)
-    
-class DrawInstanceDiffusionTracking:
-    
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("image", )
-    FUNCTION = "draw"
-    CATEGORY = "KJNodes/InstanceDiffusion"
-    DESCRIPTION = """
-Draws the tracking data from  
-CreateInstanceDiffusionTracking -node.
-
-"""
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE", ),
-                "tracking": ("TRACKING", {"forceInput": True}),
-                "box_line_width": ("INT", {"default": 2, "min": 1, "max": 10, "step": 1}),
-                "draw_text": ("BOOLEAN", {"default": True}),
-                "font": (folder_paths.get_filename_list("kjnodes_fonts"), ),
-                "font_size": ("INT", {"default": 20}),
-        },
-    } 
-
-    def draw(self, image, tracking, box_line_width, draw_text, font, font_size):
-        import matplotlib.cm as cm
-
-        modified_images = []
-        
-        colormap = cm.get_cmap('rainbow', len(tracking))
-        if draw_text:
-            font_path = folder_paths.get_full_path("kjnodes_fonts", font)
-            font = ImageFont.truetype(font_path, font_size)
-
-        # Iterate over each image in the batch
-        for i in range(image.shape[0]):
-            # Extract the current image and convert it to a PIL image
-            current_image = image[i, :, :, :].permute(2, 0, 1)
-            pil_image = transforms.ToPILImage()(current_image)
-            
-            draw = ImageDraw.Draw(pil_image)
-            
-            # Iterate over the bounding boxes for the current image
-            for j, (class_name, class_data) in enumerate(tracking.items()):
-                for class_id, bbox_list in class_data.items():
-                    # Check if the current index is within the bounds of the bbox_list
-                    if i < len(bbox_list):
-                        bbox = bbox_list[i]
-                        # Ensure bbox is a list or tuple before unpacking
-                        if isinstance(bbox, (list, tuple)):
-                            x1, y1, x2, y2, _, _ = bbox
-                            # Convert coordinates to integers
-                            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
-                            # Generate a color from the rainbow colormap
-                            color = tuple(int(255 * x) for x in colormap(j / len(tracking)))[:3]
-                            # Draw the bounding box on the image with the generated color
-                            draw.rectangle([x1, y1, x2, y2], outline=color, width=box_line_width)
-                            if draw_text:
-                                # Draw the class name and ID as text above the box with the generated color
-                                text = f"{class_id}.{class_name}"
-                                # Calculate the width and height of the text
-                                _, _, text_width, text_height = draw.textbbox((0, 0), text=text, font=font)
-                                # Position the text above the top-left corner of the box
-                                text_position = (x1, y1 - text_height)
-                                draw.text(text_position, text, fill=color, font=font)
-                        else:
-                            print(f"Unexpected data type for bbox: {type(bbox)}")
-            
-            # Convert the drawn image back to a torch tensor and adjust back to (H, W, C)
-            modified_image_tensor = transforms.ToTensor()(pil_image).permute(1, 2, 0)
-            modified_images.append(modified_image_tensor)
-        
-        # Stack the modified images back into a batch
-        image_tensor_batch = torch.stack(modified_images).cpu().float()
-        
-        return image_tensor_batch,
-
-class PointsEditor:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "points_store": ("STRING", {"multiline": False}),
-                "coordinates": ("STRING", {"multiline": False}),
-                "neg_coordinates": ("STRING", {"multiline": False}),
-                "bbox_store": ("STRING", {"multiline": False}),
-                "bboxes": ("STRING", {"multiline": False}),
-                "bbox_format": (
-                [   
-                    'xyxy',
-                    'xywh',
-                ],
-                ),
-                "width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
-                "height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
-                "normalize": ("BOOLEAN", {"default": False}),
-            },
-            "optional": {
-                "bg_image": ("IMAGE", ),
-            },
-        }
-
-    RETURN_TYPES = ("STRING", "STRING", "BBOX", "MASK", "IMAGE")
-    RETURN_NAMES = ("positive_coords", "negative_coords", "bbox", "bbox_mask", "cropped_image")
-    FUNCTION = "pointdata"
-    CATEGORY = "KJNodes/experimental"
-    DESCRIPTION = """
-# WORK IN PROGRESS  
-Do not count on this as part of your workflow yet,  
-probably contains lots of bugs and stability is not  
-guaranteed!!  
-  
-## Graphical editor to create coordinates
-
-**Shift + click** to add a positive (green) point.
-**Shift + right click** to add a negative (red) point.
-**Ctrl + click** to draw a box.  
-**Right click on a point** to delete it.    
-Note that you can't delete from start/end of the points array.  
-  
-To add an image select the node and copy/paste or drag in the image.  
-Or from the bg_image input on queue (first frame of the batch).  
-
-**THE IMAGE IS SAVED TO THE NODE AND WORKFLOW METADATA**  
-you can clear the image from the context menu by right clicking on the canvas  
-
-"""
-
-    def pointdata(self, points_store, bbox_store, width, height, coordinates, neg_coordinates, normalize, bboxes, bbox_format="xyxy", bg_image=None):
-        coordinates = json.loads(coordinates)
-        pos_coordinates = []
-        for coord in coordinates:
-            coord['x'] = int(round(coord['x']))
-            coord['y'] = int(round(coord['y']))
-            if normalize:
-                norm_x = coord['x'] / width
-                norm_y = coord['y'] / height
-                pos_coordinates.append({'x': norm_x, 'y': norm_y})
-            else:
-                pos_coordinates.append({'x': coord['x'], 'y': coord['y']})
-
-        if neg_coordinates:
-            coordinates = json.loads(neg_coordinates)
-            neg_coordinates = []
-            for coord in coordinates:
-                coord['x'] = int(round(coord['x']))
-                coord['y'] = int(round(coord['y']))
-                if normalize:
-                    norm_x = coord['x'] / width
-                    norm_y = coord['y'] / height
-                    neg_coordinates.append({'x': norm_x, 'y': norm_y})
-                else:
-                    neg_coordinates.append({'x': coord['x'], 'y': coord['y']})
-
-        # Create a blank mask
-        mask = np.zeros((height, width), dtype=np.uint8)
-        bboxes = json.loads(bboxes)
-        print(bboxes)
-        valid_bboxes = []
-        for bbox in bboxes:
-            if (bbox.get("startX") is None or
-                bbox.get("startY") is None or
-                bbox.get("endX") is None or
-                bbox.get("endY") is None):
-                continue  # Skip this bounding box if any value is None
-            else:                
-                # Ensure that endX and endY are greater than startX and startY
-                x_min = min(int(bbox["startX"]), int(bbox["endX"]))
-                y_min = min(int(bbox["startY"]), int(bbox["endY"]))
-                x_max = max(int(bbox["startX"]), int(bbox["endX"]))
-                y_max = max(int(bbox["startY"]), int(bbox["endY"]))
-                
-                valid_bboxes.append((x_min, y_min, x_max, y_max))
-
-            bboxes_xyxy = []
-            for bbox in valid_bboxes:
-                x_min, y_min, x_max, y_max = bbox
-                bboxes_xyxy.append((x_min, y_min, x_max, y_max))
-                mask[y_min:y_max, x_min:x_max] = 1  # Fill the bounding box area with 1s
-
-            if bbox_format == "xywh":
-                bboxes_xywh = []
-                for bbox in valid_bboxes:
-                    x_min, y_min, x_max, y_max = bbox
-                    width = x_max - x_min
-                    height = y_max - y_min
-                    bboxes_xywh.append((x_min, y_min, width, height))
-                bboxes = bboxes_xywh
-            else:
-                bboxes = bboxes_xyxy           
-
-        mask_tensor = torch.from_numpy(mask)
-        mask_tensor = mask_tensor.unsqueeze(0).float().cpu()
-
-        if bg_image is not None and len(valid_bboxes) > 0:
-            x_min, y_min, x_max, y_max = bboxes[0]
-            cropped_image = bg_image[:, y_min:y_max, x_min:x_max, :]
-
-        elif bg_image is not None:
-            cropped_image = bg_image
-
-        if bg_image is None:
-            return (json.dumps(pos_coordinates), json.dumps(neg_coordinates), bboxes, mask_tensor)
-        else:
-            transform = transforms.ToPILImage()
-            image = transform(bg_image[0].permute(2, 0, 1))
-            buffered = io.BytesIO()
-            image.save(buffered, format="JPEG", quality=75)
-
-            # Step 3: Encode the image bytes to a Base64 string
-            img_bytes = buffered.getvalue()
-            img_base64 = base64.b64encode(img_bytes).decode('utf-8')
-        
-            return {
-                "ui": {"bg_image": [img_base64]}, 
-                "result": (json.dumps(pos_coordinates), json.dumps(neg_coordinates), bboxes, mask_tensor, cropped_image)
-            }
-
-class CutAndDragOnPath:
-    RETURN_TYPES = ("IMAGE", "MASK",)
-    RETURN_NAMES = ("image","mask", )
-    FUNCTION = "cutanddrag" 
-    CATEGORY = "KJNodes/image"
-    DESCRIPTION = """
-Cuts the masked area from the image, and drags it along the path. If inpaint is enabled, and no bg_image is provided, the cut area is filled using cv2 TELEA algorithm.
-"""
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "coordinates": ("STRING", {"forceInput": True}),
-                "mask": ("MASK",),
-                "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                "inpaint": ("BOOLEAN", {"default": True}),
-        },
-        "optional": {
-            "bg_image": ("IMAGE",),
-        }
-    }
-
-    def cutanddrag(self, image, coordinates, mask, frame_width, frame_height, inpaint, bg_image=None):
-        # Parse coordinates
-        if len(coordinates) < 10:
-            coords_list = []
-            for coords in coordinates:
-                coords = json.loads(coords.replace("'", '"'))
-                coords_list.append(coords)
-        else:
-            coords = json.loads(coordinates.replace("'", '"'))
-            coords_list = [coords]
-
-        batch_size = len(coords_list[0])
-        images_list = []
-        masks_list = []
-
-        # Convert input image and mask to PIL
-        input_image = tensor2pil(image)[0]
-        input_mask = tensor2pil(mask)[0]
-
-        # Find masked region bounds
-        mask_array = np.array(input_mask)
-        y_indices, x_indices = np.where(mask_array > 0)
-        if len(x_indices) == 0 or len(y_indices) == 0:
-            return (image, mask)
-            
-        x_min, x_max = x_indices.min(), x_indices.max()
-        y_min, y_max = y_indices.min(), y_indices.max()
-        
-        # Cut out the masked region
-        cut_width = x_max - x_min
-        cut_height = y_max - y_min
-        cut_image = input_image.crop((x_min, y_min, x_max, y_max))
-        cut_mask = input_mask.crop((x_min, y_min, x_max, y_max))
-        
-        # Create inpainted background
-        if bg_image is None:
-            background = input_image.copy()
-            # Inpaint the cut area
-            if inpaint:
-                import cv2
-                border = 5 # Create small border around cut area for better inpainting
-                fill_mask = Image.new("L", background.size, 0)
-                draw = ImageDraw.Draw(fill_mask)
-                draw.rectangle([x_min-border, y_min-border, x_max+border, y_max+border], fill=255)
-                background = cv2.inpaint(
-                    np.array(background), 
-                    np.array(fill_mask), 
-                    inpaintRadius=3, 
-                    flags=cv2.INPAINT_TELEA
-                )
-                background = Image.fromarray(background)
-        else:
-            background = tensor2pil(bg_image)[0]
-        
-        # Create batch of images with cut region at different positions
-        for i in range(batch_size):
-            # Create new image
-            new_image = background.copy()
-            new_mask = Image.new("L", (frame_width, frame_height), 0)
-
-            # Get target position from coordinates
-            for coords in coords_list:
-                target_x = int(coords[i]['x'] - cut_width/2)
-                target_y = int(coords[i]['y'] - cut_height/2)
-
-                # Paste cut region at new position
-                new_image.paste(cut_image, (target_x, target_y), cut_mask)
-                new_mask.paste(cut_mask, (target_x, target_y))
-
-            # Convert to tensor and append
-            image_tensor = pil2tensor(new_image)
-            mask_tensor = pil2tensor(new_mask)
-            
-            images_list.append(image_tensor)
-            masks_list.append(mask_tensor)
-
-        # Stack tensors into batches
-        out_images = torch.cat(images_list, dim=0).cpu().float()
-        out_masks = torch.cat(masks_list, dim=0)
-
-        return (out_images, out_masks)

custom_nodes/ComfyUI-KJNodes-main/nodes/intrinsic_lora_nodes.py

@@ -1,115 +0,0 @@
-import folder_paths
-import os
-import torch
-import torch.nn.functional as F
-from comfy.utils import ProgressBar, load_torch_file
-import comfy.sample
-from nodes import CLIPTextEncode
-
-script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-folder_paths.add_model_folder_path("intrinsic_loras", os.path.join(script_directory, "intrinsic_loras"))
-
-class Intrinsic_lora_sampling:
-    def __init__(self):
-        self.loaded_lora = None
-        
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                "lora_name": (folder_paths.get_filename_list("intrinsic_loras"), ),
-                "task": (
-                [   
-                    'depth map',
-                    'surface normals',
-                    'albedo',
-                    'shading',
-                ],
-                {
-                "default": 'depth map'
-                    }),
-                "text": ("STRING", {"multiline": True, "default": ""}),
-                "clip": ("CLIP", ),
-                "vae": ("VAE", ),
-                "per_batch": ("INT", {"default": 16, "min": 1, "max": 4096, "step": 1}),
-        },
-            "optional": {
-            "image": ("IMAGE",),
-            "optional_latent": ("LATENT",),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE", "LATENT",)
-    FUNCTION = "onestepsample"
-    CATEGORY = "KJNodes"
-    DESCRIPTION = """
-Sampler to use the intrinsic loras:  
-https://github.com/duxiaodan/intrinsic-lora  
-These LoRAs are tiny and thus included  
-with this node pack.
-"""
-
-    def onestepsample(self, model, lora_name, clip, vae, text, task, per_batch, image=None, optional_latent=None):
-        pbar = ProgressBar(3)
-
-        if optional_latent is None:
-            image_list = []
-            for start_idx in range(0, image.shape[0], per_batch):
-                sub_pixels = vae.vae_encode_crop_pixels(image[start_idx:start_idx+per_batch])
-                image_list.append(vae.encode(sub_pixels[:,:,:,:3]))
-            sample = torch.cat(image_list, dim=0)
-        else:
-            sample = optional_latent["samples"]
-        noise = torch.zeros(sample.size(), dtype=sample.dtype, layout=sample.layout, device="cpu")
-        prompt = task + "," + text
-        positive, = CLIPTextEncode.encode(self, clip, prompt)
-        negative = positive #negative shouldn't do anything in this scenario
-
-        pbar.update(1)
-     
-        #custom model sampling to pass latent through as it is
-        class X0_PassThrough(comfy.model_sampling.EPS):
-            def calculate_denoised(self, sigma, model_output, model_input):
-                return model_output
-            def calculate_input(self, sigma, noise):
-                return noise
-        sampling_base = comfy.model_sampling.ModelSamplingDiscrete
-        sampling_type = X0_PassThrough
-
-        class ModelSamplingAdvanced(sampling_base, sampling_type):
-            pass
-        model_sampling = ModelSamplingAdvanced(model.model.model_config)
-
-        #load lora
-        model_clone = model.clone()
-        lora_path = folder_paths.get_full_path("intrinsic_loras", lora_name)        
-        lora = load_torch_file(lora_path, safe_load=True)
-        self.loaded_lora = (lora_path, lora)
-
-        model_clone_with_lora = comfy.sd.load_lora_for_models(model_clone, None, lora, 1.0, 0)[0]
-
-        model_clone_with_lora.add_object_patch("model_sampling", model_sampling)
-
-        samples = {"samples": comfy.sample.sample(model_clone_with_lora, noise, 1, 1.0, "euler", "simple", positive, negative, sample,
-                                  denoise=1.0, disable_noise=True, start_step=0, last_step=1,
-                                  force_full_denoise=True, noise_mask=None, callback=None, disable_pbar=True, seed=None)}
-        pbar.update(1)
-
-        decoded = []
-        for start_idx in range(0, samples["samples"].shape[0], per_batch):
-            decoded.append(vae.decode(samples["samples"][start_idx:start_idx+per_batch]))
-        image_out = torch.cat(decoded, dim=0)
-
-        pbar.update(1)
-
-        if task == 'depth map':
-            imax = image_out.max()
-            imin = image_out.min()
-            image_out = (image_out-imin)/(imax-imin)
-            image_out = torch.max(image_out, dim=3, keepdim=True)[0].repeat(1, 1, 1, 3)
-        elif task == 'surface normals':
-            image_out = F.normalize(image_out * 2 - 1, dim=3) / 2 + 0.5
-            image_out = 1.0 - image_out
-        else:
-            image_out = image_out.clamp(-1.,1.)
-            
-        return (image_out, samples,)

custom_nodes/ComfyUI-KJNodes-main/nodes/mask_nodes.py

@@ -1,1397 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torchvision.transforms import functional as TF
-from PIL import Image, ImageDraw, ImageFilter, ImageFont
-import scipy.ndimage
-import numpy as np
-from contextlib import nullcontext
-import os
-
-import model_management
-from comfy.utils import ProgressBar
-from comfy.utils import common_upscale
-from nodes import MAX_RESOLUTION
-
-import folder_paths
-
-from ..utility.utility import tensor2pil, pil2tensor
-
-script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-class BatchCLIPSeg:
-
-    def __init__(self):
-        pass
-    
-    @classmethod
-    def INPUT_TYPES(s):
-       
-        return {"required":
-                    {
-                        "images": ("IMAGE",),
-                        "text": ("STRING", {"multiline": False}),
-                        "threshold": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 10.0, "step": 0.001}),
-                        "binary_mask": ("BOOLEAN", {"default": True}),
-                        "combine_mask": ("BOOLEAN", {"default": False}),
-                        "use_cuda": ("BOOLEAN", {"default": True}),
-                     },
-                     "optional":
-                    {
-                        "blur_sigma": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}),
-                        "opt_model": ("CLIPSEGMODEL", ),
-                        "prev_mask": ("MASK", {"default": None}),
-                        "image_bg_level": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
-                        "invert": ("BOOLEAN", {"default": False}),
-                    }
-                }
-
-    CATEGORY = "KJNodes/masking"
-    RETURN_TYPES = ("MASK", "IMAGE", )
-    RETURN_NAMES = ("Mask", "Image", )
-    FUNCTION = "segment_image"
-    DESCRIPTION = """
-Segments an image or batch of images using CLIPSeg.
-"""
-
-    def segment_image(self, images, text, threshold, binary_mask, combine_mask, use_cuda, blur_sigma=0.0, opt_model=None, prev_mask=None, invert= False, image_bg_level=0.5):
-        from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
-        import torchvision.transforms as transforms
-        offload_device = model_management.unet_offload_device()
-        device = model_management.get_torch_device()
-        if not use_cuda:
-            device = torch.device("cpu")
-        dtype = model_management.unet_dtype()
-
-        if opt_model is None:
-            checkpoint_path = os.path.join(folder_paths.models_dir,'clip_seg', 'clipseg-rd64-refined-fp16')
-            if not hasattr(self, "model"):
-                try:
-                    if not os.path.exists(checkpoint_path):
-                        from huggingface_hub import snapshot_download
-                        snapshot_download(repo_id="Kijai/clipseg-rd64-refined-fp16", local_dir=checkpoint_path, local_dir_use_symlinks=False)
-                    self.model = CLIPSegForImageSegmentation.from_pretrained(checkpoint_path)
-                except:
-                    checkpoint_path = "CIDAS/clipseg-rd64-refined"
-                    self.model = CLIPSegForImageSegmentation.from_pretrained(checkpoint_path)
-            processor = CLIPSegProcessor.from_pretrained(checkpoint_path)
-
-        else:
-            self.model = opt_model['model']
-            processor = opt_model['processor']
-
-        self.model.to(dtype).to(device)
-
-        B, H, W, C = images.shape
-        images = images.to(device)
-        
-        autocast_condition = (dtype != torch.float32) and not model_management.is_device_mps(device)
-        with torch.autocast(model_management.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
-
-            PIL_images = [Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) for image in images ]
-            prompt = [text] * len(images)
-            input_prc = processor(text=prompt, images=PIL_images, return_tensors="pt")
-
-            for key in input_prc:
-                input_prc[key] = input_prc[key].to(device)
-            outputs = self.model(**input_prc)
-
-        mask_tensor = torch.sigmoid(outputs.logits)
-        mask_tensor = (mask_tensor - mask_tensor.min()) / (mask_tensor.max() - mask_tensor.min())
-        mask_tensor = torch.where(mask_tensor > (threshold), mask_tensor, torch.tensor(0, dtype=torch.float))
-        print(mask_tensor.shape)
-        if len(mask_tensor.shape) == 2:
-            mask_tensor = mask_tensor.unsqueeze(0)
-        mask_tensor = F.interpolate(mask_tensor.unsqueeze(1), size=(H, W), mode='nearest')
-        mask_tensor = mask_tensor.squeeze(1)
-
-        self.model.to(offload_device)
-        
-        if binary_mask:
-            mask_tensor = (mask_tensor > 0).float()
-        if blur_sigma > 0:
-            kernel_size = int(6 * int(blur_sigma) + 1) 
-            blur = transforms.GaussianBlur(kernel_size=(kernel_size, kernel_size), sigma=(blur_sigma, blur_sigma))
-            mask_tensor = blur(mask_tensor)
-
-        if combine_mask:
-            mask_tensor = torch.max(mask_tensor, dim=0)[0]
-            mask_tensor = mask_tensor.unsqueeze(0).repeat(len(images),1,1)
-
-        del outputs
-        model_management.soft_empty_cache()
-
-        if prev_mask is not None:
-            if prev_mask.shape != mask_tensor.shape:
-                prev_mask = F.interpolate(prev_mask.unsqueeze(1), size=(H, W), mode='nearest')
-            mask_tensor = mask_tensor + prev_mask.to(device)
-            torch.clamp(mask_tensor, min=0.0, max=1.0)
-
-        if invert:
-            mask_tensor = 1 - mask_tensor
-
-        image_tensor = images * mask_tensor.unsqueeze(-1) + (1 - mask_tensor.unsqueeze(-1)) * image_bg_level
-        image_tensor = torch.clamp(image_tensor, min=0.0, max=1.0).cpu().float()
-
-        mask_tensor = mask_tensor.cpu().float()
-    
-        return mask_tensor, image_tensor, 
-
-class DownloadAndLoadCLIPSeg:
-
-    def __init__(self):
-        pass
-    
-    @classmethod
-    def INPUT_TYPES(s):
-       
-        return {"required":
-                    {     
-                    "model": (
-                    [   'Kijai/clipseg-rd64-refined-fp16',
-                        'CIDAS/clipseg-rd64-refined',
-                    ],
-                    ),
-                     },
-                }
-
-    CATEGORY = "KJNodes/masking"
-    RETURN_TYPES = ("CLIPSEGMODEL",)
-    RETURN_NAMES = ("clipseg_model",)
-    FUNCTION = "segment_image"
-    DESCRIPTION = """
-Downloads and loads CLIPSeg model with huggingface_hub,  
-to ComfyUI/models/clip_seg
-"""
-
-    def segment_image(self, model):
-        from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
-        checkpoint_path = os.path.join(folder_paths.models_dir,'clip_seg', os.path.basename(model))
-        if not hasattr(self, "model"):
-            if not os.path.exists(checkpoint_path):
-                from huggingface_hub import snapshot_download
-                snapshot_download(repo_id=model, local_dir=checkpoint_path, local_dir_use_symlinks=False)
-            self.model = CLIPSegForImageSegmentation.from_pretrained(checkpoint_path)
-
-        processor = CLIPSegProcessor.from_pretrained(checkpoint_path)
-
-        clipseg_model = {}
-        clipseg_model['model'] = self.model
-        clipseg_model['processor'] = processor
-
-        return clipseg_model,
-
-class CreateTextMask:
-
-    RETURN_TYPES = ("IMAGE", "MASK",)
-    FUNCTION = "createtextmask"
-    CATEGORY = "KJNodes/text"
-    DESCRIPTION = """
-Creates a text image and mask.  
-Looks for fonts from this folder:  
-ComfyUI/custom_nodes/ComfyUI-KJNodes/fonts
-  
-If start_rotation and/or end_rotation are different values,  
-creates animation between them.
-"""
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                 "invert": ("BOOLEAN", {"default": False}),
-                 "frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}),
-                 "text_x": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
-                 "text_y": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
-                 "font_size": ("INT", {"default": 32,"min": 8, "max": 4096, "step": 1}),
-                 "font_color": ("STRING", {"default": "white"}),
-                 "text": ("STRING", {"default": "HELLO!", "multiline": True}),
-                 "font": (folder_paths.get_filename_list("kjnodes_fonts"), ),
-                 "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                 "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                 "start_rotation": ("INT", {"default": 0,"min": 0, "max": 359, "step": 1}),
-                 "end_rotation": ("INT", {"default": 0,"min": -359, "max": 359, "step": 1}),
-        },
-    } 
-
-    def createtextmask(self, frames, width, height, invert, text_x, text_y, text, font_size, font_color, font, start_rotation, end_rotation):
-    # Define the number of images in the batch
-        batch_size = frames
-        out = []
-        masks = []
-        rotation = start_rotation
-        if start_rotation != end_rotation:
-            rotation_increment = (end_rotation - start_rotation) / (batch_size - 1)
-
-        font_path = folder_paths.get_full_path("kjnodes_fonts", font)
-        # Generate the text
-        for i in range(batch_size):
-            image = Image.new("RGB", (width, height), "black")
-            draw = ImageDraw.Draw(image)
-            font = ImageFont.truetype(font_path, font_size)
-            
-            # Split the text into words
-            words = text.split()
-            
-            # Initialize variables for line creation
-            lines = []
-            current_line = []
-            current_line_width = 0
-            try: #new pillow  
-                # Iterate through words to create lines
-                for word in words:
-                    word_width = font.getbbox(word)[2]
-                    if current_line_width + word_width <= width - 2 * text_x:
-                        current_line.append(word)
-                        current_line_width += word_width + font.getbbox(" ")[2] # Add space width
-                    else:
-                        lines.append(" ".join(current_line))
-                        current_line = [word]
-                        current_line_width = word_width
-            except: #old pillow             
-                for word in words:
-                    word_width = font.getsize(word)[0]
-                    if current_line_width + word_width <= width - 2 * text_x:
-                        current_line.append(word)
-                        current_line_width += word_width + font.getsize(" ")[0] # Add space width
-                    else:
-                        lines.append(" ".join(current_line))
-                        current_line = [word]
-                        current_line_width = word_width
-            
-            # Add the last line if it's not empty
-            if current_line:
-                lines.append(" ".join(current_line))
-            
-            # Draw each line of text separately
-            y_offset = text_y
-            for line in lines:
-                text_width = font.getlength(line)
-                text_height = font_size
-                text_center_x = text_x + text_width / 2
-                text_center_y = y_offset + text_height / 2
-                try:
-                    draw.text((text_x, y_offset), line, font=font, fill=font_color, features=['-liga'])
-                except:
-                    draw.text((text_x, y_offset), line, font=font, fill=font_color)
-                y_offset += text_height # Move to the next line
-            
-            if start_rotation != end_rotation:
-                image = image.rotate(rotation, center=(text_center_x, text_center_y))
-                rotation += rotation_increment
-            
-            image = np.array(image).astype(np.float32) / 255.0
-            image = torch.from_numpy(image)[None,]
-            mask = image[:, :, :, 0] 
-            masks.append(mask)
-            out.append(image)
-            
-        if invert:
-            return (1.0 - torch.cat(out, dim=0), 1.0 - torch.cat(masks, dim=0),)
-        return (torch.cat(out, dim=0),torch.cat(masks, dim=0),)
-
-class ColorToMask:
-    
-    RETURN_TYPES = ("MASK",)
-    FUNCTION = "clip"
-    CATEGORY = "KJNodes/masking"
-    DESCRIPTION = """
-Converts chosen RGB value to a mask.  
-With batch inputs, the **per_batch**  
-controls the number of images processed at once.
-"""
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                 "images": ("IMAGE",),
-                 "invert": ("BOOLEAN", {"default": False}),
-                 "red": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
-                 "green": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
-                 "blue": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
-                 "threshold": ("INT", {"default": 10,"min": 0, "max": 255, "step": 1}),
-                 "per_batch": ("INT", {"default": 16, "min": 1, "max": 4096, "step": 1}),
-        },
-    } 
-
-    def clip(self, images, red, green, blue, threshold, invert, per_batch):
-
-        color = torch.tensor([red, green, blue], dtype=torch.uint8)  
-        black = torch.tensor([0, 0, 0], dtype=torch.uint8)
-        white = torch.tensor([255, 255, 255], dtype=torch.uint8)
-        
-        if invert:
-            black, white = white, black
-
-        steps = images.shape[0]
-        pbar = ProgressBar(steps)
-        tensors_out = []
-        
-        for start_idx in range(0, images.shape[0], per_batch):
-
-            # Calculate color distances
-            color_distances = torch.norm(images[start_idx:start_idx+per_batch] * 255 - color, dim=-1)
-            
-            # Create a mask based on the threshold
-            mask = color_distances <= threshold
-            
-            # Apply the mask to create new images
-            mask_out = torch.where(mask.unsqueeze(-1), white, black).float()
-            mask_out = mask_out.mean(dim=-1)
-
-            tensors_out.append(mask_out.cpu())
-            batch_count = mask_out.shape[0]
-            pbar.update(batch_count)
-       
-        tensors_out = torch.cat(tensors_out, dim=0)
-        tensors_out = torch.clamp(tensors_out, min=0.0, max=1.0)
-        return tensors_out,
-      
-class CreateFluidMask:
-    
-    RETURN_TYPES = ("IMAGE", "MASK")
-    FUNCTION = "createfluidmask"
-    CATEGORY = "KJNodes/masking/generate"
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                 "invert": ("BOOLEAN", {"default": False}),
-                 "frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}),
-                 "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
-                 "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
-                 "inflow_count": ("INT", {"default": 3,"min": 0, "max": 255, "step": 1}),
-                 "inflow_velocity": ("INT", {"default": 1,"min": 0, "max": 255, "step": 1}),
-                 "inflow_radius": ("INT", {"default": 8,"min": 0, "max": 255, "step": 1}),
-                 "inflow_padding": ("INT", {"default": 50,"min": 0, "max": 255, "step": 1}),
-                 "inflow_duration": ("INT", {"default": 60,"min": 0, "max": 255, "step": 1}),
-        },
-    } 
-    #using code from https://github.com/GregTJ/stable-fluids
-    def createfluidmask(self, frames, width, height, invert, inflow_count, inflow_velocity, inflow_radius, inflow_padding, inflow_duration):
-        from ..utility.fluid import Fluid
-        try:
-            from scipy.special import erf
-        except:
-            from scipy.spatial import erf
-        out = []
-        masks = []
-        RESOLUTION = width, height
-        DURATION = frames
-
-        INFLOW_PADDING = inflow_padding
-        INFLOW_DURATION = inflow_duration
-        INFLOW_RADIUS = inflow_radius
-        INFLOW_VELOCITY = inflow_velocity
-        INFLOW_COUNT = inflow_count
-
-        print('Generating fluid solver, this may take some time.')
-        fluid = Fluid(RESOLUTION, 'dye')
-
-        center = np.floor_divide(RESOLUTION, 2)
-        r = np.min(center) - INFLOW_PADDING
-
-        points = np.linspace(-np.pi, np.pi, INFLOW_COUNT, endpoint=False)
-        points = tuple(np.array((np.cos(p), np.sin(p))) for p in points)
-        normals = tuple(-p for p in points)
-        points = tuple(r * p + center for p in points)
-
-        inflow_velocity = np.zeros_like(fluid.velocity)
-        inflow_dye = np.zeros(fluid.shape)
-        for p, n in zip(points, normals):
-            mask = np.linalg.norm(fluid.indices - p[:, None, None], axis=0) <= INFLOW_RADIUS
-            inflow_velocity[:, mask] += n[:, None] * INFLOW_VELOCITY
-            inflow_dye[mask] = 1
-
-        
-        for f in range(DURATION):
-            print(f'Computing frame {f + 1} of {DURATION}.')
-            if f <= INFLOW_DURATION:
-                fluid.velocity += inflow_velocity
-                fluid.dye += inflow_dye
-
-            curl = fluid.step()[1]
-            # Using the error function to make the contrast a bit higher. 
-            # Any other sigmoid function e.g. smoothstep would work.
-            curl = (erf(curl * 2) + 1) / 4
-
-            color = np.dstack((curl, np.ones(fluid.shape), fluid.dye))
-            color = (np.clip(color, 0, 1) * 255).astype('uint8')
-            image = np.array(color).astype(np.float32) / 255.0
-            image = torch.from_numpy(image)[None,]
-            mask = image[:, :, :, 0] 
-            masks.append(mask)
-            out.append(image)
-        
-        if invert:
-            return (1.0 - torch.cat(out, dim=0),1.0 - torch.cat(masks, dim=0),)
-        return (torch.cat(out, dim=0),torch.cat(masks, dim=0),)
-
-class CreateAudioMask:
-       
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "createaudiomask"
-    CATEGORY = "KJNodes/deprecated"
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                 "invert": ("BOOLEAN", {"default": False}),
-                 "frames": ("INT", {"default": 16,"min": 1, "max": 255, "step": 1}),
-                 "scale": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 2.0, "step": 0.01}),
-                 "audio_path": ("STRING", {"default": "audio.wav"}),
-                 "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
-                 "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
-        },
-    } 
-
-    def createaudiomask(self, frames, width, height, invert, audio_path, scale):
-        try:
-            import librosa
-        except ImportError:
-            raise Exception("Can not import librosa. Install it with 'pip install librosa'")
-        batch_size = frames
-        out = []
-        masks = []
-        if audio_path == "audio.wav": #I don't know why relative path won't work otherwise...
-            audio_path = os.path.join(script_directory, audio_path)
-        audio, sr = librosa.load(audio_path)
-        spectrogram = np.abs(librosa.stft(audio))
-        
-        for i in range(batch_size):
-           image = Image.new("RGB", (width, height), "black")
-           draw = ImageDraw.Draw(image)
-           frame = spectrogram[:, i]
-           circle_radius = int(height * np.mean(frame))
-           circle_radius *= scale
-           circle_center = (width // 2, height // 2)  # Calculate the center of the image
-
-           draw.ellipse([(circle_center[0] - circle_radius, circle_center[1] - circle_radius),
-                      (circle_center[0] + circle_radius, circle_center[1] + circle_radius)],
-                      fill='white')
-             
-           image = np.array(image).astype(np.float32) / 255.0
-           image = torch.from_numpy(image)[None,]
-           mask = image[:, :, :, 0] 
-           masks.append(mask)
-           out.append(image)
-
-        if invert:
-            return (1.0 - torch.cat(out, dim=0),)
-        return (torch.cat(out, dim=0),torch.cat(masks, dim=0),)
-       
-class CreateGradientMask:
-    
-    RETURN_TYPES = ("MASK",)
-    FUNCTION = "createmask"
-    CATEGORY = "KJNodes/masking/generate"
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                 "invert": ("BOOLEAN", {"default": False}),
-                 "frames": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
-                 "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
-                 "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
-        },
-    } 
-    def createmask(self, frames, width, height, invert):
-        # Define the number of images in the batch
-        batch_size = frames
-        out = []
-        # Create an empty array to store the image batch
-        image_batch = np.zeros((batch_size, height, width), dtype=np.float32)
-        # Generate the black to white gradient for each image
-        for i in range(batch_size):
-            gradient = np.linspace(1.0, 0.0, width, dtype=np.float32)
-            time = i / frames  # Calculate the time variable
-            offset_gradient = gradient - time  # Offset the gradient values based on time
-            image_batch[i] = offset_gradient.reshape(1, -1)
-        output = torch.from_numpy(image_batch)
-        mask = output
-        out.append(mask)
-        if invert:
-            return (1.0 - torch.cat(out, dim=0),)
-        return (torch.cat(out, dim=0),)
-
-class CreateFadeMask:
-    
-    RETURN_TYPES = ("MASK",)
-    FUNCTION = "createfademask"
-    CATEGORY = "KJNodes/deprecated"
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                 "invert": ("BOOLEAN", {"default": False}),
-                 "frames": ("INT", {"default": 2,"min": 2, "max": 10000, "step": 1}),
-                 "width": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
-                 "height": ("INT", {"default": 256,"min": 16, "max": 4096, "step": 1}),
-                 "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],),
-                 "start_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}),
-                 "midpoint_level": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 1.0, "step": 0.01}),
-                 "end_level": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}),
-                 "midpoint_frame": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
-        },
-    } 
-    
-    def createfademask(self, frames, width, height, invert, interpolation, start_level, midpoint_level, end_level, midpoint_frame):
-        def ease_in(t):
-            return t * t
-
-        def ease_out(t):
-            return 1 - (1 - t) * (1 - t)
-
-        def ease_in_out(t):
-            return 3 * t * t - 2 * t * t * t
-
-        batch_size = frames
-        out = []
-        image_batch = np.zeros((batch_size, height, width), dtype=np.float32)
-
-        if midpoint_frame == 0:
-            midpoint_frame = batch_size // 2
-
-        for i in range(batch_size):
-            if i <= midpoint_frame:
-                t = i / midpoint_frame
-                if interpolation == "ease_in":
-                    t = ease_in(t)
-                elif interpolation == "ease_out":
-                    t = ease_out(t)
-                elif interpolation == "ease_in_out":
-                    t = ease_in_out(t)
-                color = start_level - t * (start_level - midpoint_level)
-            else:
-                t = (i - midpoint_frame) / (batch_size - midpoint_frame)
-                if interpolation == "ease_in":
-                    t = ease_in(t)
-                elif interpolation == "ease_out":
-                    t = ease_out(t)
-                elif interpolation == "ease_in_out":
-                    t = ease_in_out(t)
-                color = midpoint_level - t * (midpoint_level - end_level)
-
-            color = np.clip(color, 0, 255)
-            image = np.full((height, width), color, dtype=np.float32)
-            image_batch[i] = image
-
-        output = torch.from_numpy(image_batch)
-        mask = output
-        out.append(mask)
-
-        if invert:
-            return (1.0 - torch.cat(out, dim=0),)
-        return (torch.cat(out, dim=0),)
-
-class CreateFadeMaskAdvanced:
-    
-    RETURN_TYPES = ("MASK",)
-    FUNCTION = "createfademask"
-    CATEGORY = "KJNodes/masking/generate"
-    DESCRIPTION = """
-Create a batch of masks interpolated between given frames and values. 
-Uses same syntax as Fizz' BatchValueSchedule.
-First value is the frame index (not that this starts from 0, not 1) 
-and the second value inside the brackets is the float value of the mask in range 0.0 - 1.0  
-
-For example the default values:  
-0:(0.0)  
-7:(1.0)  
-15:(0.0)  
-  
-Would create a mask batch fo 16 frames, starting from black, 
-interpolating with the chosen curve to fully white at the 8th frame, 
-and interpolating from that to fully black at the 16th frame.
-"""
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                 "points_string": ("STRING", {"default": "0:(0.0),\n7:(1.0),\n15:(0.0)\n", "multiline": True}),
-                 "invert": ("BOOLEAN", {"default": False}),
-                 "frames": ("INT", {"default": 16,"min": 2, "max": 10000, "step": 1}),
-                 "width": ("INT", {"default": 512,"min": 1, "max": 4096, "step": 1}),
-                 "height": ("INT", {"default": 512,"min": 1, "max": 4096, "step": 1}),
-                 "interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],),
-        },
-    } 
-    
-    def createfademask(self, frames, width, height, invert, points_string, interpolation):
-        def ease_in(t):
-            return t * t
-        
-        def ease_out(t):
-            return 1 - (1 - t) * (1 - t)
-
-        def ease_in_out(t):
-            return 3 * t * t - 2 * t * t * t
-        
-        # Parse the input string into a list of tuples
-        points = []
-        points_string = points_string.rstrip(',\n')
-        for point_str in points_string.split(','):
-            frame_str, color_str = point_str.split(':')
-            frame = int(frame_str.strip())
-            color = float(color_str.strip()[1:-1])  # Remove parentheses around color
-            points.append((frame, color))
-
-        # Check if the last frame is already in the points
-        if len(points) == 0 or points[-1][0] != frames - 1:
-            # If not, add it with the color of the last specified frame
-            points.append((frames - 1, points[-1][1] if points else 0))
-
-        # Sort the points by frame number
-        points.sort(key=lambda x: x[0])
-
-        batch_size = frames
-        out = []
-        image_batch = np.zeros((batch_size, height, width), dtype=np.float32)
-
-        # Index of the next point to interpolate towards
-        next_point = 1
-
-        for i in range(batch_size):
-            while next_point < len(points) and i > points[next_point][0]:
-                next_point += 1
-
-            # Interpolate between the previous point and the next point
-            prev_point = next_point - 1
-            t = (i - points[prev_point][0]) / (points[next_point][0] - points[prev_point][0])
-            if interpolation == "ease_in":
-                t = ease_in(t)
-            elif interpolation == "ease_out":
-                t = ease_out(t)
-            elif interpolation == "ease_in_out":
-                t = ease_in_out(t)
-            elif interpolation == "linear":
-                pass  # No need to modify `t` for linear interpolation
-
-            color = points[prev_point][1] - t * (points[prev_point][1] - points[next_point][1])
-            color = np.clip(color, 0, 255)
-            image = np.full((height, width), color, dtype=np.float32)
-            image_batch[i] = image
-
-        output = torch.from_numpy(image_batch)
-        mask = output
-        out.append(mask)
-
-        if invert:
-            return (1.0 - torch.cat(out, dim=0),)
-        return (torch.cat(out, dim=0),)
-
-class CreateMagicMask:
-    
-    RETURN_TYPES = ("MASK", "MASK",)
-    RETURN_NAMES = ("mask", "mask_inverted",)
-    FUNCTION = "createmagicmask"
-    CATEGORY = "KJNodes/masking/generate"
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                 "frames": ("INT", {"default": 16,"min": 2, "max": 4096, "step": 1}),
-                 "depth": ("INT", {"default": 12,"min": 1, "max": 500, "step": 1}),
-                 "distortion": ("FLOAT", {"default": 1.5,"min": 0.0, "max": 100.0, "step": 0.01}),
-                 "seed": ("INT", {"default": 123,"min": 0, "max": 99999999, "step": 1}),
-                 "transitions": ("INT", {"default": 1,"min": 1, "max": 20, "step": 1}),
-                 "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                 "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-        },
-    } 
-
-    def createmagicmask(self, frames, transitions, depth, distortion, seed, frame_width, frame_height):
-        from ..utility.magictex import coordinate_grid, random_transform, magic
-        import matplotlib.pyplot as plt
-        rng = np.random.default_rng(seed)
-        out = []
-        coords = coordinate_grid((frame_width, frame_height))
-
-        # Calculate the number of frames for each transition
-        frames_per_transition = frames // transitions
-
-        # Generate a base set of parameters
-        base_params = {
-            "coords": random_transform(coords, rng),
-            "depth": depth,
-            "distortion": distortion,
-        }
-        for t in range(transitions):
-        # Generate a second set of parameters that is at most max_diff away from the base parameters
-            params1 = base_params.copy()
-            params2 = base_params.copy()
-
-            params1['coords'] = random_transform(coords, rng)
-            params2['coords'] = random_transform(coords, rng)
-
-            for i in range(frames_per_transition):
-                # Compute the interpolation factor
-                alpha = i / frames_per_transition
-
-                # Interpolate between the two sets of parameters
-                params = params1.copy()
-                params['coords'] = (1 - alpha) * params1['coords'] + alpha * params2['coords']
-
-                tex = magic(**params)
-
-                dpi = frame_width / 10
-                fig = plt.figure(figsize=(10, 10), dpi=dpi)
-
-                ax = fig.add_subplot(111)
-                plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
-                
-                ax.get_yaxis().set_ticks([])
-                ax.get_xaxis().set_ticks([])
-                ax.imshow(tex, aspect='auto')
-                
-                fig.canvas.draw()
-                img = np.array(fig.canvas.renderer._renderer)
-                
-                plt.close(fig)
-                
-                pil_img = Image.fromarray(img).convert("L")
-                mask = torch.tensor(np.array(pil_img)) / 255.0
-                
-                out.append(mask)
-        
-        return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),)
-        
-class CreateShapeMask:
-    
-    RETURN_TYPES = ("MASK", "MASK",)
-    RETURN_NAMES = ("mask", "mask_inverted",)
-    FUNCTION = "createshapemask"
-    CATEGORY = "KJNodes/masking/generate"
-    DESCRIPTION = """
-Creates a mask or batch of masks with the specified shape.  
-Locations are center locations.  
-Grow value is the amount to grow the shape on each frame, creating animated masks.
-"""
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "shape": (
-            [   'circle',
-                'square',
-                'triangle',
-            ],
-            {
-            "default": 'circle'
-             }),
-                "frames": ("INT", {"default": 1,"min": 1, "max": 4096, "step": 1}),
-                "location_x": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}),
-                "location_y": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}),
-                "grow": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
-                "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                "shape_width": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}),
-                "shape_height": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}),
-        },
-    } 
-
-    def createshapemask(self, frames, frame_width, frame_height, location_x, location_y, shape_width, shape_height, grow, shape):
-        # Define the number of images in the batch
-        batch_size = frames
-        out = []
-        color = "white"
-        for i in range(batch_size):
-            image = Image.new("RGB", (frame_width, frame_height), "black")
-            draw = ImageDraw.Draw(image)
-
-            # Calculate the size for this frame and ensure it's not less than 0
-            current_width = max(0, shape_width + i*grow)
-            current_height = max(0, shape_height + i*grow)
-
-            if shape == 'circle' or shape == 'square':
-                # Define the bounding box for the shape
-                left_up_point = (location_x - current_width // 2, location_y - current_height // 2)
-                right_down_point = (location_x + current_width // 2, location_y + current_height // 2)
-                two_points = [left_up_point, right_down_point]
-
-                if shape == 'circle':
-                    draw.ellipse(two_points, fill=color)
-                elif shape == 'square':
-                    draw.rectangle(two_points, fill=color)
-                    
-            elif shape == 'triangle':
-                # Define the points for the triangle
-                left_up_point = (location_x - current_width // 2, location_y + current_height // 2) # bottom left
-                right_down_point = (location_x + current_width // 2, location_y + current_height // 2) # bottom right
-                top_point = (location_x, location_y - current_height // 2) # top point
-                draw.polygon([top_point, left_up_point, right_down_point], fill=color)
-
-            image = pil2tensor(image)
-            mask = image[:, :, :, 0]
-            out.append(mask)
-        outstack = torch.cat(out, dim=0)
-        return (outstack, 1.0 - outstack,)
-    
-class CreateVoronoiMask:
-    
-    RETURN_TYPES = ("MASK", "MASK",)
-    RETURN_NAMES = ("mask", "mask_inverted",)
-    FUNCTION = "createvoronoi"
-    CATEGORY = "KJNodes/masking/generate"
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                 "frames": ("INT", {"default": 16,"min": 2, "max": 4096, "step": 1}),
-                 "num_points": ("INT", {"default": 15,"min": 1, "max": 4096, "step": 1}),
-                 "line_width": ("INT", {"default": 4,"min": 1, "max": 4096, "step": 1}),
-                 "speed": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 1.0, "step": 0.01}),
-                 "frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-                 "frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
-        },
-    } 
-
-    def createvoronoi(self, frames, num_points, line_width, speed, frame_width, frame_height):
-        from scipy.spatial import Voronoi
-        # Define the number of images in the batch
-        batch_size = frames
-        out = []
-          
-        # Calculate aspect ratio
-        aspect_ratio = frame_width / frame_height
-        
-        # Create start and end points for each point, considering the aspect ratio
-        start_points = np.random.rand(num_points, 2)
-        start_points[:, 0] *= aspect_ratio
-        
-        end_points = np.random.rand(num_points, 2)
-        end_points[:, 0] *= aspect_ratio
-
-        for i in range(batch_size):
-            # Interpolate the points' positions based on the current frame
-            t = (i * speed) / (batch_size - 1)  # normalize to [0, 1] over the frames
-            t = np.clip(t, 0, 1)  # ensure t is in [0, 1]
-            points = (1 - t) * start_points + t * end_points  # lerp
-
-            # Adjust points for aspect ratio
-            points[:, 0] *= aspect_ratio
-
-            vor = Voronoi(points)
-
-            # Create a blank image with a white background
-            fig, ax = plt.subplots()
-            plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
-            ax.set_xlim([0, aspect_ratio]); ax.set_ylim([0, 1])  # adjust x limits
-            ax.axis('off')
-            ax.margins(0, 0)
-            fig.set_size_inches(aspect_ratio * frame_height/100, frame_height/100)  # adjust figure size
-            ax.fill_between([0, 1], [0, 1], color='white')
-
-            # Plot each Voronoi ridge
-            for simplex in vor.ridge_vertices:
-                simplex = np.asarray(simplex)
-                if np.all(simplex >= 0):
-                    plt.plot(vor.vertices[simplex, 0], vor.vertices[simplex, 1], 'k-', linewidth=line_width)
-
-            fig.canvas.draw()
-            img = np.array(fig.canvas.renderer._renderer)
-
-            plt.close(fig)
-
-            pil_img = Image.fromarray(img).convert("L")
-            mask = torch.tensor(np.array(pil_img)) / 255.0
-
-            out.append(mask)
-
-        return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),)
-    
-class GetMaskSizeAndCount:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "mask": ("MASK",),
-        }}
-
-    RETURN_TYPES = ("MASK","INT", "INT", "INT",)
-    RETURN_NAMES = ("mask", "width", "height", "count",)
-    FUNCTION = "getsize"
-    CATEGORY = "KJNodes/masking"
-    DESCRIPTION = """
-Returns the width, height and batch size of the mask,  
-and passes it through unchanged.  
-
-"""
-
-    def getsize(self, mask):
-        width = mask.shape[2]
-        height = mask.shape[1]
-        count = mask.shape[0]
-        return {"ui": {
-            "text": [f"{count}x{width}x{height}"]}, 
-            "result": (mask, width, height, count) 
-        }
-
-class GrowMaskWithBlur:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "mask": ("MASK",),
-                "expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}),
-                "incremental_expandrate": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}),
-                "tapered_corners": ("BOOLEAN", {"default": True}),
-                "flip_input": ("BOOLEAN", {"default": False}),
-                "blur_radius": ("FLOAT", {
-                    "default": 0.0,
-                    "min": 0.0,
-                    "max": 100,
-                    "step": 0.1
-                }),
-                "lerp_alpha": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                "decay_factor": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-            },
-            "optional": {
-                "fill_holes": ("BOOLEAN", {"default": False}),
-            },
-        }
-
-    CATEGORY = "KJNodes/masking"
-    RETURN_TYPES = ("MASK", "MASK",)
-    RETURN_NAMES = ("mask", "mask_inverted",)
-    FUNCTION = "expand_mask"
-    DESCRIPTION = """
-# GrowMaskWithBlur
-- mask: Input mask or mask batch
-- expand: Expand or contract mask or mask batch by a given amount
-- incremental_expandrate: increase expand rate by a given amount per frame
-- tapered_corners: use tapered corners
-- flip_input: flip input mask
-- blur_radius: value higher than 0 will blur the mask
-- lerp_alpha: alpha value for interpolation between frames
-- decay_factor: decay value for interpolation between frames
-- fill_holes: fill holes in the mask (slow)"""
-    
-    def expand_mask(self, mask, expand, tapered_corners, flip_input, blur_radius, incremental_expandrate, lerp_alpha, decay_factor, fill_holes=False):
-        alpha = lerp_alpha
-        decay = decay_factor
-        if flip_input:
-            mask = 1.0 - mask
-        c = 0 if tapered_corners else 1
-        kernel = np.array([[c, 1, c],
-                           [1, 1, 1],
-                           [c, 1, c]])
-        growmask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).cpu()
-        out = []
-        previous_output = None
-        current_expand = expand
-        for m in growmask:
-            output = m.numpy().astype(np.float32)
-            for _ in range(abs(round(current_expand))):
-                if current_expand < 0:
-                    output = scipy.ndimage.grey_erosion(output, footprint=kernel)
-                else:
-                    output = scipy.ndimage.grey_dilation(output, footprint=kernel)
-            if current_expand < 0:
-                current_expand -= abs(incremental_expandrate)
-            else:
-                current_expand += abs(incremental_expandrate)
-            if fill_holes:
-                binary_mask = output > 0
-                output = scipy.ndimage.binary_fill_holes(binary_mask)
-                output = output.astype(np.float32) * 255
-            output = torch.from_numpy(output)
-            if alpha < 1.0 and previous_output is not None:
-                # Interpolate between the previous and current frame
-                output = alpha * output + (1 - alpha) * previous_output
-            if decay < 1.0 and previous_output is not None:
-                # Add the decayed previous output to the current frame
-                output += decay * previous_output
-                output = output / output.max()
-            previous_output = output
-            out.append(output)
-
-        if blur_radius != 0:
-            # Convert the tensor list to PIL images, apply blur, and convert back
-            for idx, tensor in enumerate(out):
-                # Convert tensor to PIL image
-                pil_image = tensor2pil(tensor.cpu().detach())[0]
-                # Apply Gaussian blur
-                pil_image = pil_image.filter(ImageFilter.GaussianBlur(blur_radius))
-                # Convert back to tensor
-                out[idx] = pil2tensor(pil_image)
-            blurred = torch.cat(out, dim=0)
-            return (blurred, 1.0 - blurred)
-        else:
-            return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),)
-        
-class MaskBatchMulti:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "inputcount": ("INT", {"default": 2, "min": 2, "max": 1000, "step": 1}),
-                "mask_1": ("MASK", ),
-                "mask_2": ("MASK", ),
-            },
-    }
-
-    RETURN_TYPES = ("MASK",)
-    RETURN_NAMES = ("masks",)
-    FUNCTION = "combine"
-    CATEGORY = "KJNodes/masking"
-    DESCRIPTION = """
-Creates an image batch from multiple masks.  
-You can set how many inputs the node has,  
-with the **inputcount** and clicking update.
-"""
-
-    def combine(self, inputcount, **kwargs):
-        mask = kwargs["mask_1"]
-        for c in range(1, inputcount):
-            new_mask = kwargs[f"mask_{c + 1}"]
-            if mask.shape[1:] != new_mask.shape[1:]:
-                new_mask = F.interpolate(new_mask.unsqueeze(1), size=(mask.shape[1], mask.shape[2]), mode="bicubic").squeeze(1)
-            mask = torch.cat((mask, new_mask), dim=0)
-        return (mask,)
-
-class OffsetMask:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "mask": ("MASK",),
-                "x": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
-                "y": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
-                "angle": ("INT", { "default": 0, "min": -360, "max": 360, "step": 1, "display": "number" }),
-                "duplication_factor": ("INT", { "default": 1, "min": 1, "max": 1000, "step": 1, "display": "number" }),
-                "roll": ("BOOLEAN", { "default": False }),
-                "incremental": ("BOOLEAN", { "default": False }),
-                "padding_mode": (
-            [   
-                'empty',
-                'border',
-                'reflection',
-                
-            ], {
-               "default": 'empty'
-            }),
-            }
-        }
-
-    RETURN_TYPES = ("MASK",)
-    RETURN_NAMES = ("mask",)
-    FUNCTION = "offset"
-    CATEGORY = "KJNodes/masking"
-    DESCRIPTION = """
-Offsets the mask by the specified amount.  
- - mask: Input mask or mask batch
- - x: Horizontal offset
- - y: Vertical offset
- - angle: Angle in degrees
- - roll: roll edge wrapping
- - duplication_factor: Number of times to duplicate the mask to form a batch
- - border padding_mode: Padding mode for the mask
-"""
-
-    def offset(self, mask, x, y, angle, roll=False, incremental=False, duplication_factor=1, padding_mode="empty"):
-        # Create duplicates of the mask batch
-        mask = mask.repeat(duplication_factor, 1, 1).clone()
-
-        batch_size, height, width = mask.shape
-
-        if angle != 0 and incremental:
-            for i in range(batch_size):
-                rotation_angle = angle * (i+1)
-                mask[i] = TF.rotate(mask[i].unsqueeze(0), rotation_angle).squeeze(0)
-        elif angle > 0:
-            for i in range(batch_size):
-                mask[i] = TF.rotate(mask[i].unsqueeze(0), angle).squeeze(0)
-
-        if roll:
-            if incremental:
-                for i in range(batch_size):
-                    shift_x = min(x*(i+1), width-1)
-                    shift_y = min(y*(i+1), height-1)
-                    if shift_x != 0:
-                        mask[i] = torch.roll(mask[i], shifts=shift_x, dims=1)
-                    if shift_y != 0:
-                        mask[i] = torch.roll(mask[i], shifts=shift_y, dims=0)
-            else:
-                shift_x = min(x, width-1)
-                shift_y = min(y, height-1)
-                if shift_x != 0:
-                    mask = torch.roll(mask, shifts=shift_x, dims=2)
-                if shift_y != 0:
-                    mask = torch.roll(mask, shifts=shift_y, dims=1)
-        else:
-            
-            for i in range(batch_size):
-                if incremental:
-                    temp_x = min(x * (i+1), width-1)
-                    temp_y = min(y * (i+1), height-1)
-                else:
-                    temp_x = min(x, width-1)
-                    temp_y = min(y, height-1)
-                if temp_x > 0:
-                    if padding_mode == 'empty':
-                        mask[i] = torch.cat([torch.zeros((height, temp_x)), mask[i, :, :-temp_x]], dim=1)
-                    elif padding_mode in ['replicate', 'reflect']:
-                        mask[i] = F.pad(mask[i, :, :-temp_x], (0, temp_x), mode=padding_mode)
-                elif temp_x < 0:
-                    if padding_mode == 'empty':
-                        mask[i] = torch.cat([mask[i, :, :temp_x], torch.zeros((height, -temp_x))], dim=1)
-                    elif padding_mode in ['replicate', 'reflect']:
-                        mask[i] = F.pad(mask[i, :, -temp_x:], (temp_x, 0), mode=padding_mode)
-
-                if temp_y > 0:
-                    if padding_mode == 'empty':
-                        mask[i] = torch.cat([torch.zeros((temp_y, width)), mask[i, :-temp_y, :]], dim=0)
-                    elif padding_mode in ['replicate', 'reflect']:
-                        mask[i] = F.pad(mask[i, :-temp_y, :], (0, temp_y), mode=padding_mode)
-                elif temp_y < 0:
-                    if padding_mode == 'empty':
-                        mask[i] = torch.cat([mask[i, :temp_y, :], torch.zeros((-temp_y, width))], dim=0)
-                    elif padding_mode in ['replicate', 'reflect']:
-                        mask[i] = F.pad(mask[i, -temp_y:, :], (temp_y, 0), mode=padding_mode)
-           
-        return mask,
-        
-class RoundMask:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "mask": ("MASK",),  
-        }}
-
-    RETURN_TYPES = ("MASK",)
-    FUNCTION = "round"
-    CATEGORY = "KJNodes/masking"
-    DESCRIPTION = """
-Rounds the mask or batch of masks to a binary mask.  
-<img src="https://github.com/kijai/ComfyUI-KJNodes/assets/40791699/52c85202-f74e-4b96-9dac-c8bda5ddcc40" width="300" height="250" alt="RoundMask example">
-
-"""
-
-    def round(self, mask):
-        mask = mask.round()
-        return (mask,)
-    
-class ResizeMask:
-    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "mask": ("MASK",),
-                "width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
-                "height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
-                "keep_proportions": ("BOOLEAN", { "default": False }),
-                "upscale_method": (s.upscale_methods,),
-                "crop": (["disabled","center"],),
-            }
-        }
-
-    RETURN_TYPES = ("MASK", "INT", "INT",)
-    RETURN_NAMES = ("mask", "width", "height",)
-    FUNCTION = "resize"
-    CATEGORY = "KJNodes/masking"
-    DESCRIPTION = """
-Resizes the mask or batch of masks to the specified width and height.
-"""
-
-    def resize(self, mask, width, height, keep_proportions, upscale_method,crop):
-        if keep_proportions:
-            _, oh, ow = mask.shape
-            width = ow if width == 0 else width
-            height = oh if height == 0 else height
-            ratio = min(width / ow, height / oh)
-            width = round(ow*ratio)
-            height = round(oh*ratio)
-        outputs = mask.unsqueeze(1)
-        outputs = common_upscale(outputs, width, height, upscale_method, crop)
-        outputs = outputs.squeeze(1)
-
-        return(outputs, outputs.shape[2], outputs.shape[1],)
-
-class RemapMaskRange:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "mask": ("MASK",),
-                "min": ("FLOAT", {"default": 0.0,"min": -10.0, "max": 1.0, "step": 0.01}),
-                "max": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 10.0, "step": 0.01}),
-            }
-        }
-
-    RETURN_TYPES = ("MASK",)
-    RETURN_NAMES = ("mask",)
-    FUNCTION = "remap"
-    CATEGORY = "KJNodes/masking"
-    DESCRIPTION = """
-Sets new min and max values for the mask.
-"""
-
-    def remap(self, mask, min, max):
-
-         # Find the maximum value in the mask
-        mask_max = torch.max(mask)
-        
-        # If the maximum mask value is zero, avoid division by zero by setting it to 1
-        mask_max = mask_max if mask_max > 0 else 1
-        
-        # Scale the mask values to the new range defined by min and max
-        # The highest pixel value in the mask will be scaled to max
-        scaled_mask = (mask / mask_max) * (max - min) + min
-        
-        # Clamp the values to ensure they are within [0.0, 1.0]
-        scaled_mask = torch.clamp(scaled_mask, min=0.0, max=1.0)
-        
-        return (scaled_mask, )
-
-
-def get_mask_polygon(self, mask_np):
-    import cv2
-    """Helper function to get polygon points from mask"""
-    # Find contours
-    contours, _ = cv2.findContours(mask_np, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-    
-    if not contours:
-        return None
-    
-    # Get the largest contour
-    largest_contour = max(contours, key=cv2.contourArea)
-    
-    # Approximate polygon
-    epsilon = 0.02 * cv2.arcLength(largest_contour, True)
-    polygon = cv2.approxPolyDP(largest_contour, epsilon, True)
-    
-    return polygon.squeeze()
-
-import cv2
-class SeparateMasks:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "mask": ("MASK", ),
-                "size_threshold_width" : ("INT", {"default": 256, "min": 0.0, "max": 4096, "step": 1}),
-                "size_threshold_height" : ("INT", {"default": 256, "min": 0.0, "max": 4096, "step": 1}),
-                "mode": (["convex_polygons", "area"],),
-                "max_poly_points": ("INT", {"default": 8, "min": 3, "max": 32, "step": 1}),
-
-            },
-        }
-
-    RETURN_TYPES = ("MASK",)
-    RETURN_NAMES = ("mask",)
-    FUNCTION = "separate"
-    CATEGORY = "KJNodes/masking"
-    OUTPUT_NODE = True
-    DESCRIPTION = "Separates a mask into multiple masks based on the size of the connected components."
-
-    def polygon_to_mask(self, polygon, shape):
-        mask = np.zeros((shape[0], shape[1]), dtype=np.uint8)  # Fixed shape handling
-
-        if len(polygon.shape) == 2:  # Check if polygon points are valid
-            polygon = polygon.astype(np.int32)
-            cv2.fillPoly(mask, [polygon], 1)
-        return mask
-
-    def get_mask_polygon(self, mask_np, max_points):
-        contours, _ = cv2.findContours(mask_np, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-        if not contours:
-            return None
-        
-        largest_contour = max(contours, key=cv2.contourArea)
-        hull = cv2.convexHull(largest_contour)
-        
-        # Initialize with smaller epsilon for more points
-        perimeter = cv2.arcLength(hull, True)
-        epsilon = perimeter * 0.01  # Start smaller
-        
-        min_eps = perimeter * 0.001  # Much smaller minimum
-        max_eps = perimeter * 0.2   # Smaller maximum
-        
-        best_approx = None
-        best_diff = float('inf')
-        max_iterations = 20
-        
-        #print(f"Target points: {max_points}, Perimeter: {perimeter}")
-        
-        for i in range(max_iterations):
-            curr_eps = (min_eps + max_eps) / 2
-            approx = cv2.approxPolyDP(hull, curr_eps, True)
-            points_diff = len(approx) - max_points
-            
-            #print(f"Iteration {i}: points={len(approx)}, eps={curr_eps:.4f}")
-            
-            if abs(points_diff) < best_diff:
-                best_approx = approx
-                best_diff = abs(points_diff)
-            
-            if len(approx) > max_points:
-                min_eps = curr_eps * 1.1  # More gradual adjustment
-            elif len(approx) < max_points:
-                max_eps = curr_eps * 0.9  # More gradual adjustment
-            else:
-                return approx.squeeze()
-            
-            if abs(max_eps - min_eps) < perimeter * 0.0001:  # Relative tolerance
-                break
-        
-        # If we didn't find exact match, return best approximation
-        return best_approx.squeeze() if best_approx is not None else hull.squeeze()
-
-    def separate(self, mask: torch.Tensor, size_threshold_width: int, size_threshold_height: int, max_poly_points: int, mode: str):
-        from scipy.ndimage import label, center_of_mass
-        import numpy as np
-        
-        B, H, W = mask.shape
-        separated = []
-
-        mask = mask.round()
-        
-        for b in range(B):
-            mask_np = mask[b].cpu().numpy().astype(np.uint8)
-            structure = np.ones((3, 3), dtype=np.int8)
-            labeled, ncomponents = label(mask_np, structure=structure)
-            pbar = ProgressBar(ncomponents)
-            
-            for component in range(1, ncomponents + 1):
-                component_mask_np = (labeled == component).astype(np.uint8)
-                
-                rows = np.any(component_mask_np, axis=1)
-                cols = np.any(component_mask_np, axis=0)
-                y_min, y_max = np.where(rows)[0][[0, -1]]
-                x_min, x_max = np.where(cols)[0][[0, -1]]
-                
-                width = x_max - x_min + 1
-                height = y_max - y_min + 1
-                centroid_x = (x_min + x_max) / 2  # Calculate x centroid
-                print(f"Component {component}: width={width}, height={height}, x_pos={centroid_x}")
-                
-                if width >= size_threshold_width and height >= size_threshold_height:
-                    if mode != "area":
-                        polygon = self.get_mask_polygon(component_mask_np, max_poly_points)
-                        if polygon is not None:
-                            poly_mask = self.polygon_to_mask(polygon, (H, W))
-                            poly_mask = torch.tensor(poly_mask, device=mask.device)
-                            separated.append((centroid_x, poly_mask))
-                    else:
-                        area_mask = torch.tensor(component_mask_np, device=mask.device)
-                        separated.append((centroid_x, area_mask))
-                pbar.update(1)
-        
-        if len(separated) > 0:
-            # Sort by x position and extract only the masks
-            separated.sort(key=lambda x: x[0])
-            separated = [x[1] for x in separated]
-            out_masks = torch.stack(separated, dim=0)
-            return out_masks,
-        else:
-            return torch.empty((1, 64, 64), device=mask.device),
-        

custom_nodes/ComfyUI-KJNodes-main/nodes/model_optimization_nodes.py

@@ -1,1179 +0,0 @@
-from comfy.ldm.modules import attention as comfy_attention
-import logging
-import comfy.model_patcher
-import comfy.utils
-import comfy.sd
-import torch
-import folder_paths
-import comfy.model_management as mm
-from comfy.cli_args import args
-
-orig_attention = comfy_attention.optimized_attention
-original_patch_model = comfy.model_patcher.ModelPatcher.patch_model
-original_load_lora_for_models = comfy.sd.load_lora_for_models
-
-class BaseLoaderKJ:
-    original_linear = None
-    cublas_patched = False
-
-    def _patch_modules(self, patch_cublaslinear, sage_attention):
-        from comfy.ops import disable_weight_init, CastWeightBiasOp, cast_bias_weight
-
-        if sage_attention != "disabled":
-            print("Patching comfy attention to use sageattn")
-            from sageattention import sageattn
-            def set_sage_func(sage_attention):
-                if sage_attention == "auto":
-                    def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"):
-                        return sageattn(q, k, v, is_causal=is_causal, attn_mask=attn_mask, tensor_layout=tensor_layout)
-                    return func
-                elif sage_attention == "sageattn_qk_int8_pv_fp16_cuda":
-                    from sageattention import sageattn_qk_int8_pv_fp16_cuda
-                    def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"):
-                        return sageattn_qk_int8_pv_fp16_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32", tensor_layout=tensor_layout)
-                    return func
-                elif sage_attention == "sageattn_qk_int8_pv_fp16_triton":
-                    from sageattention import sageattn_qk_int8_pv_fp16_triton
-                    def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"):
-                        return sageattn_qk_int8_pv_fp16_triton(q, k, v, is_causal=is_causal, attn_mask=attn_mask, tensor_layout=tensor_layout)
-                    return func
-                elif sage_attention == "sageattn_qk_int8_pv_fp8_cuda":
-                    from sageattention import sageattn_qk_int8_pv_fp8_cuda
-                    def func(q, k, v, is_causal=False, attn_mask=None, tensor_layout="NHD"):
-                        return sageattn_qk_int8_pv_fp8_cuda(q, k, v, is_causal=is_causal, attn_mask=attn_mask, pv_accum_dtype="fp32+fp32", tensor_layout=tensor_layout)
-                    return func
-
-            sage_func = set_sage_func(sage_attention)
-
-            @torch.compiler.disable()
-            def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
-                if skip_reshape:
-                    b, _, _, dim_head = q.shape
-                    tensor_layout="HND"
-                else:
-                    b, _, dim_head = q.shape
-                    dim_head //= heads
-                    q, k, v = map(
-                        lambda t: t.view(b, -1, heads, dim_head),
-                        (q, k, v),
-                    )
-                    tensor_layout="NHD"
-                if mask is not None:
-                    # add a batch dimension if there isn't already one
-                    if mask.ndim == 2:
-                        mask = mask.unsqueeze(0)
-                    # add a heads dimension if there isn't already one
-                    if mask.ndim == 3:
-                        mask = mask.unsqueeze(1)
-                out = sage_func(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout)
-                if tensor_layout == "HND":
-                    if not skip_output_reshape:
-                        out = (
-                            out.transpose(1, 2).reshape(b, -1, heads * dim_head)
-                        )
-                else:
-                    if skip_output_reshape:
-                        out = out.transpose(1, 2)
-                    else:
-                        out = out.reshape(b, -1, heads * dim_head)
-                return out
-
-            comfy_attention.optimized_attention = attention_sage
-            comfy.ldm.hunyuan_video.model.optimized_attention = attention_sage
-            comfy.ldm.flux.math.optimized_attention = attention_sage
-            comfy.ldm.genmo.joint_model.asymm_models_joint.optimized_attention = attention_sage
-            comfy.ldm.cosmos.blocks.optimized_attention = attention_sage
-            comfy.ldm.wan.model.optimized_attention = attention_sage
-
-        else:
-            comfy_attention.optimized_attention = orig_attention
-            comfy.ldm.hunyuan_video.model.optimized_attention = orig_attention
-            comfy.ldm.flux.math.optimized_attention = orig_attention
-            comfy.ldm.genmo.joint_model.asymm_models_joint.optimized_attention = orig_attention
-            comfy.ldm.cosmos.blocks.optimized_attention = orig_attention
-            comfy.ldm.wan.model.optimized_attention = orig_attention
-
-        if patch_cublaslinear:
-            if not BaseLoaderKJ.cublas_patched:
-                BaseLoaderKJ.original_linear = disable_weight_init.Linear
-                try:
-                    from cublas_ops import CublasLinear
-                except ImportError:
-                    raise Exception("Can't import 'torch-cublas-hgemm', install it from here https://github.com/aredden/torch-cublas-hgemm")
-
-                class PatchedLinear(CublasLinear, CastWeightBiasOp):
-                    def reset_parameters(self):
-                        pass
-
-                    def forward_comfy_cast_weights(self, input):
-                        weight, bias = cast_bias_weight(self, input)
-                        return torch.nn.functional.linear(input, weight, bias)
-
-                    def forward(self, *args, **kwargs):
-                        if self.comfy_cast_weights:
-                            return self.forward_comfy_cast_weights(*args, **kwargs)
-                        else:
-                            return super().forward(*args, **kwargs)
-
-                disable_weight_init.Linear = PatchedLinear
-                BaseLoaderKJ.cublas_patched = True
-        else:
-            if BaseLoaderKJ.cublas_patched:
-                disable_weight_init.Linear = BaseLoaderKJ.original_linear
-                BaseLoaderKJ.cublas_patched = False
-
-class PathchSageAttentionKJ(BaseLoaderKJ):
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "model": ("MODEL",),
-            "sage_attention": (["disabled", "auto", "sageattn_qk_int8_pv_fp16_cuda", "sageattn_qk_int8_pv_fp16_triton", "sageattn_qk_int8_pv_fp8_cuda"], {"default": False, "tooltip": "Global patch comfy attention to use sageattn, once patched to revert back to normal you would need to run this node again with disabled option."}),
-        }}
-
-    RETURN_TYPES = ("MODEL", )
-    FUNCTION = "patch"
-    DESCRIPTION = "Experimental node for patching attention mode. This doesn't use the model patching system and thus can't be disabled without running the node again with 'disabled' option."
-    EXPERIMENTAL = True
-    CATEGORY = "KJNodes/experimental"
-
-    def patch(self, model, sage_attention):
-        self._patch_modules(False, sage_attention)
-        return model,
- 
-class CheckpointLoaderKJ(BaseLoaderKJ):
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "ckpt_name": (folder_paths.get_filename_list("checkpoints"), {"tooltip": "The name of the checkpoint (model) to load."}),
-            "patch_cublaslinear": ("BOOLEAN", {"default": False, "tooltip": "Enable or disable the patching, won't take effect on already loaded models!"}),
-            "sage_attention": (["disabled", "auto", "sageattn_qk_int8_pv_fp16_cuda", "sageattn_qk_int8_pv_fp16_triton", "sageattn_qk_int8_pv_fp8_cuda"], {"default": False, "tooltip": "Patch comfy attention to use sageattn."}),
-        }}
-
-    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
-    FUNCTION = "patch"
-    OUTPUT_NODE = True
-    DESCRIPTION = "Experimental node for patching torch.nn.Linear with CublasLinear."
-    EXPERIMENTAL = True
-    CATEGORY = "KJNodes/experimental"
-
-    def patch(self, ckpt_name, patch_cublaslinear, sage_attention):
-        self._patch_modules(patch_cublaslinear, sage_attention)
-        from nodes import CheckpointLoaderSimple
-        model, clip, vae = CheckpointLoaderSimple.load_checkpoint(self, ckpt_name)
-        return model, clip, vae
-
-class DiffusionModelLoaderKJ(BaseLoaderKJ):
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "model_name": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "The name of the checkpoint (model) to load."}),
-            "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2", "fp16", "bf16", "fp32"],),
-            "compute_dtype": (["default", "fp16", "bf16", "fp32"], {"default": "fp16", "tooltip": "The compute dtype to use for the model."}),
-            "patch_cublaslinear": ("BOOLEAN", {"default": False, "tooltip": "Enable or disable the patching, won't take effect on already loaded models!"}),
-            "sage_attention": (["disabled", "auto", "sageattn_qk_int8_pv_fp16_cuda", "sageattn_qk_int8_pv_fp16_triton", "sageattn_qk_int8_pv_fp8_cuda"], {"default": False, "tooltip": "Patch comfy attention to use sageattn."}),
-            "enable_fp16_accumulation": ("BOOLEAN", {"default": False, "tooltip": "Enable torch.backends.cuda.matmul.allow_fp16_accumulation, requires pytorch 2.7.0 nightly."}),
-        }}
-
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch_and_load"
-    OUTPUT_NODE = True
-    DESCRIPTION = "Node for patching torch.nn.Linear with CublasLinear."
-    EXPERIMENTAL = True
-    CATEGORY = "KJNodes/experimental"
-
-    def patch_and_load(self, model_name, weight_dtype, compute_dtype, patch_cublaslinear, sage_attention, enable_fp16_accumulation):        
-        DTYPE_MAP = {
-            "fp8_e4m3fn": torch.float8_e4m3fn,
-            "fp8_e5m2": torch.float8_e5m2,
-            "fp16": torch.float16,
-            "bf16": torch.bfloat16,
-            "fp32": torch.float32
-        }
-        model_options = {}
-        if dtype := DTYPE_MAP.get(weight_dtype):
-            model_options["dtype"] = dtype
-            print(f"Setting {model_name} weight dtype to {dtype}")
-        
-        if weight_dtype == "fp8_e4m3fn_fast":
-            model_options["dtype"] = torch.float8_e4m3fn
-            model_options["fp8_optimizations"] = True
-        
-        if enable_fp16_accumulation:
-            if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"):
-                torch.backends.cuda.matmul.allow_fp16_accumulation = True
-            else:
-                raise RuntimeError("Failed to set fp16 accumulation, this requires pytorch 2.7.0 nightly currently")
-        else:
-            if hasattr(torch.backends.cuda.matmul, "allow_fp16_accumulation"):
-                torch.backends.cuda.matmul.allow_fp16_accumulation = False
-
-        unet_path = folder_paths.get_full_path_or_raise("diffusion_models", model_name)
-        model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options)
-        if dtype := DTYPE_MAP.get(compute_dtype):
-            model.set_model_compute_dtype(dtype)
-            model.force_cast_weights = False
-            print(f"Setting {model_name} compute dtype to {dtype}")
-        self._patch_modules(patch_cublaslinear, sage_attention)
-        
-        return (model,)
-
-def patched_patch_model(self, device_to=None, lowvram_model_memory=0, load_weights=True, force_patch_weights=False):
-    with self.use_ejected():
-
-        device_to = mm.get_torch_device()
-
-        full_load_override = getattr(self.model, "full_load_override", "auto")
-        if full_load_override in ["enabled", "disabled"]:
-            full_load = full_load_override == "enabled"
-        else:
-            full_load = lowvram_model_memory == 0
-
-        self.load(device_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights, full_load=full_load)
-
-        for k in self.object_patches:
-            old = comfy.utils.set_attr(self.model, k, self.object_patches[k])
-            if k not in self.object_patches_backup:
-                self.object_patches_backup[k] = old
-       
-    self.inject_model()
-    return self.model
-
-def patched_load_lora_for_models(model, clip, lora, strength_model, strength_clip):
-
-    patch_keys = list(model.object_patches_backup.keys())
-    for k in patch_keys:
-        #print("backing up object patch: ", k)
-        comfy.utils.set_attr(model.model, k, model.object_patches_backup[k])
-
-    key_map = {}
-    if model is not None:
-        key_map = comfy.lora.model_lora_keys_unet(model.model, key_map)
-    if clip is not None:
-        key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map)
-
-    lora = comfy.lora_convert.convert_lora(lora)
-    loaded = comfy.lora.load_lora(lora, key_map)
-    #print(temp_object_patches_backup)
-   
-    if model is not None:
-        new_modelpatcher = model.clone()
-        k = new_modelpatcher.add_patches(loaded, strength_model)
-    else:
-        k = ()
-        new_modelpatcher = None
-
-    if clip is not None:
-        new_clip = clip.clone()
-        k1 = new_clip.add_patches(loaded, strength_clip)
-    else:
-        k1 = ()
-        new_clip = None
-    k = set(k)
-    k1 = set(k1)
-    for x in loaded:
-        if (x not in k) and (x not in k1):
-            print("NOT LOADED {}".format(x))
-
-    if patch_keys:
-        if hasattr(model.model, "compile_settings"):
-            compile_settings = getattr(model.model, "compile_settings")
-            print("compile_settings: ", compile_settings)
-            for k in patch_keys:
-                if "diffusion_model." in k:
-                    # Remove the prefix to get the attribute path
-                    key = k.replace('diffusion_model.', '')
-                    attributes = key.split('.')
-                    # Start with the diffusion_model object
-                    block = model.get_model_object("diffusion_model")
-                    # Navigate through the attributes to get to the block
-                    for attr in attributes:
-                        if attr.isdigit():
-                            block = block[int(attr)]
-                        else:
-                            block = getattr(block, attr)
-                    # Compile the block
-                    compiled_block = torch.compile(block, mode=compile_settings["mode"], dynamic=compile_settings["dynamic"], fullgraph=compile_settings["fullgraph"], backend=compile_settings["backend"])
-                    # Add the compiled block back as an object patch
-                    model.add_object_patch(k, compiled_block)
-    return (new_modelpatcher, new_clip)
-
-class PatchModelPatcherOrder:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { 
-                    "model": ("MODEL",),
-                    "patch_order": (["object_patch_first", "weight_patch_first"], {"default": "weight_patch_first", "tooltip": "Patch the comfy patch_model function to load weight patches (LoRAs) before compiling the model"}),
-                    "full_load": (["enabled", "disabled", "auto"], {"default": "auto", "tooltip": "Disabling may help with memory issues when loading large models, when changing this you should probably force model reload to avoid issues!"}),
-                }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
-    CATEGORY = "KJNodes/experimental"
-    DESCRIPTION = "Patch the comfy patch_model function patching order, useful for torch.compile (used as object_patch) as it should come last if you want to use LoRAs with compile"
-    EXPERIMENTAL = True
-
-    def patch(self, model, patch_order, full_load):
-        comfy.model_patcher.ModelPatcher.temp_object_patches_backup = {}
-        setattr(model.model, "full_load_override", full_load)
-        if patch_order == "weight_patch_first":
-            comfy.model_patcher.ModelPatcher.patch_model = patched_patch_model
-            comfy.sd.load_lora_for_models = patched_load_lora_for_models
-        else:
-            comfy.model_patcher.ModelPatcher.patch_model = original_patch_model
-            comfy.sd.load_lora_for_models = original_load_lora_for_models
-        
-        return model,
-
-class TorchCompileModelFluxAdvanced:
-    def __init__(self):
-        self._compiled = False
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { 
-                    "model": ("MODEL",),
-                    "backend": (["inductor", "cudagraphs"],),
-                    "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
-                    "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
-                    "double_blocks": ("STRING", {"default": "0-18", "multiline": True}),
-                    "single_blocks": ("STRING", {"default": "0-37", "multiline": True}),
-                    "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}),
-                },
-                "optional": {
-                    "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}),
-                }
-                }
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
-
-    CATEGORY = "KJNodes/torchcompile"
-    EXPERIMENTAL = True
-
-    def parse_blocks(self, blocks_str):
-        blocks = []
-        for part in blocks_str.split(','):
-            part = part.strip()
-            if '-' in part:
-                start, end = map(int, part.split('-'))
-                blocks.extend(range(start, end + 1))
-            else:
-                blocks.append(int(part))
-        return blocks
-
-    def patch(self, model, backend, mode, fullgraph, single_blocks, double_blocks, dynamic, dynamo_cache_size_limit):
-        single_block_list = self.parse_blocks(single_blocks)
-        double_block_list = self.parse_blocks(double_blocks)
-        m = model.clone()
-        diffusion_model = m.get_model_object("diffusion_model")
-        torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit
-        
-        if not self._compiled:
-            try:
-                for i, block in enumerate(diffusion_model.double_blocks):
-                    if i in double_block_list:
-                        #print("Compiling double_block", i)
-                        m.add_object_patch(f"diffusion_model.double_blocks.{i}", torch.compile(block, mode=mode, dynamic=dynamic, fullgraph=fullgraph, backend=backend))
-                for i, block in enumerate(diffusion_model.single_blocks):
-                    if i in single_block_list:
-                        #print("Compiling single block", i)
-                        m.add_object_patch(f"diffusion_model.single_blocks.{i}", torch.compile(block, mode=mode, dynamic=dynamic, fullgraph=fullgraph, backend=backend))
-                self._compiled = True
-                compile_settings = {
-                    "backend": backend,
-                    "mode": mode,
-                    "fullgraph": fullgraph,
-                    "dynamic": dynamic,
-                }
-                setattr(m.model, "compile_settings", compile_settings)
-            except:
-                raise RuntimeError("Failed to compile model")
-        
-        return (m, )
-        # rest of the layers that are not patched
-        # diffusion_model.final_layer = torch.compile(diffusion_model.final_layer, mode=mode, fullgraph=fullgraph, backend=backend)
-        # diffusion_model.guidance_in = torch.compile(diffusion_model.guidance_in, mode=mode, fullgraph=fullgraph, backend=backend)
-        # diffusion_model.img_in = torch.compile(diffusion_model.img_in, mode=mode, fullgraph=fullgraph, backend=backend)
-        # diffusion_model.time_in = torch.compile(diffusion_model.time_in, mode=mode, fullgraph=fullgraph, backend=backend)
-        # diffusion_model.txt_in = torch.compile(diffusion_model.txt_in, mode=mode, fullgraph=fullgraph, backend=backend)
-        # diffusion_model.vector_in = torch.compile(diffusion_model.vector_in, mode=mode, fullgraph=fullgraph, backend=backend)
-
-class TorchCompileModelHyVideo:
-    def __init__(self):
-        self._compiled = False
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model": ("MODEL",),
-                "backend": (["inductor","cudagraphs"], {"default": "inductor"}),
-                "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
-                "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
-                "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}),
-                "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}),
-                "compile_single_blocks": ("BOOLEAN", {"default": True, "tooltip": "Compile single blocks"}),
-                "compile_double_blocks": ("BOOLEAN", {"default": True, "tooltip": "Compile double blocks"}),
-                "compile_txt_in": ("BOOLEAN", {"default": False, "tooltip": "Compile txt_in layers"}),
-                "compile_vector_in": ("BOOLEAN", {"default": False, "tooltip": "Compile vector_in layers"}),
-                "compile_final_layer": ("BOOLEAN", {"default": False, "tooltip": "Compile final layer"}),
-
-            },
-        }
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
-
-    CATEGORY = "KJNodes/torchcompile"
-    EXPERIMENTAL = True
-
-    def patch(self, model, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_single_blocks, compile_double_blocks, compile_txt_in, compile_vector_in, compile_final_layer):
-        m = model.clone()
-        diffusion_model = m.get_model_object("diffusion_model")
-        torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit
-        if not self._compiled:
-            try:
-                if compile_single_blocks:
-                    for i, block in enumerate(diffusion_model.single_blocks):
-                        compiled_block = torch.compile(block, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode)
-                        m.add_object_patch(f"diffusion_model.single_blocks.{i}", compiled_block)
-                if compile_double_blocks:
-                    for i, block in enumerate(diffusion_model.double_blocks):
-                        compiled_block = torch.compile(block, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode)
-                        m.add_object_patch(f"diffusion_model.double_blocks.{i}", compiled_block)
-                if compile_txt_in:
-                    compiled_block = torch.compile(diffusion_model.txt_in, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode)
-                    m.add_object_patch("diffusion_model.txt_in", compiled_block)
-                if compile_vector_in:
-                    compiled_block = torch.compile(diffusion_model.vector_in, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode)
-                    m.add_object_patch("diffusion_model.vector_in", compiled_block)
-                if compile_final_layer:
-                    compiled_block = torch.compile(diffusion_model.final_layer, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode)
-                    m.add_object_patch("diffusion_model.final_layer", compiled_block)
-                self._compiled = True
-                compile_settings = {
-                    "backend": backend,
-                    "mode": mode,
-                    "fullgraph": fullgraph,
-                    "dynamic": dynamic,
-                }
-                setattr(m.model, "compile_settings", compile_settings)
-            except:
-                raise RuntimeError("Failed to compile model")
-        return (m, )
-    
-class TorchCompileModelWanVideo:
-    def __init__(self):
-        self._compiled = False
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model": ("MODEL",),
-                "backend": (["inductor","cudagraphs"], {"default": "inductor"}),
-                "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
-                "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
-                "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}),
-                "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}),
-                "compile_transformer_blocks_only": ("BOOLEAN", {"default": False, "tooltip": "Compile only transformer blocks"}),
-            },
-        }
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
-
-    CATEGORY = "KJNodes/torchcompile"
-    EXPERIMENTAL = True
-
-    def patch(self, model, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_transformer_blocks_only):
-        m = model.clone()
-        diffusion_model = m.get_model_object("diffusion_model")
-        torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit
-        is_compiled = hasattr(model.model.diffusion_model.blocks[0], "_orig_mod")
-        if is_compiled:
-            logging.info(f"Already compiled, not reapplying")
-        else:
-            logging.info(f"Not compiled, applying")
-        try:
-            if compile_transformer_blocks_only:
-                for i, block in enumerate(diffusion_model.blocks):
-                    if is_compiled:
-                        compiled_block = torch.compile(block._orig_mod, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode)
-                    else:
-                        compiled_block = torch.compile(block, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode)
-                    m.add_object_patch(f"diffusion_model.blocks.{i}", compiled_block)
-            else:
-                compiled_model = torch.compile(diffusion_model, fullgraph=fullgraph, dynamic=dynamic, backend=backend, mode=mode)
-                m.add_object_patch("diffusion_model", compiled_model)
-
-            compile_settings = {
-                "backend": backend,
-                "mode": mode,
-                "fullgraph": fullgraph,
-                "dynamic": dynamic,
-            }
-            setattr(m.model, "compile_settings", compile_settings)
-        except:
-            raise RuntimeError("Failed to compile model")
-        return (m, )
-
-class TorchCompileVAE:
-    def __init__(self):
-        self._compiled_encoder = False
-        self._compiled_decoder = False
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { 
-                    "vae": ("VAE",),
-                    "backend": (["inductor", "cudagraphs"],),
-                    "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
-                    "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
-                    "compile_encoder": ("BOOLEAN", {"default": True, "tooltip": "Compile encoder"}),
-                    "compile_decoder": ("BOOLEAN", {"default": True, "tooltip": "Compile decoder"}),
-                }}
-    RETURN_TYPES = ("VAE",)
-    FUNCTION = "compile"
-
-    CATEGORY = "KJNodes/torchcompile"
-    EXPERIMENTAL = True
-
-    def compile(self, vae, backend, mode, fullgraph, compile_encoder, compile_decoder):
-        if compile_encoder:
-            if not self._compiled_encoder:
-                encoder_name = "encoder"
-                if hasattr(vae.first_stage_model, "taesd_encoder"):
-                    encoder_name = "taesd_encoder"
-
-                try:
-                    setattr(
-                        vae.first_stage_model,
-                        encoder_name,
-                        torch.compile(
-                            getattr(vae.first_stage_model, encoder_name),
-                            mode=mode,
-                            fullgraph=fullgraph,
-                            backend=backend,
-                        ),
-                    )
-                    self._compiled_encoder = True
-                except:
-                    raise RuntimeError("Failed to compile model")
-        if compile_decoder:
-            if not self._compiled_decoder:
-                decoder_name = "decoder"
-                if hasattr(vae.first_stage_model, "taesd_decoder"):
-                    decoder_name = "taesd_decoder"
-
-                try:
-                    setattr(
-                        vae.first_stage_model,
-                        decoder_name,
-                        torch.compile(
-                            getattr(vae.first_stage_model, decoder_name),
-                            mode=mode,
-                            fullgraph=fullgraph,
-                            backend=backend,
-                        ),
-                    )
-                    self._compiled_decoder = True
-                except:
-                    raise RuntimeError("Failed to compile model")
-        return (vae, )
-
-class TorchCompileControlNet:
-    def __init__(self):
-        self._compiled= False
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { 
-                    "controlnet": ("CONTROL_NET",),
-                    "backend": (["inductor", "cudagraphs"],),
-                    "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
-                    "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
-                }}
-    RETURN_TYPES = ("CONTROL_NET",)
-    FUNCTION = "compile"
-
-    CATEGORY = "KJNodes/torchcompile"
-    EXPERIMENTAL = True
-
-    def compile(self, controlnet, backend, mode, fullgraph):
-        if not self._compiled:
-            try:
-                # for i, block in enumerate(controlnet.control_model.double_blocks):
-                #     print("Compiling controlnet double_block", i)
-                #     controlnet.control_model.double_blocks[i] = torch.compile(block, mode=mode, fullgraph=fullgraph, backend=backend)
-                controlnet.control_model = torch.compile(controlnet.control_model, mode=mode, fullgraph=fullgraph, backend=backend)
-                self._compiled = True
-            except:
-                self._compiled = False
-                raise RuntimeError("Failed to compile model")
-       
-        return (controlnet, )
-
-class TorchCompileLTXModel:
-    def __init__(self):
-        self._compiled = False
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { 
-                    "model": ("MODEL",),
-                    "backend": (["inductor", "cudagraphs"],),
-                    "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
-                    "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
-                    "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}),
-                }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
-
-    CATEGORY = "KJNodes/torchcompile"
-    EXPERIMENTAL = True
-
-    def patch(self, model, backend, mode, fullgraph, dynamic):
-        m = model.clone()
-        diffusion_model = m.get_model_object("diffusion_model")
-        
-        if not self._compiled:
-            try:
-                for i, block in enumerate(diffusion_model.transformer_blocks):
-                        compiled_block = torch.compile(block, mode=mode, dynamic=dynamic, fullgraph=fullgraph, backend=backend)
-                        m.add_object_patch(f"diffusion_model.transformer_blocks.{i}", compiled_block)
-                self._compiled = True
-                compile_settings = {
-                    "backend": backend,
-                    "mode": mode,
-                    "fullgraph": fullgraph,
-                    "dynamic": dynamic,
-                }
-                setattr(m.model, "compile_settings", compile_settings)
-               
-            except:
-                raise RuntimeError("Failed to compile model")           
-        
-        return (m, )
-      
-class TorchCompileCosmosModel:
-    def __init__(self):
-        self._compiled = False
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { 
-                    "model": ("MODEL",),
-                    "backend": (["inductor", "cudagraphs"],),
-                    "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
-                    "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
-                    "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}),
-                    "dynamo_cache_size_limit": ("INT", {"default": 64, "tooltip": "Set the dynamo cache size limit"}),
-                }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
-
-    CATEGORY = "KJNodes/torchcompile"
-    EXPERIMENTAL = True
-
-    def patch(self, model, backend, mode, fullgraph, dynamic, dynamo_cache_size_limit):
-        
-        m = model.clone()
-        diffusion_model = m.get_model_object("diffusion_model")
-        torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit
-        
-        if not self._compiled:
-            try:
-                for name, block in diffusion_model.blocks.items():
-                    #print(f"Compiling block {name}")
-                    compiled_block = torch.compile(block, mode=mode, dynamic=dynamic, fullgraph=fullgraph, backend=backend)
-                    m.add_object_patch(f"diffusion_model.blocks.{name}", compiled_block)
-                    #diffusion_model.blocks[name] = compiled_block
-
-                self._compiled = True
-                compile_settings = {
-                    "backend": backend,
-                    "mode": mode,
-                    "fullgraph": fullgraph,
-                    "dynamic": dynamic,
-                }
-                setattr(m.model, "compile_settings", compile_settings)
-               
-            except:
-                raise RuntimeError("Failed to compile model")           
-        
-        return (m, )
-
-
-#teacache
-
-try:
-    from comfy.ldm.wan.model import sinusoidal_embedding_1d
-except:
-    pass
-from einops import repeat
-from unittest.mock import patch
-from contextlib import nullcontext
-import numpy as np
-
-def relative_l1_distance(last_tensor, current_tensor):
-    l1_distance = torch.abs(last_tensor - current_tensor).mean()
-    norm = torch.abs(last_tensor).mean()
-    relative_l1_distance = l1_distance / norm
-    return relative_l1_distance.to(torch.float32)
-
-def teacache_wanvideo_forward_orig(self, x, t, context, clip_fea=None, freqs=None, transformer_options={}, **kwargs):
-        # embeddings
-        x = self.patch_embedding(x.float()).to(x.dtype)
-        grid_sizes = x.shape[2:]
-        x = x.flatten(2).transpose(1, 2)
-
-        # time embeddings
-        e = self.time_embedding(
-            sinusoidal_embedding_1d(self.freq_dim, t).to(dtype=x[0].dtype))
-        e0 = self.time_projection(e).unflatten(1, (6, self.dim))
-
-        # context
-        context = self.text_embedding(context)
-        if clip_fea is not None and self.img_emb is not None:
-            context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
-            context = torch.concat([context_clip, context], dim=1)
-
-        @torch.compiler.disable()
-        def tea_cache(x, e0, e, kwargs):
-            #teacache for cond and uncond separately
-            rel_l1_thresh = transformer_options["rel_l1_thresh"]
-            
-            is_cond = True if transformer_options["cond_or_uncond"] == [0] else False
-
-            should_calc = True
-            suffix = "cond" if is_cond else "uncond"
-
-            # Init cache dict if not exists
-            if not hasattr(self, 'teacache_state'):
-                self.teacache_state = {
-                    'cond': {'accumulated_rel_l1_distance': 0, 'prev_input': None, 
-                            'teacache_skipped_steps': 0, 'previous_residual': None},
-                    'uncond': {'accumulated_rel_l1_distance': 0, 'prev_input': None,
-                            'teacache_skipped_steps': 0, 'previous_residual': None}
-                }
-                logging.info("\nTeaCache: Initialized")
-
-            cache = self.teacache_state[suffix]
-
-            if cache['prev_input'] is not None:
-                if transformer_options["coefficients"] == []:
-                    temb_relative_l1 = relative_l1_distance(cache['prev_input'], e0)
-                    curr_acc_dist = cache['accumulated_rel_l1_distance'] + temb_relative_l1
-                else:
-                    rescale_func = np.poly1d(transformer_options["coefficients"])
-                    curr_acc_dist = cache['accumulated_rel_l1_distance'] + rescale_func(((e-cache['prev_input']).abs().mean() / cache['prev_input'].abs().mean()).cpu().item())
-                try:
-                    if curr_acc_dist < rel_l1_thresh:
-                        should_calc = False
-                        cache['accumulated_rel_l1_distance'] = curr_acc_dist
-                    else:
-                        should_calc = True
-                        cache['accumulated_rel_l1_distance'] = 0
-                except:
-                    should_calc = True
-                    cache['accumulated_rel_l1_distance'] = 0
-
-            if transformer_options["coefficients"] == []:
-                cache['prev_input'] = e0.clone().detach()
-            else:
-                cache['prev_input'] = e.clone().detach()
-
-            if not should_calc:
-                x += cache['previous_residual'].to(x.device)
-                cache['teacache_skipped_steps'] += 1
-                #print(f"TeaCache: Skipping {suffix} step")
-            return should_calc, cache
-        
-        if not transformer_options:
-            raise RuntimeError("Can't access transformer_options, this requires ComfyUI nightly version from Mar 14, 2025 or later")
-
-        teacache_enabled = transformer_options.get("teacache_enabled", False)
-        if not teacache_enabled:
-            should_calc = True
-        else:
-            should_calc, cache = tea_cache(x, e0, e, kwargs)
-        
-        if should_calc:
-            original_x = x.clone().detach()
-            patches_replace = transformer_options.get("patches_replace", {})
-            blocks_replace = patches_replace.get("dit", {})
-            for i, block in enumerate(self.blocks):
-                if ("double_block", i) in blocks_replace:
-                    def block_wrap(args):
-                        out = {}
-                        out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"])
-                        return out
-                    out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap, "transformer_options": transformer_options})
-                    x = out["img"]
-                else:
-                    x = block(x, e=e0, freqs=freqs, context=context)
-
-            if teacache_enabled:
-                cache['previous_residual']  = (x - original_x).to(transformer_options["teacache_device"])
-          
-        # head
-        x = self.head(x, e)
-
-        # unpatchify
-        x = self.unpatchify(x, grid_sizes)
-        return x
-
-class WanVideoTeaCacheKJ:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model": ("MODEL",),
-                "rel_l1_thresh": ("FLOAT", {"default": 0.275, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Threshold for to determine when to apply the cache, compromise between speed and accuracy. When using coefficients a good value range is something between 0.2-0.4 for all but 1.3B model, which should be about 10 times smaller, same as when not using coefficients."}),
-                "start_percent": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The start percentage of the steps to use with TeaCache."}),
-                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The end percentage of the steps to use with TeaCache."}),
-                "cache_device": (["main_device", "offload_device"], {"default": "offload_device", "tooltip": "Device to cache to"}),
-                "coefficients": (["disabled", "1.3B", "14B", "i2v_480", "i2v_720"], {"default": "i2v_480", "tooltip": "Coefficients for rescaling the relative l1 distance, if disabled the threshold value should be about 10 times smaller than the value used with coefficients."}),
-            }
-        }
-    
-    RETURN_TYPES = ("MODEL",)
-    RETURN_NAMES = ("model",)
-    FUNCTION = "patch_teacache"
-    CATEGORY = "KJNodes/teacache"
-    DESCRIPTION = """
-Patch WanVideo model to use TeaCache. Speeds up inference by caching the output and  
-applying it instead of doing the step.  Best results are achieved by choosing the  
-appropriate coefficients for the model. Early steps should never be skipped, with too  
-aggressive values this can happen and the motion suffers. Starting later can help with that too.   
-When NOT using coefficients, the threshold value should be  
-about 10 times smaller than the value used with coefficients.  
-
-Official recommended values https://github.com/ali-vilab/TeaCache/tree/main/TeaCache4Wan2.1:
-
-
-<pre style='font-family:monospace'>
-+-------------------+--------+---------+--------+
-|       Model       |  Low   | Medium  |  High  |
-+-------------------+--------+---------+--------+
-| Wan2.1 t2v 1.3B  |  0.05  |  0.07   |  0.08  |
-| Wan2.1 t2v 14B   |  0.14  |  0.15   |  0.20  |
-| Wan2.1 i2v 480P  |  0.13  |  0.19   |  0.26  |
-| Wan2.1 i2v 720P  |  0.18  |  0.20   |  0.30  |
-+-------------------+--------+---------+--------+
-</pre> 
-"""
-    EXPERIMENTAL = True
-
-    def patch_teacache(self, model, rel_l1_thresh, start_percent, end_percent, cache_device, coefficients):
-        if rel_l1_thresh == 0:
-            return (model,)
-
-        if coefficients == "disabled" and rel_l1_thresh > 0.1:
-            logging.warning("Threshold value is too high for TeaCache without coefficients, consider using coefficients for better results.")
-        if coefficients != "disabled" and rel_l1_thresh < 0.1 and "1.3B" not in coefficients:
-            logging.warning("Threshold value is too low for TeaCache with coefficients, consider using higher threshold value for better results.")
-        
-        # type_str = str(type(model.model.model_config).__name__)
-        #if model.model.diffusion_model.dim == 1536:
-        #    model_type ="1.3B"
-        # else:
-        #     if "WAN21_T2V" in type_str:
-        #         model_type = "14B"
-        #     elif "WAN21_I2V" in type_str:
-        #         model_type = "i2v_480"
-        #     else:
-        #         model_type = "i2v_720" #how to detect this?
-  
-       
-        teacache_coefficients_map = {
-            "disabled": [],
-            "1.3B": [2.39676752e+03, -1.31110545e+03, 2.01331979e+02, -8.29855975e+00, 1.37887774e-01],
-            "14B": [-5784.54975374, 5449.50911966, -1811.16591783, 256.27178429, -13.02252404],
-            "i2v_480": [-3.02331670e+02, 2.23948934e+02, -5.25463970e+01, 5.87348440e+00, -2.01973289e-01],
-            "i2v_720": [-114.36346466, 65.26524496, -18.82220707, 4.91518089, -0.23412683],
-        }
-        coefficients = teacache_coefficients_map[coefficients]
-        
-        teacache_device = mm.get_torch_device() if cache_device == "main_device" else mm.unet_offload_device()
-
-        model_clone = model.clone()
-        if 'transformer_options' not in model_clone.model_options:
-            model_clone.model_options['transformer_options'] = {}
-        model_clone.model_options["transformer_options"]["rel_l1_thresh"] = rel_l1_thresh
-        model_clone.model_options["transformer_options"]["teacache_device"] = teacache_device
-        model_clone.model_options["transformer_options"]["coefficients"] = coefficients
-        diffusion_model = model_clone.get_model_object("diffusion_model")
-                
-        def outer_wrapper(start_percent, end_percent):        
-            def unet_wrapper_function(model_function, kwargs):
-                input = kwargs["input"]
-                timestep = kwargs["timestep"]
-                c = kwargs["c"]
-                sigmas = c["transformer_options"]["sample_sigmas"]
-                cond_or_uncond = kwargs["cond_or_uncond"]
-                last_step = (len(sigmas) - 1)
-             
-                matched_step_index = (sigmas == timestep[0] ).nonzero()
-                if len(matched_step_index) > 0:
-                    current_step_index = matched_step_index.item()
-                else:
-                    for i in range(len(sigmas) - 1):
-                        # walk from beginning of steps until crossing the timestep
-                        if (sigmas[i] - timestep[0]) * (sigmas[i + 1] - timestep[0]) <= 0:
-                            current_step_index = i
-                            break
-                    else:
-                        current_step_index = 0
-
-                if current_step_index == 0:
-                    if hasattr(diffusion_model, "teacache_state"):
-                        delattr(diffusion_model, "teacache_state")
-                        logging.info("\nResetting TeaCache state")
-                
-                current_percent = current_step_index / (len(sigmas) - 1)
-                c["transformer_options"]["current_percent"] = current_percent
-                if start_percent <= current_percent <= end_percent:
-                    c["transformer_options"]["teacache_enabled"] = True
-                
-                context = patch.multiple(
-                    diffusion_model, 
-                    forward_orig=teacache_wanvideo_forward_orig.__get__(diffusion_model, diffusion_model.__class__)
-                )
-
-                with context:
-                    out = model_function(input, timestep, **c)
-                    if current_step_index+1 == last_step and hasattr(diffusion_model, "teacache_state"):
-                        if len(cond_or_uncond) == 1 and cond_or_uncond[0] == 0:
-                            skipped_steps_cond = diffusion_model.teacache_state["cond"]["teacache_skipped_steps"]
-                            skipped_steps_uncond = diffusion_model.teacache_state["uncond"]["teacache_skipped_steps"]
-                            logging.info("-----------------------------------")
-                            logging.info(f"TeaCache skipped:")
-                            logging.info(f"{skipped_steps_cond} cond steps")
-                            logging.info(f"{skipped_steps_uncond} uncond step")
-                            logging.info(f"out of {last_step} steps")
-                            logging.info("-----------------------------------")
-                        elif len(cond_or_uncond) == 2:
-                            skipped_steps_cond = diffusion_model.teacache_state["uncond"]["teacache_skipped_steps"]
-                            logging.info("-----------------------------------")
-                            logging.info(f"TeaCache skipped:")
-                            logging.info(f"{skipped_steps_cond} cond steps")
-                            logging.info(f"out of {last_step} steps")
-                            logging.info("-----------------------------------")
-                        
-                    return out
-            return unet_wrapper_function
-
-        model_clone.set_model_unet_function_wrapper(outer_wrapper(start_percent=start_percent, end_percent=end_percent))
-
-        return (model_clone,)
-
-
-
-from comfy.ldm.modules.attention import optimized_attention
-from comfy.ldm.flux.math import apply_rope
-
-def modified_wan_self_attention_forward(self, x, freqs):
-    r"""
-    Args:
-        x(Tensor): Shape [B, L, num_heads, C / num_heads]
-        freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
-    """
-    b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
-
-    # query, key, value function
-    def qkv_fn(x):
-        q = self.norm_q(self.q(x)).view(b, s, n, d)
-        k = self.norm_k(self.k(x)).view(b, s, n, d)
-        v = self.v(x).view(b, s, n * d)
-        return q, k, v
-
-    q, k, v = qkv_fn(x)
-
-    q, k = apply_rope(q, k, freqs)
-
-    feta_scores = get_feta_scores(q, k, self.num_frames, self.enhance_weight)
-
-    x = optimized_attention(
-        q.view(b, s, n * d),
-        k.view(b, s, n * d),
-        v,
-        heads=self.num_heads,
-    )
-
-    x = self.o(x)
-
-    x *= feta_scores
-
-    return x
-    
-from einops import rearrange
-def get_feta_scores(query, key, num_frames, enhance_weight):
-    img_q, img_k = query, key #torch.Size([2, 9216, 12, 128])
-    
-    _, ST, num_heads, head_dim = img_q.shape
-    spatial_dim = ST / num_frames
-    spatial_dim = int(spatial_dim)
-
-    query_image = rearrange(
-        img_q, "B (T S) N C -> (B S) N T C", T=num_frames, S=spatial_dim, N=num_heads, C=head_dim
-    )
-    key_image = rearrange(
-        img_k, "B (T S) N C -> (B S) N T C", T=num_frames, S=spatial_dim, N=num_heads, C=head_dim
-    )
-
-    return feta_score(query_image, key_image, head_dim, num_frames, enhance_weight)
-
-def feta_score(query_image, key_image, head_dim, num_frames, enhance_weight):
-    scale = head_dim**-0.5
-    query_image = query_image * scale
-    attn_temp = query_image @ key_image.transpose(-2, -1)  # translate attn to float32
-    attn_temp = attn_temp.to(torch.float32)
-    attn_temp = attn_temp.softmax(dim=-1)
-
-    # Reshape to [batch_size * num_tokens, num_frames, num_frames]
-    attn_temp = attn_temp.reshape(-1, num_frames, num_frames)
-
-    # Create a mask for diagonal elements
-    diag_mask = torch.eye(num_frames, device=attn_temp.device).bool()
-    diag_mask = diag_mask.unsqueeze(0).expand(attn_temp.shape[0], -1, -1)
-
-    # Zero out diagonal elements
-    attn_wo_diag = attn_temp.masked_fill(diag_mask, 0)
-
-    # Calculate mean for each token's attention matrix
-    # Number of off-diagonal elements per matrix is n*n - n
-    num_off_diag = num_frames * num_frames - num_frames
-    mean_scores = attn_wo_diag.sum(dim=(1, 2)) / num_off_diag
-
-    enhance_scores = mean_scores.mean() * (num_frames + enhance_weight)
-    enhance_scores = enhance_scores.clamp(min=1)
-    return enhance_scores
-
-import types
-class WanAttentionPatch:
-    def __init__(self, num_frames, weight):
-        self.num_frames = num_frames
-        self.enhance_weight = weight
-        
-    def __get__(self, obj, objtype=None):
-        # Create bound method with stored parameters
-        def wrapped_attention(self_module, *args, **kwargs):
-            self_module.num_frames = self.num_frames
-            self_module.enhance_weight = self.enhance_weight
-            return modified_wan_self_attention_forward(self_module, *args, **kwargs)
-        return types.MethodType(wrapped_attention, obj)
-
-class WanVideoEnhanceAVideoKJ:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model": ("MODEL",),
-                "latent": ("LATENT", {"tooltip": "Only used to get the latent count"}),
-                "weight": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Strength of the enhance effect"}),
-           }
-        }
-    
-    RETURN_TYPES = ("MODEL",)
-    RETURN_NAMES = ("model",)
-    FUNCTION = "enhance"
-    CATEGORY = "KJNodes/experimental"
-    DESCRIPTION = "https://github.com/NUS-HPC-AI-Lab/Enhance-A-Video"
-    EXPERIMENTAL = True
-
-    def enhance(self, model, weight, latent):
-        if weight == 0:
-            return (model,)
-        
-        num_frames = latent["samples"].shape[2]
-
-        model_clone = model.clone()
-        if 'transformer_options' not in model_clone.model_options:
-            model_clone.model_options['transformer_options'] = {}
-        model_clone.model_options["transformer_options"]["enhance_weight"] = weight
-        diffusion_model = model_clone.get_model_object("diffusion_model")
-
-        compile_settings = getattr(model.model, "compile_settings", None)
-        for idx, block in enumerate(diffusion_model.blocks):
-            patched_attn = WanAttentionPatch(num_frames, weight).__get__(block.self_attn, block.__class__)
-            if compile_settings is not None:
-                patched_attn = torch.compile(patched_attn, mode=compile_settings["mode"], dynamic=compile_settings["dynamic"], fullgraph=compile_settings["fullgraph"], backend=compile_settings["backend"])
-            
-            model_clone.add_object_patch(f"diffusion_model.blocks.{idx}.self_attn.forward", patched_attn)
-            
-        return (model_clone,)
-    
-class SkipLayerGuidanceWanVideo:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"model": ("MODEL", ),
-                             "blocks": ("STRING", {"default": "10", "multiline": False}),
-                             "start_percent": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.001}),
-                             "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
-                            }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "slg"
-    EXPERIMENTAL = True
-    DESCRIPTION = "Simplified skip layer guidance that only skips the uncond on selected blocks"
-
-    CATEGORY = "advanced/guidance"
-
-    def slg(self, model, start_percent, end_percent, blocks):
-        def skip(args, extra_args):
-            transformer_options = extra_args.get("transformer_options", {})
-            original_block = extra_args["original_block"]
-
-            if not transformer_options:
-                raise ValueError("transformer_options not found in extra_args, currently SkipLayerGuidanceWanVideo only works with TeaCacheKJ")
-            if start_percent <= transformer_options["current_percent"] <= end_percent:
-                if args["img"].shape[0] == 2:
-                    prev_img_uncond = args["img"][0].unsqueeze(0)
-
-                    new_args = {
-                        "img": args["img"][1],
-                        "txt": args["txt"][1],
-                        "vec": args["vec"][1],
-                        "pe": args["pe"][1]
-                    }
-                    
-                    block_out = original_block(new_args)
-
-                    out = {
-                        "img": torch.cat([prev_img_uncond, block_out["img"]], dim=0),
-                        "txt": args["txt"],
-                        "vec": args["vec"],
-                        "pe": args["pe"]
-                    }
-                else:
-                    if transformer_options.get("cond_or_uncond") == [0]:
-                        out = original_block(args)
-                    else:
-                        out = args
-            else:
-                out = original_block(args)
-            return out
-
-        block_list = [int(x.strip()) for x in blocks.split(",")]
-        blocks = [int(i) for i in block_list]
-        logging.info(f"Selected blocks to skip uncond on: {blocks}")
-
-        m = model.clone()
-
-        for b in blocks:
-            #m.set_model_patch_replace(skip, "dit", "double_block", b)
-            model_options = m.model_options["transformer_options"].copy()
-            if "patches_replace" not in model_options:
-                model_options["patches_replace"] = {}
-            else:
-                model_options["patches_replace"] = model_options["patches_replace"].copy()
-
-            if "dit" not in model_options["patches_replace"]:
-                model_options["patches_replace"]["dit"] = {}
-            else:
-                model_options["patches_replace"]["dit"] = model_options["patches_replace"]["dit"].copy()
-
-            block = ("double_block", b)
-
-            model_options["patches_replace"]["dit"][block] = skip
-            m.model_options["transformer_options"] = model_options
-            
-
-        return (m, )

custom_nodes/ComfyUI-KJNodes-main/pyproject.toml

@@ -1,15 +0,0 @@
-[project]
-name = "comfyui-kjnodes"
-description = "Various quality of life -nodes for ComfyUI, mostly just visual stuff to improve usability."
-version = "1.0.8"
-license = {file = "LICENSE"}
-dependencies = ["librosa", "numpy", "pillow>=10.3.0", "scipy", "color-matcher", "matplotlib", "huggingface_hub"]
-
-[project.urls]
-Repository = "https://github.com/kijai/ComfyUI-KJNodes"
-#  Used by Comfy Registry https://comfyregistry.org
-
-[tool.comfy]
-PublisherId = "kijai"
-DisplayName = "ComfyUI-KJNodes"
-Icon = ""

custom_nodes/ComfyUI-KJNodes-main/requirements.txt

@@ -1,7 +0,0 @@
-pillow>=10.3.0
-scipy
-color-matcher
-matplotlib
-huggingface_hub
-mss
-opencv-python

custom_nodes/ComfyUI-KJNodes-main/utility/fluid.py

@@ -1,67 +0,0 @@
-import numpy as np
-from scipy.ndimage import map_coordinates, spline_filter
-from scipy.sparse.linalg import factorized
-
-from .numerical import difference, operator
-
-
-class Fluid:
-    def __init__(self, shape, *quantities, pressure_order=1, advect_order=3):
-        self.shape = shape
-        self.dimensions = len(shape)
-
-        # Prototyping is simplified by dynamically 
-        # creating advected quantities as needed.
-        self.quantities = quantities
-        for q in quantities:
-            setattr(self, q, np.zeros(shape))
-
-        self.indices = np.indices(shape)
-        self.velocity = np.zeros((self.dimensions, *shape))
-
-        laplacian = operator(shape, difference(2, pressure_order))
-        self.pressure_solver = factorized(laplacian)
-        
-        self.advect_order = advect_order
-
-    def step(self):
-        # Advection is computed backwards in time as described in Stable Fluids.
-        advection_map = self.indices - self.velocity
-
-        # SciPy's spline filter introduces checkerboard divergence.
-        # A linear blend of the filtered and unfiltered fields based
-        # on some value epsilon eliminates this error.
-        def advect(field, filter_epsilon=10e-2, mode='constant'):
-            filtered = spline_filter(field, order=self.advect_order, mode=mode)
-            field = filtered * (1 - filter_epsilon) + field * filter_epsilon
-            return map_coordinates(field, advection_map, prefilter=False, order=self.advect_order, mode=mode)
-
-        # Apply advection to each axis of the
-        # velocity field and each user-defined quantity.
-        for d in range(self.dimensions):
-            self.velocity[d] = advect(self.velocity[d])
-
-        for q in self.quantities:
-            setattr(self, q, advect(getattr(self, q)))
-
-        # Compute the jacobian at each point in the
-        # velocity field to extract curl and divergence.
-        jacobian_shape = (self.dimensions,) * 2
-        partials = tuple(np.gradient(d) for d in self.velocity)
-        jacobian = np.stack(partials).reshape(*jacobian_shape, *self.shape)
-
-        divergence = jacobian.trace()
-
-        # If this curl calculation is extended to 3D, the y-axis value must be negated.
-        # This corresponds to the coefficients of the levi-civita symbol in that dimension.
-        # Higher dimensions do not have a vector -> scalar, or vector -> vector,
-        # correspondence between velocity and curl due to differing isomorphisms
-        # between exterior powers in dimensions != 2 or 3 respectively.
-        curl_mask = np.triu(np.ones(jacobian_shape, dtype=bool), k=1)
-        curl = (jacobian[curl_mask] - jacobian[curl_mask.T]).squeeze()
-
-        # Apply the pressure correction to the fluid's velocity field.
-        pressure = self.pressure_solver(divergence.flatten()).reshape(self.shape)
-        self.velocity -= np.gradient(pressure)
-
-        return divergence, curl, pressure

custom_nodes/ComfyUI-KJNodes-main/utility/magictex.py

@@ -1,95 +0,0 @@
-"""Generates psychedelic color textures in the spirit of Blender's magic texture shader using Python/Numpy
-
-https://github.com/cheind/magic-texture
-"""
-from typing import Tuple, Optional
-import numpy as np
-
-
-def coordinate_grid(shape: Tuple[int, int], dtype=np.float32):
-    """Returns a three-dimensional coordinate grid of given shape for use in `magic`."""
-    x = np.linspace(-1, 1, shape[1], endpoint=True, dtype=dtype)
-    y = np.linspace(-1, 1, shape[0], endpoint=True, dtype=dtype)
-    X, Y = np.meshgrid(x, y)
-    XYZ = np.stack((X, Y, np.ones_like(X)), -1)
-    return XYZ
-
-
-def random_transform(coords: np.ndarray, rng: np.random.Generator = None):
-    """Returns randomly transformed coordinates"""
-    H, W = coords.shape[:2]
-    rng = rng or np.random.default_rng()
-    m = rng.uniform(-1.0, 1.0, size=(3, 3)).astype(coords.dtype)
-    return (coords.reshape(-1, 3) @ m.T).reshape(H, W, 3)
-
-
-def magic(
-    coords: np.ndarray,
-    depth: Optional[int] = None,
-    distortion: Optional[int] = None,
-    rng: np.random.Generator = None,
-):
-    """Returns color magic color texture.
-
-    The implementation is based on Blender's (https://www.blender.org/) magic
-    texture shader. The following adaptions have been made:
-     - we exchange the nested if-cascade by a probabilistic iterative approach
-
-    Kwargs
-    ------
-    coords: HxWx3 array
-        Coordinates transformed into colors by this method. See
-        `magictex.coordinate_grid` to generate the default.
-    depth: int (optional)
-        Number of transformations applied. Higher numbers lead to more
-        nested patterns. If not specified, randomly sampled.
-    distortion: float (optional)
-        Distortion of patterns. Larger values indicate more distortion,
-        lower values tend to generate smoother patterns. If not specified,
-        randomly sampled.
-    rng: np.random.Generator
-        Optional random generator to draw samples from.
-
-    Returns
-    -------
-    colors: HxWx3 array
-        Three channel color image in range [0,1]
-    """
-    rng = rng or np.random.default_rng()
-    if distortion is None:
-        distortion = rng.uniform(1, 4)
-    if depth is None:
-        depth = rng.integers(1, 5)
-
-    H, W = coords.shape[:2]
-    XYZ = coords
-    x = np.sin((XYZ[..., 0] + XYZ[..., 1] + XYZ[..., 2]) * distortion)
-    y = np.cos((-XYZ[..., 0] + XYZ[..., 1] - XYZ[..., 2]) * distortion)
-    z = -np.cos((-XYZ[..., 0] - XYZ[..., 1] + XYZ[..., 2]) * distortion)
-
-    if depth > 0:
-        x *= distortion
-        y *= distortion
-        z *= distortion
-        y = -np.cos(x - y + z)
-        y *= distortion
-
-    xyz = [x, y, z]
-    fns = [np.cos, np.sin]
-    for _ in range(1, depth):
-        axis = rng.choice(3)
-        fn = fns[rng.choice(2)]
-        signs = rng.binomial(n=1, p=0.5, size=4) * 2 - 1
-
-        xyz[axis] = signs[-1] * fn(
-            signs[0] * xyz[0] + signs[1] * xyz[1] + signs[2] * xyz[2]
-        )
-        xyz[axis] *= distortion
-
-    x, y, z = xyz
-    x /= 2 * distortion
-    y /= 2 * distortion
-    z /= 2 * distortion
-    c = 0.5 - np.stack((x, y, z), -1)
-    np.clip(c, 0, 1.0)
-    return c

custom_nodes/ComfyUI-KJNodes-main/utility/numerical.py

@@ -1,25 +0,0 @@
-from functools import reduce
-from itertools import cycle
-from math import factorial
-
-import numpy as np
-import scipy.sparse as sp
-
-
-def difference(derivative, accuracy=1):
-    # Central differences implemented based on the article here:
-    # http://web.media.mit.edu/~crtaylor/calculator.html
-    derivative += 1
-    radius = accuracy + derivative // 2 - 1
-    points = range(-radius, radius + 1)
-    coefficients = np.linalg.inv(np.vander(points))
-    return coefficients[-derivative] * factorial(derivative - 1), points
-
-
-def operator(shape, *differences):
-    # Credit to Philip Zucker for figuring out
-    # that kronsum's argument order is reversed.
-    # Without that bit of wisdom I'd have lost it.
-    differences = zip(shape, cycle(differences))
-    factors = (sp.diags(*diff, shape=(dim,) * 2) for dim, diff in differences)
-    return reduce(lambda a, f: sp.kronsum(f, a, format='csc'), factors)

custom_nodes/ComfyUI-KJNodes-main/utility/utility.py

@@ -1,39 +0,0 @@
-import torch
-import numpy as np
-from PIL import Image
-from typing import Union, List
-
-# Utility functions from mtb nodes: https://github.com/melMass/comfy_mtb
-def pil2tensor(image: Union[Image.Image, List[Image.Image]]) -> torch.Tensor:
-    if isinstance(image, list):
-        return torch.cat([pil2tensor(img) for img in image], dim=0)
-
-    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
-
-
-def np2tensor(img_np: Union[np.ndarray, List[np.ndarray]]) -> torch.Tensor:
-    if isinstance(img_np, list):
-        return torch.cat([np2tensor(img) for img in img_np], dim=0)
-
-    return torch.from_numpy(img_np.astype(np.float32) / 255.0).unsqueeze(0)
-
-
-def tensor2np(tensor: torch.Tensor):
-    if len(tensor.shape) == 3:  # Single image
-        return np.clip(255.0 * tensor.cpu().numpy(), 0, 255).astype(np.uint8)
-    else:  # Batch of images
-        return [np.clip(255.0 * t.cpu().numpy(), 0, 255).astype(np.uint8) for t in tensor]
-    
-def tensor2pil(image: torch.Tensor) -> List[Image.Image]:
-    batch_count = image.size(0) if len(image.shape) > 3 else 1
-    if batch_count > 1:
-        out = []
-        for i in range(batch_count):
-            out.extend(tensor2pil(image[i]))
-        return out
-
-    return [
-        Image.fromarray(
-            np.clip(255.0 * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
-        )
-    ]

custom_nodes/ComfyUI-KJNodes-main/web/js/appearance.js

@@ -1,23 +0,0 @@
-import { app } from "../../../scripts/app.js";
-
-app.registerExtension({
-    name: "KJNodes.appearance",
-        nodeCreated(node) {
-            switch (node.comfyClass) {
-                case "INTConstant":
-                    node.setSize([200, 58]);
-                    node.color = "#1b4669";
-                    node.bgcolor = "#29699c";
-                    break;
-                case "FloatConstant":
-                    node.setSize([200, 58]);
-                    node.color = LGraphCanvas.node_colors.green.color;
-                    node.bgcolor = LGraphCanvas.node_colors.green.bgcolor;
-                    break;
-                case "ConditioningMultiCombine":
-                    node.color = LGraphCanvas.node_colors.brown.color;
-                    node.bgcolor = LGraphCanvas.node_colors.brown.bgcolor;
-                    break;
-            }
-        }
-});

custom_nodes/ComfyUI-KJNodes-main/web/js/browserstatus.js

@@ -1,55 +0,0 @@
-import { api } from "../../../scripts/api.js";
-import { app } from "../../../scripts/app.js";
-
-app.registerExtension({
-    name: "KJNodes.browserstatus",
-    setup() {
-        if (!app.ui.settings.getSettingValue("KJNodes.browserStatus")) {
-            return;
-        }
-        api.addEventListener("status", ({ detail }) => {
-            let title = "ComfyUI";
-            let favicon = "green";
-            let queueRemaining = detail && detail.exec_info.queue_remaining;
-
-            if (queueRemaining) {
-                favicon = "red";
-                title = `00% - ${queueRemaining} | ${title}`;
-            } 
-            let link = document.querySelector("link[rel~='icon']");
-            if (!link) {
-                link = document.createElement("link");
-                link.rel = "icon";
-                document.head.appendChild(link);
-            }
-            link.href = new URL(`../${favicon}.png`, import.meta.url);
-            document.title = title;
-        });
-        //add progress to the title
-        api.addEventListener("progress", ({ detail }) => {
-			const { value, max } = detail;
-			const progress = Math.floor((value / max) * 100);
-			let title = document.title;
-		
-			if (!isNaN(progress) && progress >= 0 && progress <= 100) {
-				const paddedProgress = String(progress).padStart(2, '0');
-				title = `${paddedProgress}% ${title.replace(/^\d+%\s/, '')}`;
-			}
-			document.title = title;
-		});
-    },
-    init() {
-        if (!app.ui.settings.getSettingValue("KJNodes.browserStatus")) {
-            return;
-        }
-        const pythongossFeed = app.extensions.find(
-            (e) => e.name === 'pysssss.FaviconStatus',
-          )
-          if (pythongossFeed) {
-            console.warn("KJNodes - Overriding pysssss.FaviconStatus")
-            pythongossFeed.setup = function() {
-                console.warn("Disabled by KJNodes")
-            };
-          }
-    },
-});

custom_nodes/ComfyUI-KJNodes-main/web/js/contextmenu.js

@@ -1,147 +0,0 @@
-import { app } from "../../../scripts/app.js";
-
-// Adds context menu entries, code partly from pyssssscustom-scripts
-
-function addMenuHandler(nodeType, cb) {
-	const getOpts = nodeType.prototype.getExtraMenuOptions;
-	nodeType.prototype.getExtraMenuOptions = function () {
-		const r = getOpts.apply(this, arguments);
-		cb.apply(this, arguments);
-		return r;
-	};
-}
-
-function addNode(name, nextTo, options) {
-	console.log("name:", name);
-	console.log("nextTo:", nextTo);
-	options = { side: "left", select: true, shiftY: 0, shiftX: 0, ...(options || {}) };
-	const node = LiteGraph.createNode(name);
-	app.graph.add(node);
-	
-	node.pos = [
-		options.side === "left" ? nextTo.pos[0] - (node.size[0] + options.offset): nextTo.pos[0] + nextTo.size[0] + options.offset,
-		
-		nextTo.pos[1] + options.shiftY,
-	];
-	if (options.select) {
-		app.canvas.selectNode(node, false);
-	}
-	return node;
-}
-
-app.registerExtension({
-	name: "KJNodesContextmenu",
-	async beforeRegisterNodeDef(nodeType, nodeData, app) {
-		if (nodeData.input && nodeData.input.required) {
-			addMenuHandler(nodeType, function (_, options) {
-				options.unshift(
-					{
-					content: "Add GetNode",
-					callback: () => {addNode("GetNode", this, { side:"left", offset: 30});}
-					},
-					{
-					content: "Add SetNode",
-					callback: () => {addNode("SetNode", this, { side:"right", offset: 30 });
-					},
-				});
-			});
-		}
-	},
-		async setup(app) {
-			const updateSlots = (value) => {
-				const valuesToAddToIn = ["GetNode"];
-				const valuesToAddToOut = ["SetNode"];
-				// Remove entries if they exist
-				for (const arr of Object.values(LiteGraph.slot_types_default_in)) {
-					for (const valueToAdd of valuesToAddToIn) {
-						const idx = arr.indexOf(valueToAdd);
-						if (idx !== -1) {
-							arr.splice(idx, 1);
-						}
-					}
-				}
-			
-				for (const arr of Object.values(LiteGraph.slot_types_default_out)) {
-					for (const valueToAdd of valuesToAddToOut) {
-						const idx = arr.indexOf(valueToAdd);
-						if (idx !== -1) {
-							arr.splice(idx, 1);
-						}
-					}
-				}
-				if (value!="disabled") {
-					for (const arr of Object.values(LiteGraph.slot_types_default_in)) {
-						for (const valueToAdd of valuesToAddToIn) {
-							const idx = arr.indexOf(valueToAdd);
-							if (idx !== -1) {
-								arr.splice(idx, 1);
-							}
-							if (value === "top") {
-								arr.unshift(valueToAdd);
-							} else {
-								arr.push(valueToAdd);
-							}
-						}
-					}
-			
-					for (const arr of Object.values(LiteGraph.slot_types_default_out)) {
-						for (const valueToAdd of valuesToAddToOut) {
-							const idx = arr.indexOf(valueToAdd);
-							if (idx !== -1) {
-								arr.splice(idx, 1);
-							}
-							if (value === "top") {
-								arr.unshift(valueToAdd);
-							} else {
-								arr.push(valueToAdd);
-							}
-						}
-					}
-				}
-			};
-			
-			app.ui.settings.addSetting({
-				id: "KJNodes.SetGetMenu",
-				name: "KJNodes: Make Set/Get -nodes defaults",
-				tooltip: 'Adds Set/Get nodes to the top or bottom of the list of available node suggestions.',
-				options: ['disabled', 'top', 'bottom'],
-				defaultValue: 'disabled',
-				type: "combo",
-				onChange: updateSlots,
-				
-			});
-			app.ui.settings.addSetting({
-				id: "KJNodes.MiddleClickDefault",
-				name: "KJNodes: Middle click default node adding",
-				defaultValue: false,
-				type: "boolean",
-				onChange: (value) => {
-					LiteGraph.middle_click_slot_add_default_node = value;
-				},
-			});
-			app.ui.settings.addSetting({
-				id: "KJNodes.nodeAutoColor",
-				name: "KJNodes: Automatically set node colors",
-				type: "boolean",
-				defaultValue: true,
-			});
-			app.ui.settings.addSetting({
-				id: "KJNodes.helpPopup",
-				name: "KJNodes: Help popups",
-				defaultValue: true,
-				type: "boolean",
-			});
-			app.ui.settings.addSetting({
-				id: "KJNodes.disablePrefix",
-				name: "KJNodes: Disable automatic Set_ and Get_ prefix",
-				defaultValue: true,
-				type: "boolean",
-			});
-			app.ui.settings.addSetting({
-				id: "KJNodes.browserStatus",
-				name: "KJNodes: 🟢 Stoplight browser status icon 🔴",
-				defaultValue: false,
-				type: "boolean",
-			});
-}
-});

custom_nodes/ComfyUI-KJNodes-main/web/js/fast_preview.js

@@ -1,95 +0,0 @@
-import { app } from '../../../scripts/app.js'
-
-//from melmass
-export function makeUUID() {
-  let dt = new Date().getTime()
-  const uuid = 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, (c) => {
-    const r = ((dt + Math.random() * 16) % 16) | 0
-    dt = Math.floor(dt / 16)
-    return (c === 'x' ? r : (r & 0x3) | 0x8).toString(16)
-  })
-  return uuid
-}
-
-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) {
-    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;
-  }
-}
-app.registerExtension({
-  name: 'KJNodes.FastPreview',
-
-  async beforeRegisterNodeDef(nodeType, nodeData) {
-    if (nodeData?.name === 'FastPreview') {
-      chainCallback(nodeType.prototype, "onNodeCreated", function () {
-
-        var element = document.createElement("div");
-        this.uuid = makeUUID()
-        element.id = `fast-preview-${this.uuid}`
-
-        this.previewWidget = this.addDOMWidget(nodeData.name, "FastPreviewWidget", element, {
-          serialize: false,
-          hideOnZoom: false,
-        });
-
-        this.previewer = new Previewer(this);
-
-        this.setSize([550, 550]);
-        this.resizable = false;
-        this.previewWidget.parentEl = document.createElement("div");
-        this.previewWidget.parentEl.className = "fast-preview";
-        this.previewWidget.parentEl.id = `fast-preview-${this.uuid}`
-        element.appendChild(this.previewWidget.parentEl);
-        
-        chainCallback(this, "onExecuted", function (message) {
-          let bg_image = message["bg_image"];
-          this.properties.imgData = {
-            name: "bg_image",
-            base64: bg_image
-          };
-          this.previewer.refreshBackgroundImage(this);
-        });
-       
-
-      }); // onAfterGraphConfigured
-    }//node created
-  } //before register
-})//register
-
-class Previewer {
-  constructor(context) {
-    this.node = context;
-    this.previousWidth = null;
-    this.previousHeight = null;
-  }
-  refreshBackgroundImage = () => {
-    const imgData = this.node?.properties?.imgData;
-    if (imgData?.base64) {
-      const base64String = imgData.base64;
-      const imageUrl = `data:${imgData.type};base64,${base64String}`;
-      const img = new Image();
-      img.src = imageUrl;
-      img.onload = () => {
-        const { width, height } = img;
-        if (width !== this.previousWidth || height !== this.previousHeight) {
-          this.node.setSize([width, height]);
-          this.previousWidth = width;
-          this.previousHeight = height;
-        }
-        this.node.previewWidget.element.style.backgroundImage = `url(${imageUrl})`;
-      };
-    }
-  };
-  }

custom_nodes/ComfyUI-KJNodes-main/web/js/help_popup.js

@@ -1,326 +0,0 @@
-import { app } from "../../../scripts/app.js";
-
-// code based on mtb nodes by Mel Massadian https://github.com/melMass/comfy_mtb/
-export const loadScript = (
-  FILE_URL,
-  async = true,
-  type = 'text/javascript',
-) => {
-  return new Promise((resolve, reject) => {
-    try {
-      // Check if the script already exists
-      const existingScript = document.querySelector(`script[src="${FILE_URL}"]`)
-      if (existingScript) {
-        resolve({ status: true, message: 'Script already loaded' })
-        return
-      }
-
-      const scriptEle = document.createElement('script')
-      scriptEle.type = type
-      scriptEle.async = async
-      scriptEle.src = FILE_URL
-
-      scriptEle.addEventListener('load', (ev) => {
-        resolve({ status: true })
-      })
-
-      scriptEle.addEventListener('error', (ev) => {
-        reject({
-          status: false,
-          message: `Failed to load the script ${FILE_URL}`,
-        })
-      })
-
-      document.body.appendChild(scriptEle)
-    } catch (error) {
-      reject(error)
-    }
-  })
-}
-
-loadScript('/kjweb_async/marked.min.js').catch((e) => {
-  console.log(e)
-})
-loadScript('/kjweb_async/purify.min.js').catch((e) => {
-  console.log(e)
-})
-
-const categories = ["KJNodes", "SUPIR", "VoiceCraft", "Marigold", "IC-Light", "WanVideoWrapper"];
-app.registerExtension({
-	name: "KJNodes.HelpPopup",
-	async beforeRegisterNodeDef(nodeType, nodeData) {
-  
-  if (app.ui.settings.getSettingValue("KJNodes.helpPopup") === false) {
-    return;
-    }
-		try {
-			categories.forEach(category => {
-        if (nodeData?.category?.startsWith(category)) {
-            addDocumentation(nodeData, nodeType);
-        }
-        else return
-    });
-		} catch (error) {
-			console.error("Error in registering KJNodes.HelpPopup", error);
-		}
-	},
-});
-
-const create_documentation_stylesheet = () => {
-    const tag = 'kj-documentation-stylesheet'
-
-    let styleTag = document.head.querySelector(tag)
-
-    if (!styleTag) {
-      styleTag = document.createElement('style')
-      styleTag.type = 'text/css'
-      styleTag.id = tag
-      styleTag.innerHTML = `
-      .kj-documentation-popup {
-        background: var(--comfy-menu-bg);
-        position: absolute;
-        color: var(--fg-color);
-        font: 12px monospace;
-        line-height: 1.5em;
-        padding: 10px;
-        border-radius: 10px;
-        border-style: solid;
-        border-width: medium;
-        border-color: var(--border-color);
-        z-index: 5;
-        overflow: hidden;
-       }
-       .content-wrapper {
-        overflow: auto;
-        max-height: 100%;
-        /* Scrollbar styling for Chrome */
-        &::-webkit-scrollbar {
-           width: 6px;
-        }
-        &::-webkit-scrollbar-track {
-           background: var(--bg-color);
-        }
-        &::-webkit-scrollbar-thumb {
-           background-color: var(--fg-color);
-           border-radius: 6px;
-           border: 3px solid var(--bg-color);
-        }
-       
-        /* Scrollbar styling for Firefox */
-        scrollbar-width: thin;
-        scrollbar-color: var(--fg-color) var(--bg-color);
-        a {
-          color: yellow;
-        }
-        a:visited {
-          color: orange;
-        }
-        a:hover {
-          color: red;
-        }
-       }
-        `
-      document.head.appendChild(styleTag)
-    }
-  }
-
-  /** Add documentation widget to the selected node */
-  export const addDocumentation = (
-    nodeData,
-    nodeType,
-    opts = { icon_size: 14, icon_margin: 4 },) => {
-
-    opts = opts || {}
-    const iconSize = opts.icon_size ? opts.icon_size : 14
-    const iconMargin = opts.icon_margin ? opts.icon_margin : 4
-    let docElement = null
-    let contentWrapper = null
-    //if no description in the node python code, don't do anything
-    if (!nodeData.description) {
-      return
-    }
-
-    const drawFg = nodeType.prototype.onDrawForeground
-    nodeType.prototype.onDrawForeground = function (ctx) {
-      const r = drawFg ? drawFg.apply(this, arguments) : undefined
-      if (this.flags.collapsed) return r
-
-      // icon position
-      const x = this.size[0] - iconSize - iconMargin
-      
-      // create the popup
-      if (this.show_doc && docElement === null) {
-        docElement = document.createElement('div')
-        contentWrapper = document.createElement('div');
-        docElement.appendChild(contentWrapper);
-
-        create_documentation_stylesheet()
-        contentWrapper.classList.add('content-wrapper');
-        docElement.classList.add('kj-documentation-popup')
-        
-        //parse the string from the python node code to html with marked, and sanitize the html with DOMPurify
-        contentWrapper.innerHTML = DOMPurify.sanitize(marked.parse(nodeData.description,))
-
-        // resize handle
-        const resizeHandle = document.createElement('div');
-        resizeHandle.style.width = '0';
-        resizeHandle.style.height = '0';
-        resizeHandle.style.position = 'absolute';
-        resizeHandle.style.bottom = '0';
-        resizeHandle.style.right = '0';
-        resizeHandle.style.cursor = 'se-resize';
-        
-        // Add pseudo-elements to create a triangle shape
-        const borderColor = getComputedStyle(document.documentElement).getPropertyValue('--border-color').trim();
-        resizeHandle.style.borderTop = '10px solid transparent';
-        resizeHandle.style.borderLeft = '10px solid transparent';
-        resizeHandle.style.borderBottom = `10px solid ${borderColor}`;
-        resizeHandle.style.borderRight = `10px solid ${borderColor}`;
-
-        docElement.appendChild(resizeHandle)
-        let isResizing = false
-        let startX, startY, startWidth, startHeight
-
-        resizeHandle.addEventListener('mousedown', function (e) {
-          e.preventDefault();
-          e.stopPropagation();
-          isResizing = true;
-          startX = e.clientX;
-          startY = e.clientY;
-          startWidth = parseInt(document.defaultView.getComputedStyle(docElement).width, 10);
-          startHeight = parseInt(document.defaultView.getComputedStyle(docElement).height, 10);
-         },
-         { signal: this.docCtrl.signal },
-         );
-
-        // close button
-        const closeButton = document.createElement('div');
-        closeButton.textContent = '❌';
-        closeButton.style.position = 'absolute';
-        closeButton.style.top = '0';
-        closeButton.style.right = '0';
-        closeButton.style.cursor = 'pointer';
-        closeButton.style.padding = '5px';
-        closeButton.style.color = 'red';
-        closeButton.style.fontSize = '12px';
-
-        docElement.appendChild(closeButton)
-
-        closeButton.addEventListener('mousedown', (e) => {
-          e.stopPropagation();
-          this.show_doc = !this.show_doc
-          docElement.parentNode.removeChild(docElement)
-          docElement = null
-          if (contentWrapper) {
-            contentWrapper.remove()
-            contentWrapper = null
-          }
-         },
-         { signal: this.docCtrl.signal },
-         );
-         
-        document.addEventListener('mousemove', function (e) {
-          if (!isResizing) return;
-          const scale = app.canvas.ds.scale;
-          const newWidth = startWidth + (e.clientX - startX) / scale;
-          const newHeight = startHeight + (e.clientY - startY) / scale;;
-          docElement.style.width = `${newWidth}px`;
-          docElement.style.height = `${newHeight}px`;
-         },
-         { signal: this.docCtrl.signal },
-         );
-
-        document.addEventListener('mouseup', function () {
-          isResizing = false
-        },
-        { signal: this.docCtrl.signal },
-        )
-
-        document.body.appendChild(docElement)
-      }
-      // close the popup
-      else if (!this.show_doc && docElement !== null) {
-        docElement.parentNode.removeChild(docElement)
-        docElement = null
-      }
-      // update position of the popup
-      if (this.show_doc && docElement !== null) {
-        const rect = ctx.canvas.getBoundingClientRect()
-        const scaleX = rect.width / ctx.canvas.width
-        const scaleY = rect.height / ctx.canvas.height
-
-        const transform = new DOMMatrix()
-        .scaleSelf(scaleX, scaleY)
-        .multiplySelf(ctx.getTransform())
-        .translateSelf(this.size[0] * scaleX * Math.max(1.0,window.devicePixelRatio) , 0)
-        .translateSelf(10, -32)
-        
-        const scale = new DOMMatrix()
-        .scaleSelf(transform.a, transform.d);
-        const bcr = app.canvas.canvas.getBoundingClientRect()
-
-        const styleObject = {
-          transformOrigin: '0 0',
-          transform: scale,
-          left: `${transform.a + bcr.x + transform.e}px`,
-          top: `${transform.d + bcr.y + transform.f}px`,
-         };
-        Object.assign(docElement.style, styleObject);
-      }
-
-      ctx.save()
-      ctx.translate(x - 2, iconSize - 34)
-      ctx.scale(iconSize / 32, iconSize / 32)
-      ctx.strokeStyle = 'rgba(255,255,255,0.3)'
-      ctx.lineCap = 'round'
-      ctx.lineJoin = 'round'
-      ctx.lineWidth = 2.4
-      ctx.font = 'bold 36px monospace'
-      ctx.fillStyle = 'orange';
-      ctx.fillText('?', 0, 24)
-      ctx.restore()
-      return r
-    }
-    // handle clicking of the icon
-    const mouseDown = nodeType.prototype.onMouseDown
-    nodeType.prototype.onMouseDown = function (e, localPos, canvas) {
-      const r = mouseDown ? mouseDown.apply(this, arguments) : undefined
-      const iconX = this.size[0] - iconSize - iconMargin
-      const iconY = iconSize - 34
-      if (
-        localPos[0] > iconX &&
-        localPos[0] < iconX + iconSize &&
-        localPos[1] > iconY &&
-        localPos[1] < iconY + iconSize
-      ) {
-        if (this.show_doc === undefined) {
-          this.show_doc = true
-        } else {
-          this.show_doc = !this.show_doc
-        }
-        if (this.show_doc) {
-          this.docCtrl = new AbortController()
-        } else {
-          this.docCtrl.abort()
-        }
-        return true;
-      }
-      return r;
-    }
-    const onRem = nodeType.prototype.onRemoved
-
-    nodeType.prototype.onRemoved = function () {
-      const r = onRem ? onRem.apply(this, []) : undefined
-  
-      if (docElement) {
-        docElement.remove()
-        docElement = null
-      }
-  
-      if (contentWrapper) {
-        contentWrapper.remove()
-        contentWrapper = null
-      }
-      return r
-    }
-}

custom_nodes/ComfyUI-KJNodes-main/web/js/jsnodes.js

@@ -1,374 +0,0 @@
-import { app } from "../../../scripts/app.js";
-import { applyTextReplacements } from "../../../scripts/utils.js";
-
-app.registerExtension({
-	name: "KJNodes.jsnodes",
-	async beforeRegisterNodeDef(nodeType, nodeData, app) {
-		if(!nodeData?.category?.startsWith("KJNodes")) {
-			return;
-		  }
-		switch (nodeData.name) {
-			case "ConditioningMultiCombine":
-				nodeType.prototype.onNodeCreated = function () {
-				this.cond_type = "CONDITIONING"
-				this.inputs_offset = nodeData.name.includes("selective")?1:0
-				this.addWidget("button", "Update inputs", null, () => {
-					if (!this.inputs) {
-						this.inputs = [];
-					}
-					const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"];
-					    if(target_number_of_inputs===this.inputs.length)return; // already set, do nothing
-
-					    if(target_number_of_inputs < this.inputs.length){
-    						for(let i = this.inputs.length; i>=this.inputs_offset+target_number_of_inputs; i--)
-							      this.removeInput(i)
-					    }
-                        else{
-						    for(let i = this.inputs.length+1-this.inputs_offset; i <= target_number_of_inputs; ++i)
-						    	this.addInput(`conditioning_${i}`, this.cond_type)
-                        }
-					});
-				}
-				break;
-			case "ImageBatchMulti":
-			case "ImageAddMulti":
-			case "ImageConcatMulti":
-			case "CrossFadeImagesMulti":
-			case "TransitionImagesMulti":
-				nodeType.prototype.onNodeCreated = function () {
-				this._type = "IMAGE"
-				this.inputs_offset = nodeData.name.includes("selective")?1:0
-				this.addWidget("button", "Update inputs", null, () => {
-					if (!this.inputs) {
-						this.inputs = [];
-					}
-					const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"];
-						if(target_number_of_inputs===this.inputs.length)return; // already set, do nothing
-
-						if(target_number_of_inputs < this.inputs.length){
-							for(let i = this.inputs.length; i>=this.inputs_offset+target_number_of_inputs; i--)
-									this.removeInput(i)
-						}
-						else{
-							for(let i = this.inputs.length+1-this.inputs_offset; i <= target_number_of_inputs; ++i)
-								this.addInput(`image_${i}`, this._type)
-						}
-					});
-				}
-				break;
-			case "MaskBatchMulti":
-				nodeType.prototype.onNodeCreated = function () {
-				this._type = "MASK"
-				this.inputs_offset = nodeData.name.includes("selective")?1:0
-				this.addWidget("button", "Update inputs", null, () => {
-					if (!this.inputs) {
-						this.inputs = [];
-					}
-					const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"];
-						if(target_number_of_inputs===this.inputs.length)return; // already set, do nothing
-
-						if(target_number_of_inputs < this.inputs.length){
-							for(let i = this.inputs.length; i>=this.inputs_offset+target_number_of_inputs; i--)
-									this.removeInput(i)
-						}
-						else{
-							for(let i = this.inputs.length+1-this.inputs_offset; i <= target_number_of_inputs; ++i)
-								this.addInput(`mask_${i}`, this._type)
-							}
-						});
-					}
-					break;
-			
-			case "FluxBlockLoraSelect":
-			case "HunyuanVideoBlockLoraSelect":
-				nodeType.prototype.onNodeCreated = function () {
-					this.addWidget("button", "Set all", null, () => {
-						const userInput = prompt("Enter the values to set for widgets (e.g., s0,1,2-7=2.0, d0,1,2-7=2.0, or 1.0):", "");
-						if (userInput) {
-							const regex = /([sd])?(\d+(?:,\d+|-?\d+)*?)?=(\d+(\.\d+)?)/;
-							const match = userInput.match(regex);
-							if (match) {
-								const type = match[1];
-								const indicesPart = match[2];
-								const value = parseFloat(match[3]);
-			
-								let targetWidgets = [];
-								if (type === 's') {
-									targetWidgets = this.widgets.filter(widget => widget.name.includes("single"));
-								} else if (type === 'd') {
-									targetWidgets = this.widgets.filter(widget => widget.name.includes("double"));
-								} else {
-									targetWidgets = this.widgets; // No type specified, all widgets
-								}
-			
-								if (indicesPart) {
-									const indices = indicesPart.split(',').flatMap(part => {
-										if (part.includes('-')) {
-											const [start, end] = part.split('-').map(Number);
-											return Array.from({ length: end - start + 1 }, (_, i) => start + i);
-										}
-										return Number(part);
-									});
-			
-									for (const index of indices) {
-										if (index < targetWidgets.length) {
-											targetWidgets[index].value = value;
-										}
-									}
-								} else {
-									// No indices provided, set value for all target widgets
-									for (const widget of targetWidgets) {
-										widget.value = value;
-									}
-								}
-							} else if (!isNaN(parseFloat(userInput))) {
-								// Single value provided, set it for all widgets
-								const value = parseFloat(userInput);
-								for (const widget of this.widgets) {
-									widget.value = value;
-								}
-							} else {
-								alert("Invalid input format. Please use the format s0,1,2-7=2.0, d0,1,2-7=2.0, or 1.0");
-							}
-						} else {
-							alert("Invalid input. Please enter a value.");
-						}
-					});
-				};
-				break;
-
-			case "GetMaskSizeAndCount":
-				const onGetMaskSizeConnectInput = nodeType.prototype.onConnectInput;
-				nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) {
-					const v = onGetMaskSizeConnectInput? onGetMaskSizeConnectInput.apply(this, arguments): undefined
-					this.outputs[1]["label"] = "width"
-					this.outputs[2]["label"] = "height" 
-					this.outputs[3]["label"] = "count"
-					return v;
-				}
-				const onGetMaskSizeExecuted = nodeType.prototype.onAfterExecuteNode;
-				nodeType.prototype.onExecuted = function(message) {
-					const r = onGetMaskSizeExecuted? onGetMaskSizeExecuted.apply(this,arguments): undefined
-					let values = message["text"].toString().split('x').map(Number);
-					this.outputs[1]["label"] = values[1] + " width"
-					this.outputs[2]["label"] = values[2] + " height" 
-					this.outputs[3]["label"] = values[0] + " count" 
-					return r
-				}
-				break;
-			
-			case "GetImageSizeAndCount":
-				const onGetImageSizeConnectInput = nodeType.prototype.onConnectInput;
-				nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) {
-					console.log(this)
-					const v = onGetImageSizeConnectInput? onGetImageSizeConnectInput.apply(this, arguments): undefined
-					//console.log(this)
-					this.outputs[1]["label"] = "width"
-					this.outputs[2]["label"] = "height" 
-					this.outputs[3]["label"] = "count"
-					return v;
-				}
-				//const onGetImageSizeExecuted = nodeType.prototype.onExecuted;
-				const onGetImageSizeExecuted = nodeType.prototype.onAfterExecuteNode;
-				nodeType.prototype.onExecuted = function(message) {
-					console.log(this)
-					const r = onGetImageSizeExecuted? onGetImageSizeExecuted.apply(this,arguments): undefined
-					let values = message["text"].toString().split('x').map(Number);
-					console.log(values)
-					this.outputs[1]["label"] = values[1] + " width"
-					this.outputs[2]["label"] = values[2] + " height" 
-					this.outputs[3]["label"] = values[0] + " count" 
-					return r
-				}
-				break;
-
-			case "PreviewAnimation":
-				const onPreviewAnimationConnectInput = nodeType.prototype.onConnectInput;
-				nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) {
-					const v = onPreviewAnimationConnectInput? onPreviewAnimationConnectInput.apply(this, arguments): undefined
-					this.title = "Preview Animation"
-					return v;
-				}
-				const onPreviewAnimationExecuted = nodeType.prototype.onAfterExecuteNode;
-				nodeType.prototype.onExecuted = function(message) {
-					const r = onPreviewAnimationExecuted? onPreviewAnimationExecuted.apply(this,arguments): undefined
-					let values = message["text"].toString();
-					this.title = "Preview Animation " + values
-					return r
-				}
-				break;
-
-			case "VRAM_Debug":
-				const onVRAM_DebugConnectInput = nodeType.prototype.onConnectInput;
-				nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) {
-					const v = onVRAM_DebugConnectInput? onVRAM_DebugConnectInput.apply(this, arguments): undefined
-					this.outputs[3]["label"] = "freemem_before"
-					this.outputs[4]["label"] = "freemem_after" 
-					return v;
-				}
-				const onVRAM_DebugExecuted = nodeType.prototype.onAfterExecuteNode;
-				nodeType.prototype.onExecuted = function(message) {
-					const r = onVRAM_DebugExecuted? onVRAM_DebugExecuted.apply(this,arguments): undefined
-					let values = message["text"].toString().split('x');
-					this.outputs[3]["label"] = values[0] + "   freemem_before"
-					this.outputs[4]["label"] = values[1] + "      freemem_after" 
-					return r
-				}
-				break;
-
-			case "JoinStringMulti":
-				const originalOnNodeCreated = nodeType.prototype.onNodeCreated || function() {};
-				nodeType.prototype.onNodeCreated = function () {
-					originalOnNodeCreated.apply(this, arguments);
-			
-					this._type = "STRING";
-					this.inputs_offset = nodeData.name.includes("selective") ? 1 : 0;
-					this.addWidget("button", "Update inputs", null, () => {
-						if (!this.inputs) {
-							this.inputs = [];
-						}
-						const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"];
-						if (target_number_of_inputs === this.inputs.length) return; // already set, do nothing
-			
-						if (target_number_of_inputs < this.inputs.length) {
-							for (let i = this.inputs.length; i >= this.inputs_offset + target_number_of_inputs; i--)
-								this.removeInput(i);
-						} else {
-							for (let i = this.inputs.length + 1 - this.inputs_offset; i <= target_number_of_inputs; ++i)
-								this.addInput(`string_${i}`, this._type);
-						}
-					});
-				}
-				break;
-			case "SoundReactive":
-				nodeType.prototype.onNodeCreated = function () {
-					let audioContext;
-					let microphoneStream;
-					let animationFrameId;
-					let analyser;
-					let dataArray;
-					let startRangeHz;
-    				let endRangeHz;
-					let smoothingFactor = 0.5;
-					let smoothedSoundLevel = 0;
-				
-					// Function to update the widget value in real-time
-					const updateWidgetValueInRealTime = () => {
-						// Ensure analyser and dataArray are defined before using them
-						if (analyser && dataArray) {
-							analyser.getByteFrequencyData(dataArray);
-
-							const startRangeHzWidget = this.widgets.find(w => w.name === "start_range_hz");
-							if (startRangeHzWidget) startRangeHz = startRangeHzWidget.value;
-							const endRangeHzWidget = this.widgets.find(w => w.name === "end_range_hz");
-							if (endRangeHzWidget) endRangeHz = endRangeHzWidget.value;
-							const smoothingFactorWidget = this.widgets.find(w => w.name === "smoothing_factor");
-							if (smoothingFactorWidget) smoothingFactor = smoothingFactorWidget.value;
-
-							// Calculate frequency bin width (frequency resolution)
-							const frequencyBinWidth = audioContext.sampleRate / analyser.fftSize;	
-							// Convert the widget values from Hz to indices
-							const startRangeIndex = Math.floor(startRangeHz / frequencyBinWidth);
-							const endRangeIndex = Math.floor(endRangeHz / frequencyBinWidth);
-
-							// Function to calculate the average value for a frequency range
-							const calculateAverage = (start, end) => {
-								const sum = dataArray.slice(start, end).reduce((acc, val) => acc + val, 0);
-								const average = sum / (end - start);
-
-								// Apply exponential moving average smoothing
-    							smoothedSoundLevel = (average * (1 - smoothingFactor)) + (smoothedSoundLevel * smoothingFactor);
-								return smoothedSoundLevel;
-							};
-							// Calculate the average levels for each frequency range
-							const soundLevel = calculateAverage(startRangeIndex, endRangeIndex);
-							
-							// Update the widget values
-
-							const lowLevelWidget = this.widgets.find(w => w.name === "sound_level");
-							if (lowLevelWidget) lowLevelWidget.value = soundLevel;
-
-							animationFrameId = requestAnimationFrame(updateWidgetValueInRealTime);
-						}
-					};
-				
-					// Function to start capturing audio from the microphone
-					const startMicrophoneCapture = () => {
-						// Only create the audio context and analyser once
-						if (!audioContext) {
-							audioContext = new (window.AudioContext || window.webkitAudioContext)();
-							// Access the sample rate of the audio context
-							console.log(`Sample rate: ${audioContext.sampleRate}Hz`);
-							analyser = audioContext.createAnalyser();
-							analyser.fftSize = 2048;
-							dataArray = new Uint8Array(analyser.frequencyBinCount);
-							// Get the range values from widgets (assumed to be in Hz)
-							const lowRangeWidget = this.widgets.find(w => w.name === "low_range_hz");
-							if (lowRangeWidget) startRangeHz = lowRangeWidget.value;
-				
-							const midRangeWidget = this.widgets.find(w => w.name === "mid_range_hz");
-							if (midRangeWidget) endRangeHz = midRangeWidget.value;
-						}
-						
-						navigator.mediaDevices.getUserMedia({ audio: true }).then(stream => {
-							microphoneStream = stream;
-							const microphone = audioContext.createMediaStreamSource(stream);
-							microphone.connect(analyser);
-							updateWidgetValueInRealTime();
-						}).catch(error => {
-							console.error('Access to microphone was denied or an error occurred:', error);
-						});
-					};
-				
-					// Function to stop capturing audio from the microphone
-					const stopMicrophoneCapture = () => {
-						if (animationFrameId) {
-							cancelAnimationFrame(animationFrameId);
-						}
-						if (microphoneStream) {
-							microphoneStream.getTracks().forEach(track => track.stop());
-						}
-						if (audioContext) {
-							audioContext.close();
-							// Reset audioContext to ensure it can be created again when starting
-							audioContext = null;
-						}
-					};
-				
-					// Add start button
-					this.addWidget("button", "Start mic capture", null, startMicrophoneCapture);
-				
-					// Add stop button
-					this.addWidget("button", "Stop mic capture", null, stopMicrophoneCapture);
-				};
-			break;
-		case "SaveImageKJ":
-			const onNodeCreated = nodeType.prototype.onNodeCreated;
-			nodeType.prototype.onNodeCreated = function() {
-				const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : void 0;
-				const widget = this.widgets.find((w) => w.name === "filename_prefix");
-				widget.serializeValue = () => {
-				return applyTextReplacements(app, widget.value);
-				};
-				return r;
-			};
-			break;
-			
-		}	
-		
-	},
-	async setup() {
-		// to keep Set/Get node virtual connections visible when offscreen
-		const originalComputeVisibleNodes = LGraphCanvas.prototype.computeVisibleNodes;
-		LGraphCanvas.prototype.computeVisibleNodes = function () {
-			const visibleNodesSet = new Set(originalComputeVisibleNodes.apply(this, arguments));
-			for (const node of this.graph._nodes) {
-				if ((node.type === "SetNode" || node.type === "GetNode") && node.drawConnection) {
-					visibleNodesSet.add(node);
-				}
-			}
-			return Array.from(visibleNodesSet);
-		};
-
-	}
-});

custom_nodes/ComfyUI-KJNodes-main/web/js/point_editor.js

@@ -1,736 +0,0 @@
-import { app } from '../../../scripts/app.js'
-
-//from melmass
-export function makeUUID() {
-  let dt = new Date().getTime()
-  const uuid = 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, (c) => {
-    const r = ((dt + Math.random() * 16) % 16) | 0
-    dt = Math.floor(dt / 16)
-    return (c === 'x' ? r : (r & 0x3) | 0x8).toString(16)
-  })
-  return uuid
-}
-
-export const loadScript = (
-  FILE_URL,
-  async = true,
-  type = 'text/javascript',
-) => {
-  return new Promise((resolve, reject) => {
-    try {
-      // Check if the script already exists
-      const existingScript = document.querySelector(`script[src="${FILE_URL}"]`)
-      if (existingScript) {
-        resolve({ status: true, message: 'Script already loaded' })
-        return
-      }
-
-      const scriptEle = document.createElement('script')
-      scriptEle.type = type
-      scriptEle.async = async
-      scriptEle.src = FILE_URL
-
-      scriptEle.addEventListener('load', (ev) => {
-        resolve({ status: true })
-      })
-
-      scriptEle.addEventListener('error', (ev) => {
-        reject({
-          status: false,
-          message: `Failed to load the script ${FILE_URL}`,
-        })
-      })
-
-      document.body.appendChild(scriptEle)
-    } catch (error) {
-      reject(error)
-    }
-  })
-}
-const create_documentation_stylesheet = () => {
-  const tag = 'kj-pointseditor-stylesheet'
-
-  let styleTag = document.head.querySelector(tag)
-
-  if (!styleTag) {
-    styleTag = document.createElement('style')
-    styleTag.type = 'text/css'
-    styleTag.id = tag
-    styleTag.innerHTML = `
-       .points-editor {
-
-        position: absolute;
-
-        font: 12px monospace;
-        line-height: 1.5em;
-        padding: 10px;
-        z-index: 0;
-        overflow: hidden;
-       }
-        `
-    document.head.appendChild(styleTag)
-  }
-}
-
-loadScript('/kjweb_async/svg-path-properties.min.js').catch((e) => {
-  console.log(e)
-})
-loadScript('/kjweb_async/protovis.min.js').catch((e) => {
-  console.log(e)
-})
-create_documentation_stylesheet()
-
-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) {
-    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;
-  }
-}
-app.registerExtension({
-  name: 'KJNodes.PointEditor',
-
-  async beforeRegisterNodeDef(nodeType, nodeData) {
-    if (nodeData?.name === 'PointsEditor') {
-      chainCallback(nodeType.prototype, "onNodeCreated", function () {
-
-        hideWidgetForGood(this, this.widgets.find(w => w.name === "coordinates"))
-        hideWidgetForGood(this, this.widgets.find(w => w.name === "neg_coordinates"))
-        hideWidgetForGood(this, this.widgets.find(w => w.name === "bboxes"))
-
-        var element = document.createElement("div");
-        this.uuid = makeUUID()
-        element.id = `points-editor-${this.uuid}`
-
-        // fake image widget to allow copy/paste
-        const fakeimagewidget = this.addWidget("COMBO", "image", null, () => { }, {});
-        hideWidgetForGood(this, fakeimagewidget)
-
-        this.pointsEditor = this.addDOMWidget(nodeData.name, "PointsEditorWidget", element, {
-          serialize: false,
-          hideOnZoom: false,
-        });
-
-        // context menu
-        this.contextMenu = document.createElement("div");
-        this.contextMenu.id = "context-menu";
-        this.contextMenu.style.display = "none";
-        this.contextMenu.style.position = "absolute";
-        this.contextMenu.style.backgroundColor = "#202020";
-        this.contextMenu.style.minWidth = "100px";
-        this.contextMenu.style.boxShadow = "0px 8px 16px 0px rgba(0,0,0,0.2)";
-        this.contextMenu.style.zIndex = "100";
-        this.contextMenu.style.padding = "5px";
-
-        function styleMenuItem(menuItem) {
-          menuItem.style.display = "block";
-          menuItem.style.padding = "5px";
-          menuItem.style.color = "#FFF";
-          menuItem.style.fontFamily = "Arial, sans-serif";
-          menuItem.style.fontSize = "16px";
-          menuItem.style.textDecoration = "none";
-          menuItem.style.marginBottom = "5px";
-        }
-        function createMenuItem(id, textContent) {
-          let menuItem = document.createElement("a");
-          menuItem.href = "#";
-          menuItem.id = `menu-item-${id}`;
-          menuItem.textContent = textContent;
-          styleMenuItem(menuItem);
-          return menuItem;
-        }
-
-        // Create an array of menu items using the createMenuItem function
-        this.menuItems = [
-          createMenuItem(0, "Load Image"),
-          createMenuItem(1, "Clear Image"),
-        ];
-
-        // Add mouseover and mouseout event listeners to each menu item for styling
-        this.menuItems.forEach(menuItem => {
-          menuItem.addEventListener('mouseover', function () {
-            this.style.backgroundColor = "gray";
-          });
-
-          menuItem.addEventListener('mouseout', function () {
-            this.style.backgroundColor = "#202020";
-          });
-        });
-
-        // Append each menu item to the context menu
-        this.menuItems.forEach(menuItem => {
-          this.contextMenu.appendChild(menuItem);
-        });
-
-        document.body.appendChild(this.contextMenu);
-
-        this.addWidget("button", "New canvas", null, () => {
-          if (!this.properties || !("points" in this.properties)) {
-            this.editor = new PointsEditor(this);
-            this.addProperty("points", this.constructor.type, "string");
-            this.addProperty("neg_points", this.constructor.type, "string");
-
-          }
-          else {
-            this.editor = new PointsEditor(this, true);
-          }
-        });
-
-        this.setSize([550, 550]);
-        this.resizable = false;
-        this.pointsEditor.parentEl = document.createElement("div");
-        this.pointsEditor.parentEl.className = "points-editor";
-        this.pointsEditor.parentEl.id = `points-editor-${this.uuid}`
-        element.appendChild(this.pointsEditor.parentEl);
-
-        chainCallback(this, "onConfigure", function () {
-          try {
-            this.editor = new PointsEditor(this);
-          } catch (error) {
-            console.error("An error occurred while configuring the editor:", error);
-          }
-        });
-        chainCallback(this, "onExecuted", function (message) {
-          let bg_image = message["bg_image"];
-          this.properties.imgData = {
-            name: "bg_image",
-            base64: bg_image
-          };
-          this.editor.refreshBackgroundImage(this);
-        });
-
-      }); // onAfterGraphConfigured
-    }//node created
-  } //before register
-})//register
-
-class PointsEditor {
-  constructor(context, reset = false) {
-    this.node = context;
-    this.reset = reset;
-    const self = this; // Keep a reference to the main class context
-
-    console.log("creatingPointEditor")
-
-    this.node.pasteFile = (file) => {
-      if (file.type.startsWith("image/")) {
-        this.handleImageFile(file);
-        return true;
-      }
-      return false;
-    };
-
-    this.node.onDragOver = function (e) {
-      if (e.dataTransfer && e.dataTransfer.items) {
-        return [...e.dataTransfer.items].some(f => f.kind === "file" && f.type.startsWith("image/"));
-      }
-      return false;
-    };
-
-    // On drop upload files
-    this.node.onDragDrop = (e) => {
-      console.log("onDragDrop called");
-      let handled = false;
-      for (const file of e.dataTransfer.files) {
-        if (file.type.startsWith("image/")) {
-          this.handleImageFile(file);
-          handled = true;
-        }
-      }
-      return handled;
-    };
-
-    // context menu
-    this.createContextMenu();
-
-    if (reset && context.pointsEditor.element) {
-      context.pointsEditor.element.innerHTML = ''; // Clear the container
-    }
-    this.pos_coordWidget = context.widgets.find(w => w.name === "coordinates");
-    this.neg_coordWidget = context.widgets.find(w => w.name === "neg_coordinates");
-    this.pointsStoreWidget = context.widgets.find(w => w.name === "points_store");
-    this.widthWidget = context.widgets.find(w => w.name === "width");
-    this.heightWidget = context.widgets.find(w => w.name === "height");
-    this.bboxStoreWidget = context.widgets.find(w => w.name === "bbox_store");
-    this.bboxWidget = context.widgets.find(w => w.name === "bboxes");
-
-    //widget callbacks
-    this.widthWidget.callback = () => {
-      this.width = this.widthWidget.value;
-      if (this.width > 256) {
-        context.setSize([this.width + 45, context.size[1]]);
-      }
-      this.vis.width(this.width);
-      this.updateData();
-    }
-    this.heightWidget.callback = () => {
-      this.height = this.heightWidget.value
-      this.vis.height(this.height)
-      context.setSize([context.size[0], this.height + 300]);
-      this.updateData();
-    }
-    this.pointsStoreWidget.callback = () => {
-      this.points = JSON.parse(pointsStoreWidget.value).positive;
-      this.neg_points = JSON.parse(pointsStoreWidget.value).negative;
-      this.updateData();
-    }
-    this.bboxStoreWidget.callback = () => {
-      this.bbox = JSON.parse(bboxStoreWidget.value)
-      this.updateData();
-    }
-
-    this.width = this.widthWidget.value;
-    this.height = this.heightWidget.value;
-    var i = 3;
-    this.points = [];
-    this.neg_points = [];
-    this.bbox = [{}];
-    var drawing = false;
-
-    // Initialize or reset points array
-    if (!reset && this.pointsStoreWidget.value != "") {
-      this.points = JSON.parse(this.pointsStoreWidget.value).positive;
-      this.neg_points = JSON.parse(this.pointsStoreWidget.value).negative;
-      this.bbox = JSON.parse(this.bboxStoreWidget.value);
-      console.log(this.bbox)
-    } else {
-      this.points = [
-        {
-          x: this.width / 2, // Middle point horizontally centered
-          y: this.height / 2 // Middle point vertically centered
-        }
-      ];
-      this.neg_points = [
-        {
-          x: 0, // Middle point horizontally centered
-          y: 0 // Middle point vertically centered
-        }
-      ];
-      const combinedPoints = {
-        positive: this.points,
-        negative: this.neg_points,
-      };
-      this.pointsStoreWidget.value = JSON.stringify(combinedPoints);
-      this.bboxStoreWidget.value = JSON.stringify(this.bbox);
-    }
-
-    //create main canvas panel
-    this.vis = new pv.Panel()
-      .width(this.width)
-      .height(this.height)
-      .fillStyle("#222")
-      .strokeStyle("gray")
-      .lineWidth(2)
-      .antialias(false)
-      .margin(10)
-      .event("mousedown", function () { 
-        if (pv.event.shiftKey && pv.event.button === 2) { // Use pv.event to access the event object
-          let scaledMouse = {
-            x: this.mouse().x / app.canvas.ds.scale,
-            y: this.mouse().y / app.canvas.ds.scale
-          };
-          i = self.neg_points.push(scaledMouse) - 1;
-          self.updateData();
-          return this;
-        }
-        else if (pv.event.shiftKey) {
-          let scaledMouse = {
-            x: this.mouse().x / app.canvas.ds.scale,
-            y: this.mouse().y / app.canvas.ds.scale
-          };
-          i = self.points.push(scaledMouse) - 1;
-          self.updateData();
-          return this;
-        }
-        else if (pv.event.ctrlKey) {
-          console.log("start drawing at " + this.mouse().x / app.canvas.ds.scale + ", " + this.mouse().y / app.canvas.ds.scale);
-          drawing = true;
-          self.bbox[0].startX = this.mouse().x / app.canvas.ds.scale;
-          self.bbox[0].startY = this.mouse().y / app.canvas.ds.scale;
-        }
-        else if (pv.event.button === 2) {
-          self.node.contextMenu.style.display = 'block';
-          self.node.contextMenu.style.left = `${pv.event.clientX}px`;
-          self.node.contextMenu.style.top = `${pv.event.clientY}px`;
-        }
-      })
-      .event("mousemove", function () {
-        if (drawing) {
-          self.bbox[0].endX = this.mouse().x / app.canvas.ds.scale;
-          self.bbox[0].endY = this.mouse().y / app.canvas.ds.scale;
-          self.vis.render();
-        }
-      })
-      .event("mouseup", function () {
-        console.log("end drawing at " + this.mouse().x / app.canvas.ds.scale + ", " + this.mouse().y / app.canvas.ds.scale);
-        drawing = false;
-        self.updateData();
-      });
-
-    this.backgroundImage = this.vis.add(pv.Image).visible(false)
-    
-    //create bounding box
-    this.bounding_box = this.vis.add(pv.Area)
-      .data(function () {
-        if (drawing || (self.bbox && self.bbox[0] && Object.keys(self.bbox[0]).length > 0)) {
-          return [self.bbox[0].startX, self.bbox[0].endX];
-        } else {
-          return [];
-        }
-      })
-      .bottom(function () {return self.height - Math.max(self.bbox[0].startY, self.bbox[0].endY); })
-      .left(function (d) {return d; })
-      .height(function () {return Math.abs(self.bbox[0].startY - self.bbox[0].endY);})
-      .fillStyle("rgba(70, 130, 180, 0.5)")
-      .strokeStyle("steelblue")
-      .visible(function () {return drawing || Object.keys(self.bbox[0]).length > 0; })
-      .add(pv.Dot)
-        .visible(function () {return drawing || Object.keys(self.bbox[0]).length > 0; })
-        .data(() => {
-          if (self.bbox && Object.keys(self.bbox[0]).length > 0) {
-            return [{
-              x: self.bbox[0].endX,
-              y: self.bbox[0].endY
-            }];
-          } else {
-            return [];
-          }
-        })
-        .left(d => d.x)
-        .top(d => d.y)
-        .radius(Math.log(Math.min(self.width, self.height)) * 1)
-        .shape("square")
-        .cursor("move")
-        .strokeStyle("steelblue")
-        .lineWidth(2)
-        .fillStyle(function () { return "rgba(100, 100, 100, 0.6)"; })
-        .event("mousedown", pv.Behavior.drag())
-        .event("drag", function () {
-          let adjustedX = this.mouse().x / app.canvas.ds.scale; // Adjust the new position by the inverse of the scale factor
-          let adjustedY = this.mouse().y / app.canvas.ds.scale; 
-
-          // Adjust the new position if it would place the dot outside the bounds of the vis.Panel
-          adjustedX = Math.max(0, Math.min(self.vis.width(), adjustedX));
-          adjustedY = Math.max(0, Math.min(self.vis.height(), adjustedY));
-          self.bbox[0].endX = this.mouse().x / app.canvas.ds.scale;
-          self.bbox[0].endY = this.mouse().y / app.canvas.ds.scale;
-          self.vis.render();
-        })
-        .event("dragend", function () {
-          self.updateData();
-        });
-
-    //create positive points
-    this.vis.add(pv.Dot)
-      .data(() => this.points)
-      .left(d => d.x)
-      .top(d => d.y)
-      .radius(Math.log(Math.min(self.width, self.height)) * 4)
-      .shape("circle")
-      .cursor("move")
-      .strokeStyle(function () { return i == this.index ? "#07f907" : "#139613"; })
-      .lineWidth(4)
-      .fillStyle(function () { return "rgba(100, 100, 100, 0.6)"; })
-      .event("mousedown", pv.Behavior.drag())
-      .event("dragstart", function () {
-        i = this.index;
-      })
-      .event("dragend", function () {
-        if (pv.event.button === 2 && i !== 0 && i !== self.points.length - 1) {
-          this.index = i;
-          self.points.splice(i--, 1);
-        }
-        self.updateData();
-
-      })
-      .event("drag", function () {
-        let adjustedX = this.mouse().x / app.canvas.ds.scale; // Adjust the new X position by the inverse of the scale factor
-        let adjustedY = this.mouse().y / app.canvas.ds.scale; // Adjust the new Y position by the inverse of the scale factor
-        // Determine the bounds of the vis.Panel
-        const panelWidth = self.vis.width();
-        const panelHeight = self.vis.height();
-
-        // Adjust the new position if it would place the dot outside the bounds of the vis.Panel
-        adjustedX = Math.max(0, Math.min(panelWidth, adjustedX));
-        adjustedY = Math.max(0, Math.min(panelHeight, adjustedY));
-        self.points[this.index] = { x: adjustedX, y: adjustedY }; // Update the point's position
-        self.vis.render(); // Re-render the visualization to reflect the new position
-      })
-
-      .anchor("center")
-      .add(pv.Label)
-      .left(d => d.x < this.width / 2 ? d.x + 30 : d.x - 35) // Shift label to right if on left half, otherwise shift to left
-      .top(d => d.y < this.height / 2 ? d.y + 25 : d.y - 25)  // Shift label down if on top half, otherwise shift up
-      .font(25 + "px sans-serif")
-      .text(d => {return this.points.indexOf(d); })
-      .textStyle("#139613")
-      .textShadow("2px 2px 2px black")
-      .add(pv.Dot) // Add smaller point in the center
-      .data(() => this.points)
-        .left(d => d.x)
-        .top(d => d.y)
-        .radius(2)  // Smaller radius for the center point
-        .shape("circle")
-        .fillStyle("red")  // Color for the center point
-        .lineWidth(1);  // Stroke thickness for the center point
-
-    //create negative points
-    this.vis.add(pv.Dot)
-      .data(() => this.neg_points)
-      .left(d => d.x)
-      .top(d => d.y)
-      .radius(Math.log(Math.min(self.width, self.height)) * 4)
-      .shape("circle")
-      .cursor("move")
-      .strokeStyle(function () { return i == this.index ? "#f91111" : "#891616"; })
-      .lineWidth(4)
-      .fillStyle(function () { return "rgba(100, 100, 100, 0.6)"; })
-      .event("mousedown", pv.Behavior.drag())
-      .event("dragstart", function () {
-        i = this.index;
-      })
-      .event("dragend", function () {
-        if (pv.event.button === 2 && i !== 0 && i !== self.neg_points.length - 1) {
-          this.index = i;
-          self.neg_points.splice(i--, 1);
-        }
-        self.updateData();
-
-      })
-      .event("drag", function () {
-        let adjustedX = this.mouse().x / app.canvas.ds.scale; // Adjust the new X position by the inverse of the scale factor
-        let adjustedY = this.mouse().y / app.canvas.ds.scale; // Adjust the new Y position by the inverse of the scale factor
-        // Determine the bounds of the vis.Panel
-        const panelWidth = self.vis.width();
-        const panelHeight = self.vis.height();
-
-        // Adjust the new position if it would place the dot outside the bounds of the vis.Panel
-        adjustedX = Math.max(0, Math.min(panelWidth, adjustedX));
-        adjustedY = Math.max(0, Math.min(panelHeight, adjustedY));
-        self.neg_points[this.index] = { x: adjustedX, y: adjustedY }; // Update the point's position
-        self.vis.render(); // Re-render the visualization to reflect the new position
-      })
-      .anchor("center")
-      .add(pv.Label)
-        .left(d => d.x < this.width / 2 ? d.x + 30 : d.x - 35) // Shift label to right if on left half, otherwise shift to left
-        .top(d => d.y < this.height / 2 ? d.y + 25 : d.y - 25)  // Shift label down if on top half, otherwise shift up
-        .font(25 + "px sans-serif")
-        .text(d => {return this.neg_points.indexOf(d); })
-        .textStyle("red")
-        .textShadow("2px 2px 2px black")
-      .add(pv.Dot) // Add smaller point in the center
-        .data(() => this.neg_points)
-        .left(d => d.x)
-        .top(d => d.y)
-        .radius(2)  // Smaller radius for the center point
-        .shape("circle")
-        .fillStyle("red")  // Color for the center point
-        .lineWidth(1);  // Stroke thickness for the center point
-
-    if (this.points.length != 0) {
-      this.vis.render();
-    }
-
-    var svgElement = this.vis.canvas();
-    svgElement.style['zIndex'] = "2"
-    svgElement.style['position'] = "relative"
-    this.node.pointsEditor.element.appendChild(svgElement);
-
-    if (this.width > 256) {
-      this.node.setSize([this.width + 45, this.node.size[1]]);
-    }
-    this.node.setSize([this.node.size[0], this.height + 300]);
-    this.updateData();
-    this.refreshBackgroundImage();
-
-  }//end constructor
-
-  updateData = () => {
-    if (!this.points || this.points.length === 0) {
-      console.log("no points");
-      return;
-    }
-    const combinedPoints = {
-      positive: this.points,
-      negative: this.neg_points,
-    };
-    this.pointsStoreWidget.value = JSON.stringify(combinedPoints);
-    this.pos_coordWidget.value = JSON.stringify(this.points);
-    this.neg_coordWidget.value = JSON.stringify(this.neg_points);
-
-    if (this.bbox.length != 0) {    
-      let bboxString = JSON.stringify(this.bbox);
-      this.bboxStoreWidget.value = bboxString;
-      this.bboxWidget.value = bboxString;
-      }
-    
-    this.vis.render();
-    };
-
-  handleImageLoad = (img, file, base64String) => {
-    console.log(img.width, img.height); // Access width and height here
-    this.widthWidget.value = img.width;
-    this.heightWidget.value = img.height;
-
-    if (img.width != this.vis.width() || img.height != this.vis.height()) {
-      if (img.width > 256) {
-        this.node.setSize([img.width + 45, this.node.size[1]]);
-      }
-      this.node.setSize([this.node.size[0], img.height + 300]);
-      this.vis.width(img.width);
-      this.vis.height(img.height);
-      this.height = img.height;
-      this.width = img.width;
-      this.updateData();
-    }
-    this.backgroundImage.url(file ? URL.createObjectURL(file) : `data:${this.node.properties.imgData.type};base64,${base64String}`).visible(true).root.render();
-    };
-
-  processImage = (img, file) => {
-    const canvas = document.createElement('canvas');
-    const ctx = canvas.getContext('2d');
-
-    const maxWidth = 800; // maximum width
-    const maxHeight = 600; // maximum height
-    let width = img.width;
-    let height = img.height;
-
-    // Calculate the new dimensions while preserving the aspect ratio
-    if (width > height) {
-      if (width > maxWidth) {
-        height *= maxWidth / width;
-        width = maxWidth;
-      }
-    } else {
-      if (height > maxHeight) {
-        width *= maxHeight / height;
-        height = maxHeight;
-      }
-    }
-
-    canvas.width = width;
-    canvas.height = height;
-    ctx.drawImage(img, 0, 0, width, height);
-
-    // Get the compressed image data as a Base64 string
-    const base64String = canvas.toDataURL('image/jpeg', 0.5).replace('data:', '').replace(/^.+,/, ''); // 0.5 is the quality from 0 to 1
-
-    this.node.properties.imgData = {
-      name: file.name,
-      lastModified: file.lastModified,
-      size: file.size,
-      type: file.type,
-      base64: base64String
-    };
-    handleImageLoad(img, file, base64String);
-};
-
-  handleImageFile = (file) => {
-    const reader = new FileReader();
-    reader.onloadend = () => {
-      const img = new Image();
-      img.src = reader.result;
-      img.onload = () => processImage(img, file);
-    };
-    reader.readAsDataURL(file);
-
-    const imageUrl = URL.createObjectURL(file);
-    const img = new Image();
-    img.src = imageUrl;
-    img.onload = () => this.handleImageLoad(img, file, null);
-  };
-
-  refreshBackgroundImage = () => {
-    if (this.node.properties.imgData && this.node.properties.imgData.base64) {
-      const base64String = this.node.properties.imgData.base64;
-      const imageUrl = `data:${this.node.properties.imgData.type};base64,${base64String}`;
-      const img = new Image();
-      img.src = imageUrl;
-      img.onload = () => this.handleImageLoad(img, null, base64String);
-    }
-  };
-
-  createContextMenu = () => {
-    self = this;
-    document.addEventListener('contextmenu', function (e) {
-      e.preventDefault();
-    });
-
-    document.addEventListener('click', function (e) {
-      if (!self.node.contextMenu.contains(e.target)) {
-        self.node.contextMenu.style.display = 'none';
-      }
-    });
-
-    this.node.menuItems.forEach((menuItem, index) => {
-      self = this;
-      menuItem.addEventListener('click', function (e) {
-        e.preventDefault();
-        switch (index) {
-          case 0:
-            // Create file input element
-            const fileInput = document.createElement('input');
-            fileInput.type = 'file';
-            fileInput.accept = 'image/*'; // Accept only image files
-
-            // Listen for file selection
-            fileInput.addEventListener('change', function (event) {
-              const file = event.target.files[0]; // Get the selected file
-
-              if (file) {
-                const imageUrl = URL.createObjectURL(file);
-                let img = new Image();
-                img.src = imageUrl;
-                img.onload = () => self.handleImageLoad(img, file, null);
-              }
-            });
-
-            fileInput.click();
-            
-            self.node.contextMenu.style.display = 'none';
-            break;
-          case 1:
-            self.backgroundImage.visible(false).root.render();
-            self.node.properties.imgData = null;
-            self.node.contextMenu.style.display = 'none';
-            break;
-        }
-      });
-    });
-  }//end createContextMenu
-}//end class
-
-
-//from melmass
-export function hideWidgetForGood(node, widget, suffix = '') {
-  widget.origType = widget.type
-  widget.origComputeSize = widget.computeSize
-  widget.origSerializeValue = widget.serializeValue
-  widget.computeSize = () => [0, -4] // -4 is due to the gap litegraph adds between widgets automatically
-  widget.type = "converted-widget" + suffix
-  // widget.serializeValue = () => {
-  //     // Prevent serializing the widget if we have no input linked
-  //     const w = node.inputs?.find((i) => i.widget?.name === widget.name);
-  //     if (w?.link == null) {
-  //         return undefined;
-  //     }
-  //     return widget.origSerializeValue ? widget.origSerializeValue() : widget.value;
-  // };
-
-  // Hide any linked widgets, e.g. seed+seedControl
-  if (widget.linkedWidgets) {
-    for (const w of widget.linkedWidgets) {
-      hideWidgetForGood(node, w, ':' + widget.name)
-    }
-  }
-}

custom_nodes/ComfyUI-KJNodes-main/web/js/setgetnodes.js

@@ -1,564 +0,0 @@
-import { app } from "../../../scripts/app.js";
-
-//based on diffus3's SetGet: https://github.com/diffus3/ComfyUI-extensions
-
-// Nodes that allow you to tunnel connections for cleaner graphs
-function setColorAndBgColor(type) {
-    const colorMap = {
-        "MODEL": LGraphCanvas.node_colors.blue,
-        "LATENT": LGraphCanvas.node_colors.purple,
-        "VAE": LGraphCanvas.node_colors.red,
-        "CONDITIONING": LGraphCanvas.node_colors.brown,
-        "IMAGE": LGraphCanvas.node_colors.pale_blue,
-        "CLIP": LGraphCanvas.node_colors.yellow,
-        "FLOAT": LGraphCanvas.node_colors.green,
-		"MASK": { color: "#1c5715", bgcolor: "#1f401b"},
-		"INT": { color: "#1b4669", bgcolor: "#29699c"},
-		"CONTROL_NET": { color: "#156653", bgcolor: "#1c453b"},
-		"NOISE": { color: "#2e2e2e", bgcolor: "#242121"},
-		"GUIDER": { color: "#3c7878", bgcolor: "#1c453b"},
-		"SAMPLER": { color: "#614a4a", bgcolor: "#3b2c2c"},
-		"SIGMAS": { color: "#485248", bgcolor: "#272e27"},
-
-    };
-
-    const colors = colorMap[type];
-    if (colors) {
-        this.color = colors.color;
-        this.bgcolor = colors.bgcolor;
-    }
-}
-let disablePrefix = app.ui.settings.getSettingValue("KJNodes.disablePrefix")
-const LGraphNode = LiteGraph.LGraphNode
-
-function showAlert(message) {
-  app.extensionManager.toast.add({
-    severity: 'warn',
-    summary: "KJ Get/Set",
-    detail: `${message}. Most likely you're missing custom nodes`,
-    life: 5000,
-  })
-}
-app.registerExtension({
-	name: "SetNode",
-	registerCustomNodes() {
-		class SetNode extends LGraphNode {
-			defaultVisibility = true;
-			serialize_widgets = true;
-			drawConnection = false;
-			currentGetters = null;
-			slotColor = "#FFF";
-			canvas = app.canvas;
-			menuEntry = "Show connections";
-
-			constructor(title) {
-				super(title)
-				if (!this.properties) {
-					this.properties = {
-						"previousName": ""
-					};
-				}
-				this.properties.showOutputText = SetNode.defaultVisibility;
-
-				const node = this;
-
-				this.addWidget(
-					"text", 
-					"Constant", 
-					'', 
-					(s, t, u, v, x) => {
-						node.validateName(node.graph);
-						if(this.widgets[0].value !== ''){
-							this.title = (!disablePrefix ? "Set_" : "") + this.widgets[0].value;
-						}
-						this.update();
-						this.properties.previousName = this.widgets[0].value;
-					}, 
-					{}
-				)
-				
-				this.addInput("*", "*");
-				this.addOutput("*", '*');
-
-				this.onConnectionsChange = function(
-					slotType,	//1 = input, 2 = output
-					slot,
-					isChangeConnect,
-                    link_info,
-                    output
-				) {
-					//On Disconnect
-					if (slotType == 1 && !isChangeConnect) {
-						if(this.inputs[slot].name === ''){
-							this.inputs[slot].type = '*';
-							this.inputs[slot].name = '*';
-							this.title = "Set"
-						}
-					}
-					if (slotType == 2 && !isChangeConnect) {
-						this.outputs[slot].type = '*';
-						this.outputs[slot].name = '*';
-						
-					}	
-					//On Connect
-					if (link_info && node.graph && slotType == 1 && isChangeConnect) {
-						const fromNode = node.graph._nodes.find((otherNode) => otherNode.id == link_info.origin_id);
-						
-						if (fromNode && fromNode.outputs && fromNode.outputs[link_info.origin_slot]) {
-							const type = fromNode.outputs[link_info.origin_slot].type;
-						
-							if (this.title === "Set"){
-								this.title = (!disablePrefix ? "Set_" : "") + type;
-							}
-							if (this.widgets[0].value === '*'){
-								this.widgets[0].value = type	
-							}
-							
-							this.validateName(node.graph);
-							this.inputs[0].type = type;
-							this.inputs[0].name = type;
-							
-							if (app.ui.settings.getSettingValue("KJNodes.nodeAutoColor")){
-								setColorAndBgColor.call(this, type);	
-							}
-						} else {
-                showAlert("node input undefined.")
-						}
-					}
-					if (link_info && node.graph && slotType == 2 && isChangeConnect) {
-						const fromNode = node.graph._nodes.find((otherNode) => otherNode.id == link_info.origin_id);
-						
-						if (fromNode && fromNode.inputs && fromNode.inputs[link_info.origin_slot]) {
-							const type = fromNode.inputs[link_info.origin_slot].type;
-							
-							this.outputs[0].type = type;
-							this.outputs[0].name = type;
-						} else {
-							showAlert('node output undefined');
-						}
-					}
-					
-
-					//Update either way
-					this.update();
-				}
-
-				this.validateName = function(graph) {
-					let widgetValue = node.widgets[0].value;
-				
-					if (widgetValue !== '') {
-						let tries = 0;
-						const existingValues = new Set();
-				
-						graph._nodes.forEach(otherNode => {
-							if (otherNode !== this && otherNode.type === 'SetNode') {
-								existingValues.add(otherNode.widgets[0].value);
-							}
-						});
-				
-						while (existingValues.has(widgetValue)) {
-							widgetValue = node.widgets[0].value + "_" + tries;
-							tries++;
-						}
-				
-						node.widgets[0].value = widgetValue;
-						this.update();
-					}
-				}
-
-				this.clone = function () {
-					const cloned = SetNode.prototype.clone.apply(this);
-					cloned.inputs[0].name = '*';
-					cloned.inputs[0].type = '*';
-					cloned.value = '';
-					cloned.properties.previousName = '';
-					cloned.size = cloned.computeSize();
-					return cloned;
-				};
-
-				this.onAdded = function(graph) {
-					this.validateName(graph);
-				}
-
-
-				this.update = function() {
-					if (!node.graph) {
-						return;
-					}
-				
-					const getters = this.findGetters(node.graph);
-					getters.forEach(getter => {
-						getter.setType(this.inputs[0].type);
-					});
-				
-					if (this.widgets[0].value) {
-						const gettersWithPreviousName = this.findGetters(node.graph, true);
-						gettersWithPreviousName.forEach(getter => {
-							getter.setName(this.widgets[0].value);
-						});
-					}
-				
-					const allGetters = node.graph._nodes.filter(otherNode => otherNode.type === "GetNode");
-					allGetters.forEach(otherNode => {
-						if (otherNode.setComboValues) {
-							otherNode.setComboValues();
-						}
-					});
-				}
-
-
-				this.findGetters = function(graph, checkForPreviousName) {
-					const name = checkForPreviousName ? this.properties.previousName : this.widgets[0].value;
-					return graph._nodes.filter(otherNode => otherNode.type === 'GetNode' && otherNode.widgets[0].value === name && name !== '');
-				}
-
-				
-				// This node is purely frontend and does not impact the resulting prompt so should not be serialized
-				this.isVirtualNode = true;
-			}
-				
-
-			onRemoved() {
-				const allGetters = this.graph._nodes.filter((otherNode) => otherNode.type == "GetNode");
-				allGetters.forEach((otherNode) => {
-					if (otherNode.setComboValues) {
-						otherNode.setComboValues([this]);
-					}
-				})
-			}
-			getExtraMenuOptions(_, options) {
-				this.menuEntry = this.drawConnection ? "Hide connections" : "Show connections";
-				options.unshift(
-					{
-						content: this.menuEntry,
-						callback: () => {
-							this.currentGetters = this.findGetters(this.graph);								
-							if (this.currentGetters.length == 0) return;
-							let linkType = (this.currentGetters[0].outputs[0].type);	
-							this.slotColor = this.canvas.default_connection_color_byType[linkType]
-							this.menuEntry = this.drawConnection ? "Hide connections" : "Show connections";
-							this.drawConnection = !this.drawConnection;
-							this.canvas.setDirty(true, true);
-							
-						},
-						has_submenu: true,
-						submenu: {
-							title: "Color",
-                            options: [ 
-								{
-								content: "Highlight",
-								callback: () => {
-									this.slotColor = "orange"
-									this.canvas.setDirty(true, true);
-									}
-								}
-							],
-						},
-					},
-					{
-						content: "Hide all connections",
-						callback: () => {
-							const allGetters = this.graph._nodes.filter(otherNode => otherNode.type === "GetNode" || otherNode.type === "SetNode");
-							allGetters.forEach(otherNode => {
-								otherNode.drawConnection = false;
-								console.log(otherNode);
-							});
-							
-							this.menuEntry = "Show connections";
-							this.drawConnection = false
-							this.canvas.setDirty(true, true);
-							
-						},
-					
-					},
-				);
-				// Dynamically add a submenu for all getters
-				this.currentGetters = this.findGetters(this.graph);
-				if (this.currentGetters) {
-					
-					let gettersSubmenu = this.currentGetters.map(getter => ({
-						
-						content: `${getter.title} id: ${getter.id}`,
-						callback: () => {
-							this.canvas.centerOnNode(getter);
-							this.canvas.selectNode(getter, false);
-							this.canvas.setDirty(true, true);
-							
-						},
-					}));
-			
-					options.unshift({
-						content: "Getters",
-						has_submenu: true,
-						submenu: {
-							title: "GetNodes",
-                            options: gettersSubmenu,
-						}
-					});
-				}
-			}
-			
-			
-			onDrawForeground(ctx, lGraphCanvas) {
-				if (this.drawConnection) {
-					this._drawVirtualLinks(lGraphCanvas, ctx);
-				}
-			}
-			// onDrawCollapsed(ctx, lGraphCanvas) {
-			// 	if (this.drawConnection) {
-			// 		this._drawVirtualLinks(lGraphCanvas, ctx);
-			// 	}
-			// }
-			_drawVirtualLinks(lGraphCanvas, ctx) {
-				if (!this.currentGetters?.length) return;
-				var title = this.getTitle ? this.getTitle() : this.title;
-				var title_width = ctx.measureText(title).width;
-				if (!this.flags.collapsed) {
-					var start_node_slotpos = [
-						this.size[0],
-						LiteGraph.NODE_TITLE_HEIGHT * 0.5,
-						];
-				}
-				else {
-					
-					var start_node_slotpos = [
-						title_width + 55,
-						-15,
-
-						];
-				}
-				// Provide a default link object with necessary properties, to avoid errors as link can't be null anymore
-				const defaultLink = { type: 'default', color: this.slotColor };
-
-				for (const getter of this.currentGetters) {
-					if (!this.flags.collapsed) {
-					var end_node_slotpos = this.getConnectionPos(false, 0);
-					end_node_slotpos = [
-						getter.pos[0] - end_node_slotpos[0] + this.size[0],
-						getter.pos[1] - end_node_slotpos[1]
-						];
-					}
-					else {
-						var end_node_slotpos = this.getConnectionPos(false, 0);
-						end_node_slotpos = [
-						getter.pos[0] - end_node_slotpos[0] + title_width + 50,
-						getter.pos[1] - end_node_slotpos[1] - 30
-						];
-					}
-					lGraphCanvas.renderLink(
-						ctx,
-						start_node_slotpos,
-						end_node_slotpos,
-						defaultLink,
-						false,
-						null,
-						this.slotColor,
-						LiteGraph.RIGHT,
-						LiteGraph.LEFT
-					);
-				}
-			}
-		}
-
-		LiteGraph.registerNodeType(
-			"SetNode",
-			Object.assign(SetNode, {
-				title: "Set",
-			})
-		);
-
-		SetNode.category = "KJNodes";
-	},
-});
-
-app.registerExtension({
-	name: "GetNode",
-	registerCustomNodes() {
-		class GetNode extends LGraphNode {
-
-			defaultVisibility = true;
-			serialize_widgets = true;
-			drawConnection = false;
-			slotColor = "#FFF";
-			currentSetter = null;
-			canvas = app.canvas;
-
-			constructor(title) {
-				super(title)
-				if (!this.properties) {
-					this.properties = {};
-				}
-				this.properties.showOutputText = GetNode.defaultVisibility;
-				const node = this;
-				this.addWidget(
-					"combo",
-					"Constant",
-					"",
-					(e) => {
-						this.onRename();
-					},
-					{
-						values: () => {
-                            const setterNodes = node.graph._nodes.filter((otherNode) => otherNode.type == 'SetNode');
-                            return setterNodes.map((otherNode) => otherNode.widgets[0].value).sort();
-                        }
-					}
-				)
-
-				this.addOutput("*", '*');			
-				this.onConnectionsChange = function(
-					slotType,	//0 = output, 1 = input
-					slot,	//self-explanatory
-					isChangeConnect,
-                    link_info,
-                    output
-				) {
-					this.validateLinks();	
-				}
-
-				this.setName = function(name) {
-					node.widgets[0].value = name;
-					node.onRename();
-					node.serialize();
-				}
-				
-				this.onRename = function() {
-					const setter = this.findSetter(node.graph);
-					if (setter) {
-						let linkType = (setter.inputs[0].type);
-						
-						this.setType(linkType);
-						this.title = (!disablePrefix ? "Get_" : "") + setter.widgets[0].value;
-						
-						if (app.ui.settings.getSettingValue("KJNodes.nodeAutoColor")){
-							setColorAndBgColor.call(this, linkType);	
-						}
-
-					} else {
-						this.setType('*');
-					}
-				}
-
-				this.clone = function () {
-					const cloned = GetNode.prototype.clone.apply(this);
-					cloned.size = cloned.computeSize();
-					return cloned;
-				};
-
-				this.validateLinks = function() {
-					if (this.outputs[0].type !== '*' && this.outputs[0].links) {
-						this.outputs[0].links.filter(linkId => {
-							const link = node.graph.links[linkId];
-							return link && (!link.type.split(",").includes(this.outputs[0].type) && link.type !== '*');
-						}).forEach(linkId => {
-							node.graph.removeLink(linkId);
-						});
-					}
-				};
-
-				this.setType = function(type) {
-					this.outputs[0].name = type;
-					this.outputs[0].type = type;
-					this.validateLinks();
-				}
-
-				this.findSetter = function(graph) {
-					const name = this.widgets[0].value;
-					const foundNode = graph._nodes.find(otherNode => otherNode.type === 'SetNode' && otherNode.widgets[0].value === name && name !== '');
-					return foundNode;
-				};
-
-				this.goToSetter = function() {
-					const setter = this.findSetter(this.graph);	
-					this.canvas.centerOnNode(setter);
-					this.canvas.selectNode(setter, false);
-				};
-				
-				// This node is purely frontend and does not impact the resulting prompt so should not be serialized
-				this.isVirtualNode = true;
-			}
-			
-			getInputLink(slot) {
-				const setter = this.findSetter(this.graph);
-			
-				if (setter) {
-					const slotInfo = setter.inputs[slot];
-					const link = this.graph.links[slotInfo.link];
-					return link;
-				} else {
-					const errorMessage = "No SetNode found for " + this.widgets[0].value + "(" + this.type + ")";
-					showAlert(errorMessage);
-					//throw new Error(errorMessage);
-				}
-			}
-			onAdded(graph) {
-			}
-			getExtraMenuOptions(_, options) {
-				let menuEntry = this.drawConnection ? "Hide connections" : "Show connections";
-				
-				options.unshift(
-					{
-						content: "Go to setter",
-						callback: () => {
-							this.goToSetter();
-						},
-					},
-					{
-						content: menuEntry,
-						callback: () => {
-							this.currentSetter = this.findSetter(this.graph);
-							if (this.currentSetter.length == 0) return;
-							let linkType = (this.currentSetter.inputs[0].type);	
-							this.drawConnection = !this.drawConnection;
-							this.slotColor = this.canvas.default_connection_color_byType[linkType]
-							menuEntry = this.drawConnection ? "Hide connections" : "Show connections";
-							this.canvas.setDirty(true, true);
-						},
-					},
-				);
-			}
-
-			onDrawForeground(ctx, lGraphCanvas) {
-				if (this.drawConnection) {
-					this._drawVirtualLink(lGraphCanvas, ctx);
-				}
-			}
-			// onDrawCollapsed(ctx, lGraphCanvas) {
-			// 	if (this.drawConnection) {
-			// 		this._drawVirtualLink(lGraphCanvas, ctx);
-			// 	}
-			// }
-			_drawVirtualLink(lGraphCanvas, ctx) {
-				if (!this.currentSetter) return;
-
-				// Provide a default link object with necessary properties, to avoid errors as link can't be null anymore
-				const defaultLink = { type: 'default', color: this.slotColor };
-				
-				let start_node_slotpos = this.currentSetter.getConnectionPos(false, 0);
-				start_node_slotpos = [
-					start_node_slotpos[0] - this.pos[0],
-					start_node_slotpos[1] - this.pos[1],
-				];
-				let end_node_slotpos = [0, -LiteGraph.NODE_TITLE_HEIGHT * 0.5];
-				lGraphCanvas.renderLink(
-					ctx,
-					start_node_slotpos,
-					end_node_slotpos,
-					defaultLink,
-					false,
-					null,
-					this.slotColor
-				);
-			}
-		}
-
-		LiteGraph.registerNodeType(
-			"GetNode",
-			Object.assign(GetNode, {
-				title: "Get",
-			})
-		);
-
-		GetNode.category = "KJNodes";
-	},
-});

custom_nodes/ComfyUI-KJNodes-main/web/js/spline_editor.js

@@ -1,866 +0,0 @@
-import { app } from '../../../scripts/app.js'
-
-//from melmass
-export function makeUUID() {
-  let dt = new Date().getTime()
-  const uuid = 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, (c) => {
-    const r = ((dt + Math.random() * 16) % 16) | 0
-    dt = Math.floor(dt / 16)
-    return (c === 'x' ? r : (r & 0x3) | 0x8).toString(16)
-  })
-  return uuid
-}
-
-export const loadScript = (
-    FILE_URL,
-    async = true,
-    type = 'text/javascript',
-  ) => {
-    return new Promise((resolve, reject) => {
-      try {
-        // Check if the script already exists
-        const existingScript = document.querySelector(`script[src="${FILE_URL}"]`)
-        if (existingScript) {
-          resolve({ status: true, message: 'Script already loaded' })
-          return
-        }
-  
-        const scriptEle = document.createElement('script')
-        scriptEle.type = type
-        scriptEle.async = async
-        scriptEle.src = FILE_URL
-  
-        scriptEle.addEventListener('load', (ev) => {
-          resolve({ status: true })
-        })
-  
-        scriptEle.addEventListener('error', (ev) => {
-          reject({
-            status: false,
-            message: `Failed to load the script ${FILE_URL}`,
-          })
-        })
-  
-        document.body.appendChild(scriptEle)
-      } catch (error) {
-        reject(error)
-      }
-    })
-  }
-  const create_documentation_stylesheet = () => {
-    const tag = 'kj-splineditor-stylesheet'
-
-    let styleTag = document.head.querySelector(tag)
-
-    if (!styleTag) {
-      styleTag = document.createElement('style')
-      styleTag.type = 'text/css'
-      styleTag.id = tag
-      styleTag.innerHTML = `
-       .spline-editor {
-
-        position: absolute;
-
-        font: 12px monospace;
-        line-height: 1.5em;
-        padding: 10px;
-        z-index: 0;
-        overflow: hidden;
-       }
-        `
-      document.head.appendChild(styleTag)
-    }
-  }
-
-loadScript('/kjweb_async/svg-path-properties.min.js').catch((e) => {
-    console.log(e)
-})
-loadScript('/kjweb_async/protovis.min.js').catch((e) => {
-  console.log(e)
-})
-create_documentation_stylesheet()
-
-function chainCallback(object, property, callback) {
-  if (object == undefined) {
-      //This should not happen.
-      console.error("Tried to add callback to non-existant object")
-      return;
-  }
-  if (property in object) {
-      const callback_orig = object[property]
-      object[property] = function () {
-          const r = callback_orig.apply(this, arguments);
-          callback.apply(this, arguments);
-          return r
-      };
-  } else {
-      object[property] = callback;
-  }
-}
-app.registerExtension({
-    name: 'KJNodes.SplineEditor', 
-    
-    async beforeRegisterNodeDef(nodeType, nodeData) {
-        if (nodeData?.name === 'SplineEditor') {
-          chainCallback(nodeType.prototype, "onNodeCreated", function () {
-            
-            hideWidgetForGood(this, this.widgets.find(w => w.name === "coordinates"))
-
-            var element = document.createElement("div");
-            this.uuid = makeUUID()
-            element.id = `spline-editor-${this.uuid}`
-
-            // fake image widget to allow copy/paste
-            const fakeimagewidget = this.addWidget("COMBO", "image", null, () => { }, {});
-            hideWidgetForGood(this, fakeimagewidget)
-
-            this.splineEditor = this.addDOMWidget(nodeData.name, "SplineEditorWidget", element, {
-            serialize: false,
-            hideOnZoom: false,
-            });
-
-            // context menu
-            this.contextMenu = document.createElement("div");
-            this.contextMenu.className = 'spline-editor-context-menu';
-            this.contextMenu.id = "context-menu";
-            this.contextMenu.style.display = "none";
-            this.contextMenu.style.position = "absolute";
-            this.contextMenu.style.backgroundColor = "#202020";
-            this.contextMenu.style.minWidth = "100px";
-            this.contextMenu.style.boxShadow = "0px 8px 16px 0px rgba(0,0,0,0.2)";
-            this.contextMenu.style.zIndex = "100";
-            this.contextMenu.style.padding = "5px";
-
-            function styleMenuItem(menuItem) {
-              menuItem.style.display = "block";
-              menuItem.style.padding = "5px";
-              menuItem.style.color = "#FFF";
-              menuItem.style.fontFamily = "Arial, sans-serif";
-              menuItem.style.fontSize = "16px";
-              menuItem.style.textDecoration = "none";
-              menuItem.style.marginBottom = "5px";
-            }
-            function createMenuItem(id, textContent) {
-              let menuItem = document.createElement("a");
-              menuItem.href = "#";
-              menuItem.id = `menu-item-${id}`;
-              menuItem.textContent = textContent;
-              styleMenuItem(menuItem);
-              return menuItem;
-            }
-            
-            // Create an array of menu items using the createMenuItem function
-            this.menuItems = [
-              createMenuItem(0, "Toggle handles"),
-              createMenuItem(1, "Display sample points"),
-              createMenuItem(2, "Switch point shape"),
-              createMenuItem(3, "Background image"),
-              createMenuItem(4, "Invert point order"),
-              createMenuItem(5, "Clear Image"),
-            ];
-            
-            // Add mouseover and mouseout event listeners to each menu item for styling
-            this.menuItems.forEach(menuItem => {
-              menuItem.addEventListener('mouseover', function() {
-                this.style.backgroundColor = "gray";
-              });
-            
-              menuItem.addEventListener('mouseout', function() {
-                this.style.backgroundColor = "#202020";
-              });
-            });
-            
-            // Append each menu item to the context menu
-            this.menuItems.forEach(menuItem => {
-              this.contextMenu.appendChild(menuItem);
-            });
-
-            document.body.appendChild(this.contextMenu);
-
-            this.addWidget("button", "New spline", null, () => {
-              if (!this.properties || !("points" in this.properties)) {
-              this.editor = new SplineEditor(this);
-                this.addProperty("points", this.constructor.type, "string");
-              }
-              else {
-              this.editor = new SplineEditor(this, true);
-              }
-            });
-            
-            this.setSize([550, 950]);
-            this.resizable = false;
-            this.splineEditor.parentEl = document.createElement("div");
-            this.splineEditor.parentEl.className = "spline-editor";
-            this.splineEditor.parentEl.id = `spline-editor-${this.uuid}`
-            element.appendChild(this.splineEditor.parentEl);
-            
-          chainCallback(this, "onConfigure", function () {
-            try {
-              this.editor = new SplineEditor(this);
-            } catch (error) {
-              console.error("An error occurred while configuring the editor:", error);
-            }
-          });
-          chainCallback(this, "onExecuted", function (message) {
-            let bg_image = message["bg_image"];
-            this.properties.imgData = {
-              name: "bg_image",
-              base64: bg_image
-            };
-            this.editor.refreshBackgroundImage(this);
-                });
-              
-          }); // onAfterGraphConfigured
-        }//node created
-      } //before register
-})//register
-
-
-class SplineEditor{
-  constructor(context, reset = false) {
-  this.node = context;
-  this.reset=reset;
-  const self = this;
-  console.log("creatingSplineEditor")
-
-  this.node.pasteFile = (file) => {
-    if (file.type.startsWith("image/")) {
-      this.handleImageFile(file);
-      return true;
-    }
-    return false;
-  };
-
-  this.node.onDragOver = function (e) {
-    if (e.dataTransfer && e.dataTransfer.items) {
-      return [...e.dataTransfer.items].some(f => f.kind === "file" && f.type.startsWith("image/"));
-    }
-    return false;
-  };
-
-  // On drop upload files
-  this.node.onDragDrop = (e) => {
-    console.log("onDragDrop called");
-    let handled = false;
-    for (const file of e.dataTransfer.files) {
-      if (file.type.startsWith("image/")) {
-        this.handleImageFile(file);
-        handled = true;
-      }
-    }
-    return handled;
-  };
-
-  // context menu
-  this.createContextMenu();
-  
-
-  this.dotShape = "circle";
-  this.drawSamplePoints = false;
-  
-  if (reset && context.splineEditor.element) {
-    context.splineEditor.element.innerHTML = ''; // Clear the container
-  }
-  this.coordWidget = context.widgets.find(w => w.name === "coordinates");
-  this.interpolationWidget = context.widgets.find(w => w.name === "interpolation");
-  this.pointsWidget = context.widgets.find(w => w.name === "points_to_sample");
-  this.pointsStoreWidget = context.widgets.find(w => w.name === "points_store");
-  this.tensionWidget = context.widgets.find(w => w.name === "tension");
-  this.minValueWidget = context.widgets.find(w => w.name === "min_value");
-  this.maxValueWidget = context.widgets.find(w => w.name === "max_value");
-  this.samplingMethodWidget = context.widgets.find(w => w.name === "sampling_method");
-  this.widthWidget = context.widgets.find(w => w.name === "mask_width");
-  this.heightWidget = context.widgets.find(w => w.name === "mask_height");
-
-  this.interpolation = this.interpolationWidget.value
-  this.tension = this.tensionWidget.value
-  this.points_to_sample = this.pointsWidget.value
-  this.rangeMin = this.minValueWidget.value
-  this.rangeMax = this.maxValueWidget.value
-  this.pointsLayer = null;
-  this.samplingMethod = this.samplingMethodWidget.value
-  
-  if (this.samplingMethod == "path") {
-    this.dotShape = "triangle"
-  }
-  
-  
-  this.interpolationWidget.callback = () => {
-    this.interpolation = this.interpolationWidget.value
-    this.updatePath();
-  }
-  this.samplingMethodWidget.callback = () => {
-    this.samplingMethod = this.samplingMethodWidget.value
-    if (this.samplingMethod == "path") {
-      this.dotShape = "triangle"
-    }
-    else if (this.samplingMethod == "controlpoints") {
-      this.dotShape = "circle"
-      this.drawSamplePoints = true;
-    }
-    this.updatePath();
-  }
-  this.tensionWidget.callback = () => {
-    this.tension = this.tensionWidget.value
-    this.updatePath();
-  }
-  this.pointsWidget.callback = () => {
-    this.points_to_sample = this.pointsWidget.value
-    this.updatePath();
-  }
-  this.minValueWidget.callback = () => {
-    this.rangeMin = this.minValueWidget.value
-    this.updatePath();
-  }
-  this.maxValueWidget.callback = () => {
-    this.rangeMax = this.maxValueWidget.value
-    this.updatePath();
-  }
-  this.widthWidget.callback = () => {
-    this.width = this.widthWidget.value;
-    if (this.width > 256) {
-        context.setSize([this.width + 45, context.size[1]]);
-    }
-    this.vis.width(this.width);
-    this.updatePath();
-}
-this.heightWidget.callback = () => {
-    this.height = this.heightWidget.value
-    this.vis.height(this.height)
-    context.setSize([context.size[0], this.height + 430]);
-    this.updatePath();
-  }
-  this.pointsStoreWidget.callback = () => {
-    points = JSON.parse(this.pointsStoreWidget.value);
-    this.updatePath();
-  }
-  
- // Initialize or reset points array
- this.drawHandles = false;
- this.drawRuler = true;
- var hoverIndex = -1;
- var isDragging = false;
- this.width = this.widthWidget.value;
- this.height = this.heightWidget.value;
- var i = 3;
- this.points = [];
-
- if (!reset && this.pointsStoreWidget.value != "") {
-  this.points = JSON.parse(this.pointsStoreWidget.value);
- } else {
-  this.points = pv.range(1, 4).map((i, index) => {
-    if (index === 0) {
-      // First point at the bottom-left corner
-      return { x: 0, y: this.height };
-    } else if (index === 2) {
-      // Last point at the top-right corner
-      return { x: this.width, y: 0 };
-    } else {
-      // Other points remain as they were
-      return {
-        x: i * this.width / 5,
-        y: 50 + Math.random() * (this.height - 100)
-      };
-    }
-  });
-    this.pointsStoreWidget.value = JSON.stringify(this.points);
- }
-  
-  this.vis = new pv.Panel()
-  .width(this.width)
-  .height(this.height)
-  .fillStyle("#222")
-  .strokeStyle("gray")
-  .lineWidth(2)
-  .antialias(false)
-  .margin(10)
-  .event("mousedown", function () {
-    if (pv.event.shiftKey) { // Use pv.event to access the event object
-        let scaledMouse = {
-        x: this.mouse().x / app.canvas.ds.scale,
-        y: this.mouse().y / app.canvas.ds.scale
-        };
-        i = self.points.push(scaledMouse) - 1;
-        self.updatePath();
-        return this;
-    }
-    else if (pv.event.ctrlKey) {
-       // Capture the clicked location
-       let clickedPoint = {
-        x: this.mouse().x / app.canvas.ds.scale,
-        y: this.mouse().y / app.canvas.ds.scale
-        };
-
-        // Find the two closest points to the clicked location
-        let { point1Index, point2Index } = self.findClosestPoints(self.points, clickedPoint);
-
-        // Calculate the midpoint between the two closest points
-        let midpoint = {
-            x: (self.points[point1Index].x + self.points[point2Index].x) / 2,
-            y: (self.points[point1Index].y + self.points[point2Index].y) / 2
-        };
-
-        // Insert the midpoint into the array
-        self.points.splice(point2Index, 0, midpoint);
-        i = point2Index;
-        self.updatePath();
-    }
-    else if (pv.event.button === 2) {
-      self.node.contextMenu.style.display = 'block';
-      self.node.contextMenu.style.left = `${pv.event.clientX}px`;
-      self.node.contextMenu.style.top = `${pv.event.clientY}px`;
-      }
-    })
-    this.backgroundImage = this.vis.add(pv.Image).visible(false)
-
-  this.vis.add(pv.Rule)
-    .data(pv.range(0, this.height, 64))
-    .bottom(d =>  d)
-    .strokeStyle("gray")
-    .lineWidth(3)
-    .visible(() => self.drawRuler)
-
-  // vis.add(pv.Rule)
-  //   .data(pv.range(0, points_to_sample, 1))
-  //   .left(d =>  d * 512 / (points_to_sample - 1))
-  //   .strokeStyle("gray")
-  //   .lineWidth(2)
-
-  this.vis.add(pv.Line)
-    .data(() => this.points)
-    .left(d => d.x)
-    .top(d => d.y)
-    .interpolate(() => this.interpolation)
-    .tension(() => this.tension)
-    .segmented(() => false)
-    .strokeStyle(pv.Colors.category10().by(pv.index))
-    .lineWidth(3)
-    
-    this.vis.add(pv.Dot)
-    .data(() => this.points)
-    .left(d => d.x)
-    .top(d => d.y)
-    .radius(10)
-    .shape(function() {
-      return self.dotShape;
-   })
-   .angle(function() {
-    const index = this.index;
-    let angle = 0;
-
-    if (self.dotShape === "triangle") {
-      let dxNext = 0, dyNext = 0;
-      if (index < self.points.length - 1) {
-        dxNext = self.points[index + 1].x - self.points[index].x;
-        dyNext = self.points[index + 1].y - self.points[index].y;
-      }
-
-      let dxPrev = 0, dyPrev = 0;
-      if (index > 0) {
-        dxPrev = self.points[index].x - self.points[index - 1].x;
-        dyPrev = self.points[index].y - self.points[index - 1].y;
-      }
-
-      const dx = (dxNext + dxPrev) / 2;
-      const dy = (dyNext + dyPrev) / 2;
-
-      angle = Math.atan2(dy, dx);
-      angle -= Math.PI / 2;
-      angle = (angle + 2 * Math.PI) % (2 * Math.PI);
-    }
-
-    return angle;
- })
-    .cursor("move")
-    .strokeStyle(function () { return i == this.index ? "#ff7f0e" : "#1f77b4"; })
-    .fillStyle(function () { return "rgba(100, 100, 100, 0.3)"; })
-    .event("mousedown", pv.Behavior.drag())
-    .event("dragstart", function () {
-        i = this.index;
-        hoverIndex = this.index;
-        isDragging = true;
-        if (pv.event.button === 2 && i !== 0 && i !== self.points.length - 1) {
-          self.points.splice(i--, 1);
-          self.vis.render();
-        }
-        return this;
-    })
-    .event("dragend", function() {
-      if (this.pathElements !== null) {
-        self.updatePath();
-      }
-        isDragging = false;
-    })
-    .event("drag", function () {
-      let adjustedX = this.mouse().x / app.canvas.ds.scale; // Adjust the new X position by the inverse of the scale factor
-      let adjustedY = this.mouse().y / app.canvas.ds.scale; // Adjust the new Y position by the inverse of the scale factor
-       // Determine the bounds of the vis.Panel
-      const panelWidth = self.vis.width();
-      const panelHeight = self.vis.height();
-
-      // Adjust the new position if it would place the dot outside the bounds of the vis.Panel
-      adjustedX = Math.max(0, Math.min(panelWidth, adjustedX));
-      adjustedY = Math.max(0, Math.min(panelHeight, adjustedY));
-      self.points[this.index] = { x: adjustedX, y: adjustedY }; // Update the point's position
-      self.vis.render(); // Re-render the visualization to reflect the new position
-   })
-    .event("mouseover", function() {
-      hoverIndex = this.index; // Set the hover index to the index of the hovered dot
-      self.vis.render(); // Re-render the visualization
-    })
-    .event("mouseout", function() {
-      !isDragging && (hoverIndex = -1); // Reset the hover index when the mouse leaves the dot
-      self.vis.render(); // Re-render the visualization
-    })
-    .anchor("center")
-    .add(pv.Label)
-    .visible(function() {
-      return hoverIndex === this.index; // Only show the label for the hovered dot
-    })
-    .left(d => d.x < this.width / 2 ? d.x + 80 : d.x - 70) // Shift label to right if on left half, otherwise shift to left
-    .top(d => d.y < this.height / 2 ? d.y + 20 : d.y - 20)  // Shift label down if on top half, otherwise shift up
-    .font(12 + "px sans-serif")
-    .text(d => {
-      if (this.samplingMethod == "path") {
-        return `X: ${Math.round(d.x)}, Y: ${Math.round(d.y)}`;
-      } else {
-          let frame = Math.round((d.x / self.width) * self.points_to_sample);
-          let normalizedY = (1.0 - (d.y / self.height) - 0.0) * (self.rangeMax - self.rangeMin) + self.rangeMin;
-          let normalizedX = (d.x / self.width);
-          return `F: ${frame}, X: ${normalizedX.toFixed(2)}, Y: ${normalizedY.toFixed(2)}`;
-      }
-      })
-    .textStyle("orange")
- 
-    if (this.points.length != 0) {
-      this.vis.render();
-    }
-    var svgElement = this.vis.canvas();
-    svgElement.style['zIndex'] = "2"
-    svgElement.style['position'] = "relative"
-    this.node.splineEditor.element.appendChild(svgElement);
-    this.pathElements = svgElement.getElementsByTagName('path'); // Get all path elements
-
-    if (this.width > 256) {
-      this.node.setSize([this.width + 45, this.node.size[1]]);
-    }
-    this.node.setSize([this.node.size[0], this.height + 430]);
-    this.updatePath();
-    this.refreshBackgroundImage();
-}
-
-  updatePath = () => {
-    if (!this.points || this.points.length === 0) {
-      console.log("no points");
-      return;
-    }
-    if (this.samplingMethod != "controlpoints") {
-      var coords = this.samplePoints(this.pathElements[0], this.points_to_sample, this.samplingMethod, this.width);
-    }
-    else {
-      var coords = this.points
-    }
-
-    if (this.drawSamplePoints) {
-      if (this.pointsLayer) {
-        // Update the data of the existing points layer
-        this.pointsLayer.data(coords);
-      } else {
-          // Create the points layer if it doesn't exist
-          this.pointsLayer = this.vis.add(pv.Dot)
-              .data(coords)
-              .left(function(d) { return d.x; })
-              .top(function(d) { return d.y; })
-              .radius(5) // Adjust the radius as needed
-              .fillStyle("red") // Change the color as needed
-              .strokeStyle("black") // Change the stroke color as needed
-              .lineWidth(1); // Adjust the line width as needed
-        }
-    } else {
-        if (this.pointsLayer) {
-          // Remove the points layer
-          this.pointsLayer.data([]);
-          this.vis.render();
-        }
-    }
-    let coordsString = JSON.stringify(coords);
-    this.pointsStoreWidget.value = JSON.stringify(this.points);
-    if (this.coordWidget) {
-      this.coordWidget.value = coordsString;
-      }
-    this.vis.render();
-  };
-    handleImageLoad = (img, file, base64String) => {
-      console.log(img.width, img.height); // Access width and height here
-      this.widthWidget.value = img.width;
-      this.heightWidget.value = img.height;
-      this.drawRuler = false;
-
-      if (img.width != this.vis.width() || img.height != this.vis.height()) {
-        if (img.width > 256) {
-          this.node.setSize([img.width + 45, this.node.size[1]]);
-        }
-        this.node.setSize([this.node.size[0], img.height + 500]);
-        this.vis.width(img.width);
-        this.vis.height(img.height);
-        this.height = img.height;
-        this.width = img.width;
-        
-        this.updatePath();
-      }
-      this.backgroundImage.url(file ? URL.createObjectURL(file) : `data:${this.node.properties.imgData.type};base64,${base64String}`).visible(true).root.render();
-      };
-
-    processImage = (img, file) => {
-      const canvas = document.createElement('canvas');
-      const ctx = canvas.getContext('2d');
-
-      const maxWidth = 800; // maximum width
-      const maxHeight = 600; // maximum height
-      let width = img.width;
-      let height = img.height;
-
-      // Calculate the new dimensions while preserving the aspect ratio
-      if (width > height) {
-        if (width > maxWidth) {
-          height *= maxWidth / width;
-          width = maxWidth;
-        }
-      } else {
-        if (height > maxHeight) {
-          width *= maxHeight / height;
-          height = maxHeight;
-        }
-      }
-
-      canvas.width = width;
-      canvas.height = height;
-      ctx.drawImage(img, 0, 0, width, height);
-
-      // Get the compressed image data as a Base64 string
-      const base64String = canvas.toDataURL('image/jpeg', 0.5).replace('data:', '').replace(/^.+,/, ''); // 0.5 is the quality from 0 to 1
-
-      this.node.properties.imgData = {
-        name: file.name,
-        lastModified: file.lastModified,
-        size: file.size,
-        type: file.type,
-        base64: base64String
-      };
-      handleImageLoad(img, file, base64String);
-    };
-
-    handleImageFile = (file) => {
-      const reader = new FileReader();
-      reader.onloadend = () => {
-        const img = new Image();
-        img.src = reader.result;
-        img.onload = () => processImage(img, file);
-      };
-      reader.readAsDataURL(file);
-
-      const imageUrl = URL.createObjectURL(file);
-      const img = new Image();
-      img.src = imageUrl;
-      img.onload = () => this.handleImageLoad(img, file, null);
-    };
-
-    refreshBackgroundImage = () => {
-      if (this.node.properties.imgData && this.node.properties.imgData.base64) {
-        const base64String = this.node.properties.imgData.base64;
-        const imageUrl = `data:${this.node.properties.imgData.type};base64,${base64String}`;
-        const img = new Image();
-        img.src = imageUrl;
-        img.onload = () => this.handleImageLoad(img, null, base64String);
-      }
-    };
-
-  createContextMenu = () => {
-    self = this;
-    document.addEventListener('contextmenu', function (e) {
-      e.preventDefault();
-      
-    });
-
-    document.addEventListener('click', function (e) {
-      document.querySelectorAll('.spline-editor-context-menu').forEach(menu => {
-        menu.style.display = 'none';
-    });
-    });
-
-    this.node.menuItems.forEach((menuItem, index) => {
-      self = this;
-      menuItem.addEventListener('click', function (e) {
-        e.preventDefault();
-        switch (index) {
-          case 0:
-            e.preventDefault();
-            if (!self.drawHandles) {
-              self.drawHandles = true
-              self.vis.add(pv.Line)
-              .data(() => self.points.map((point, index) => ({
-                  start: point,
-                  end: [index]
-              })))
-              .left(d => d.start.x)
-              .top(d => d.start.y)
-              .interpolate("linear")
-              .tension(0) // Straight lines
-              .strokeStyle("#ff7f0e") // Same color as control points
-              .lineWidth(1)
-              .visible(() => self.drawHandles);
-              self.vis.render();
-            } else {
-              self.drawHandles = false
-              self.vis.render();
-            }
-            self.node.contextMenu.style.display = 'none';
-            break;
-          case 1:
-            e.preventDefault();
-            self.drawSamplePoints = !self.drawSamplePoints;
-            self.updatePath();
-            break;
-          case 2:
-            e.preventDefault();
-            if (self.dotShape == "circle"){
-              self.dotShape = "triangle"
-            }
-            else {
-              self.dotShape = "circle"
-            }
-            console.log(self.dotShape)
-            self.updatePath();
-            break;
-          case 3:
-            // Create file input element
-            const fileInput = document.createElement('input');
-            fileInput.type = 'file';
-            fileInput.accept = 'image/*'; // Accept only image files
-
-            // Listen for file selection
-            fileInput.addEventListener('change', function (event) {
-              const file = event.target.files[0]; // Get the selected file
-
-              if (file) {
-                const imageUrl = URL.createObjectURL(file);
-                let img = new Image();
-                img.src = imageUrl;
-                img.onload = () => self.handleImageLoad(img, file, null);
-              }
-            });
-
-              fileInput.click();
-            
-            self.node.contextMenu.style.display = 'none';
-            break;
-          case 4:
-            e.preventDefault();
-            self.points.reverse();
-            self.updatePath();
-          break;
-          case 5:
-            self.backgroundImage.visible(false).root.render();
-            self.node.properties.imgData = null;
-            self.node.contextMenu.style.display = 'none';
-          break;
-        }
-      });
-    });
-  }
-
-  samplePoints(svgPathElement, numSamples, samplingMethod, width) {
-  var svgWidth = width; // Fixed width of the SVG element
-  var pathLength = svgPathElement.getTotalLength();
-  var points = [];
-
-  for (var i = 0; i < numSamples; i++) {
-      if (samplingMethod === "time") {
-        // Calculate the x-coordinate for the current sample based on the SVG's width
-        var x = (svgWidth / (numSamples - 1)) * i;
-        // Find the point on the path that intersects the vertical line at the calculated x-coordinate
-        var point = this.findPointAtX(svgPathElement, x, pathLength);
-        }
-      else if (samplingMethod === "path") {
-        // Calculate the distance along the path for the current sample
-        var distance = (pathLength / (numSamples - 1)) * i;
-        // Get the point at the current distance
-        var point = svgPathElement.getPointAtLength(distance);
-      }
-
-      // Add the point to the array of points
-      points.push({ x: point.x, y: point.y });
-  }
-  return points;
-  }
-
-  findClosestPoints(points, clickedPoint) {
-      // Calculate distances from clickedPoint to each point in the array
-      let distances = points.map(point => {
-          let dx = clickedPoint.x - point.x;
-          let dy = clickedPoint.y - point.y;
-          return { index: points.indexOf(point), distance: Math.sqrt(dx * dx + dy * dy) };
-      });
-      // Sort distances and get the indices of the two closest points
-      let sortedDistances = distances.sort((a, b) => a.distance - b.distance);
-      let closestPoint1Index = sortedDistances[0].index;
-      let closestPoint2Index = sortedDistances[1].index;
-      // Ensure point1Index is always the smaller index
-    if (closestPoint1Index > closestPoint2Index) {
-      [closestPoint1Index, closestPoint2Index] = [closestPoint2Index, closestPoint1Index];
-      }
-      return { point1Index: closestPoint1Index, point2Index: closestPoint2Index };
-    }
-
-  findPointAtX(svgPathElement, targetX, pathLength) {
-    let low = 0;
-    let high = pathLength;
-    let bestPoint = svgPathElement.getPointAtLength(0);
-
-    while (low <= high) {
-        let mid = low + (high - low) / 2;
-        let point = svgPathElement.getPointAtLength(mid);
-
-        if (Math.abs(point.x - targetX) < 1) {
-            return point; // The point is close enough to the target
-        }
-
-        if (point.x < targetX) {
-            low = mid + 1;
-        } else {
-            high = mid - 1;
-        }
-
-        // Keep track of the closest point found so far
-        if (Math.abs(point.x - targetX) < Math.abs(bestPoint.x - targetX)) {
-            bestPoint = point;
-        }
-    }
-
-    // Return the closest point found
-    return bestPoint;
-  }
-}
-//from melmass
-export function hideWidgetForGood(node, widget, suffix = '') {
-  widget.origType = widget.type
-  widget.origComputeSize = widget.computeSize
-  widget.origSerializeValue = widget.serializeValue
-  widget.computeSize = () => [0, -4] // -4 is due to the gap litegraph adds between widgets automatically
-  widget.type = "converted-widget" + suffix
-  // widget.serializeValue = () => {
-  //     // Prevent serializing the widget if we have no input linked
-  //     const w = node.inputs?.find((i) => i.widget?.name === widget.name);
-  //     if (w?.link == null) {
-  //         return undefined;
-  //     }
-  //     return widget.origSerializeValue ? widget.origSerializeValue() : widget.value;
-  // };
-
-  // Hide any linked widgets, e.g. seed+seedControl
-  if (widget.linkedWidgets) {
-    for (const w of widget.linkedWidgets) {
-      hideWidgetForGood(node, w, ':' + widget.name)
-    }
-  }
-}