Add custom nodes, Civitai loras (LFS), and vast.ai setup script

Includes 30 custom nodes committed directly, 7 Civitai-exclusive
loras stored via Git LFS, and a setup script that installs all
dependencies and downloads HuggingFace-hosted models on vast.ai.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

custom_nodes/ComfyUI-KJNodes/.gitignore

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+__pycache__
+/venv
+*.code-workspace
+.history
+.vscode
+*.ckpt
+*.pth
+types
+models
+jsconfig.json
+custom_dimensions.json

custom_nodes/ComfyUI-KJNodes/LICENSE

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+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  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
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  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.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    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.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+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/README.md

@@ -0,0 +1,65 @@
+# 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/__init__.py

@@ -0,0 +1,295 @@
+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 *
+from .nodes.lora_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},
+    "WanImageToVideoSVIPro": {"class": WanImageToVideoSVIPro, "name": "Wan Image To Video SVIPro"},
+    #masking
+    "DrawMaskOnImage": {"class": DrawMaskOnImage, "name": "Draw Mask On Image"},
+    "DownloadAndLoadCLIPSeg": {"class": DownloadAndLoadCLIPSeg, "name": "(Down)load CLIPSeg"},
+    "BatchCLIPSeg": {"class": BatchCLIPSeg, "name": "Batch CLIPSeg"},
+    "BlockifyMask": {"class": BlockifyMask, "name": "Blockify Mask"},
+    "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"},
+    "ConsolidateMasksKJ": {"class": ConsolidateMasksKJ, "name": "Consolidate Masks"},
+    #images
+    "AddLabel": {"class": AddLabel, "name": "Add Label"},
+    "ColorMatch": {"class": ColorMatch, "name": "Color Match"},
+    "ColorMatchV2": {"class": ColorMatchV2, "name": "Color Match V2"},
+    "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"},
+    "GetLatentSizeAndCount": {"class": GetLatentSizeAndCount, "name": "Get Latent Size & Count"},
+    "GetImageSizeAndCount": {"class": GetImageSizeAndCount, "name": "Get Image Size & Count"},
+    "FastPreview": {"class": FastPreview, "name": "Fast Preview"},
+    "ImageBatchFilter": {"class": ImageBatchFilter, "name": "Image Batch Filter"},
+    "ImageAndMaskPreview": {"class": ImageAndMaskPreview},
+    "ImageAddMulti": {"class": ImageAddMulti, "name": "Image Add Multi"},
+    "ImageBatchJoinWithTransition": {"class": ImageBatchJoinWithTransition, "name": "Image Batch Join With Transition"},
+    "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"},
+    "ImageBatchExtendWithOverlap": {"class": ImageBatchExtendWithOverlap, "name": "Image Batch Extend With Overlap"},
+    "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 (deprecated)"},
+    "ImageResizeKJv2": {"class": ImageResizeKJv2, "name": "Resize Image v2"},
+    "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)"},
+    "LoadVideosFromFolder": {"class": LoadVideosFromFolder, "name": "Load Videos From Folder"},
+    "MergeImageChannels": {"class": MergeImageChannels, "name": "Merge Image Channels"},
+    "PadImageBatchInterleaved": {"class": PadImageBatchInterleaved, "name": "Pad Image Batch Interleaved"},
+    "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"},
+    "SimpleCalculatorKJ": {"class": SimpleCalculatorKJ, "name": "Simple Calculator KJ"},
+    "SomethingToString": {"class": SomethingToString, "name": "Something To String"},
+    "Sleep": {"class": Sleep, "name": "Sleep"},
+    "VRAM_Debug": {"class": VRAM_Debug, "name": "VRAM Debug"},
+    "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"},
+    "DiffusionModelSelector": {"class": DiffusionModelSelector, "name": "Diffusion Model Selector"},
+    "LazySwitchKJ": {"class": LazySwitchKJ, "name": "Lazy Switch KJ"},
+    "VisualizeSigmasKJ": {"class": VisualizeSigmasKJ, "name": "Visualize Sigmas KJ"},
+    #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
+    "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"},
+    "DiTBlockLoraLoader": {"class": DiTBlockLoraLoader, "name": "DiT Block Lora Loader"},
+    "FluxBlockLoraSelect": {"class": FluxBlockLoraSelect, "name": "Flux Block Lora Select"},
+    "HunyuanVideoBlockLoraSelect": {"class": HunyuanVideoBlockLoraSelect, "name": "Hunyuan Video Block Lora Select"},
+    "Wan21BlockLoraSelect": {"class": Wan21BlockLoraSelect, "name": "Wan21 Block Lora Select"},
+    "LTX2BlockLoraSelect": {"class": LTX2BlockLoraSelect, "name": "LTX2 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"},
+    "TorchCompileModelFluxAdvancedV2": {"class": TorchCompileModelFluxAdvancedV2, "name": "TorchCompileModelFluxAdvancedV2"},
+    "TorchCompileVAE": {"class": TorchCompileVAE, "name": "TorchCompileVAE"},
+    "TorchCompileControlNet": {"class": TorchCompileControlNet, "name": "TorchCompileControlNet"},
+    "TorchCompileModelWanVideoV2": {"class": TorchCompileModelWanVideoV2, "name": "TorchCompileModelWanVideoV2"},
+    "PathchSageAttentionKJ": {"class": PathchSageAttentionKJ, "name": "Patch Sage Attention KJ"},
+    "LeapfusionHunyuanI2VPatcher": {"class": LeapfusionHunyuanI2V, "name": "Leapfusion Hunyuan I2V Patcher"},
+    "VAELoaderKJ": {"class": VAELoaderKJ, "name": "VAELoader KJ"},
+    "VAEDecodeLoopKJ": {"class": VAEDecodeLoopKJ, "name": "VAE Decode Loop 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"},
+    "CFGZeroStarAndInit": {"class": CFGZeroStarAndInit, "name": "CFG Zero Star/Init"},
+    "ModelPatchTorchSettings": {"class": ModelPatchTorchSettings, "name": "Model Patch Torch Settings"},
+    "WanVideoNAG": {"class": WanVideoNAG, "name": "WanVideoNAG"},
+    "GGUFLoaderKJ": {"class": GGUFLoaderKJ, "name": "GGUF Loader KJ"},
+    "LatentInpaintTTM": {"class": LatentInpaintTTM, "name": "Latent Inpaint TTM"},
+    "NABLA_AttentionKJ": {"class": NABLA_AttentionKJ, "name": "NABLA Attention KJ"},
+    "TorchCompileModelAdvanced": {"class": TorchCompileModelAdvanced, "name": "TorchCompileModelAdvanced"},
+    "StartRecordCUDAMemoryHistory": {"class": StartRecordCUDAMemoryHistory, "name": "Start Recording CUDAMemory History"},
+    "EndRecordCUDAMemoryHistory": {"class": EndRecordCUDAMemoryHistory, "name": "End Recording CUDAMemory History"},
+    "VisualizeCUDAMemoryHistory": {"class": VisualizeCUDAMemoryHistory, "name": "Visualize CUDAMemory History"},
+    "PreviewLatentNoiseMask": {"class": PreviewLatentNoiseMask, "name": "Preview Latent Noise Mask"},
+    "ModelMemoryUseReportPatch": {"class": ModelMemoryUseReportPatch, "name": "Model Memory Use Report Patch"},
+    "ModelMemoryUsageFactorOverride": {"class": ModelMemoryUsageFactorOverride, "name": "Model Memory Usage Factor Override"},
+    "WanChunkFeedForward": {"class": WanChunkFeedForward, "name": "Wan ChunkFeedForward"},
+    "SamplerSelfRefineVideo": {"class": SamplerSelfRefineVideo, "name": "Sampler SelfRefineVideo"},
+
+    #instance diffusion
+    "CreateInstanceDiffusionTracking": {"class": CreateInstanceDiffusionTracking},
+    "AppendInstanceDiffusionTracking": {"class": AppendInstanceDiffusionTracking},
+    "DrawInstanceDiffusionTracking": {"class": DrawInstanceDiffusionTracking},
+
+    #lora
+    "LoraExtractKJ": {"class": LoraExtractKJ, "name": "LoraExtractKJ"},
+    "LoraReduceRankKJ": {"class": LoraReduceRank, "name": "LoraReduceRank"},
+
+    #tracks
+    "GetTrackRange": {"class": GetTrackRange, "name": "Get Track Range"},
+    "AddNoiseToTrackPath": {"class": AddNoiseToTrackPath, "name": "Add Noise To Track"},
+
+    # deprecated
+    "PatchModelPatcherOrder": {"class": PatchModelPatcherOrder, "name": "Patch Model Patcher Order"},
+    "TorchCompileModelFluxAdvanced": {"class": DeprecatedCompileNodeKJ, "name": "TorchCompileModelFluxAdvanced"},
+    "TorchCompileLTXModel": {"class": DeprecatedCompileNodeKJ, "name": "TorchCompileLTXModel"},
+    "TorchCompileCosmosModel": {"class": DeprecatedCompileNodeKJ, "name": "TorchCompileCosmosModel"},
+    "TorchCompileModelHyVideo": {"class": DeprecatedCompileNodeKJ, "name": "TorchCompileModelHyVideo"},
+    "TorchCompileModelQwenImage": {"class": DeprecatedCompileNodeKJ, "name": "TorchCompileModelQwenImage"},
+    "TorchCompileModelWanVideo": {"class": DeprecatedCompileNodeKJ, "name": "TorchCompileModelWanVideo"},
+}
+
+#ltxv
+try:
+    from .nodes.ltxv_nodes import *
+    NODE_CONFIG.update({
+    "LTXVEnhanceAVideoKJ": {"class": LTXVEnhanceAVideoKJ, "name": "LTXV Enhance A Video KJ"},
+    "LTXVAddGuideMulti": {"class": LTXVAddGuideMulti, "name": "LTXV Add Guide Multi"},
+    "LTXVAddGuidesFromBatch": {"class": LTXVAddGuidesFromBatch, "name": "LTXV Add Guides From Batch"},
+    "LTXVAudioVideoMask": {"class": LTXVAudioVideoMask, "name": "LTXV Audio Video Mask"},
+    "LTX2_NAG": {"class": LTX2_NAG, "name": "LTX2 NAG"},
+    "LTXVChunkFeedForward": {"class": LTXVChunkFeedForward, "name": "LTXV Chunk Feed Forward"},
+    "LTX2SamplingPreviewOverride": {"class": LTX2SamplingPreviewOverride, "name": "LTX2 Sampling Preview Override"},
+    "LTX2AudioLatentNormalizingSampling": {"class": LTX2AudioLatentNormalizingSampling, "name": "LTX2 Audio Latent Normalizing Sampling"},
+    "LTXVImgToVideoInplaceKJ": {"class": LTXVImgToVideoInplaceKJ, "name": "LTXV Img To Video Inplace KJ"},
+    "LTX2AttentionTunerPatch": {"class": LTX2AttentionTunerPatch, "name": "LTX2 Attention Tuner Patch"},
+    "LTX2MemoryEfficientSageAttentionPatch": {"class": LTX2MemoryEfficientSageAttentionPatch, "name": "LTX2 Memory Efficient Sage Attention Patch"},
+    "LTX2LoraLoaderAdvanced": {"class": LTX2LoraLoaderAdvanced, "name": "LTX2 Lora Loader Advanced"},
+    })
+except Exception as e:
+    logging.warning(f"KJNodes: LTXV nodes could not be imported. LTXV nodes will be unavailable. Error: {e}")
+
+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/custom_dimensions_example.json

@@ -0,0 +1,22 @@
+[
+    {
+        "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/intrinsic_loras/intrinsic_loras.txt

@@ -0,0 +1,4 @@
+source for the loras:
+https://github.com/duxiaodan/intrinsic-lora
+
+Renamed and conveted to .safetensors

custom_nodes/ComfyUI-KJNodes/kjweb_async/protovis.min.js

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+pv.Dom=function(b){this.$map=b};pv.Dom.prototype.$leaf=function(b){return typeof b!="object"};pv.Dom.prototype.leaf=function(b){if(arguments.length){this.$leaf=b;return this}return this.$leaf};pv.Dom.prototype.root=function(b){function c(g){var h=new pv.Dom.Node;for(var i in g){var j=g[i];h.appendChild(d(j)?new pv.Dom.Node(j):c(j)).nodeName=i}return h}var d=this.$leaf,f=c(this.$map);f.nodeName=b;return f};pv.Dom.prototype.nodes=function(){return this.root().nodes()};
+pv.Dom.Node=function(b){this.nodeValue=b;this.childNodes=[]};a=pv.Dom.Node.prototype;a.parentNode=null;a.firstChild=null;a.lastChild=null;a.previousSibling=null;a.nextSibling=null;
+a.removeChild=function(b){var c=this.childNodes.indexOf(b);if(c==-1)throw new Error("child not found");this.childNodes.splice(c,1);if(b.previousSibling)b.previousSibling.nextSibling=b.nextSibling;else this.firstChild=b.nextSibling;if(b.nextSibling)b.nextSibling.previousSibling=b.previousSibling;else this.lastChild=b.previousSibling;delete b.nextSibling;delete b.previousSibling;delete b.parentNode;return b};
+a.appendChild=function(b){b.parentNode&&b.parentNode.removeChild(b);b.parentNode=this;if(b.previousSibling=this.lastChild)this.lastChild.nextSibling=b;else this.firstChild=b;this.lastChild=b;this.childNodes.push(b);return b};
+a.insertBefore=function(b,c){if(!c)return this.appendChild(b);var d=this.childNodes.indexOf(c);if(d==-1)throw new Error("child not found");b.parentNode&&b.parentNode.removeChild(b);b.parentNode=this;b.nextSibling=c;if(b.previousSibling=c.previousSibling)c.previousSibling.nextSibling=b;else{if(c==this.lastChild)this.lastChild=b;this.firstChild=b}this.childNodes.splice(d,0,b);return b};
+a.replaceChild=function(b,c){var d=this.childNodes.indexOf(c);if(d==-1)throw new Error("child not found");b.parentNode&&b.parentNode.removeChild(b);b.parentNode=this;b.nextSibling=c.nextSibling;if(b.previousSibling=c.previousSibling)c.previousSibling.nextSibling=b;else this.firstChild=b;if(c.nextSibling)c.nextSibling.previousSibling=b;else this.lastChild=b;this.childNodes[d]=b;return c};a.visitBefore=function(b){function c(d,f){b(d,f);for(d=d.firstChild;d;d=d.nextSibling)c(d,f+1)}c(this,0)};
+a.visitAfter=function(b){function c(d,f){for(var g=d.firstChild;g;g=g.nextSibling)c(g,f+1);b(d,f)}c(this,0)};a.sort=function(b){if(this.firstChild){this.childNodes.sort(b);var c=this.firstChild=this.childNodes[0],d;delete c.previousSibling;for(var f=1;f<this.childNodes.length;f++){c.sort(b);d=this.childNodes[f];d.previousSibling=c;c=c.nextSibling=d}this.lastChild=c;delete c.nextSibling;c.sort(b)}return this};
+a.reverse=function(){var b=[];this.visitAfter(function(c){for(;c.lastChild;)b.push(c.removeChild(c.lastChild));for(var d;d=b.pop();)c.insertBefore(d,c.firstChild)});return this};a.nodes=function(){function b(d){c.push(d);d.childNodes.forEach(b)}var c=[];b(this,c);return c};
+a.toggle=function(b){if(b)return this.toggled?this.visitBefore(function(d){d.toggled&&d.toggle()}):this.visitAfter(function(d){d.toggled||d.toggle()});b=this;if(b.toggled){for(var c;c=b.toggled.pop();)b.appendChild(c);delete b.toggled}else if(b.lastChild)for(b.toggled=[];b.lastChild;)b.toggled.push(b.removeChild(b.lastChild))};pv.nodes=function(b){for(var c=new pv.Dom.Node,d=0;d<b.length;d++)c.appendChild(new pv.Dom.Node(b[d]));return c.nodes()};pv.tree=function(b){return new pv.Tree(b)};
+pv.Tree=function(b){this.array=b};pv.Tree.prototype.keys=function(b){this.k=b;return this};pv.Tree.prototype.value=function(b){this.v=b;return this};pv.Tree.prototype.map=function(){for(var b={},c={},d=0;d<this.array.length;d++){c.index=d;for(var f=this.array[d],g=this.k.call(c,f),h=b,i=0;i<g.length-1;i++)h=h[g[i]]||(h[g[i]]={});h[g[i]]=this.v?this.v.call(c,f):f}return b};pv.nest=function(b){return new pv.Nest(b)};pv.Nest=function(b){this.array=b;this.keys=[]};a=pv.Nest.prototype;
+a.key=function(b){this.keys.push(b);return this};a.sortKeys=function(b){this.keys[this.keys.length-1].order=b||pv.naturalOrder;return this};a.sortValues=function(b){this.order=b||pv.naturalOrder;return this};a.map=function(){for(var b={},c=[],d,f=0;f<this.array.length;f++){var g=this.array[f],h=b;for(d=0;d<this.keys.length-1;d++){var i=this.keys[d](g);h[i]||(h[i]={});h=h[i]}i=this.keys[d](g);if(!h[i]){d=[];c.push(d);h[i]=d}h[i].push(g)}if(this.order)for(d=0;d<c.length;d++)c[d].sort(this.order);return b};
+a.entries=function(){function b(d){var f=[];for(var g in d){var h=d[g];f.push({key:g,values:h instanceof Array?h:b(h)})}return f}function c(d,f){var g=this.keys[f].order;g&&d.sort(function(i,j){return g(i.key,j.key)});if(++f<this.keys.length)for(var h=0;h<d.length;h++)c.call(this,d[h].values,f);return d}return c.call(this,b(this.map()),0)};a.rollup=function(b){function c(d){for(var f in d){var g=d[f];if(g instanceof Array)d[f]=b(g);else c(g)}return d}return c(this.map())};pv.flatten=function(b){return new pv.Flatten(b)};
+pv.Flatten=function(b){this.map=b;this.keys=[]};pv.Flatten.prototype.key=function(b,c){this.keys.push({name:b,value:c});delete this.$leaf;return this};pv.Flatten.prototype.leaf=function(b){this.keys.length=0;this.$leaf=b;return this};
+pv.Flatten.prototype.array=function(){function b(i,j){if(j<f.length-1)for(var k in i){d.push(k);b(i[k],j+1);d.pop()}else c.push(d.concat(i))}var c=[],d=[],f=this.keys,g=this.$leaf;if(g){function h(i,j){if(g(i))c.push({keys:d.slice(),value:i});else for(var k in i){d.push(k);h(i[k],j+1);d.pop()}}h(this.map,0);return c}b(this.map,0);return c.map(function(i){for(var j={},k=0;k<f.length;k++){var l=f[k],q=i[k];j[l.name]=l.value?l.value.call(null,q):q}return j})};
+pv.vector=function(b,c){return new pv.Vector(b,c)};pv.Vector=function(b,c){this.x=b;this.y=c};a=pv.Vector.prototype;a.perp=function(){return new pv.Vector(-this.y,this.x)};a.norm=function(){var b=this.length();return this.times(b?1/b:1)};a.length=function(){return Math.sqrt(this.x*this.x+this.y*this.y)};a.times=function(b){return new pv.Vector(this.x*b,this.y*b)};a.plus=function(b,c){return arguments.length==1?new pv.Vector(this.x+b.x,this.y+b.y):new pv.Vector(this.x+b,this.y+c)};
+a.minus=function(b,c){return arguments.length==1?new pv.Vector(this.x-b.x,this.y-b.y):new pv.Vector(this.x-b,this.y-c)};a.dot=function(b,c){return arguments.length==1?this.x*b.x+this.y*b.y:this.x*b+this.y*c};pv.Transform=function(){};pv.Transform.prototype={k:1,x:0,y:0};pv.Transform.identity=new pv.Transform;pv.Transform.prototype.translate=function(b,c){var d=new pv.Transform;d.k=this.k;d.x=this.k*b+this.x;d.y=this.k*c+this.y;return d};
+pv.Transform.prototype.scale=function(b){var c=new pv.Transform;c.k=this.k*b;c.x=this.x;c.y=this.y;return c};pv.Transform.prototype.invert=function(){var b=new pv.Transform,c=1/this.k;b.k=c;b.x=-this.x*c;b.y=-this.y*c;return b};pv.Transform.prototype.times=function(b){var c=new pv.Transform;c.k=this.k*b.k;c.x=this.k*b.x+this.x;c.y=this.k*b.y+this.y;return c};pv.Scale=function(){};
+pv.Scale.interpolator=function(b,c){if(typeof b=="number")return function(d){return d*(c-b)+b};b=pv.color(b).rgb();c=pv.color(c).rgb();return function(d){var f=b.a*(1-d)+c.a*d;if(f<1.0E-5)f=0;return b.a==0?pv.rgb(c.r,c.g,c.b,f):c.a==0?pv.rgb(b.r,b.g,b.b,f):pv.rgb(Math.round(b.r*(1-d)+c.r*d),Math.round(b.g*(1-d)+c.g*d),Math.round(b.b*(1-d)+c.b*d),f)}};
+pv.Scale.quantitative=function(){function b(n){return new Date(n)}function c(n){var p=pv.search(d,n);if(p<0)p=-p-2;p=Math.max(0,Math.min(h.length-1,p));return h[p]((k(n)-f[p])/(f[p+1]-f[p]))}var d=[0,1],f=[0,1],g=[0,1],h=[pv.identity],i=Number,j=false,k=pv.identity,l=pv.identity,q=String;c.transform=function(n,p){k=function(m){return j?-n(-m):n(m)};l=function(m){return j?-p(-m):p(m)};f=d.map(k);return this};c.domain=function(n,p,m){if(arguments.length){var r;if(n instanceof Array){if(arguments.length<
+2)p=pv.identity;if(arguments.length<3)m=p;r=n.length&&p(n[0]);d=n.length?[pv.min(n,p),pv.max(n,m)]:[]}else{r=n;d=Array.prototype.slice.call(arguments).map(Number)}if(d.length){if(d.length==1)d=[d[0],d[0]]}else d=[-Infinity,Infinity];j=(d[0]||d[d.length-1])<0;f=d.map(k);i=r instanceof Date?b:Number;return this}return d.map(i)};c.range=function(){if(arguments.length){g=Array.prototype.slice.call(arguments);if(g.length){if(g.length==1)g=[g[0],g[0]]}else g=[-Infinity,Infinity];h=[];for(var n=0;n<g.length-
+1;n++)h.push(pv.Scale.interpolator(g[n],g[n+1]));return this}return g};c.invert=function(n){var p=pv.search(g,n);if(p<0)p=-p-2;p=Math.max(0,Math.min(h.length-1,p));return i(l(f[p]+(n-g[p])/(g[p+1]-g[p])*(f[p+1]-f[p])))};c.ticks=function(n){var p=d[0],m=d[d.length-1],r=m<p,s=r?m:p;m=r?p:m;var t=m-s;if(!t||!isFinite(t)){if(i==b)q=pv.Format.date("%x");return[i(s)]}if(i==b){function x(w,y){switch(y){case 31536E6:w.setMonth(0);case 2592E6:w.setDate(1);case 6048E5:y==6048E5&&w.setDate(w.getDate()-w.getDay());
+case 864E5:w.setHours(0);case 36E5:w.setMinutes(0);case 6E4:w.setSeconds(0);case 1E3:w.setMilliseconds(0)}}var u,o,v=1;if(t>=94608E6){p=31536E6;u="%Y";o=function(w){w.setFullYear(w.getFullYear()+v)}}else if(t>=7776E6){p=2592E6;u="%m/%Y";o=function(w){w.setMonth(w.getMonth()+v)}}else if(t>=18144E5){p=6048E5;u="%m/%d";o=function(w){w.setDate(w.getDate()+7*v)}}else if(t>=2592E5){p=864E5;u="%m/%d";o=function(w){w.setDate(w.getDate()+v)}}else if(t>=108E5){p=36E5;u="%I:%M %p";o=function(w){w.setHours(w.getHours()+
+v)}}else if(t>=18E4){p=6E4;u="%I:%M %p";o=function(w){w.setMinutes(w.getMinutes()+v)}}else if(t>=3E3){p=1E3;u="%I:%M:%S";o=function(w){w.setSeconds(w.getSeconds()+v)}}else{p=1;u="%S.%Qs";o=function(w){w.setTime(w.getTime()+v)}}q=pv.Format.date(u);s=new Date(s);u=[];x(s,p);t=t/p;if(t>10)switch(p){case 36E5:v=t>20?6:3;s.setHours(Math.floor(s.getHours()/v)*v);break;case 2592E6:v=3;s.setMonth(Math.floor(s.getMonth()/v)*v);break;case 6E4:v=t>30?15:t>15?10:5;s.setMinutes(Math.floor(s.getMinutes()/v)*v);
+break;case 1E3:v=t>90?15:t>60?10:5;s.setSeconds(Math.floor(s.getSeconds()/v)*v);break;case 1:v=t>1E3?250:t>200?100:t>100?50:t>50?25:5;s.setMilliseconds(Math.floor(s.getMilliseconds()/v)*v);break;default:v=pv.logCeil(t/15,10);if(t/v<2)v/=5;else if(t/v<5)v/=2;s.setFullYear(Math.floor(s.getFullYear()/v)*v);break}for(;;){o(s);if(s>m)break;u.push(new Date(s))}return r?u.reverse():u}arguments.length||(n=10);v=pv.logFloor(t/n,10);p=n/(t/v);if(p<=0.15)v*=10;else if(p<=0.35)v*=5;else if(p<=0.75)v*=2;p=Math.ceil(s/
+v)*v;m=Math.floor(m/v)*v;q=pv.Format.number().fractionDigits(Math.max(0,-Math.floor(pv.log(v,10)+0.01)));m=pv.range(p,m+v,v);return r?m.reverse():m};c.tickFormat=function(n){return q(n)};c.nice=function(){if(d.length!=2)return this;var n=d[0],p=d[d.length-1],m=p<n,r=m?p:n;n=m?n:p;p=n-r;if(!p||!isFinite(p))return this;p=Math.pow(10,Math.round(Math.log(p)/Math.log(10))-1);d=[Math.floor(r/p)*p,Math.ceil(n/p)*p];m&&d.reverse();f=d.map(k);return this};c.by=function(n){function p(){return c(n.apply(this,
+arguments))}for(var m in c)p[m]=c[m];return p};c.domain.apply(c,arguments);return c};pv.Scale.linear=function(){var b=pv.Scale.quantitative();b.domain.apply(b,arguments);return b};
+pv.Scale.log=function(){var b=pv.Scale.quantitative(1,10),c,d,f=function(h){return Math.log(h)/d},g=function(h){return Math.pow(c,h)};b.ticks=function(){var h=b.domain(),i=h[0]<0,j=Math.floor(i?-f(-h[0]):f(h[0])),k=Math.ceil(i?-f(-h[1]):f(h[1])),l=[];if(i)for(l.push(-g(-j));j++<k;)for(i=c-1;i>0;i--)l.push(-g(-j)*i);else{for(;j<k;j++)for(i=1;i<c;i++)l.push(g(j)*i);l.push(g(j))}for(j=0;l[j]<h[0];j++);for(k=l.length;l[k-1]>h[1];k--);return l.slice(j,k)};b.tickFormat=function(h){return h.toPrecision(1)};
+b.nice=function(){var h=b.domain();return b.domain(pv.logFloor(h[0],c),pv.logCeil(h[1],c))};b.base=function(h){if(arguments.length){c=Number(h);d=Math.log(c);b.transform(f,g);return this}return c};b.domain.apply(b,arguments);return b.base(10)};pv.Scale.root=function(){var b=pv.Scale.quantitative();b.power=function(c){if(arguments.length){var d=Number(c),f=1/d;b.transform(function(g){return Math.pow(g,f)},function(g){return Math.pow(g,d)});return this}return d};b.domain.apply(b,arguments);return b.power(2)};
+pv.Scale.ordinal=function(){function b(g){g in d||(d[g]=c.push(g)-1);return f[d[g]%f.length]}var c=[],d={},f=[];b.domain=function(g,h){if(arguments.length){g=g instanceof Array?arguments.length>1?pv.map(g,h):g:Array.prototype.slice.call(arguments);c=[];for(var i={},j=0;j<g.length;j++){var k=g[j];if(!(k in i)){i[k]=true;c.push(k)}}d=pv.numerate(c);return this}return c};b.range=function(g,h){if(arguments.length){f=g instanceof Array?arguments.length>1?pv.map(g,h):g:Array.prototype.slice.call(arguments);
+if(typeof f[0]=="string")f=f.map(pv.color);return this}return f};b.split=function(g,h){var i=(h-g)/this.domain().length;f=pv.range(g+i/2,h,i);return this};b.splitFlush=function(g,h){var i=this.domain().length,j=(h-g)/(i-1);f=i==1?[(g+h)/2]:pv.range(g,h+j/2,j);return this};b.splitBanded=function(g,h,i){if(arguments.length<3)i=1;if(i<0){var j=this.domain().length;j=(h-g- -i*j)/(j+1);f=pv.range(g+j,h,j-i);f.band=-i}else{j=(h-g)/(this.domain().length+(1-i));f=pv.range(g+j*(1-i),h,j);f.band=j*i}return this};
+b.by=function(g){function h(){return b(g.apply(this,arguments))}for(var i in b)h[i]=b[i];return h};b.domain.apply(b,arguments);return b};
+pv.Scale.quantile=function(){function b(i){return h(Math.max(0,Math.min(d,pv.search.index(f,i)-1))/d)}var c=-1,d=-1,f=[],g=[],h=pv.Scale.linear();b.quantiles=function(i){if(arguments.length){c=Number(i);if(c<0){f=[g[0]].concat(g);d=g.length-1}else{f=[];f[0]=g[0];for(var j=1;j<=c;j++)f[j]=g[~~(j*(g.length-1)/c)];d=c-1}return this}return f};b.domain=function(i,j){if(arguments.length){g=i instanceof Array?pv.map(i,j):Array.prototype.slice.call(arguments);g.sort(pv.naturalOrder);b.quantiles(c);return this}return g};
+b.range=function(){if(arguments.length){h.range.apply(h,arguments);return this}return h.range()};b.by=function(i){function j(){return b(i.apply(this,arguments))}for(var k in b)j[k]=b[k];return j};b.domain.apply(b,arguments);return b};
+pv.histogram=function(b,c){var d=true;return{bins:function(f){var g=pv.map(b,c),h=[];arguments.length||(f=pv.Scale.linear(g).ticks());for(var i=0;i<f.length-1;i++){var j=h[i]=[];j.x=f[i];j.dx=f[i+1]-f[i];j.y=0}for(i=0;i<g.length;i++){j=pv.search.index(f,g[i])-1;j=h[Math.max(0,Math.min(h.length-1,j))];j.y++;j.push(b[i])}if(!d)for(i=0;i<h.length;i++)h[i].y/=g.length;return h},frequency:function(f){if(arguments.length){d=Boolean(f);return this}return d}}};
+pv.color=function(b){if(b.rgb)return b.rgb();var c=/([a-z]+)\((.*)\)/i.exec(b);if(c){var d=c[2].split(","),f=1;switch(c[1]){case "hsla":case "rgba":f=parseFloat(d[3]);if(!f)return pv.Color.transparent;break}switch(c[1]){case "hsla":case "hsl":b=parseFloat(d[0]);var g=parseFloat(d[1])/100;d=parseFloat(d[2])/100;return(new pv.Color.Hsl(b,g,d,f)).rgb();case "rgba":case "rgb":function h(k){var l=parseFloat(k);return k[k.length-1]=="%"?Math.round(l*2.55):l}g=h(d[0]);var i=h(d[1]),j=h(d[2]);return pv.rgb(g,
+i,j,f)}}if(f=pv.Color.names[b])return f;if(b.charAt(0)=="#"){if(b.length==4){g=b.charAt(1);g+=g;i=b.charAt(2);i+=i;j=b.charAt(3);j+=j}else if(b.length==7){g=b.substring(1,3);i=b.substring(3,5);j=b.substring(5,7)}return pv.rgb(parseInt(g,16),parseInt(i,16),parseInt(j,16),1)}return new pv.Color(b,1)};pv.Color=function(b,c){this.color=b;this.opacity=c};pv.Color.prototype.brighter=function(b){return this.rgb().brighter(b)};pv.Color.prototype.darker=function(b){return this.rgb().darker(b)};
+pv.rgb=function(b,c,d,f){return new pv.Color.Rgb(b,c,d,arguments.length==4?f:1)};pv.Color.Rgb=function(b,c,d,f){pv.Color.call(this,f?"rgb("+b+","+c+","+d+")":"none",f);this.r=b;this.g=c;this.b=d;this.a=f};pv.Color.Rgb.prototype=pv.extend(pv.Color);a=pv.Color.Rgb.prototype;a.red=function(b){return pv.rgb(b,this.g,this.b,this.a)};a.green=function(b){return pv.rgb(this.r,b,this.b,this.a)};a.blue=function(b){return pv.rgb(this.r,this.g,b,this.a)};
+a.alpha=function(b){return pv.rgb(this.r,this.g,this.b,b)};a.rgb=function(){return this};a.brighter=function(b){b=Math.pow(0.7,arguments.length?b:1);var c=this.r,d=this.g,f=this.b;if(!c&&!d&&!f)return pv.rgb(30,30,30,this.a);if(c&&c<30)c=30;if(d&&d<30)d=30;if(f&&f<30)f=30;return pv.rgb(Math.min(255,Math.floor(c/b)),Math.min(255,Math.floor(d/b)),Math.min(255,Math.floor(f/b)),this.a)};
+a.darker=function(b){b=Math.pow(0.7,arguments.length?b:1);return pv.rgb(Math.max(0,Math.floor(b*this.r)),Math.max(0,Math.floor(b*this.g)),Math.max(0,Math.floor(b*this.b)),this.a)};pv.hsl=function(b,c,d,f){return new pv.Color.Hsl(b,c,d,arguments.length==4?f:1)};pv.Color.Hsl=function(b,c,d,f){pv.Color.call(this,"hsl("+b+","+c*100+"%,"+d*100+"%)",f);this.h=b;this.s=c;this.l=d;this.a=f};pv.Color.Hsl.prototype=pv.extend(pv.Color);a=pv.Color.Hsl.prototype;
+a.hue=function(b){return pv.hsl(b,this.s,this.l,this.a)};a.saturation=function(b){return pv.hsl(this.h,b,this.l,this.a)};a.lightness=function(b){return pv.hsl(this.h,this.s,b,this.a)};a.alpha=function(b){return pv.hsl(this.h,this.s,this.l,b)};
+a.rgb=function(){function b(j){if(j>360)j-=360;else if(j<0)j+=360;if(j<60)return i+(h-i)*j/60;if(j<180)return h;if(j<240)return i+(h-i)*(240-j)/60;return i}function c(j){return Math.round(b(j)*255)}var d=this.h,f=this.s,g=this.l;d%=360;if(d<0)d+=360;f=Math.max(0,Math.min(f,1));g=Math.max(0,Math.min(g,1));var h=g<=0.5?g*(1+f):g+f-g*f,i=2*g-h;return pv.rgb(c(d+120),c(d),c(d-120),this.a)};
+pv.Color.names={aliceblue:"#f0f8ff",antiquewhite:"#faebd7",aqua:"#00ffff",aquamarine:"#7fffd4",azure:"#f0ffff",beige:"#f5f5dc",bisque:"#ffe4c4",black:"#000000",blanchedalmond:"#ffebcd",blue:"#0000ff",blueviolet:"#8a2be2",brown:"#a52a2a",burlywood:"#deb887",cadetblue:"#5f9ea0",chartreuse:"#7fff00",chocolate:"#d2691e",coral:"#ff7f50",cornflowerblue:"#6495ed",cornsilk:"#fff8dc",crimson:"#dc143c",cyan:"#00ffff",darkblue:"#00008b",darkcyan:"#008b8b",darkgoldenrod:"#b8860b",darkgray:"#a9a9a9",darkgreen:"#006400",
+darkgrey:"#a9a9a9",darkkhaki:"#bdb76b",darkmagenta:"#8b008b",darkolivegreen:"#556b2f",darkorange:"#ff8c00",darkorchid:"#9932cc",darkred:"#8b0000",darksalmon:"#e9967a",darkseagreen:"#8fbc8f",darkslateblue:"#483d8b",darkslategray:"#2f4f4f",darkslategrey:"#2f4f4f",darkturquoise:"#00ced1",darkviolet:"#9400d3",deeppink:"#ff1493",deepskyblue:"#00bfff",dimgray:"#696969",dimgrey:"#696969",dodgerblue:"#1e90ff",firebrick:"#b22222",floralwhite:"#fffaf0",forestgreen:"#228b22",fuchsia:"#ff00ff",gainsboro:"#dcdcdc",
+ghostwhite:"#f8f8ff",gold:"#ffd700",goldenrod:"#daa520",gray:"#808080",green:"#008000",greenyellow:"#adff2f",grey:"#808080",honeydew:"#f0fff0",hotpink:"#ff69b4",indianred:"#cd5c5c",indigo:"#4b0082",ivory:"#fffff0",khaki:"#f0e68c",lavender:"#e6e6fa",lavenderblush:"#fff0f5",lawngreen:"#7cfc00",lemonchiffon:"#fffacd",lightblue:"#add8e6",lightcoral:"#f08080",lightcyan:"#e0ffff",lightgoldenrodyellow:"#fafad2",lightgray:"#d3d3d3",lightgreen:"#90ee90",lightgrey:"#d3d3d3",lightpink:"#ffb6c1",lightsalmon:"#ffa07a",
+lightseagreen:"#20b2aa",lightskyblue:"#87cefa",lightslategray:"#778899",lightslategrey:"#778899",lightsteelblue:"#b0c4de",lightyellow:"#ffffe0",lime:"#00ff00",limegreen:"#32cd32",linen:"#faf0e6",magenta:"#ff00ff",maroon:"#800000",mediumaquamarine:"#66cdaa",mediumblue:"#0000cd",mediumorchid:"#ba55d3",mediumpurple:"#9370db",mediumseagreen:"#3cb371",mediumslateblue:"#7b68ee",mediumspringgreen:"#00fa9a",mediumturquoise:"#48d1cc",mediumvioletred:"#c71585",midnightblue:"#191970",mintcream:"#f5fffa",mistyrose:"#ffe4e1",
+moccasin:"#ffe4b5",navajowhite:"#ffdead",navy:"#000080",oldlace:"#fdf5e6",olive:"#808000",olivedrab:"#6b8e23",orange:"#ffa500",orangered:"#ff4500",orchid:"#da70d6",palegoldenrod:"#eee8aa",palegreen:"#98fb98",paleturquoise:"#afeeee",palevioletred:"#db7093",papayawhip:"#ffefd5",peachpuff:"#ffdab9",peru:"#cd853f",pink:"#ffc0cb",plum:"#dda0dd",powderblue:"#b0e0e6",purple:"#800080",red:"#ff0000",rosybrown:"#bc8f8f",royalblue:"#4169e1",saddlebrown:"#8b4513",salmon:"#fa8072",sandybrown:"#f4a460",seagreen:"#2e8b57",
+seashell:"#fff5ee",sienna:"#a0522d",silver:"#c0c0c0",skyblue:"#87ceeb",slateblue:"#6a5acd",slategray:"#708090",slategrey:"#708090",snow:"#fffafa",springgreen:"#00ff7f",steelblue:"#4682b4",tan:"#d2b48c",teal:"#008080",thistle:"#d8bfd8",tomato:"#ff6347",turquoise:"#40e0d0",violet:"#ee82ee",wheat:"#f5deb3",white:"#ffffff",whitesmoke:"#f5f5f5",yellow:"#ffff00",yellowgreen:"#9acd32",transparent:pv.Color.transparent=pv.rgb(0,0,0,0)};(function(){var b=pv.Color.names;for(var c in b)b[c]=pv.color(b[c])})();
+pv.colors=function(){var b=pv.Scale.ordinal();b.range.apply(b,arguments);return b};pv.Colors={};pv.Colors.category10=function(){var b=pv.colors("#1f77b4","#ff7f0e","#2ca02c","#d62728","#9467bd","#8c564b","#e377c2","#7f7f7f","#bcbd22","#17becf");b.domain.apply(b,arguments);return b};
+pv.Colors.category20=function(){var b=pv.colors("#1f77b4","#aec7e8","#ff7f0e","#ffbb78","#2ca02c","#98df8a","#d62728","#ff9896","#9467bd","#c5b0d5","#8c564b","#c49c94","#e377c2","#f7b6d2","#7f7f7f","#c7c7c7","#bcbd22","#dbdb8d","#17becf","#9edae5");b.domain.apply(b,arguments);return b};
+pv.Colors.category19=function(){var b=pv.colors("#9c9ede","#7375b5","#4a5584","#cedb9c","#b5cf6b","#8ca252","#637939","#e7cb94","#e7ba52","#bd9e39","#8c6d31","#e7969c","#d6616b","#ad494a","#843c39","#de9ed6","#ce6dbd","#a55194","#7b4173");b.domain.apply(b,arguments);return b};pv.ramp=function(){var b=pv.Scale.linear();b.range.apply(b,arguments);return b};
+pv.Scene=pv.SvgScene={svg:"http://www.w3.org/2000/svg",xmlns:"http://www.w3.org/2000/xmlns",xlink:"http://www.w3.org/1999/xlink",xhtml:"http://www.w3.org/1999/xhtml",scale:1,events:["DOMMouseScroll","mousewheel","mousedown","mouseup","mouseover","mouseout","mousemove","click","dblclick"],implicit:{svg:{"shape-rendering":"auto","pointer-events":"painted",x:0,y:0,dy:0,"text-anchor":"start",transform:"translate(0,0)",fill:"none","fill-opacity":1,stroke:"none","stroke-opacity":1,"stroke-width":1.5,"stroke-linejoin":"miter"},
+css:{font:"10px sans-serif"}}};pv.SvgScene.updateAll=function(b){if(b.length&&b[0].reverse&&b.type!="line"&&b.type!="area"){for(var c=pv.extend(b),d=0,f=b.length-1;f>=0;d++,f--)c[d]=b[f];b=c}this.removeSiblings(this[b.type](b))};pv.SvgScene.create=function(b){return document.createElementNS(this.svg,b)};
+pv.SvgScene.expect=function(b,c,d,f){if(b){if(b.tagName=="a")b=b.firstChild;if(b.tagName!=c){c=this.create(c);b.parentNode.replaceChild(c,b);b=c}}else b=this.create(c);for(var g in d){c=d[g];if(c==this.implicit.svg[g])c=null;c==null?b.removeAttribute(g):b.setAttribute(g,c)}for(g in f){c=f[g];if(c==this.implicit.css[g])c=null;if(c==null)b.style.removeProperty(g);else b.style[g]=c}return b};
+pv.SvgScene.append=function(b,c,d){b.$scene={scenes:c,index:d};b=this.title(b,c[d]);b.parentNode||c.$g.appendChild(b);return b.nextSibling};pv.SvgScene.title=function(b,c){var d=b.parentNode;if(d&&d.tagName!="a")d=null;if(c.title){if(!d){d=this.create("a");b.parentNode&&b.parentNode.replaceChild(d,b);d.appendChild(b)}d.setAttributeNS(this.xlink,"title",c.title);return d}d&&d.parentNode.replaceChild(b,d);return b};
+pv.SvgScene.dispatch=pv.listener(function(b){var c=b.target.$scene;if(c){var d=b.type;switch(d){case "DOMMouseScroll":d="mousewheel";b.wheel=-480*b.detail;break;case "mousewheel":b.wheel=(window.opera?12:1)*b.wheelDelta;break}pv.Mark.dispatch(d,c.scenes,c.index)&&b.preventDefault()}});pv.SvgScene.removeSiblings=function(b){for(;b;){var c=b.nextSibling;b.parentNode.removeChild(b);b=c}};pv.SvgScene.undefined=function(){};
+pv.SvgScene.pathBasis=function(){function b(f,g,h,i,j){return{x:f[0]*g.left+f[1]*h.left+f[2]*i.left+f[3]*j.left,y:f[0]*g.top+f[1]*h.top+f[2]*i.top+f[3]*j.top}}var c=[[1/6,2/3,1/6,0],[0,2/3,1/3,0],[0,1/3,2/3,0],[0,1/6,2/3,1/6]],d=function(f,g,h,i){var j=b(c[1],f,g,h,i),k=b(c[2],f,g,h,i);f=b(c[3],f,g,h,i);return"C"+j.x+","+j.y+","+k.x+","+k.y+","+f.x+","+f.y};d.segment=function(f,g,h,i){var j=b(c[0],f,g,h,i),k=b(c[1],f,g,h,i),l=b(c[2],f,g,h,i);f=b(c[3],f,g,h,i);return"M"+j.x+","+j.y+"C"+k.x+","+k.y+
+","+l.x+","+l.y+","+f.x+","+f.y};return d}();pv.SvgScene.curveBasis=function(b){if(b.length<=2)return"";var c="",d=b[0],f=d,g=d,h=b[1];c+=this.pathBasis(d,f,g,h);for(var i=2;i<b.length;i++){d=f;f=g;g=h;h=b[i];c+=this.pathBasis(d,f,g,h)}c+=this.pathBasis(f,g,h,h);c+=this.pathBasis(g,h,h,h);return c};
+pv.SvgScene.curveBasisSegments=function(b){if(b.length<=2)return"";var c=[],d=b[0],f=d,g=d,h=b[1],i=this.pathBasis.segment(d,f,g,h);d=f;f=g;g=h;h=b[2];c.push(i+this.pathBasis(d,f,g,h));for(i=3;i<b.length;i++){d=f;f=g;g=h;h=b[i];c.push(this.pathBasis.segment(d,f,g,h))}c.push(this.pathBasis.segment(f,g,h,h)+this.pathBasis(g,h,h,h));return c};
+pv.SvgScene.curveHermite=function(b,c){if(c.length<1||b.length!=c.length&&b.length!=c.length+2)return"";var d=b.length!=c.length,f="",g=b[0],h=b[1],i=c[0],j=i,k=1;if(d){f+="Q"+(h.left-i.x*2/3)+","+(h.top-i.y*2/3)+","+h.left+","+h.top;g=b[1];k=2}if(c.length>1){j=c[1];h=b[k];k++;f+="C"+(g.left+i.x)+","+(g.top+i.y)+","+(h.left-j.x)+","+(h.top-j.y)+","+h.left+","+h.top;for(g=2;g<c.length;g++,k++){h=b[k];j=c[g];f+="S"+(h.left-j.x)+","+(h.top-j.y)+","+h.left+","+h.top}}if(d){b=b[k];f+="Q"+(h.left+j.x*2/
+3)+","+(h.top+j.y*2/3)+","+b.left+","+b.top}return f};
+pv.SvgScene.curveHermiteSegments=function(b,c){if(c.length<1||b.length!=c.length&&b.length!=c.length+2)return[];var d=b.length!=c.length,f=[],g=b[0],h=g,i=c[0],j=i,k=1;if(d){h=b[1];f.push("M"+g.left+","+g.top+"Q"+(h.left-j.x*2/3)+","+(h.top-j.y*2/3)+","+h.left+","+h.top);k=2}for(var l=1;l<c.length;l++,k++){g=h;i=j;h=b[k];j=c[l];f.push("M"+g.left+","+g.top+"C"+(g.left+i.x)+","+(g.top+i.y)+","+(h.left-j.x)+","+(h.top-j.y)+","+h.left+","+h.top)}if(d){b=b[k];f.push("M"+h.left+","+h.top+"Q"+(h.left+j.x*
+2/3)+","+(h.top+j.y*2/3)+","+b.left+","+b.top)}return f};pv.SvgScene.cardinalTangents=function(b,c){var d=[];c=(1-c)/2;for(var f=b[0],g=b[1],h=b[2],i=3;i<b.length;i++){d.push({x:c*(h.left-f.left),y:c*(h.top-f.top)});f=g;g=h;h=b[i]}d.push({x:c*(h.left-f.left),y:c*(h.top-f.top)});return d};pv.SvgScene.curveCardinal=function(b,c){if(b.length<=2)return"";return this.curveHermite(b,this.cardinalTangents(b,c))};
+pv.SvgScene.curveCardinalSegments=function(b,c){if(b.length<=2)return"";return this.curveHermiteSegments(b,this.cardinalTangents(b,c))};
+pv.SvgScene.monotoneTangents=function(b){var c=[],d=[],f=[],g=[],h=0;for(h=0;h<b.length-1;h++)d[h]=(b[h+1].top-b[h].top)/(b[h+1].left-b[h].left);f[0]=d[0];g[0]=b[1].left-b[0].left;for(h=1;h<b.length-1;h++){f[h]=(d[h-1]+d[h])/2;g[h]=(b[h+1].left-b[h-1].left)/2}f[h]=d[h-1];g[h]=b[h].left-b[h-1].left;for(h=0;h<b.length-1;h++)if(d[h]==0){f[h]=0;f[h+1]=0}for(h=0;h<b.length-1;h++)if(!(Math.abs(f[h])<1.0E-5||Math.abs(f[h+1])<1.0E-5)){var i=f[h]/d[h],j=f[h+1]/d[h],k=i*i+j*j;if(k>9){k=3/Math.sqrt(k);f[h]=
+k*i*d[h];f[h+1]=k*j*d[h]}}for(h=0;h<b.length;h++){d=1+f[h]*f[h];c.push({x:g[h]/3/d,y:f[h]*g[h]/3/d})}return c};pv.SvgScene.curveMonotone=function(b){if(b.length<=2)return"";return this.curveHermite(b,this.monotoneTangents(b))};pv.SvgScene.curveMonotoneSegments=function(b){if(b.length<=2)return"";return this.curveHermiteSegments(b,this.monotoneTangents(b))};
+pv.SvgScene.area=function(b){function c(n,p){for(var m=[],r=[],s=p;n<=s;n++,p--){var t=b[n],x=b[p];t=t.left+","+t.top;x=x.left+x.width+","+(x.top+x.height);if(n<s){var u=b[n+1],o=b[p-1];switch(g.interpolate){case "step-before":t+="V"+u.top;x+="H"+(o.left+o.width);break;case "step-after":t+="H"+u.left;x+="V"+(o.top+o.height);break}}m.push(t);r.push(x)}return m.concat(r).join("L")}function d(n,p){for(var m=[],r=[],s=p;n<=s;n++,p--){var t=b[p];m.push(b[n]);r.push({left:t.left+t.width,top:t.top+t.height})}if(g.interpolate==
+"basis"){n=pv.SvgScene.curveBasis(m);p=pv.SvgScene.curveBasis(r)}else if(g.interpolate=="cardinal"){n=pv.SvgScene.curveCardinal(m,g.tension);p=pv.SvgScene.curveCardinal(r,g.tension)}else{n=pv.SvgScene.curveMonotone(m);p=pv.SvgScene.curveMonotone(r)}return m[0].left+","+m[0].top+n+"L"+r[0].left+","+r[0].top+p}var f=b.$g.firstChild;if(!b.length)return f;var g=b[0];if(g.segmented)return this.areaSegment(b);if(!g.visible)return f;var h=g.fillStyle,i=g.strokeStyle;if(!h.opacity&&!i.opacity)return f;for(var j=
+[],k,l=0;l<b.length;l++){k=b[l];if(k.width||k.height){for(var q=l+1;q<b.length;q++){k=b[q];if(!k.width&&!k.height)break}l&&g.interpolate!="step-after"&&l--;q<b.length&&g.interpolate!="step-before"&&q++;j.push((q-l>2&&(g.interpolate=="basis"||g.interpolate=="cardinal"||g.interpolate=="monotone")?d:c)(l,q-1));l=q-1}}if(!j.length)return f;f=this.expect(f,"path",{"shape-rendering":g.antialias?null:"crispEdges","pointer-events":g.events,cursor:g.cursor,d:"M"+j.join("ZM")+"Z",fill:h.color,"fill-opacity":h.opacity||
+null,stroke:i.color,"stroke-opacity":i.opacity||null,"stroke-width":i.opacity?g.lineWidth/this.scale:null});return this.append(f,b,0)};
+pv.SvgScene.areaSegment=function(b){var c=b.$g.firstChild,d=b[0],f,g;if(d.interpolate=="basis"||d.interpolate=="cardinal"||d.interpolate=="monotone"){f=[];g=[];for(var h=0,i=b.length;h<i;h++){var j=b[i-h-1];f.push(b[h]);g.push({left:j.left+j.width,top:j.top+j.height})}if(d.interpolate=="basis"){f=this.curveBasisSegments(f);g=this.curveBasisSegments(g)}else if(d.interpolate=="cardinal"){f=this.curveCardinalSegments(f,d.tension);g=this.curveCardinalSegments(g,d.tension)}else{f=this.curveMonotoneSegments(f);
+g=this.curveMonotoneSegments(g)}}h=0;for(i=b.length-1;h<i;h++){d=b[h];var k=b[h+1];if(d.visible&&k.visible){var l=d.fillStyle,q=d.strokeStyle;if(l.opacity||q.opacity){if(f){j=f[h];k="L"+g[i-h-1].substr(1);j=j+k+"Z"}else{var n=d;j=k;switch(d.interpolate){case "step-before":n=k;break;case "step-after":j=d;break}j="M"+d.left+","+n.top+"L"+k.left+","+j.top+"L"+(k.left+k.width)+","+(j.top+j.height)+"L"+(d.left+d.width)+","+(n.top+n.height)+"Z"}c=this.expect(c,"path",{"shape-rendering":d.antialias?null:
+"crispEdges","pointer-events":d.events,cursor:d.cursor,d:j,fill:l.color,"fill-opacity":l.opacity||null,stroke:q.color,"stroke-opacity":q.opacity||null,"stroke-width":q.opacity?d.lineWidth/this.scale:null});c=this.append(c,b,h)}}}return c};
+pv.SvgScene.bar=function(b){for(var c=b.$g.firstChild,d=0;d<b.length;d++){var f=b[d];if(f.visible){var g=f.fillStyle,h=f.strokeStyle;if(g.opacity||h.opacity){c=this.expect(c,"rect",{"shape-rendering":f.antialias?null:"crispEdges","pointer-events":f.events,cursor:f.cursor,x:f.left,y:f.top,width:Math.max(1.0E-10,f.width),height:Math.max(1.0E-10,f.height),fill:g.color,"fill-opacity":g.opacity||null,stroke:h.color,"stroke-opacity":h.opacity||null,"stroke-width":h.opacity?f.lineWidth/this.scale:null});
+c=this.append(c,b,d)}}}return c};
+pv.SvgScene.dot=function(b){for(var c=b.$g.firstChild,d=0;d<b.length;d++){var f=b[d];if(f.visible){var g=f.fillStyle,h=f.strokeStyle;if(g.opacity||h.opacity){var i=f.radius,j=null;switch(f.shape){case "cross":j="M"+-i+","+-i+"L"+i+","+i+"M"+i+","+-i+"L"+-i+","+i;break;case "triangle":j=i;var k=i*1.1547;j="M0,"+j+"L"+k+","+-j+" "+-k+","+-j+"Z";break;case "diamond":i*=Math.SQRT2;j="M0,"+-i+"L"+i+",0 0,"+i+" "+-i+",0Z";break;case "square":j="M"+-i+","+-i+"L"+i+","+-i+" "+i+","+i+" "+-i+","+i+"Z";break;
+case "tick":j="M0,0L0,"+-f.size;break;case "bar":j="M0,"+f.size/2+"L0,"+-(f.size/2);break}g={"shape-rendering":f.antialias?null:"crispEdges","pointer-events":f.events,cursor:f.cursor,fill:g.color,"fill-opacity":g.opacity||null,stroke:h.color,"stroke-opacity":h.opacity||null,"stroke-width":h.opacity?f.lineWidth/this.scale:null};if(j){g.transform="translate("+f.left+","+f.top+")";if(f.angle)g.transform+=" rotate("+180*f.angle/Math.PI+")";g.d=j;c=this.expect(c,"path",g)}else{g.cx=f.left;g.cy=f.top;g.r=
+i;c=this.expect(c,"circle",g)}c=this.append(c,b,d)}}}return c};
+pv.SvgScene.image=function(b){for(var c=b.$g.firstChild,d=0;d<b.length;d++){var f=b[d];if(f.visible){c=this.fill(c,b,d);if(f.image){c=this.expect(c,"foreignObject",{cursor:f.cursor,x:f.left,y:f.top,width:f.width,height:f.height});var g=c.firstChild||c.appendChild(document.createElementNS(this.xhtml,"canvas"));g.$scene={scenes:b,index:d};g.style.width=f.width;g.style.height=f.height;g.width=f.imageWidth;g.height=f.imageHeight;g.getContext("2d").putImageData(f.image,0,0)}else{c=this.expect(c,"image",
+{preserveAspectRatio:"none",cursor:f.cursor,x:f.left,y:f.top,width:f.width,height:f.height});c.setAttributeNS(this.xlink,"href",f.url)}c=this.append(c,b,d);c=this.stroke(c,b,d)}}return c};
+pv.SvgScene.label=function(b){for(var c=b.$g.firstChild,d=0;d<b.length;d++){var f=b[d];if(f.visible){var g=f.textStyle;if(g.opacity&&f.text){var h=0,i=0,j=0,k="start";switch(f.textBaseline){case "middle":j=".35em";break;case "top":j=".71em";i=f.textMargin;break;case "bottom":i="-"+f.textMargin;break}switch(f.textAlign){case "right":k="end";h="-"+f.textMargin;break;case "center":k="middle";break;case "left":h=f.textMargin;break}c=this.expect(c,"text",{"pointer-events":f.events,cursor:f.cursor,x:h,
+y:i,dy:j,transform:"translate("+f.left+","+f.top+")"+(f.textAngle?" rotate("+180*f.textAngle/Math.PI+")":"")+(this.scale!=1?" scale("+1/this.scale+")":""),fill:g.color,"fill-opacity":g.opacity||null,"text-anchor":k},{font:f.font,"text-shadow":f.textShadow,"text-decoration":f.textDecoration});if(c.firstChild)c.firstChild.nodeValue=f.text;else c.appendChild(document.createTextNode(f.text));c=this.append(c,b,d)}}}return c};
+pv.SvgScene.line=function(b){var c=b.$g.firstChild;if(b.length<2)return c;var d=b[0];if(d.segmented)return this.lineSegment(b);if(!d.visible)return c;var f=d.fillStyle,g=d.strokeStyle;if(!f.opacity&&!g.opacity)return c;var h="M"+d.left+","+d.top;if(b.length>2&&(d.interpolate=="basis"||d.interpolate=="cardinal"||d.interpolate=="monotone"))switch(d.interpolate){case "basis":h+=this.curveBasis(b);break;case "cardinal":h+=this.curveCardinal(b,d.tension);break;case "monotone":h+=this.curveMonotone(b);
+break}else for(var i=1;i<b.length;i++)h+=this.pathSegment(b[i-1],b[i]);c=this.expect(c,"path",{"shape-rendering":d.antialias?null:"crispEdges","pointer-events":d.events,cursor:d.cursor,d:h,fill:f.color,"fill-opacity":f.opacity||null,stroke:g.color,"stroke-opacity":g.opacity||null,"stroke-width":g.opacity?d.lineWidth/this.scale:null,"stroke-linejoin":d.lineJoin});return this.append(c,b,0)};
+pv.SvgScene.lineSegment=function(b){var c=b.$g.firstChild,d=b[0],f;switch(d.interpolate){case "basis":f=this.curveBasisSegments(b);break;case "cardinal":f=this.curveCardinalSegments(b,d.tension);break;case "monotone":f=this.curveMonotoneSegments(b);break}d=0;for(var g=b.length-1;d<g;d++){var h=b[d],i=b[d+1];if(h.visible&&i.visible){var j=h.strokeStyle,k=pv.Color.transparent;if(j.opacity){if(h.interpolate=="linear"&&h.lineJoin=="miter"){k=j;j=pv.Color.transparent;i=this.pathJoin(b[d-1],h,i,b[d+2])}else i=
+f?f[d]:"M"+h.left+","+h.top+this.pathSegment(h,i);c=this.expect(c,"path",{"shape-rendering":h.antialias?null:"crispEdges","pointer-events":h.events,cursor:h.cursor,d:i,fill:k.color,"fill-opacity":k.opacity||null,stroke:j.color,"stroke-opacity":j.opacity||null,"stroke-width":j.opacity?h.lineWidth/this.scale:null,"stroke-linejoin":h.lineJoin});c=this.append(c,b,d)}}}return c};
+pv.SvgScene.pathSegment=function(b,c){var d=1;switch(b.interpolate){case "polar-reverse":d=0;case "polar":var f=c.left-b.left,g=c.top-b.top;b=1-b.eccentricity;f=Math.sqrt(f*f+g*g)/(2*b);if(b<=0||b>1)break;return"A"+f+","+f+" 0 0,"+d+" "+c.left+","+c.top;case "step-before":return"V"+c.top+"H"+c.left;case "step-after":return"H"+c.left+"V"+c.top}return"L"+c.left+","+c.top};pv.SvgScene.lineIntersect=function(b,c,d,f){return b.plus(c.times(d.minus(b).dot(f.perp())/c.dot(f.perp())))};
+pv.SvgScene.pathJoin=function(b,c,d,f){var g=pv.vector(c.left,c.top);d=pv.vector(d.left,d.top);var h=d.minus(g),i=h.perp().norm(),j=i.times(c.lineWidth/(2*this.scale));c=g.plus(j);var k=d.plus(j),l=d.minus(j);j=g.minus(j);if(b&&b.visible){b=g.minus(b.left,b.top).perp().norm().plus(i);j=this.lineIntersect(g,b,j,h);c=this.lineIntersect(g,b,c,h)}if(f&&f.visible){f=pv.vector(f.left,f.top).minus(d).perp().norm().plus(i);l=this.lineIntersect(d,f,l,h);k=this.lineIntersect(d,f,k,h)}return"M"+c.x+","+c.y+
+"L"+k.x+","+k.y+" "+l.x+","+l.y+" "+j.x+","+j.y};
+pv.SvgScene.panel=function(b){for(var c=b.$g,d=c&&c.firstChild,f=0;f<b.length;f++){var g=b[f];if(g.visible){if(!b.parent){g.canvas.style.display="inline-block";if(c&&c.parentNode!=g.canvas)d=(c=g.canvas.firstChild)&&c.firstChild;if(!c){c=g.canvas.appendChild(this.create("svg"));c.setAttribute("font-size","10px");c.setAttribute("font-family","sans-serif");c.setAttribute("fill","none");c.setAttribute("stroke","none");c.setAttribute("stroke-width",1.5);for(var h=0;h<this.events.length;h++)c.addEventListener(this.events[h],
+this.dispatch,false);d=c.firstChild}b.$g=c;c.setAttribute("width",g.width+g.left+g.right);c.setAttribute("height",g.height+g.top+g.bottom)}if(g.overflow=="hidden"){h=pv.id().toString(36);var i=this.expect(d,"g",{"clip-path":"url(#"+h+")"});i.parentNode||c.appendChild(i);b.$g=c=i;d=i.firstChild;d=this.expect(d,"clipPath",{id:h});h=d.firstChild||d.appendChild(this.create("rect"));h.setAttribute("x",g.left);h.setAttribute("y",g.top);h.setAttribute("width",g.width);h.setAttribute("height",g.height);d.parentNode||
+c.appendChild(d);d=d.nextSibling}d=this.fill(d,b,f);var j=this.scale,k=g.transform,l=g.left+k.x,q=g.top+k.y;this.scale*=k.k;for(h=0;h<g.children.length;h++){g.children[h].$g=d=this.expect(d,"g",{transform:"translate("+l+","+q+")"+(k.k!=1?" scale("+k.k+")":"")});this.updateAll(g.children[h]);d.parentNode||c.appendChild(d);d=d.nextSibling}this.scale=j;d=this.stroke(d,b,f);if(g.overflow=="hidden"){b.$g=c=i.parentNode;d=i.nextSibling}}}return d};
+pv.SvgScene.fill=function(b,c,d){var f=c[d],g=f.fillStyle;if(g.opacity||f.events=="all"){b=this.expect(b,"rect",{"shape-rendering":f.antialias?null:"crispEdges","pointer-events":f.events,cursor:f.cursor,x:f.left,y:f.top,width:f.width,height:f.height,fill:g.color,"fill-opacity":g.opacity,stroke:null});b=this.append(b,c,d)}return b};
+pv.SvgScene.stroke=function(b,c,d){var f=c[d],g=f.strokeStyle;if(g.opacity||f.events=="all"){b=this.expect(b,"rect",{"shape-rendering":f.antialias?null:"crispEdges","pointer-events":f.events=="all"?"stroke":f.events,cursor:f.cursor,x:f.left,y:f.top,width:Math.max(1.0E-10,f.width),height:Math.max(1.0E-10,f.height),fill:null,stroke:g.color,"stroke-opacity":g.opacity,"stroke-width":f.lineWidth/this.scale});b=this.append(b,c,d)}return b};
+pv.SvgScene.rule=function(b){for(var c=b.$g.firstChild,d=0;d<b.length;d++){var f=b[d];if(f.visible){var g=f.strokeStyle;if(g.opacity){c=this.expect(c,"line",{"shape-rendering":f.antialias?null:"crispEdges","pointer-events":f.events,cursor:f.cursor,x1:f.left,y1:f.top,x2:f.left+f.width,y2:f.top+f.height,stroke:g.color,"stroke-opacity":g.opacity,"stroke-width":f.lineWidth/this.scale});c=this.append(c,b,d)}}}return c};
+pv.SvgScene.wedge=function(b){for(var c=b.$g.firstChild,d=0;d<b.length;d++){var f=b[d];if(f.visible){var g=f.fillStyle,h=f.strokeStyle;if(g.opacity||h.opacity){var i=f.innerRadius,j=f.outerRadius,k=Math.abs(f.angle);if(k>=2*Math.PI)i=i?"M0,"+j+"A"+j+","+j+" 0 1,1 0,"+-j+"A"+j+","+j+" 0 1,1 0,"+j+"M0,"+i+"A"+i+","+i+" 0 1,1 0,"+-i+"A"+i+","+i+" 0 1,1 0,"+i+"Z":"M0,"+j+"A"+j+","+j+" 0 1,1 0,"+-j+"A"+j+","+j+" 0 1,1 0,"+j+"Z";else{var l=Math.min(f.startAngle,f.endAngle),q=Math.max(f.startAngle,f.endAngle),
+n=Math.cos(l),p=Math.cos(q);l=Math.sin(l);q=Math.sin(q);i=i?"M"+j*n+","+j*l+"A"+j+","+j+" 0 "+(k<Math.PI?"0":"1")+",1 "+j*p+","+j*q+"L"+i*p+","+i*q+"A"+i+","+i+" 0 "+(k<Math.PI?"0":"1")+",0 "+i*n+","+i*l+"Z":"M"+j*n+","+j*l+"A"+j+","+j+" 0 "+(k<Math.PI?"0":"1")+",1 "+j*p+","+j*q+"L0,0Z"}c=this.expect(c,"path",{"shape-rendering":f.antialias?null:"crispEdges","pointer-events":f.events,cursor:f.cursor,transform:"translate("+f.left+","+f.top+")",d:i,fill:g.color,"fill-rule":"evenodd","fill-opacity":g.opacity||
+null,stroke:h.color,"stroke-opacity":h.opacity||null,"stroke-width":h.opacity?f.lineWidth/this.scale:null});c=this.append(c,b,d)}}}return c};pv.Mark=function(){this.$properties=[];this.$handlers={}};pv.Mark.prototype.properties={};pv.Mark.cast={};pv.Mark.prototype.property=function(b,c){if(!this.hasOwnProperty("properties"))this.properties=pv.extend(this.properties);this.properties[b]=true;pv.Mark.prototype.propertyMethod(b,false,pv.Mark.cast[b]=c);return this};
+pv.Mark.prototype.propertyMethod=function(b,c,d){d||(d=pv.Mark.cast[b]);this[b]=function(f){if(c&&this.scene){var g=this.scene.defs;if(arguments.length){g[b]={id:f==null?0:pv.id(),value:f!=null&&d?d(f):f};return this}return g[b]?g[b].value:null}if(arguments.length){g=!c<<1|typeof f=="function";this.propertyValue(b,g&1&&d?function(){var h=f.apply(this,arguments);return h!=null?d(h):null}:f!=null&&d?d(f):f).type=g;return this}return this.instance()[b]}};
+pv.Mark.prototype.propertyValue=function(b,c){var d=this.$properties;c={name:b,id:pv.id(),value:c};for(var f=0;f<d.length;f++)if(d[f].name==b){d.splice(f,1);break}d.push(c);return c};pv.Mark.prototype.property("data").property("visible",Boolean).property("left",Number).property("right",Number).property("top",Number).property("bottom",Number).property("cursor",String).property("title",String).property("reverse",Boolean).property("antialias",Boolean).property("events",String);a=pv.Mark.prototype;
+a.childIndex=-1;a.index=-1;a.scale=1;a.defaults=(new pv.Mark).data(function(b){return[b]}).visible(true).antialias(true).events("painted");a.extend=function(b){this.proto=b;this.target=b.target;return this};a.add=function(b){return this.parent.add(b).extend(this)};a.def=function(b,c){this.propertyMethod(b,true);return this[b](arguments.length>1?c:null)};
+a.anchor=function(b){b||(b="center");return(new pv.Anchor(this)).name(b).data(function(){return this.scene.target.map(function(c){return c.data})}).visible(function(){return this.scene.target[this.index].visible}).left(function(){var c=this.scene.target[this.index],d=c.width||0;switch(this.name()){case "bottom":case "top":case "center":return c.left+d/2;case "left":return null}return c.left+d}).top(function(){var c=this.scene.target[this.index],d=c.height||0;switch(this.name()){case "left":case "right":case "center":return c.top+
+d/2;case "top":return null}return c.top+d}).right(function(){var c=this.scene.target[this.index];return this.name()=="left"?c.right+(c.width||0):null}).bottom(function(){var c=this.scene.target[this.index];return this.name()=="top"?c.bottom+(c.height||0):null}).textAlign(function(){switch(this.name()){case "bottom":case "top":case "center":return"center";case "right":return"right"}return"left"}).textBaseline(function(){switch(this.name()){case "right":case "left":case "center":return"middle";case "top":return"top"}return"bottom"})};
+a.anchorTarget=function(){return this.target};a.margin=function(b){return this.left(b).right(b).top(b).bottom(b)};a.instance=function(b){var c=this.scene||this.parent.instance(-1).children[this.childIndex],d=!arguments.length||this.hasOwnProperty("index")?this.index:b;return c[d<0?c.length-1:d]};
+a.instances=function(b){for(var c=this,d=[],f;!(f=c.scene);){b=b.parent;d.push({index:b.index,childIndex:c.childIndex});c=c.parent}for(;d.length;){b=d.pop();f=f[b.index].children[b.childIndex]}if(this.hasOwnProperty("index")){d=pv.extend(f[this.index]);d.right=d.top=d.left=d.bottom=0;return[d]}return f};a.first=function(){return this.scene[0]};a.last=function(){return this.scene[this.scene.length-1]};a.sibling=function(){return this.index==0?null:this.scene[this.index-1]};
+a.cousin=function(){var b=this.parent;return(b=b&&b.sibling())&&b.children?b.children[this.childIndex][this.index]:null};
+a.render=function(){function b(i,j,k){i.scale=k;if(j<g.length){f.unshift(null);if(i.hasOwnProperty("index"))c(i,j,k);else{for(var l=0,q=i.scene.length;l<q;l++){i.index=l;c(i,j,k)}delete i.index}f.shift()}else{i.build();pv.Scene.scale=k;pv.Scene.updateAll(i.scene)}delete i.scale}function c(i,j,k){var l=i.scene[i.index],q;if(l.visible){var n=g[j],p=i.children[n];for(q=0;q<n;q++)i.children[q].scene=l.children[q];f[0]=l.data;if(p.scene)b(p,j+1,k*l.transform.k);else{p.scene=l.children[n];b(p,j+1,k*l.transform.k);
+delete p.scene}for(q=0;q<n;q++)delete i.children[q].scene}}var d=this.parent,f=pv.Mark.stack;if(d&&!this.root.scene)this.root.render();else{for(var g=[],h=this;h.parent;h=h.parent)g.unshift(h.childIndex);for(this.bind();d&&!d.hasOwnProperty("index");)d=d.parent;this.context(d?d.scene:undefined,d?d.index:-1,function(){b(this.root,0,1)})}};pv.Mark.stack=[];a=pv.Mark.prototype;
+a.bind=function(){function b(j){do for(var k=j.$properties,l=k.length-1;l>=0;l--){var q=k[l];if(!(q.name in c)){c[q.name]=q;switch(q.name){case "data":f=q;break;case "visible":g=q;break;default:d[q.type].push(q);break}}}while(j=j.proto)}var c={},d=[[],[],[],[]],f,g;b(this);b(this.defaults);d[1].reverse();d[3].reverse();var h=this;do for(var i in h.properties)i in c||d[2].push(c[i]={name:i,type:2,value:null});while(h=h.proto);h=d[0].concat(d[1]);for(i=0;i<h.length;i++)this.propertyMethod(h[i].name,
+true);this.binds={properties:c,data:f,defs:h,required:[g],optional:pv.blend(d)}};
+a.build=function(){var b=this.scene,c=pv.Mark.stack;if(!b){b=this.scene=[];b.mark=this;b.type=this.type;b.childIndex=this.childIndex;if(this.parent){b.parent=this.parent.scene;b.parentIndex=this.parent.index}}if(this.target)b.target=this.target.instances(b);if(this.binds.defs.length){var d=b.defs;if(!d)b.defs=d={};for(var f=0;f<this.binds.defs.length;f++){var g=this.binds.defs[f],h=d[g.name];if(!h||g.id>h.id)d[g.name]={id:0,value:g.type&1?g.value.apply(this,c):g.value}}}d=this.binds.data;d=d.type&
+1?d.value.apply(this,c):d.value;c.unshift(null);b.length=d.length;for(f=0;f<d.length;f++){pv.Mark.prototype.index=this.index=f;(g=b[f])||(b[f]=g={});g.data=c[0]=d[f];this.buildInstance(g)}pv.Mark.prototype.index=-1;delete this.index;c.shift();return this};a.buildProperties=function(b,c){for(var d=0,f=c.length;d<f;d++){var g=c[d],h=g.value;switch(g.type){case 0:case 1:h=this.scene.defs[g.name].value;break;case 3:h=h.apply(this,pv.Mark.stack);break}b[g.name]=h}};
+a.buildInstance=function(b){this.buildProperties(b,this.binds.required);if(b.visible){this.buildProperties(b,this.binds.optional);this.buildImplied(b)}};
+a.buildImplied=function(b){var c=b.left,d=b.right,f=b.top,g=b.bottom,h=this.properties,i=h.width?b.width:0,j=h.height?b.height:0,k=this.parent?this.parent.width():i+c+d;if(i==null)i=k-(d=d||0)-(c=c||0);else if(d==null)if(c==null)c=d=(k-i)/2;else d=k-i-(c=c||0);else if(c==null)c=k-i-d;k=this.parent?this.parent.height():j+f+g;if(j==null)j=k-(f=f||0)-(g=g||0);else if(g==null)g=f==null?(f=(k-j)/2):k-j-(f=f||0);else if(f==null)f=k-j-g;b.left=c;b.right=d;b.top=f;b.bottom=g;if(h.width)b.width=i;if(h.height)b.height=
+j;if(h.textStyle&&!b.textStyle)b.textStyle=pv.Color.transparent;if(h.fillStyle&&!b.fillStyle)b.fillStyle=pv.Color.transparent;if(h.strokeStyle&&!b.strokeStyle)b.strokeStyle=pv.Color.transparent};
+a.mouse=function(){var b=pv.event.pageX||0,c=pv.event.pageY||0,d=this.root.canvas();do{b-=d.offsetLeft;c-=d.offsetTop}while(d=d.offsetParent);d=pv.Transform.identity;var f=this.properties.transform?this:this.parent,g=[];do g.push(f);while(f=f.parent);for(;f=g.pop();)d=d.translate(f.left(),f.top()).times(f.transform());d=d.invert();return pv.vector(b*d.k+d.x,c*d.k+d.y)};a.event=function(b,c){this.$handlers[b]=pv.functor(c);return this};
+a.context=function(b,c,d){function f(l,q){pv.Mark.scene=l;h.index=q;if(l){var n=l.mark,p=n,m=[];do{m.push(p);i.push(l[q].data);p.index=q;p.scene=l;q=l.parentIndex;l=l.parent}while(p=p.parent);l=m.length-1;for(q=1;l>0;l--){p=m[l];p.scale=q;q*=p.scene[p.index].transform.k}if(n.children){l=0;for(m=n.children.length;l<m;l++){p=n.children[l];p.scene=n.scene[n.index].children[l];p.scale=q}}}}function g(l){if(l){l=l.mark;var q;if(l.children)for(var n=0,p=l.children.length;n<p;n++){q=l.children[n];delete q.scene;
+delete q.scale}q=l;do{i.pop();if(q.parent){delete q.scene;delete q.scale}delete q.index}while(q=q.parent)}}var h=pv.Mark.prototype,i=pv.Mark.stack,j=pv.Mark.scene,k=h.index;g(j,k);f(b,c);try{d.apply(this,i)}finally{g(b,c);f(j,k)}};pv.Mark.dispatch=function(b,c,d){var f=c.mark,g=c.parent,h=f.$handlers[b];if(!h)return g&&pv.Mark.dispatch(b,g,c.parentIndex);f.context(c,d,function(){(f=h.apply(f,pv.Mark.stack))&&f.render&&f.render()});return true};
+pv.Anchor=function(b){pv.Mark.call(this);this.target=b;this.parent=b.parent};pv.Anchor.prototype=pv.extend(pv.Mark).property("name",String);pv.Anchor.prototype.extend=function(b){this.proto=b;return this};pv.Area=function(){pv.Mark.call(this)};
+pv.Area.prototype=pv.extend(pv.Mark).property("width",Number).property("height",Number).property("lineWidth",Number).property("strokeStyle",pv.color).property("fillStyle",pv.color).property("segmented",Boolean).property("interpolate",String).property("tension",Number);pv.Area.prototype.type="area";pv.Area.prototype.defaults=(new pv.Area).extend(pv.Mark.prototype.defaults).lineWidth(1.5).fillStyle(pv.Colors.category20().by(pv.parent)).interpolate("linear").tension(0.7);
+pv.Area.prototype.buildImplied=function(b){if(b.height==null)b.height=0;if(b.width==null)b.width=0;pv.Mark.prototype.buildImplied.call(this,b)};pv.Area.fixed={lineWidth:1,lineJoin:1,strokeStyle:1,fillStyle:1,segmented:1,interpolate:1,tension:1};
+pv.Area.prototype.bind=function(){pv.Mark.prototype.bind.call(this);var b=this.binds,c=b.required;b=b.optional;for(var d=0,f=b.length;d<f;d++){var g=b[d];g.fixed=g.name in pv.Area.fixed;if(g.name=="segmented"){c.push(g);b.splice(d,1);d--;f--}}this.binds.$required=c;this.binds.$optional=b};
+pv.Area.prototype.buildInstance=function(b){var c=this.binds;if(this.index){var d=c.fixed;if(!d){d=c.fixed=[];function f(i){return!i.fixed||(d.push(i),false)}c.required=c.required.filter(f);if(!this.scene[0].segmented)c.optional=c.optional.filter(f)}c=0;for(var g=d.length;c<g;c++){var h=d[c].name;b[h]=this.scene[0][h]}}else{c.required=c.$required;c.optional=c.$optional;c.fixed=null}pv.Mark.prototype.buildInstance.call(this,b)};
+pv.Area.prototype.anchor=function(b){return pv.Mark.prototype.anchor.call(this,b).interpolate(function(){return this.scene.target[this.index].interpolate}).eccentricity(function(){return this.scene.target[this.index].eccentricity}).tension(function(){return this.scene.target[this.index].tension})};pv.Bar=function(){pv.Mark.call(this)};
+pv.Bar.prototype=pv.extend(pv.Mark).property("width",Number).property("height",Number).property("lineWidth",Number).property("strokeStyle",pv.color).property("fillStyle",pv.color);pv.Bar.prototype.type="bar";pv.Bar.prototype.defaults=(new pv.Bar).extend(pv.Mark.prototype.defaults).lineWidth(1.5).fillStyle(pv.Colors.category20().by(pv.parent));pv.Dot=function(){pv.Mark.call(this)};
+pv.Dot.prototype=pv.extend(pv.Mark).property("size",Number).property("radius",Number).property("shape",String).property("angle",Number).property("lineWidth",Number).property("strokeStyle",pv.color).property("fillStyle",pv.color);pv.Dot.prototype.type="dot";pv.Dot.prototype.defaults=(new pv.Dot).extend(pv.Mark.prototype.defaults).size(20).shape("circle").lineWidth(1.5).strokeStyle(pv.Colors.category10().by(pv.parent));
+pv.Dot.prototype.anchor=function(b){return pv.Mark.prototype.anchor.call(this,b).left(function(){var c=this.scene.target[this.index];switch(this.name()){case "bottom":case "top":case "center":return c.left;case "left":return null}return c.left+c.radius}).right(function(){var c=this.scene.target[this.index];return this.name()=="left"?c.right+c.radius:null}).top(function(){var c=this.scene.target[this.index];switch(this.name()){case "left":case "right":case "center":return c.top;case "top":return null}return c.top+
+c.radius}).bottom(function(){var c=this.scene.target[this.index];return this.name()=="top"?c.bottom+c.radius:null}).textAlign(function(){switch(this.name()){case "left":return"right";case "bottom":case "top":case "center":return"center"}return"left"}).textBaseline(function(){switch(this.name()){case "right":case "left":case "center":return"middle";case "bottom":return"top"}return"bottom"})};
+pv.Dot.prototype.buildImplied=function(b){if(b.radius==null)b.radius=Math.sqrt(b.size);else if(b.size==null)b.size=b.radius*b.radius;pv.Mark.prototype.buildImplied.call(this,b)};pv.Label=function(){pv.Mark.call(this)};
+pv.Label.prototype=pv.extend(pv.Mark).property("text",String).property("font",String).property("textAngle",Number).property("textStyle",pv.color).property("textAlign",String).property("textBaseline",String).property("textMargin",Number).property("textDecoration",String).property("textShadow",String);pv.Label.prototype.type="label";pv.Label.prototype.defaults=(new pv.Label).extend(pv.Mark.prototype.defaults).events("none").text(pv.identity).font("10px sans-serif").textAngle(0).textStyle("black").textAlign("left").textBaseline("bottom").textMargin(3);
+pv.Line=function(){pv.Mark.call(this)};pv.Line.prototype=pv.extend(pv.Mark).property("lineWidth",Number).property("lineJoin",String).property("strokeStyle",pv.color).property("fillStyle",pv.color).property("segmented",Boolean).property("interpolate",String).property("eccentricity",Number).property("tension",Number);a=pv.Line.prototype;a.type="line";a.defaults=(new pv.Line).extend(pv.Mark.prototype.defaults).lineJoin("miter").lineWidth(1.5).strokeStyle(pv.Colors.category10().by(pv.parent)).interpolate("linear").eccentricity(0).tension(0.7);
+a.bind=pv.Area.prototype.bind;a.buildInstance=pv.Area.prototype.buildInstance;a.anchor=function(b){return pv.Area.prototype.anchor.call(this,b).textAlign(function(){switch(this.name()){case "left":return"right";case "bottom":case "top":case "center":return"center";case "right":return"left"}}).textBaseline(function(){switch(this.name()){case "right":case "left":case "center":return"middle";case "top":return"bottom";case "bottom":return"top"}})};pv.Rule=function(){pv.Mark.call(this)};
+pv.Rule.prototype=pv.extend(pv.Mark).property("width",Number).property("height",Number).property("lineWidth",Number).property("strokeStyle",pv.color);pv.Rule.prototype.type="rule";pv.Rule.prototype.defaults=(new pv.Rule).extend(pv.Mark.prototype.defaults).lineWidth(1).strokeStyle("black").antialias(false);pv.Rule.prototype.anchor=pv.Line.prototype.anchor;
+pv.Rule.prototype.buildImplied=function(b){var c=b.left,d=b.right;if(b.width!=null||c==null&&d==null||d!=null&&c!=null)b.height=0;else b.width=0;pv.Mark.prototype.buildImplied.call(this,b)};pv.Panel=function(){pv.Bar.call(this);this.children=[];this.root=this;this.$dom=pv.$&&pv.$.s};pv.Panel.prototype=pv.extend(pv.Bar).property("transform").property("overflow",String).property("canvas",function(b){return typeof b=="string"?document.getElementById(b):b});a=pv.Panel.prototype;a.type="panel";
+a.defaults=(new pv.Panel).extend(pv.Bar.prototype.defaults).fillStyle(null).overflow("visible");a.anchor=function(b){b=pv.Bar.prototype.anchor.call(this,b);b.parent=this;return b};a.add=function(b){b=new b;b.parent=this;b.root=this.root;b.childIndex=this.children.length;this.children.push(b);return b};a.bind=function(){pv.Mark.prototype.bind.call(this);for(var b=0;b<this.children.length;b++)this.children[b].bind()};
+a.buildInstance=function(b){pv.Bar.prototype.buildInstance.call(this,b);if(b.visible){if(!b.children)b.children=[];var c=this.scale*b.transform.k,d,f=this.children.length;pv.Mark.prototype.index=-1;for(var g=0;g<f;g++){d=this.children[g];d.scene=b.children[g];d.scale=c;d.build()}for(g=0;g<f;g++){d=this.children[g];b.children[g]=d.scene;delete d.scene;delete d.scale}b.children.length=f}};
+a.buildImplied=function(b){if(!this.parent){var c=b.canvas;if(c){if(c.$panel!=this)for(c.$panel=this;c.lastChild;)c.removeChild(c.lastChild);var d;if(b.width==null){d=parseFloat(pv.css(c,"width"));b.width=d-b.left-b.right}if(b.height==null){d=parseFloat(pv.css(c,"height"));b.height=d-b.top-b.bottom}}else{d=this.$canvas||(this.$canvas=[]);if(!(c=d[this.index])){c=d[this.index]=document.createElement("span");if(this.$dom)this.$dom.parentNode.insertBefore(c,this.$dom);else{for(d=document.body;d.lastChild&&
+d.lastChild.tagName;)d=d.lastChild;if(d!=document.body)d=d.parentNode;d.appendChild(c)}}}b.canvas=c}if(!b.transform)b.transform=pv.Transform.identity;pv.Mark.prototype.buildImplied.call(this,b)};pv.Image=function(){pv.Bar.call(this)};pv.Image.prototype=pv.extend(pv.Bar).property("url",String).property("imageWidth",Number).property("imageHeight",Number);a=pv.Image.prototype;a.type="image";a.defaults=(new pv.Image).extend(pv.Bar.prototype.defaults).fillStyle(null);
+a.image=function(b){this.$image=function(){var c=b.apply(this,arguments);return c==null?pv.Color.transparent:typeof c=="string"?pv.color(c):c};return this};a.bind=function(){pv.Bar.prototype.bind.call(this);var b=this.binds,c=this;do b.image=c.$image;while(!b.image&&(c=c.proto))};
+a.buildImplied=function(b){pv.Bar.prototype.buildImplied.call(this,b);if(b.visible){if(b.imageWidth==null)b.imageWidth=b.width;if(b.imageHeight==null)b.imageHeight=b.height;if(b.url==null&&this.binds.image){var c=this.$canvas||(this.$canvas=document.createElement("canvas")),d=c.getContext("2d"),f=b.imageWidth,g=b.imageHeight,h=pv.Mark.stack;c.width=f;c.height=g;b=(b.image=d.createImageData(f,g)).data;h.unshift(null,null);for(d=c=0;c<g;c++){h[1]=c;for(var i=0;i<f;i++){h[0]=i;var j=this.binds.image.apply(this,
+h);b[d++]=j.r;b[d++]=j.g;b[d++]=j.b;b[d++]=255*j.a}}h.splice(0,2)}}};pv.Wedge=function(){pv.Mark.call(this)};pv.Wedge.prototype=pv.extend(pv.Mark).property("startAngle",Number).property("endAngle",Number).property("angle",Number).property("innerRadius",Number).property("outerRadius",Number).property("lineWidth",Number).property("strokeStyle",pv.color).property("fillStyle",pv.color);a=pv.Wedge.prototype;a.type="wedge";
+a.defaults=(new pv.Wedge).extend(pv.Mark.prototype.defaults).startAngle(function(){var b=this.sibling();return b?b.endAngle:-Math.PI/2}).innerRadius(0).lineWidth(1.5).strokeStyle(null).fillStyle(pv.Colors.category20().by(pv.index));a.midRadius=function(){return(this.innerRadius()+this.outerRadius())/2};a.midAngle=function(){return(this.startAngle()+this.endAngle())/2};
+a.anchor=function(b){function c(g){return g.innerRadius||g.angle<2*Math.PI}function d(g){return(g.innerRadius+g.outerRadius)/2}function f(g){return(g.startAngle+g.endAngle)/2}return pv.Mark.prototype.anchor.call(this,b).left(function(){var g=this.scene.target[this.index];if(c(g))switch(this.name()){case "outer":return g.left+g.outerRadius*Math.cos(f(g));case "inner":return g.left+g.innerRadius*Math.cos(f(g));case "start":return g.left+d(g)*Math.cos(g.startAngle);case "center":return g.left+d(g)*Math.cos(f(g));
+case "end":return g.left+d(g)*Math.cos(g.endAngle)}return g.left}).top(function(){var g=this.scene.target[this.index];if(c(g))switch(this.name()){case "outer":return g.top+g.outerRadius*Math.sin(f(g));case "inner":return g.top+g.innerRadius*Math.sin(f(g));case "start":return g.top+d(g)*Math.sin(g.startAngle);case "center":return g.top+d(g)*Math.sin(f(g));case "end":return g.top+d(g)*Math.sin(g.endAngle)}return g.top}).textAlign(function(){var g=this.scene.target[this.index];if(c(g))switch(this.name()){case "outer":return pv.Wedge.upright(f(g))?
+"right":"left";case "inner":return pv.Wedge.upright(f(g))?"left":"right"}return"center"}).textBaseline(function(){var g=this.scene.target[this.index];if(c(g))switch(this.name()){case "start":return pv.Wedge.upright(g.startAngle)?"top":"bottom";case "end":return pv.Wedge.upright(g.endAngle)?"bottom":"top"}return"middle"}).textAngle(function(){var g=this.scene.target[this.index],h=0;if(c(g))switch(this.name()){case "center":case "inner":case "outer":h=f(g);break;case "start":h=g.startAngle;break;case "end":h=
+g.endAngle;break}return pv.Wedge.upright(h)?h:h+Math.PI})};pv.Wedge.upright=function(b){b%=2*Math.PI;b=b<0?2*Math.PI+b:b;return b<Math.PI/2||b>=3*Math.PI/2};pv.Wedge.prototype.buildImplied=function(b){if(b.angle==null)b.angle=b.endAngle-b.startAngle;else if(b.endAngle==null)b.endAngle=b.startAngle+b.angle;pv.Mark.prototype.buildImplied.call(this,b)};pv.simulation=function(b){return new pv.Simulation(b)};pv.Simulation=function(b){for(var c=0;c<b.length;c++)this.particle(b[c])};a=pv.Simulation.prototype;
+a.particle=function(b){b.next=this.particles;if(isNaN(b.px))b.px=b.x;if(isNaN(b.py))b.py=b.y;if(isNaN(b.fx))b.fx=0;if(isNaN(b.fy))b.fy=0;this.particles=b;return this};a.force=function(b){b.next=this.forces;this.forces=b;return this};a.constraint=function(b){b.next=this.constraints;this.constraints=b;return this};
+a.stabilize=function(b){var c;arguments.length||(b=3);for(var d=0;d<b;d++){var f=new pv.Quadtree(this.particles);for(c=this.constraints;c;c=c.next)c.apply(this.particles,f)}for(c=this.particles;c;c=c.next){c.px=c.x;c.py=c.y}return this};
+a.step=function(){var b;for(b=this.particles;b;b=b.next){var c=b.px,d=b.py;b.px=b.x;b.py=b.y;b.x+=b.vx=b.x-c+b.fx;b.y+=b.vy=b.y-d+b.fy}c=new pv.Quadtree(this.particles);for(b=this.constraints;b;b=b.next)b.apply(this.particles,c);for(b=this.particles;b;b=b.next)b.fx=b.fy=0;for(b=this.forces;b;b=b.next)b.apply(this.particles,c)};
+pv.Quadtree=function(b){function c(l,q,n,p,m,r){if(!(isNaN(q.x)||isNaN(q.y)))if(l.leaf)if(l.p){if(!(Math.abs(l.p.x-q.x)+Math.abs(l.p.y-q.y)<0.01)){var s=l.p;l.p=null;d(l,s,n,p,m,r)}d(l,q,n,p,m,r)}else l.p=q;else d(l,q,n,p,m,r)}function d(l,q,n,p,m,r){var s=(n+m)*0.5,t=(p+r)*0.5,x=q.x>=s,u=q.y>=t;l.leaf=false;switch((u<<1)+x){case 0:l=l.c1||(l.c1=new pv.Quadtree.Node);break;case 1:l=l.c2||(l.c2=new pv.Quadtree.Node);break;case 2:l=l.c3||(l.c3=new pv.Quadtree.Node);break;case 3:l=l.c4||(l.c4=new pv.Quadtree.Node);
+break}if(x)n=s;else m=s;if(u)p=t;else r=t;c(l,q,n,p,m,r)}var f,g=Number.POSITIVE_INFINITY,h=g,i=Number.NEGATIVE_INFINITY,j=i;for(f=b;f;f=f.next){if(f.x<g)g=f.x;if(f.y<h)h=f.y;if(f.x>i)i=f.x;if(f.y>j)j=f.y}f=i-g;var k=j-h;if(f>k)j=h+f;else i=g+k;this.xMin=g;this.yMin=h;this.xMax=i;this.yMax=j;this.root=new pv.Quadtree.Node;for(f=b;f;f=f.next)c(this.root,f,g,h,i,j)};pv.Quadtree.Node=function(){this.leaf=true;this.p=this.c4=this.c3=this.c2=this.c1=null};pv.Force={};
+pv.Force.charge=function(b){function c(l){function q(m){c(m);l.cn+=m.cn;n+=m.cn*m.cx;p+=m.cn*m.cy}var n=0,p=0;l.cn=0;if(!l.leaf){l.c1&&q(l.c1);l.c2&&q(l.c2);l.c3&&q(l.c3);l.c4&&q(l.c4)}if(l.p){l.cn+=b;n+=b*l.p.x;p+=b*l.p.y}l.cx=n/l.cn;l.cy=p/l.cn}function d(l,q,n,p,m,r){var s=l.cx-q.x,t=l.cy-q.y,x=1/Math.sqrt(s*s+t*t);if(l.leaf&&l.p!=q||(m-n)*x<j){if(!(x<i)){if(x>g)x=g;l=l.cn*x*x*x;s=s*l;t=t*l;q.fx+=s;q.fy+=t}}else if(!l.leaf){var u=(n+m)*0.5,o=(p+r)*0.5;l.c1&&d(l.c1,q,n,p,u,o);l.c2&&d(l.c2,q,u,p,
+m,o);l.c3&&d(l.c3,q,n,o,u,r);l.c4&&d(l.c4,q,u,o,m,r);if(!(x<i)){if(x>g)x=g;if(l.p&&l.p!=q){l=b*x*x*x;s=s*l;t=t*l;q.fx+=s;q.fy+=t}}}}var f=2,g=1/f,h=500,i=1/h,j=0.9,k={};arguments.length||(b=-40);k.constant=function(l){if(arguments.length){b=Number(l);return k}return b};k.domain=function(l,q){if(arguments.length){f=Number(l);g=1/f;h=Number(q);i=1/h;return k}return[f,h]};k.theta=function(l){if(arguments.length){j=Number(l);return k}return j};k.apply=function(l,q){c(q.root);for(l=l;l;l=l.next)d(q.root,
+l,q.xMin,q.yMin,q.xMax,q.yMax)};return k};pv.Force.drag=function(b){var c={};arguments.length||(b=0.1);c.constant=function(d){if(arguments.length){b=d;return c}return b};c.apply=function(d){if(b)for(d=d;d;d=d.next){d.fx-=b*d.vx;d.fy-=b*d.vy}};return c};
+pv.Force.spring=function(b){var c=0.1,d=20,f,g,h={};arguments.length||(b=0.1);h.links=function(i){if(arguments.length){f=i;g=i.map(function(j){return 1/Math.sqrt(Math.max(j.sourceNode.linkDegree,j.targetNode.linkDegree))});return h}return f};h.constant=function(i){if(arguments.length){b=Number(i);return h}return b};h.damping=function(i){if(arguments.length){c=Number(i);return h}return c};h.length=function(i){if(arguments.length){d=Number(i);return h}return d};h.apply=function(){for(var i=0;i<f.length;i++){var j=
+f[i].sourceNode,k=f[i].targetNode,l=j.x-k.x,q=j.y-k.y,n=Math.sqrt(l*l+q*q),p=n?1/n:1;p=(b*g[i]*(n-d)+c*g[i]*(l*(j.vx-k.vx)+q*(j.vy-k.vy))*p)*p;l=-p*(n?l:0.01*(0.5-Math.random()));q=-p*(n?q:0.01*(0.5-Math.random()));j.fx+=l;j.fy+=q;k.fx-=l;k.fy-=q}};return h};pv.Constraint={};
+pv.Constraint.collision=function(b){function c(l,q,n,p,m,r){if(!l.leaf){var s=(n+m)*0.5,t=(p+r)*0.5,x=t<j,u=s>g,o=s<i;if(t>h){l.c1&&u&&c(l.c1,q,n,p,s,t);l.c2&&o&&c(l.c2,q,s,p,m,t)}if(x){l.c3&&u&&c(l.c3,q,n,t,s,r);l.c4&&o&&c(l.c4,q,s,t,m,r)}}if(l.p&&l.p!=q){n=q.x-l.p.x;p=q.y-l.p.y;m=Math.sqrt(n*n+p*p);r=f+b(l.p);if(m<r){m=(m-r)/m*0.5;n*=m;p*=m;q.x-=n;q.y-=p;l.p.x+=n;l.p.y+=p}}}var d=1,f,g,h,i,j,k={};arguments.length||(f=10);k.repeat=function(l){if(arguments.length){d=Number(l);return k}return d};k.apply=
+function(l,q){var n,p,m=-Infinity;for(n=l;n;n=n.next){p=b(n);if(p>m)m=p}for(var r=0;r<d;r++)for(n=l;n;n=n.next){p=(f=b(n))+m;g=n.x-p;i=n.x+p;h=n.y-p;j=n.y+p;c(q.root,n,q.xMin,q.yMin,q.xMax,q.yMax)}};return k};pv.Constraint.position=function(b){var c=1,d={};arguments.length||(b=function(f){return f.fix});d.alpha=function(f){if(arguments.length){c=Number(f);return d}return c};d.apply=function(f){for(f=f;f;f=f.next){var g=b(f);if(g){f.x+=(g.x-f.x)*c;f.y+=(g.y-f.y)*c;f.fx=f.fy=f.vx=f.vy=0}}};return d};
+pv.Constraint.bound=function(){var b={},c,d;b.x=function(f,g){if(arguments.length){c={min:Math.min(f,g),max:Math.max(f,g)};return this}return c};b.y=function(f,g){if(arguments.length){d={min:Math.min(f,g),max:Math.max(f,g)};return this}return d};b.apply=function(f){if(c)for(var g=f;g;g=g.next)g.x=g.x<c.min?c.min:g.x>c.max?c.max:g.x;if(d)for(g=f;g;g=g.next)g.y=g.y<d.min?d.min:g.y>d.max?d.max:g.y};return b};pv.Layout=function(){pv.Panel.call(this)};pv.Layout.prototype=pv.extend(pv.Panel);
+pv.Layout.prototype.property=function(b,c){if(!this.hasOwnProperty("properties"))this.properties=pv.extend(this.properties);this.properties[b]=true;this.propertyMethod(b,false,pv.Mark.cast[b]=c);return this};
+pv.Layout.Network=function(){pv.Layout.call(this);var b=this;this.$id=pv.id();(this.node=(new pv.Mark).data(function(){return b.nodes()}).strokeStyle("#1f77b4").fillStyle("#fff").left(function(c){return c.x}).top(function(c){return c.y})).parent=this;this.link=(new pv.Mark).extend(this.node).data(function(c){return[c.sourceNode,c.targetNode]}).fillStyle(null).lineWidth(function(c,d){return d.linkValue*1.5}).strokeStyle("rgba(0,0,0,.2)");this.link.add=function(c){return b.add(pv.Panel).data(function(){return b.links()}).add(c).extend(this)};
+(this.label=(new pv.Mark).extend(this.node).textMargin(7).textBaseline("middle").text(function(c){return c.nodeName||c.nodeValue}).textAngle(function(c){c=c.midAngle;return pv.Wedge.upright(c)?c:c+Math.PI}).textAlign(function(c){return pv.Wedge.upright(c.midAngle)?"left":"right"})).parent=this};
+pv.Layout.Network.prototype=pv.extend(pv.Layout).property("nodes",function(b){return b.map(function(c,d){if(typeof c!="object")c={nodeValue:c};c.index=d;return c})}).property("links",function(b){return b.map(function(c){if(isNaN(c.linkValue))c.linkValue=isNaN(c.value)?1:c.value;return c})});pv.Layout.Network.prototype.reset=function(){this.$id=pv.id();return this};pv.Layout.Network.prototype.buildProperties=function(b,c){if((b.$id||0)<this.$id)pv.Layout.prototype.buildProperties.call(this,b,c)};
+pv.Layout.Network.prototype.buildImplied=function(b){pv.Layout.prototype.buildImplied.call(this,b);if(b.$id>=this.$id)return true;b.$id=this.$id;b.nodes.forEach(function(c){c.linkDegree=0});b.links.forEach(function(c){var d=c.linkValue;(c.sourceNode||(c.sourceNode=b.nodes[c.source])).linkDegree+=d;(c.targetNode||(c.targetNode=b.nodes[c.target])).linkDegree+=d})};pv.Layout.Hierarchy=function(){pv.Layout.Network.call(this);this.link.strokeStyle("#ccc")};pv.Layout.Hierarchy.prototype=pv.extend(pv.Layout.Network);
+pv.Layout.Hierarchy.prototype.buildImplied=function(b){if(!b.links)b.links=pv.Layout.Hierarchy.links.call(this);pv.Layout.Network.prototype.buildImplied.call(this,b)};pv.Layout.Hierarchy.links=function(){return this.nodes().filter(function(b){return b.parentNode}).map(function(b){return{sourceNode:b,targetNode:b.parentNode,linkValue:1}})};
+pv.Layout.Hierarchy.NodeLink={buildImplied:function(b){function c(m){return m.parentNode?m.depth*(n-q)+q:0}function d(m){return m.parentNode?(m.breadth-0.25)*2*Math.PI:0}function f(m){switch(i){case "left":return m.depth*k;case "right":return k-m.depth*k;case "top":return m.breadth*k;case "bottom":return k-m.breadth*k;case "radial":return k/2+c(m)*Math.cos(m.midAngle)}}function g(m){switch(i){case "left":return m.breadth*l;case "right":return l-m.breadth*l;case "top":return m.depth*l;case "bottom":return l-
+m.depth*l;case "radial":return l/2+c(m)*Math.sin(m.midAngle)}}var h=b.nodes,i=b.orient,j=/^(top|bottom)$/.test(i),k=b.width,l=b.height;if(i=="radial"){var q=b.innerRadius,n=b.outerRadius;if(q==null)q=0;if(n==null)n=Math.min(k,l)/2}for(b=0;b<h.length;b++){var p=h[b];p.midAngle=i=="radial"?d(p):j?Math.PI/2:0;p.x=f(p);p.y=g(p);if(p.firstChild)p.midAngle+=Math.PI}}};
+pv.Layout.Hierarchy.Fill={constructor:function(){this.node.strokeStyle("#fff").fillStyle("#ccc").width(function(b){return b.dx}).height(function(b){return b.dy}).innerRadius(function(b){return b.innerRadius}).outerRadius(function(b){return b.outerRadius}).startAngle(function(b){return b.startAngle}).angle(function(b){return b.angle});this.label.textAlign("center").left(function(b){return b.x+b.dx/2}).top(function(b){return b.y+b.dy/2});delete this.link},buildImplied:function(b){function c(o,v){return(o+
+v)/(1+v)}function d(o){switch(n){case "left":return c(o.minDepth,s)*m;case "right":return(1-c(o.maxDepth,s))*m;case "top":return o.minBreadth*m;case "bottom":return(1-o.maxBreadth)*m;case "radial":return m/2}}function f(o){switch(n){case "left":return o.minBreadth*r;case "right":return(1-o.maxBreadth)*r;case "top":return c(o.minDepth,s)*r;case "bottom":return(1-c(o.maxDepth,s))*r;case "radial":return r/2}}function g(o){switch(n){case "left":case "right":return(o.maxDepth-o.minDepth)/(1+s)*m;case "top":case "bottom":return(o.maxBreadth-
+o.minBreadth)*m;case "radial":return o.parentNode?(o.innerRadius+o.outerRadius)*Math.cos(o.midAngle):0}}function h(o){switch(n){case "left":case "right":return(o.maxBreadth-o.minBreadth)*r;case "top":case "bottom":return(o.maxDepth-o.minDepth)/(1+s)*r;case "radial":return o.parentNode?(o.innerRadius+o.outerRadius)*Math.sin(o.midAngle):0}}function i(o){return Math.max(0,c(o.minDepth,s/2))*(x-t)+t}function j(o){return c(o.maxDepth,s/2)*(x-t)+t}function k(o){return(o.parentNode?o.minBreadth-0.25:0)*
+2*Math.PI}function l(o){return(o.parentNode?o.maxBreadth-o.minBreadth:1)*2*Math.PI}var q=b.nodes,n=b.orient,p=/^(top|bottom)$/.test(n),m=b.width,r=b.height,s=-q[0].minDepth;if(n=="radial"){var t=b.innerRadius,x=b.outerRadius;if(t==null)t=0;if(t)s*=2;if(x==null)x=Math.min(m,r)/2}for(b=0;b<q.length;b++){var u=q[b];u.x=d(u);u.y=f(u);if(n=="radial"){u.innerRadius=i(u);u.outerRadius=j(u);u.startAngle=k(u);u.angle=l(u);u.midAngle=u.startAngle+u.angle/2}else u.midAngle=p?-Math.PI/2:0;u.dx=g(u);u.dy=h(u)}}};
+pv.Layout.Grid=function(){pv.Layout.call(this);var b=this;(this.cell=(new pv.Mark).data(function(){return b.scene[b.index].$grid}).width(function(){return b.width()/b.cols()}).height(function(){return b.height()/b.rows()}).left(function(){return this.width()*(this.index%b.cols())}).top(function(){return this.height()*Math.floor(this.index/b.cols())})).parent=this};pv.Layout.Grid.prototype=pv.extend(pv.Layout).property("rows").property("cols");pv.Layout.Grid.prototype.defaults=(new pv.Layout.Grid).extend(pv.Layout.prototype.defaults).rows(1).cols(1);
+pv.Layout.Grid.prototype.buildImplied=function(b){pv.Layout.prototype.buildImplied.call(this,b);var c=b.rows,d=b.cols;if(typeof d=="object")c=pv.transpose(d);if(typeof c=="object"){b.$grid=pv.blend(c);b.rows=c.length;b.cols=c[0]?c[0].length:0}else b.$grid=pv.repeat([b.data],c*d)};
+pv.Layout.Stack=function(){function b(i){return function(){return f[i](this.parent.index,this.index)}}pv.Layout.call(this);var c=this,d=function(){return null},f={t:d,l:d,r:d,b:d,w:d,h:d},g,h=c.buildImplied;this.buildImplied=function(i){h.call(this,i);var j=i.layers,k=j.length,l,q=i.orient,n=/^(top|bottom)\b/.test(q),p=this.parent[n?"height":"width"](),m=[],r=[],s=[],t=pv.Mark.stack,x={parent:{parent:this}};t.unshift(null);g=[];for(var u=0;u<k;u++){s[u]=[];r[u]=[];x.parent.index=u;t[0]=j[u];g[u]=
+this.$values.apply(x.parent,t);if(!u)l=g[u].length;t.unshift(null);for(var o=0;o<l;o++){t[0]=g[u][o];x.index=o;u||(m[o]=this.$x.apply(x,t));s[u][o]=this.$y.apply(x,t)}t.shift()}t.shift();switch(i.order){case "inside-out":var v=s.map(function(A){return pv.max.index(A)});x=pv.range(k).sort(function(A,D){return v[A]-v[D]});j=s.map(function(A){return pv.sum(A)});var w=t=0,y=[],z=[];for(u=0;u<k;u++){o=x[u];if(t<w){t+=j[o];y.push(o)}else{w+=j[o];z.push(o)}}j=z.reverse().concat(y);break;case "reverse":j=
+pv.range(k-1,-1,-1);break;default:j=pv.range(k);break}switch(i.offset){case "silohouette":for(o=0;o<l;o++){for(u=x=0;u<k;u++)x+=s[u][o];r[j[0]][o]=(p-x)/2}break;case "wiggle":for(u=x=0;u<k;u++)x+=s[u][0];r[j[0]][0]=x=(p-x)/2;for(o=1;o<l;o++){t=p=0;w=m[o]-m[o-1];for(u=0;u<k;u++)p+=s[u][o];for(u=0;u<k;u++){y=(s[j[u]][o]-s[j[u]][o-1])/(2*w);for(i=0;i<u;i++)y+=(s[j[i]][o]-s[j[i]][o-1])/w;t+=y*s[j[u]][o]}r[j[0]][o]=x-=p?t/p*w:0}break;case "expand":for(o=0;o<l;o++){for(u=i=r[j[0]][o]=0;u<k;u++)i+=s[u][o];
+if(i){i=p/i;for(u=0;u<k;u++)s[u][o]*=i}else{i=p/k;for(u=0;u<k;u++)s[u][o]=i}}break;default:for(o=0;o<l;o++)r[j[0]][o]=0;break}for(o=0;o<l;o++){x=r[j[0]][o];for(u=1;u<k;u++){x+=s[j[u-1]][o];r[j[u]][o]=x}}u=q.indexOf("-");k=n?"h":"w";n=u<0?n?"l":"b":q.charAt(u+1);q=q.charAt(0);for(var C in f)f[C]=d;f[n]=function(A,D){return m[D]};f[q]=function(A,D){return r[A][D]};f[k]=function(A,D){return s[A][D]}};this.layer=(new pv.Mark).data(function(){return g[this.parent.index]}).top(b("t")).left(b("l")).right(b("r")).bottom(b("b")).width(b("w")).height(b("h"));
+this.layer.add=function(i){return c.add(pv.Panel).data(function(){return c.layers()}).add(i).extend(this)}};pv.Layout.Stack.prototype=pv.extend(pv.Layout).property("orient",String).property("offset",String).property("order",String).property("layers");a=pv.Layout.Stack.prototype;a.defaults=(new pv.Layout.Stack).extend(pv.Layout.prototype.defaults).orient("bottom-left").offset("zero").layers([[]]);a.$x=pv.Layout.Stack.prototype.$y=function(){return 0};a.x=function(b){this.$x=pv.functor(b);return this};
+a.y=function(b){this.$y=pv.functor(b);return this};a.$values=pv.identity;a.values=function(b){this.$values=pv.functor(b);return this};
+pv.Layout.Treemap=function(){pv.Layout.Hierarchy.call(this);this.node.strokeStyle("#fff").fillStyle("rgba(31, 119, 180, .25)").width(function(b){return b.dx}).height(function(b){return b.dy});this.label.visible(function(b){return!b.firstChild}).left(function(b){return b.x+b.dx/2}).top(function(b){return b.y+b.dy/2}).textAlign("center").textAngle(function(b){return b.dx>b.dy?0:-Math.PI/2});(this.leaf=(new pv.Mark).extend(this.node).fillStyle(null).strokeStyle(null).visible(function(b){return!b.firstChild})).parent=
+this;delete this.link};pv.Layout.Treemap.prototype=pv.extend(pv.Layout.Hierarchy).property("round",Boolean).property("paddingLeft",Number).property("paddingRight",Number).property("paddingTop",Number).property("paddingBottom",Number).property("mode",String).property("order",String);a=pv.Layout.Treemap.prototype;a.defaults=(new pv.Layout.Treemap).extend(pv.Layout.Hierarchy.prototype.defaults).mode("squarify").order("ascending");a.padding=function(b){return this.paddingLeft(b).paddingRight(b).paddingTop(b).paddingBottom(b)};
+a.$size=function(b){return Number(b.nodeValue)};a.size=function(b){this.$size=pv.functor(b);return this};
+a.buildImplied=function(b){function c(r,s,t,x,u,o,v){for(var w=0,y=0;w<r.length;w++){var z=r[w];if(t){z.x=x+y;z.y=u;y+=z.dx=p(o*z.size/s);z.dy=v}else{z.x=x;z.y=u+y;z.dx=o;y+=z.dy=p(v*z.size/s)}}if(z)if(t)z.dx+=o-y;else z.dy+=v-y}function d(r,s){for(var t=-Infinity,x=Infinity,u=0,o=0;o<r.length;o++){var v=r[o].size;if(v<x)x=v;if(v>t)t=v;u+=v}u*=u;s*=s;return Math.max(s*t/u,u/(s*x))}function f(r,s){function t(A){var D=o==y,G=pv.sum(A,n),E=y?p(G/y):0;c(A,G,D,x,u,D?o:E,D?E:v);if(D){u+=E;v-=E}else{x+=
+E;o-=E}y=Math.min(o,v);return D}var x=r.x+j,u=r.y+l,o=r.dx-j-k,v=r.dy-l-q;if(m!="squarify")c(r.childNodes,r.size,m=="slice"?true:m=="dice"?false:s&1,x,u,o,v);else{var w=[];s=Infinity;var y=Math.min(o,v),z=o*v/r.size;if(!(r.size<=0)){r.visitBefore(function(A){A.size*=z});for(r=r.childNodes.slice();r.length;){var C=r[r.length-1];if(C.size){w.push(C);z=d(w,y);if(z<=s){r.pop();s=z}else{w.pop();t(w);w.length=0;s=Infinity}}else r.pop()}if(t(w))for(s=0;s<w.length;s++)w[s].dy+=v;else for(s=0;s<w.length;s++)w[s].dx+=
+o}}}if(!pv.Layout.Hierarchy.prototype.buildImplied.call(this,b)){var g=this,h=b.nodes[0],i=pv.Mark.stack,j=b.paddingLeft,k=b.paddingRight,l=b.paddingTop,q=b.paddingBottom,n=function(r){return r.size},p=b.round?Math.round:Number,m=b.mode;i.unshift(null);h.visitAfter(function(r,s){r.depth=s;r.x=r.y=r.dx=r.dy=0;r.size=r.firstChild?pv.sum(r.childNodes,function(t){return t.size}):g.$size.apply(g,(i[0]=r,i))});i.shift();switch(b.order){case "ascending":h.sort(function(r,s){return r.size-s.size});break;
+case "descending":h.sort(function(r,s){return s.size-r.size});break;case "reverse":h.reverse();break}h.x=0;h.y=0;h.dx=b.width;h.dy=b.height;h.visitBefore(f)}};pv.Layout.Tree=function(){pv.Layout.Hierarchy.call(this)};pv.Layout.Tree.prototype=pv.extend(pv.Layout.Hierarchy).property("group",Number).property("breadth",Number).property("depth",Number).property("orient",String);pv.Layout.Tree.prototype.defaults=(new pv.Layout.Tree).extend(pv.Layout.Hierarchy.prototype.defaults).group(1).breadth(15).depth(60).orient("top");
+pv.Layout.Tree.prototype.buildImplied=function(b){function c(o){var v,w,y;if(o.firstChild){v=o.firstChild;w=o.lastChild;for(var z=y=v;z;z=z.nextSibling){c(z);y=f(z,y)}j(o);w=0.5*(v.prelim+w.prelim);if(v=o.previousSibling){o.prelim=v.prelim+l(o.depth,true);o.mod=o.prelim-w}else o.prelim=w}else if(v=o.previousSibling)o.prelim=v.prelim+l(o.depth,true)}function d(o,v,w){o.breadth=o.prelim+v;v+=o.mod;for(o=o.firstChild;o;o=o.nextSibling)d(o,v,w)}function f(o,v){var w=o.previousSibling;if(w){var y=o,z=
+o,C=w;w=o.parentNode.firstChild;var A=y.mod,D=z.mod,G=C.mod,E=w.mod;C=h(C);for(y=g(y);C&&y;){C=C;y=y;w=g(w);z=h(z);z.ancestor=o;var B=C.prelim+G-(y.prelim+A)+l(C.depth,false);if(B>0){i(k(C,o,v),o,B);A+=B;D+=B}G+=C.mod;A+=y.mod;E+=w.mod;D+=z.mod;C=h(C);y=g(y)}if(C&&!h(z)){z.thread=C;z.mod+=G-D}if(y&&!g(w)){w.thread=y;w.mod+=A-E;v=o}}return v}function g(o){return o.firstChild||o.thread}function h(o){return o.lastChild||o.thread}function i(o,v,w){var y=v.number-o.number;v.change-=w/y;v.shift+=w;o.change+=
+w/y;v.prelim+=w;v.mod+=w}function j(o){var v=0,w=0;for(o=o.lastChild;o;o=o.previousSibling){o.prelim+=v;o.mod+=v;w+=o.change;v+=o.shift+w}}function k(o,v,w){return o.ancestor.parentNode==v.parentNode?o.ancestor:w}function l(o,v){return(v?1:t+1)/(m=="radial"?o:1)}function q(o){return m=="radial"?o.breadth/r:0}function n(o){switch(m){case "left":return o.depth;case "right":return x-o.depth;case "top":case "bottom":return o.breadth+x/2;case "radial":return x/2+o.depth*Math.cos(q(o))}}function p(o){switch(m){case "left":case "right":return o.breadth+
+u/2;case "top":return o.depth;case "bottom":return u-o.depth;case "radial":return u/2+o.depth*Math.sin(q(o))}}if(!pv.Layout.Hierarchy.prototype.buildImplied.call(this,b)){var m=b.orient,r=b.depth,s=b.breadth,t=b.group,x=b.width,u=b.height;b=b.nodes[0];b.visitAfter(function(o,v){o.ancestor=o;o.prelim=0;o.mod=0;o.change=0;o.shift=0;o.number=o.previousSibling?o.previousSibling.number+1:0;o.depth=v});c(b);d(b,-b.prelim,0);b.visitAfter(function(o){o.breadth*=s;o.depth*=r;o.midAngle=q(o);o.x=n(o);o.y=p(o);
+if(o.firstChild)o.midAngle+=Math.PI;delete o.breadth;delete o.depth;delete o.ancestor;delete o.prelim;delete o.mod;delete o.change;delete o.shift;delete o.number;delete o.thread})}};pv.Layout.Indent=function(){pv.Layout.Hierarchy.call(this);this.link.interpolate("step-after")};pv.Layout.Indent.prototype=pv.extend(pv.Layout.Hierarchy).property("depth",Number).property("breadth",Number);pv.Layout.Indent.prototype.defaults=(new pv.Layout.Indent).extend(pv.Layout.Hierarchy.prototype.defaults).depth(15).breadth(15);
+pv.Layout.Indent.prototype.buildImplied=function(b){function c(i,j,k){i.x=g+k++*f;i.y=h+j++*d;i.midAngle=0;for(i=i.firstChild;i;i=i.nextSibling)j=c(i,j,k);return j}if(!pv.Layout.Hierarchy.prototype.buildImplied.call(this,b)){var d=b.breadth,f=b.depth,g=0,h=0;c(b.nodes[0],1,1)}};pv.Layout.Pack=function(){pv.Layout.Hierarchy.call(this);this.node.radius(function(b){return b.radius}).strokeStyle("rgb(31, 119, 180)").fillStyle("rgba(31, 119, 180, .25)");this.label.textAlign("center");delete this.link};
+pv.Layout.Pack.prototype=pv.extend(pv.Layout.Hierarchy).property("spacing",Number).property("order",String);pv.Layout.Pack.prototype.defaults=(new pv.Layout.Pack).extend(pv.Layout.Hierarchy.prototype.defaults).spacing(1).order("ascending");pv.Layout.Pack.prototype.$radius=function(){return 1};pv.Layout.Pack.prototype.size=function(b){this.$radius=typeof b=="function"?function(){return Math.sqrt(b.apply(this,arguments))}:(b=Math.sqrt(b),function(){return b});return this};
+pv.Layout.Pack.prototype.buildImplied=function(b){function c(n){var p=pv.Mark.stack;p.unshift(null);for(var m=0,r=n.length;m<r;m++){var s=n[m];if(!s.firstChild)s.radius=i.$radius.apply(i,(p[0]=s,p))}p.shift()}function d(n){var p=[];for(n=n.firstChild;n;n=n.nextSibling){if(n.firstChild)n.radius=d(n);n.n=n.p=n;p.push(n)}switch(b.order){case "ascending":p.sort(function(m,r){return m.radius-r.radius});break;case "descending":p.sort(function(m,r){return r.radius-m.radius});break;case "reverse":p.reverse();
+break}return f(p)}function f(n){function p(B){t=Math.min(B.x-B.radius,t);x=Math.max(B.x+B.radius,x);u=Math.min(B.y-B.radius,u);o=Math.max(B.y+B.radius,o)}function m(B,F){var H=B.n;B.n=F;F.p=B;F.n=H;H.p=F}function r(B,F){B.n=F;F.p=B}function s(B,F){var H=F.x-B.x,I=F.y-B.y;B=B.radius+F.radius;return B*B-H*H-I*I>0.0010}var t=Infinity,x=-Infinity,u=Infinity,o=-Infinity,v,w,y,z,C;v=n[0];v.x=-v.radius;v.y=0;p(v);if(n.length>1){w=n[1];w.x=w.radius;w.y=0;p(w);if(n.length>2){y=n[2];g(v,w,y);p(y);m(v,y);v.p=
+y;m(y,w);w=v.n;for(var A=3;A<n.length;A++){g(v,w,y=n[A]);var D=0,G=1,E=1;for(z=w.n;z!=w;z=z.n,G++)if(s(z,y)){D=1;break}if(D==1)for(C=v.p;C!=z.p;C=C.p,E++)if(s(C,y)){if(E<G){D=-1;z=C}break}if(D==0){m(v,y);w=y;p(y)}else if(D>0){r(v,z);w=z;A--}else if(D<0){r(z,w);v=z;A--}}}}v=(t+x)/2;w=(u+o)/2;for(A=y=0;A<n.length;A++){z=n[A];z.x-=v;z.y-=w;y=Math.max(y,z.radius+Math.sqrt(z.x*z.x+z.y*z.y))}return y+b.spacing}function g(n,p,m){var r=p.radius+m.radius,s=n.radius+m.radius,t=p.x-n.x;p=p.y-n.y;var x=Math.sqrt(t*
+t+p*p),u=(s*s+x*x-r*r)/(2*s*x);r=Math.acos(u);u=u*s;s=Math.sin(r)*s;t/=x;p/=x;m.x=n.x+u*t+s*p;m.y=n.y+u*p-s*t}function h(n,p,m,r){for(var s=n.firstChild;s;s=s.nextSibling){s.x+=n.x;s.y+=n.y;h(s,p,m,r)}n.x=p+r*n.x;n.y=m+r*n.y;n.radius*=r}if(!pv.Layout.Hierarchy.prototype.buildImplied.call(this,b)){var i=this,j=b.nodes,k=j[0];c(j);k.x=0;k.y=0;k.radius=d(k);j=this.width();var l=this.height(),q=1/Math.max(2*k.radius/j,2*k.radius/l);h(k,j/2,l/2,q)}};
+pv.Layout.Force=function(){pv.Layout.Network.call(this);this.link.lineWidth(function(b,c){return Math.sqrt(c.linkValue)*1.5});this.label.textAlign("center")};
+pv.Layout.Force.prototype=pv.extend(pv.Layout.Network).property("bound",Boolean).property("iterations",Number).property("dragConstant",Number).property("chargeConstant",Number).property("chargeMinDistance",Number).property("chargeMaxDistance",Number).property("chargeTheta",Number).property("springConstant",Number).property("springDamping",Number).property("springLength",Number);pv.Layout.Force.prototype.defaults=(new pv.Layout.Force).extend(pv.Layout.Network.prototype.defaults).dragConstant(0.1).chargeConstant(-40).chargeMinDistance(2).chargeMaxDistance(500).chargeTheta(0.9).springConstant(0.1).springDamping(0.3).springLength(20);
+pv.Layout.Force.prototype.buildImplied=function(b){function c(q){return q.fix?1:q.vx*q.vx+q.vy*q.vy}if(pv.Layout.Network.prototype.buildImplied.call(this,b)){if(b=b.$force){b.next=this.binds.$force;this.binds.$force=b}}else{for(var d=this,f=b.nodes,g=b.links,h=b.iterations,i=b.width,j=b.height,k=0,l;k<f.length;k++){l=f[k];if(isNaN(l.x))l.x=i/2+40*Math.random()-20;if(isNaN(l.y))l.y=j/2+40*Math.random()-20}l=pv.simulation(f);l.force(pv.Force.drag(b.dragConstant));l.force(pv.Force.charge(b.chargeConstant).domain(b.chargeMinDistance,
+b.chargeMaxDistance).theta(b.chargeTheta));l.force(pv.Force.spring(b.springConstant).damping(b.springDamping).length(b.springLength).links(g));l.constraint(pv.Constraint.position());b.bound&&l.constraint(pv.Constraint.bound().x(6,i-6).y(6,j-6));if(h==null){l.step();l.step();b.$force=this.binds.$force={next:this.binds.$force,nodes:f,min:1.0E-4*(g.length+1),sim:l};if(!this.$timer)this.$timer=setInterval(function(){for(var q=false,n=d.binds.$force;n;n=n.next)if(pv.max(n.nodes,c)>n.min){n.sim.step();
+q=true}q&&d.render()},42)}else for(k=0;k<h;k++)l.step()}};pv.Layout.Cluster=function(){pv.Layout.Hierarchy.call(this);var b,c=this.buildImplied;this.buildImplied=function(d){c.call(this,d);b=/^(top|bottom)$/.test(d.orient)?"step-before":/^(left|right)$/.test(d.orient)?"step-after":"linear"};this.link.interpolate(function(){return b})};
+pv.Layout.Cluster.prototype=pv.extend(pv.Layout.Hierarchy).property("group",Number).property("orient",String).property("innerRadius",Number).property("outerRadius",Number);pv.Layout.Cluster.prototype.defaults=(new pv.Layout.Cluster).extend(pv.Layout.Hierarchy.prototype.defaults).group(0).orient("top");
+pv.Layout.Cluster.prototype.buildImplied=function(b){if(!pv.Layout.Hierarchy.prototype.buildImplied.call(this,b)){var c=b.nodes[0],d=b.group,f,g,h=0,i=0.5-d/2,j=undefined;c.visitAfter(function(k){if(k.firstChild)k.depth=1+pv.max(k.childNodes,function(l){return l.depth});else{if(d&&j!=k.parentNode){j=k.parentNode;h+=d}h++;k.depth=0}});f=1/h;g=1/c.depth;j=undefined;c.visitAfter(function(k){if(k.firstChild)k.breadth=pv.mean(k.childNodes,function(l){return l.breadth});else{if(d&&j!=k.parentNode){j=k.parentNode;
+i+=d}k.breadth=f*i++}k.depth=1-k.depth*g});c.visitAfter(function(k){k.minBreadth=k.firstChild?k.firstChild.minBreadth:k.breadth-f/2;k.maxBreadth=k.firstChild?k.lastChild.maxBreadth:k.breadth+f/2});c.visitBefore(function(k){k.minDepth=k.parentNode?k.parentNode.maxDepth:0;k.maxDepth=k.parentNode?k.depth+c.depth:k.minDepth+2*c.depth});c.minDepth=-g;pv.Layout.Hierarchy.NodeLink.buildImplied.call(this,b)}};pv.Layout.Cluster.Fill=function(){pv.Layout.Cluster.call(this);pv.Layout.Hierarchy.Fill.constructor.call(this)};
+pv.Layout.Cluster.Fill.prototype=pv.extend(pv.Layout.Cluster);pv.Layout.Cluster.Fill.prototype.buildImplied=function(b){pv.Layout.Cluster.prototype.buildImplied.call(this,b)||pv.Layout.Hierarchy.Fill.buildImplied.call(this,b)};pv.Layout.Partition=function(){pv.Layout.Hierarchy.call(this)};pv.Layout.Partition.prototype=pv.extend(pv.Layout.Hierarchy).property("order",String).property("orient",String).property("innerRadius",Number).property("outerRadius",Number);
+pv.Layout.Partition.prototype.defaults=(new pv.Layout.Partition).extend(pv.Layout.Hierarchy.prototype.defaults).orient("top");pv.Layout.Partition.prototype.$size=function(){return 1};pv.Layout.Partition.prototype.size=function(b){this.$size=b;return this};
+pv.Layout.Partition.prototype.buildImplied=function(b){if(!pv.Layout.Hierarchy.prototype.buildImplied.call(this,b)){var c=this,d=b.nodes[0],f=pv.Mark.stack,g=0;f.unshift(null);d.visitAfter(function(i,j){if(j>g)g=j;i.size=i.firstChild?pv.sum(i.childNodes,function(k){return k.size}):c.$size.apply(c,(f[0]=i,f))});f.shift();switch(b.order){case "ascending":d.sort(function(i,j){return i.size-j.size});break;case "descending":d.sort(function(i,j){return j.size-i.size});break}var h=1/g;d.minBreadth=0;d.breadth=
+0.5;d.maxBreadth=1;d.visitBefore(function(i){for(var j=i.minBreadth,k=i.maxBreadth-j,l=i.firstChild;l;l=l.nextSibling){l.minBreadth=j;l.maxBreadth=j+=l.size/i.size*k;l.breadth=(j+l.minBreadth)/2}});d.visitAfter(function(i,j){i.minDepth=(j-1)*h;i.maxDepth=i.depth=j*h});pv.Layout.Hierarchy.NodeLink.buildImplied.call(this,b)}};pv.Layout.Partition.Fill=function(){pv.Layout.Partition.call(this);pv.Layout.Hierarchy.Fill.constructor.call(this)};pv.Layout.Partition.Fill.prototype=pv.extend(pv.Layout.Partition);
+pv.Layout.Partition.Fill.prototype.buildImplied=function(b){pv.Layout.Partition.prototype.buildImplied.call(this,b)||pv.Layout.Hierarchy.Fill.buildImplied.call(this,b)};pv.Layout.Arc=function(){pv.Layout.Network.call(this);var b,c,d,f=this.buildImplied;this.buildImplied=function(g){f.call(this,g);c=g.directed;b=g.orient=="radial"?"linear":"polar";d=g.orient=="right"||g.orient=="top"};this.link.data(function(g){var h=g.sourceNode;g=g.targetNode;return d!=(c||h.breadth<g.breadth)?[h,g]:[g,h]}).interpolate(function(){return b})};
+pv.Layout.Arc.prototype=pv.extend(pv.Layout.Network).property("orient",String).property("directed",Boolean);pv.Layout.Arc.prototype.defaults=(new pv.Layout.Arc).extend(pv.Layout.Network.prototype.defaults).orient("bottom");pv.Layout.Arc.prototype.sort=function(b){this.$sort=b;return this};
+pv.Layout.Arc.prototype.buildImplied=function(b){function c(m){switch(h){case "top":return-Math.PI/2;case "bottom":return Math.PI/2;case "left":return Math.PI;case "right":return 0;case "radial":return(m-0.25)*2*Math.PI}}function d(m){switch(h){case "top":case "bottom":return m*k;case "left":return 0;case "right":return k;case "radial":return k/2+q*Math.cos(c(m))}}function f(m){switch(h){case "top":return 0;case "bottom":return l;case "left":case "right":return m*l;case "radial":return l/2+q*Math.sin(c(m))}}
+if(!pv.Layout.Network.prototype.buildImplied.call(this,b)){var g=b.nodes,h=b.orient,i=this.$sort,j=pv.range(g.length),k=b.width,l=b.height,q=Math.min(k,l)/2;i&&j.sort(function(m,r){return i(g[m],g[r])});for(b=0;b<g.length;b++){var n=g[j[b]],p=n.breadth=(b+0.5)/g.length;n.x=d(p);n.y=f(p);n.midAngle=c(p)}}};
+pv.Layout.Horizon=function(){pv.Layout.call(this);var b=this,c,d,f,g,h,i,j=this.buildImplied;this.buildImplied=function(k){j.call(this,k);c=k.bands;d=k.mode;f=Math.round((d=="color"?0.5:1)*k.height);g=k.backgroundStyle;h=pv.ramp(g,k.negativeStyle).domain(0,c);i=pv.ramp(g,k.positiveStyle).domain(0,c)};c=(new pv.Panel).data(function(){return pv.range(c*2)}).overflow("hidden").height(function(){return f}).top(function(k){return d=="color"?(k&1)*f:0}).fillStyle(function(k){return k?null:g});this.band=
+(new pv.Mark).top(function(k,l){return d=="mirror"&&l&1?(l+1>>1)*f:null}).bottom(function(k,l){return d=="mirror"?l&1?null:(l+1>>1)*-f:(l&1||-1)*(l+1>>1)*f}).fillStyle(function(k,l){return(l&1?h:i)((l>>1)+1)});this.band.add=function(k){return b.add(pv.Panel).extend(c).add(k).extend(this)}};pv.Layout.Horizon.prototype=pv.extend(pv.Layout).property("bands",Number).property("mode",String).property("backgroundStyle",pv.color).property("positiveStyle",pv.color).property("negativeStyle",pv.color);
+pv.Layout.Horizon.prototype.defaults=(new pv.Layout.Horizon).extend(pv.Layout.prototype.defaults).bands(2).mode("offset").backgroundStyle("white").positiveStyle("#1f77b4").negativeStyle("#d62728");
+pv.Layout.Rollup=function(){pv.Layout.Network.call(this);var b=this,c,d,f=b.buildImplied;this.buildImplied=function(g){f.call(this,g);c=g.$rollup.nodes;d=g.$rollup.links};this.node.data(function(){return c}).size(function(g){return g.nodes.length*20});this.link.interpolate("polar").eccentricity(0.8);this.link.add=function(g){return b.add(pv.Panel).data(function(){return d}).add(g).extend(this)}};pv.Layout.Rollup.prototype=pv.extend(pv.Layout.Network).property("directed",Boolean);
+pv.Layout.Rollup.prototype.x=function(b){this.$x=pv.functor(b);return this};pv.Layout.Rollup.prototype.y=function(b){this.$y=pv.functor(b);return this};
+pv.Layout.Rollup.prototype.buildImplied=function(b){function c(r){return i[r]+","+j[r]}if(!pv.Layout.Network.prototype.buildImplied.call(this,b)){var d=b.nodes,f=b.links,g=b.directed,h=d.length,i=[],j=[],k=0,l={},q={},n=pv.Mark.stack,p={parent:this};n.unshift(null);for(var m=0;m<h;m++){p.index=m;n[0]=d[m];i[m]=this.$x.apply(p,n);j[m]=this.$y.apply(p,n)}n.shift();for(m=0;m<d.length;m++){h=c(m);n=l[h];if(!n){n=l[h]=pv.extend(d[m]);n.index=k++;n.x=i[m];n.y=j[m];n.nodes=[]}n.nodes.push(d[m])}for(m=0;m<
+f.length;m++){k=f[m].targetNode;d=l[c(f[m].sourceNode.index)];k=l[c(k.index)];h=!g&&d.index>k.index?k.index+","+d.index:d.index+","+k.index;(n=q[h])||(n=q[h]={sourceNode:d,targetNode:k,linkValue:0,links:[]});n.links.push(f[m]);n.linkValue+=f[m].linkValue}b.$rollup={nodes:pv.values(l),links:pv.values(q)}}};
+pv.Layout.Matrix=function(){pv.Layout.Network.call(this);var b,c,d,f,g,h=this.buildImplied;this.buildImplied=function(i){h.call(this,i);b=i.nodes.length;c=i.width/b;d=i.height/b;f=i.$matrix.labels;g=i.$matrix.pairs};this.link.data(function(){return g}).left(function(){return c*(this.index%b)}).top(function(){return d*Math.floor(this.index/b)}).width(function(){return c}).height(function(){return d}).lineWidth(1.5).strokeStyle("#fff").fillStyle(function(i){return i.linkValue?"#555":"#eee"}).parent=
+this;delete this.link.add;this.label.data(function(){return f}).left(function(){return this.index&1?c*((this.index>>1)+0.5):0}).top(function(){return this.index&1?0:d*((this.index>>1)+0.5)}).textMargin(4).textAlign(function(){return this.index&1?"left":"right"}).textAngle(function(){return this.index&1?-Math.PI/2:0});delete this.node};pv.Layout.Matrix.prototype=pv.extend(pv.Layout.Network).property("directed",Boolean);pv.Layout.Matrix.prototype.sort=function(b){this.$sort=b;return this};
+pv.Layout.Matrix.prototype.buildImplied=function(b){if(!pv.Layout.Network.prototype.buildImplied.call(this,b)){var c=b.nodes,d=b.links,f=this.$sort,g=c.length,h=pv.range(g),i=[],j=[],k={};b.$matrix={labels:i,pairs:j};f&&h.sort(function(m,r){return f(c[m],c[r])});for(var l=0;l<g;l++)for(var q=0;q<g;q++){var n=h[l],p=h[q];j.push(k[n+"."+p]={row:l,col:q,sourceNode:c[n],targetNode:c[p],linkValue:0})}for(l=0;l<g;l++){n=h[l];i.push(c[n],c[n])}for(l=0;l<d.length;l++){i=d[l];g=i.sourceNode.index;h=i.targetNode.index;
+i=i.linkValue;k[g+"."+h].linkValue+=i;b.directed||(k[h+"."+g].linkValue+=i)}}};
+pv.Layout.Bullet=function(){pv.Layout.call(this);var b=this,c=b.buildImplied,d=b.x=pv.Scale.linear(),f,g,h,i,j;this.buildImplied=function(k){c.call(this,j=k);f=k.orient;g=/^left|right$/.test(f);h=pv.ramp("#bbb","#eee").domain(0,Math.max(1,j.ranges.length-1));i=pv.ramp("steelblue","lightsteelblue").domain(0,Math.max(1,j.measures.length-1))};(this.range=new pv.Mark).data(function(){return j.ranges}).reverse(true).left(function(){return f=="left"?0:null}).top(function(){return f=="top"?0:null}).right(function(){return f==
+"right"?0:null}).bottom(function(){return f=="bottom"?0:null}).width(function(k){return g?d(k):null}).height(function(k){return g?null:d(k)}).fillStyle(function(){return h(this.index)}).antialias(false).parent=b;(this.measure=new pv.Mark).extend(this.range).data(function(){return j.measures}).left(function(){return f=="left"?0:g?null:this.parent.width()/3.25}).top(function(){return f=="top"?0:g?this.parent.height()/3.25:null}).right(function(){return f=="right"?0:g?null:this.parent.width()/3.25}).bottom(function(){return f==
+"bottom"?0:g?this.parent.height()/3.25:null}).fillStyle(function(){return i(this.index)}).parent=b;(this.marker=new pv.Mark).data(function(){return j.markers}).left(function(k){return f=="left"?d(k):g?null:this.parent.width()/2}).top(function(k){return f=="top"?d(k):g?this.parent.height()/2:null}).right(function(k){return f=="right"?d(k):null}).bottom(function(k){return f=="bottom"?d(k):null}).strokeStyle("black").shape("bar").angle(function(){return g?0:Math.PI/2}).parent=b;(this.tick=new pv.Mark).data(function(){return d.ticks(7)}).left(function(k){return f==
+"left"?d(k):null}).top(function(k){return f=="top"?d(k):null}).right(function(k){return f=="right"?d(k):g?null:-6}).bottom(function(k){return f=="bottom"?d(k):g?-8:null}).height(function(){return g?6:null}).width(function(){return g?null:6}).parent=b};pv.Layout.Bullet.prototype=pv.extend(pv.Layout).property("orient",String).property("ranges").property("markers").property("measures").property("maximum",Number);pv.Layout.Bullet.prototype.defaults=(new pv.Layout.Bullet).extend(pv.Layout.prototype.defaults).orient("left").ranges([]).markers([]).measures([]);
+pv.Layout.Bullet.prototype.buildImplied=function(b){pv.Layout.prototype.buildImplied.call(this,b);var c=this.parent[/^left|right$/.test(b.orient)?"width":"height"]();b.maximum=b.maximum||pv.max([].concat(b.ranges,b.markers,b.measures));this.x.domain(0,b.maximum).range(0,c)};pv.Behavior={};
+pv.Behavior.drag=function(){function b(k){g=this.index;f=this.scene;var l=this.mouse();i=((h=k).fix=pv.vector(k.x,k.y)).minus(l);j={x:this.parent.width()-(k.dx||0),y:this.parent.height()-(k.dy||0)};f.mark.context(f,g,function(){this.render()});pv.Mark.dispatch("dragstart",f,g)}function c(){if(f){f.mark.context(f,g,function(){var k=this.mouse();h.x=h.fix.x=Math.max(0,Math.min(i.x+k.x,j.x));h.y=h.fix.y=Math.max(0,Math.min(i.y+k.y,j.y));this.render()});pv.Mark.dispatch("drag",f,g)}}function d(){if(f){h.fix=
+null;f.mark.context(f,g,function(){this.render()});pv.Mark.dispatch("dragend",f,g);f=null}}var f,g,h,i,j;pv.listen(window,"mousemove",c);pv.listen(window,"mouseup",d);return b};
+pv.Behavior.point=function(b){function c(l,q){l=l[q];q={cost:Infinity};for(var n=0,p=l.visible&&l.children.length;n<p;n++){var m=l.children[n],r=m.mark,s;if(r.type=="panel"){r.scene=m;for(var t=0,x=m.length;t<x;t++){r.index=t;s=c(m,t);if(s.cost<q.cost)q=s}delete r.scene;delete r.index}else if(r.$handlers.point){r=r.mouse();t=0;for(x=m.length;t<x;t++){var u=m[t];s=r.x-u.left-(u.width||0)/2;u=r.y-u.top-(u.height||0)/2;var o=i*s*s+j*u*u;if(o<q.cost){q.distance=s*s+u*u;q.cost=o;q.scene=m;q.index=t}}}}return q}
+function d(){var l=c(this.scene,this.index);if(l.cost==Infinity||l.distance>k)l=null;if(g){if(l&&g.scene==l.scene&&g.index==l.index)return;pv.Mark.dispatch("unpoint",g.scene,g.index)}if(g=l){pv.Mark.dispatch("point",l.scene,l.index);pv.listen(this.root.canvas(),"mouseout",f)}}function f(l){if(g&&!pv.ancestor(this,l.relatedTarget)){pv.Mark.dispatch("unpoint",g.scene,g.index);g=null}}var g,h=null,i=1,j=1,k=arguments.length?b*b:900;d.collapse=function(l){if(arguments.length){h=String(l);switch(h){case "y":i=
+1;j=0;break;case "x":i=0;j=1;break;default:j=i=1;break}return d}return h};return d};
+pv.Behavior.select=function(){function b(j){g=this.index;f=this.scene;i=this.mouse();h=j;h.x=i.x;h.y=i.y;h.dx=h.dy=0;pv.Mark.dispatch("selectstart",f,g)}function c(){if(f){f.mark.context(f,g,function(){var j=this.mouse();h.x=Math.max(0,Math.min(i.x,j.x));h.y=Math.max(0,Math.min(i.y,j.y));h.dx=Math.min(this.width(),Math.max(j.x,i.x))-h.x;h.dy=Math.min(this.height(),Math.max(j.y,i.y))-h.y;this.render()});pv.Mark.dispatch("select",f,g)}}function d(){if(f){pv.Mark.dispatch("selectend",f,g);f=null}}var f,
+g,h,i;pv.listen(window,"mousemove",c);pv.listen(window,"mouseup",d);return b};
+pv.Behavior.resize=function(b){function c(k){h=this.index;g=this.scene;j=this.mouse();i=k;switch(b){case "left":j.x=i.x+i.dx;break;case "right":j.x=i.x;break;case "top":j.y=i.y+i.dy;break;case "bottom":j.y=i.y;break}pv.Mark.dispatch("resizestart",g,h)}function d(){if(g){g.mark.context(g,h,function(){var k=this.mouse();i.x=Math.max(0,Math.min(j.x,k.x));i.y=Math.max(0,Math.min(j.y,k.y));i.dx=Math.min(this.parent.width(),Math.max(k.x,j.x))-i.x;i.dy=Math.min(this.parent.height(),Math.max(k.y,j.y))-i.y;
+this.render()});pv.Mark.dispatch("resize",g,h)}}function f(){if(g){pv.Mark.dispatch("resizeend",g,h);g=null}}var g,h,i,j;pv.listen(window,"mousemove",d);pv.listen(window,"mouseup",f);return c};
+pv.Behavior.pan=function(){function b(){g=this.index;f=this.scene;i=pv.vector(pv.event.pageX,pv.event.pageY);h=this.transform();j=1/(h.k*this.scale);if(k)k={x:(1-h.k)*this.width(),y:(1-h.k)*this.height()}}function c(){if(f){f.mark.context(f,g,function(){var l=h.translate((pv.event.pageX-i.x)*j,(pv.event.pageY-i.y)*j);if(k){l.x=Math.max(k.x,Math.min(0,l.x));l.y=Math.max(k.y,Math.min(0,l.y))}this.transform(l).render()});pv.Mark.dispatch("pan",f,g)}}function d(){f=null}var f,g,h,i,j,k;b.bound=function(l){if(arguments.length){k=
+Boolean(l);return this}return Boolean(k)};pv.listen(window,"mousemove",c);pv.listen(window,"mouseup",d);return b};
+pv.Behavior.zoom=function(b){function c(){var f=this.mouse(),g=pv.event.wheel*b;f=this.transform().translate(f.x,f.y).scale(g<0?1E3/(1E3-g):(1E3+g)/1E3).translate(-f.x,-f.y);if(d){f.k=Math.max(1,f.k);f.x=Math.max((1-f.k)*this.width(),Math.min(0,f.x));f.y=Math.max((1-f.k)*this.height(),Math.min(0,f.y))}this.transform(f).render();pv.Mark.dispatch("zoom",this.scene,this.index)}var d;arguments.length||(b=1/48);c.bound=function(f){if(arguments.length){d=Boolean(f);return this}return Boolean(d)};return c};
+pv.Geo=function(){};
+pv.Geo.projections={mercator:{project:function(b){return{x:b.lng/180,y:b.lat>85?1:b.lat<-85?-1:Math.log(Math.tan(Math.PI/4+pv.radians(b.lat)/2))/Math.PI}},invert:function(b){return{lng:b.x*180,lat:pv.degrees(2*Math.atan(Math.exp(b.y*Math.PI))-Math.PI/2)}}},"gall-peters":{project:function(b){return{x:b.lng/180,y:Math.sin(pv.radians(b.lat))}},invert:function(b){return{lng:b.x*180,lat:pv.degrees(Math.asin(b.y))}}},sinusoidal:{project:function(b){return{x:pv.radians(b.lng)*Math.cos(pv.radians(b.lat))/Math.PI,
+y:b.lat/90}},invert:function(b){return{lng:pv.degrees(b.x*Math.PI/Math.cos(b.y*Math.PI/2)),lat:b.y*90}}},aitoff:{project:function(b){var c=pv.radians(b.lng);b=pv.radians(b.lat);var d=Math.acos(Math.cos(b)*Math.cos(c/2));return{x:2*(d?Math.cos(b)*Math.sin(c/2)*d/Math.sin(d):0)/Math.PI,y:2*(d?Math.sin(b)*d/Math.sin(d):0)/Math.PI}},invert:function(b){var c=b.y*Math.PI/2;return{lng:pv.degrees(b.x*Math.PI/2/Math.cos(c)),lat:pv.degrees(c)}}},hammer:{project:function(b){var c=pv.radians(b.lng);b=pv.radians(b.lat);
+var d=Math.sqrt(1+Math.cos(b)*Math.cos(c/2));return{x:2*Math.SQRT2*Math.cos(b)*Math.sin(c/2)/d/3,y:Math.SQRT2*Math.sin(b)/d/1.5}},invert:function(b){var c=b.x*3;b=b.y*1.5;var d=Math.sqrt(1-c*c/16-b*b/4);return{lng:pv.degrees(2*Math.atan2(d*c,2*(2*d*d-1))),lat:pv.degrees(Math.asin(d*b))}}},identity:{project:function(b){return{x:b.lng/180,y:b.lat/90}},invert:function(b){return{lng:b.x*180,lat:b.y*90}}}};
+pv.Geo.scale=function(b){function c(m){if(!n||m.lng!=n.lng||m.lat!=n.lat){n=m;m=d(m);p={x:k(m.x),y:l(m.y)}}return p}function d(m){return j.project({lng:m.lng-q.lng,lat:m.lat})}function f(m){m=j.invert(m);m.lng+=q.lng;return m}var g={x:0,y:0},h={x:1,y:1},i=[],j=pv.Geo.projections.identity,k=pv.Scale.linear(-1,1).range(0,1),l=pv.Scale.linear(-1,1).range(1,0),q={lng:0,lat:0},n,p;c.x=function(m){return c(m).x};c.y=function(m){return c(m).y};c.ticks={lng:function(m){var r;if(i.length>1){var s=pv.Scale.linear();
+if(m==undefined)m=10;r=s.domain(i,function(t){return t.lat}).ticks(m);m=s.domain(i,function(t){return t.lng}).ticks(m)}else{r=pv.range(-80,81,10);m=pv.range(-180,181,10)}return m.map(function(t){return r.map(function(x){return{lat:x,lng:t}})})},lat:function(m){return pv.transpose(c.ticks.lng(m))}};c.invert=function(m){return f({x:k.invert(m.x),y:l.invert(m.y)})};c.domain=function(m,r){if(arguments.length){i=m instanceof Array?arguments.length>1?pv.map(m,r):m:Array.prototype.slice.call(arguments);
+if(i.length>1){var s=i.map(function(x){return x.lng}),t=i.map(function(x){return x.lat});q={lng:(pv.max(s)+pv.min(s))/2,lat:(pv.max(t)+pv.min(t))/2};s=i.map(d);k.domain(s,function(x){return x.x});l.domain(s,function(x){return x.y})}else{q={lng:0,lat:0};k.domain(-1,1);l.domain(-1,1)}n=null;return this}return i};c.range=function(m,r){if(arguments.length){if(typeof m=="object"){g={x:Number(m.x),y:Number(m.y)};h={x:Number(r.x),y:Number(r.y)}}else{g={x:0,y:0};h={x:Number(m),y:Number(r)}}k.range(g.x,h.x);
+l.range(h.y,g.y);n=null;return this}return[g,h]};c.projection=function(m){if(arguments.length){j=typeof m=="string"?pv.Geo.projections[m]||pv.Geo.projections.identity:m;return this.domain(i)}return m};c.by=function(m){function r(){return c(m.apply(this,arguments))}for(var s in c)r[s]=c[s];return r};arguments.length&&c.projection(b);return c};

custom_nodes/ComfyUI-KJNodes/nodes/audioscheduler_nodes.py

@@ -0,0 +1,251 @@
+# 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/nodes/batchcrop_nodes.py

@@ -0,0 +1,768 @@
+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}),
+                "bbox_format": (["xywh", "xyxy"], {"default": "xywh"}),
+            },
+        }
+
+    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, bbox_format):
+        image_list = []
+        for image, bbox in zip(images, bboxes):
+            # Ensure bbox is a sequence of 4 values
+            if isinstance(bbox, (list, tuple, np.ndarray)) and len(bbox) == 4:
+                if bbox_format == "xywh":
+                    x_min, y_min, width, height = bbox
+                elif bbox_format == "xyxy":
+                    x_min, y_min, x_max, y_max = bbox
+                    width = x_max - x_min
+                    height = y_max - y_min
+                else:
+                    raise ValueError(f"Unknown bbox_format: {bbox_format}")
+            else:
+                print("Invalid bbox:", bbox)
+                continue
+
+            # 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),)

custom_nodes/ComfyUI-KJNodes/nodes/intrinsic_lora_nodes.py

@@ -0,0 +1,115 @@
+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/nodes/lora_nodes.py

@@ -0,0 +1,583 @@
+import torch
+import comfy.model_management
+import comfy.utils
+import folder_paths
+import os
+import logging
+from tqdm import tqdm
+import numpy as np
+
+device = comfy.model_management.get_torch_device()
+
+CLAMP_QUANTILE = 0.99
+
+def extract_lora(diff, key, rank, algorithm, lora_type, lowrank_iters=7, adaptive_param=1.0, clamp_quantile=True):
+    """
+    Extracts LoRA weights from a weight difference tensor using SVD.
+    """
+    conv2d = (len(diff.shape) == 4)
+    kernel_size = None if not conv2d else diff.size()[2:4]
+    conv2d_3x3 = conv2d and kernel_size != (1, 1)
+    out_dim, in_dim = diff.size()[0:2]
+
+    if conv2d:
+        if conv2d_3x3:
+            diff = diff.flatten(start_dim=1)
+        else:
+            diff = diff.squeeze()
+
+    diff_float = diff.float()
+    if algorithm == "svd_lowrank":
+        U, S, V = torch.svd_lowrank(diff_float, q=min(rank, in_dim, out_dim), niter=lowrank_iters)
+        U = U @ torch.diag(S)
+        Vh = V.t()
+    else:
+        #torch.linalg.svdvals() 
+        U, S, Vh = torch.linalg.svd(diff_float)
+        # Flexible rank selection logic like locon: https://github.com/KohakuBlueleaf/LyCORIS/blob/main/tools/extract_locon.py
+        if "adaptive" in lora_type:
+            if lora_type == "adaptive_ratio":
+                min_s = torch.max(S) * adaptive_param
+                lora_rank = torch.sum(S > min_s).item()
+            elif lora_type == "adaptive_energy":
+                energy = torch.cumsum(S**2, dim=0)
+                total_energy = torch.sum(S**2)
+                threshold = adaptive_param * total_energy  # e.g., adaptive_param=0.95 for 95%
+                lora_rank = torch.sum(energy < threshold).item() + 1
+            elif lora_type == "adaptive_quantile":
+                s_cum = torch.cumsum(S, dim=0)
+                min_cum_sum = adaptive_param * torch.sum(S)
+                lora_rank = torch.sum(s_cum < min_cum_sum).item()
+            elif lora_type == "adaptive_fro":
+                S_squared = S.pow(2)
+                S_fro_sq = float(torch.sum(S_squared))
+                sum_S_squared = torch.cumsum(S_squared, dim=0) / S_fro_sq
+                lora_rank = int(torch.searchsorted(sum_S_squared, adaptive_param**2)) + 1
+                lora_rank = max(1, min(lora_rank, len(S)))
+            else:
+                pass  # Will print after capping
+            
+            # Cap adaptive rank by the specified max rank
+            lora_rank = min(lora_rank, rank)
+            
+            # Calculate and print actual fro percentage retained after capping
+            if lora_type == "adaptive_fro":
+                S_squared = S.pow(2)
+                s_fro = torch.sqrt(torch.sum(S_squared))
+                s_red_fro = torch.sqrt(torch.sum(S_squared[:lora_rank]))
+                fro_percent = float(s_red_fro / s_fro)
+                print(f"{key} Extracted LoRA rank: {lora_rank}, Frobenius retained: {fro_percent:.1%}")
+            else:
+                print(f"{key} Extracted LoRA rank: {lora_rank}")
+        else:
+            lora_rank = rank
+
+        lora_rank = max(1, lora_rank)
+        lora_rank = min(out_dim, in_dim, lora_rank)
+        
+        U = U[:, :lora_rank]
+        S = S[:lora_rank]
+        U = U @ torch.diag(S)
+        Vh = Vh[:lora_rank, :]
+
+    if clamp_quantile:
+        dist = torch.cat([U.flatten(), Vh.flatten()])
+        if dist.numel() > 100_000:
+            # Sample 100,000 elements for quantile estimation
+            idx = torch.randperm(dist.numel(), device=dist.device)[:100_000]
+            dist_sample = dist[idx]
+            hi_val = torch.quantile(dist_sample, CLAMP_QUANTILE)
+        else:
+            hi_val = torch.quantile(dist, CLAMP_QUANTILE)
+        low_val = -hi_val
+
+        U = U.clamp(low_val, hi_val)
+        Vh = Vh.clamp(low_val, hi_val)
+    if conv2d:
+        U = U.reshape(out_dim, lora_rank, 1, 1)
+        Vh = Vh.reshape(lora_rank, in_dim, kernel_size[0], kernel_size[1])
+    return (U, Vh)
+
+
+def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, lora_type, algorithm, lowrank_iters, out_dtype, bias_diff=False, adaptive_param=1.0, clamp_quantile=True):
+    comfy.model_management.load_models_gpu([model_diff], force_patch_weights=True)
+    model_diff.model.diffusion_model.cpu()
+    sd = model_diff.model_state_dict(filter_prefix=prefix_model)
+    del model_diff
+    comfy.model_management.soft_empty_cache()
+    for k, v in sd.items():
+        if isinstance(v, torch.Tensor):
+            sd[k] = v.cpu()
+
+    # Get total number of keys to process for progress bar
+    total_keys = len([k for k in sd if k.endswith(".weight") or (bias_diff and k.endswith(".bias"))])
+    
+    # Create progress bar
+    progress_bar = tqdm(total=total_keys, desc=f"Extracting LoRA ({prefix_lora.strip('.')})")
+    comfy_pbar = comfy.utils.ProgressBar(total_keys)
+
+    for k in sd:
+        if k.endswith(".weight"):
+            weight_diff = sd[k]
+            if weight_diff.ndim == 5:
+                logging.info(f"Skipping 5D tensor for key {k}") #skip patch embed
+                progress_bar.update(1)
+                comfy_pbar.update(1)
+                continue
+            if lora_type != "full":
+                if weight_diff.ndim < 2:
+                    if bias_diff:
+                        output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().to(out_dtype).cpu()
+                    progress_bar.update(1)
+                    comfy_pbar.update(1)
+                    continue
+                try:
+                    out = extract_lora(weight_diff.to(device), k, rank, algorithm, lora_type, lowrank_iters=lowrank_iters, adaptive_param=adaptive_param, clamp_quantile=clamp_quantile)
+                    output_sd["{}{}.lora_up.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[0].contiguous().to(out_dtype).cpu()
+                    output_sd["{}{}.lora_down.weight".format(prefix_lora, k[len(prefix_model):-7])] = out[1].contiguous().to(out_dtype).cpu()
+                except Exception as e:
+                    logging.warning(f"Could not generate lora weights for key {k}, error {e}")
+            else:
+                output_sd["{}{}.diff".format(prefix_lora, k[len(prefix_model):-7])] = weight_diff.contiguous().to(out_dtype).cpu()
+            
+            progress_bar.update(1)
+            comfy_pbar.update(1)
+
+        elif bias_diff and k.endswith(".bias"):
+            output_sd["{}{}.diff_b".format(prefix_lora, k[len(prefix_model):-5])] = sd[k].contiguous().to(out_dtype).cpu()
+            progress_bar.update(1)
+            comfy_pbar.update(1)
+    progress_bar.close()
+    return output_sd
+
+class LoraExtractKJ:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": 
+                {
+                    "finetuned_model": ("MODEL",),
+                    "original_model": ("MODEL",),
+                    "filename_prefix": ("STRING", {"default": "loras/ComfyUI_extracted_lora"}),
+                    "rank": ("INT", {"default": 8, "min": 1, "max": 4096, "step": 1, "tooltip": "The rank to use for standard LoRA, or maximum rank limit for adaptive methods."}),
+                    "lora_type": (["standard", "full", "adaptive_ratio", "adaptive_quantile", "adaptive_energy", "adaptive_fro"],),
+                    "algorithm": (["svd_linalg", "svd_lowrank"], {"default": "svd_linalg", "tooltip": "SVD algorithm to use, svd_lowrank is faster but less accurate."}),
+                    "lowrank_iters": ("INT", {"default": 7, "min": 1, "max": 100, "step": 1, "tooltip": "The number of subspace iterations for lowrank SVD algorithm."}),
+                    "output_dtype": (["fp16", "bf16", "fp32"], {"default": "fp16"}),
+                    "bias_diff": ("BOOLEAN", {"default": True}),
+                    "adaptive_param": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "For ratio mode, this is the ratio of the maximum singular value. For quantile mode, this is the quantile of the singular values. For fro mode, this is the Frobenius norm retention ratio."}),
+                    "clamp_quantile": ("BOOLEAN", {"default": True}),
+                },
+
+    }
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+    OUTPUT_NODE = True
+
+    CATEGORY = "KJNodes/lora"
+
+    def save(self, finetuned_model, original_model, filename_prefix, rank, lora_type, algorithm, lowrank_iters, output_dtype, bias_diff, adaptive_param, clamp_quantile):
+        if algorithm == "svd_lowrank" and lora_type != "standard":
+            raise ValueError("svd_lowrank algorithm is only supported for standard LoRA extraction.")
+
+        dtype = {"fp8_e4m3fn": torch.float8_e4m3fn, "bf16": torch.bfloat16, "fp16": torch.float16, "fp16_fast": torch.float16, "fp32": torch.float32}[output_dtype]
+        m = finetuned_model.clone()
+        kp = original_model.get_key_patches("diffusion_model.")
+        for k in kp:
+            m.add_patches({k: kp[k]}, - 1.0, 1.0)
+        model_diff = m
+
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+
+        output_sd = {}
+        if model_diff is not None:
+            output_sd = calc_lora_model(model_diff, rank, "diffusion_model.", "diffusion_model.", output_sd, lora_type, algorithm, lowrank_iters, dtype, bias_diff=bias_diff, adaptive_param=adaptive_param, clamp_quantile=clamp_quantile)
+        if "adaptive" in lora_type:
+            rank_str = f"{lora_type}_{adaptive_param:.2f}"
+        else:
+            rank_str = rank
+        output_checkpoint = f"{filename}_rank_{rank_str}_{output_dtype}_{counter:05}_.safetensors"
+        output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
+
+        comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=None)
+        return {}
+
+NODE_CLASS_MAPPINGS = {
+    "LoraExtractKJ": LoraExtractKJ
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LoraExtractKJ": "LoraExtractKJ"
+}
+
+class LoraReduceRank:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": 
+                {
+                    "lora_name": (folder_paths.get_filename_list("loras"), {"tooltip": "The name of the LoRA."}),
+                    "new_rank": ("INT", {"default": 8, "min": 1, "max": 4096, "step": 1, "tooltip": "The new rank to resize the LoRA. Acts as max rank when using dynamic_method."}),
+                    "dynamic_method": (["disabled", "sv_ratio", "sv_cumulative", "sv_fro"], {"default": "disabled", "tooltip": "Method to use for dynamically determining new alphas and dims"}),
+                    "dynamic_param": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Method to use for dynamically determining new alphas and dims"}),
+                    "output_dtype": (["match_original", "fp16", "bf16", "fp32"], {"default": "match_original", "tooltip": "Data type to save the LoRA as."}),
+                    "verbose": ("BOOLEAN", {"default": True}),
+                },
+
+    }
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+    OUTPUT_NODE = True
+    EXPERIMENTAL = True
+    DESCRIPTION = "Resize a LoRA model by reducing it's rank. Based on kohya's sd-scripts: https://github.com/kohya-ss/sd-scripts/blob/main/networks/resize_lora.py"
+
+    CATEGORY = "KJNodes/lora"
+
+    def save(self, lora_name, new_rank, output_dtype, dynamic_method, dynamic_param, verbose):
+
+        lora_path = folder_paths.get_full_path("loras", lora_name)
+        lora_sd, metadata = comfy.utils.load_torch_file(lora_path, return_metadata=True)
+
+        if output_dtype == "fp16":
+            save_dtype = torch.float16
+        elif output_dtype == "bf16":
+            save_dtype = torch.bfloat16
+        elif output_dtype == "fp32":
+            save_dtype = torch.float32
+        elif output_dtype == "match_original":
+            first_weight_key = next(k for k in lora_sd if k.endswith(".weight") and isinstance(lora_sd[k], torch.Tensor))
+            save_dtype = lora_sd[first_weight_key].dtype
+
+        new_lora_sd = {}
+        for k, v in lora_sd.items():
+            new_lora_sd[k.replace(".default", "")] = v
+        del lora_sd
+        print("Resizing Lora...")
+        output_sd, old_dim, new_alpha, rank_list = resize_lora_model(new_lora_sd, new_rank, save_dtype, device, dynamic_method, dynamic_param, verbose)
+
+        # update metadata
+        if metadata is None:
+            metadata = {}
+
+        comment = metadata.get("ss_training_comment", "")
+
+        if dynamic_method == "disabled":
+            metadata["ss_training_comment"] = f"dimension is resized from {old_dim} to {new_rank}; {comment}"
+            metadata["ss_network_dim"] = str(new_rank)
+            metadata["ss_network_alpha"] = str(new_alpha)
+        else:
+            metadata["ss_training_comment"] = f"Dynamic resize with {dynamic_method}: {dynamic_param} from {old_dim}; {comment}"
+            metadata["ss_network_dim"] = "Dynamic"
+            metadata["ss_network_alpha"] = "Dynamic"
+
+        # cast to save_dtype before calculating hashes
+        for key in list(output_sd.keys()):
+            value = output_sd[key]
+            if type(value) == torch.Tensor and value.dtype.is_floating_point and value.dtype != save_dtype:
+                output_sd[key] = value.to(save_dtype)
+
+        output_filename_prefix = "loras/" + lora_name
+
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(output_filename_prefix, self.output_dir)
+        output_dtype_str = f"_{output_dtype}" if output_dtype != "match_original" else ""
+        average_rank = str(int(np.mean(rank_list)))
+        rank_str = new_rank if dynamic_method == "disabled" else f"dynamic_{average_rank}"
+        output_checkpoint = f"{filename.replace('.safetensors', '')}_resized_from_{old_dim}_to_{rank_str}{output_dtype_str}_{counter:05}_.safetensors"
+        output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
+        print(f"Saving resized LoRA to {output_checkpoint}")
+
+        comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=metadata)
+        return {}
+
+NODE_CLASS_MAPPINGS = {
+    "LoraExtractKJ": LoraExtractKJ
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LoraExtractKJ": "LoraExtractKJ"
+}
+
+# Convert LoRA to different rank approximation (should only be used to go to lower rank)
+# This code is based off the extract_lora_from_models.py file which is based on https://github.com/cloneofsimo/lora/blob/develop/lora_diffusion/cli_svd.py
+# Thanks to cloneofsimo
+
+# This version is based on
+# https://github.com/kohya-ss/sd-scripts/blob/main/networks/resize_lora.py
+
+MIN_SV = 1e-6
+
+LORA_DOWN_UP_FORMATS = [
+    ("lora_down", "lora_up"),  # sd-scripts LoRA
+    ("lora_A", "lora_B"),  # PEFT LoRA
+    ("down", "up"),  # ControlLoRA
+]
+
+# Indexing functions
+def index_sv_cumulative(S, target):
+    original_sum = float(torch.sum(S))
+    cumulative_sums = torch.cumsum(S, dim=0) / original_sum
+    index = int(torch.searchsorted(cumulative_sums, target)) + 1
+    index = max(1, min(index, len(S) - 1))
+
+    return index
+
+
+def index_sv_fro(S, target):
+    S_squared = S.pow(2)
+    S_fro_sq = float(torch.sum(S_squared))
+    sum_S_squared = torch.cumsum(S_squared, dim=0) / S_fro_sq
+    index = int(torch.searchsorted(sum_S_squared, target**2)) + 1
+    index = max(1, min(index, len(S) - 1))
+
+    return index
+
+
+def index_sv_ratio(S, target):
+    max_sv = S[0]
+    min_sv = max_sv / target
+    index = int(torch.sum(S > min_sv).item())
+    index = max(1, min(index, len(S) - 1))
+
+    return index
+
+
+# Modified from Kohaku-blueleaf's extract/merge functions
+def extract_conv(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1):
+    out_size, in_size, kernel_size, _ = weight.size()
+    if weight.dtype != torch.float32:
+        weight = weight.to(torch.float32)
+    U, S, Vh = torch.linalg.svd(weight.reshape(out_size, -1).to(device))
+
+    param_dict = rank_resize(S, lora_rank, dynamic_method, dynamic_param, scale)
+    lora_rank = param_dict["new_rank"]
+
+    U = U[:, :lora_rank]
+    S = S[:lora_rank]
+    U = U @ torch.diag(S)
+    Vh = Vh[:lora_rank, :]
+
+    param_dict["lora_down"] = Vh.reshape(lora_rank, in_size, kernel_size, kernel_size).cpu()
+    param_dict["lora_up"] = U.reshape(out_size, lora_rank, 1, 1).cpu()
+    del U, S, Vh, weight
+    return param_dict
+
+
+def extract_linear(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1):
+    out_size, in_size = weight.size()
+
+    if weight.dtype != torch.float32:
+        weight = weight.to(torch.float32)
+    U, S, Vh = torch.linalg.svd(weight.to(device))
+
+    param_dict = rank_resize(S, lora_rank, dynamic_method, dynamic_param, scale)
+    lora_rank = param_dict["new_rank"]
+
+    U = U[:, :lora_rank]
+    S = S[:lora_rank]
+    U = U @ torch.diag(S)
+    Vh = Vh[:lora_rank, :]
+
+    param_dict["lora_down"] = Vh.reshape(lora_rank, in_size).cpu()
+    param_dict["lora_up"] = U.reshape(out_size, lora_rank).cpu()
+    del U, S, Vh, weight
+    return param_dict
+
+
+def merge_conv(lora_down, lora_up, device):
+    in_rank, in_size, kernel_size, k_ = lora_down.shape
+    out_size, out_rank, _, _ = lora_up.shape
+    assert in_rank == out_rank and kernel_size == k_, f"rank {in_rank} {out_rank} or kernel {kernel_size} {k_} mismatch"
+
+    lora_down = lora_down.to(device)
+    lora_up = lora_up.to(device)
+
+    merged = lora_up.reshape(out_size, -1) @ lora_down.reshape(in_rank, -1)
+    weight = merged.reshape(out_size, in_size, kernel_size, kernel_size)
+    del lora_up, lora_down
+    return weight
+
+
+def merge_linear(lora_down, lora_up, device):
+    in_rank, in_size = lora_down.shape
+    out_size, out_rank = lora_up.shape
+    assert in_rank == out_rank, f"rank {in_rank} {out_rank} mismatch"
+
+    lora_down = lora_down.to(device)
+    lora_up = lora_up.to(device)
+
+    weight = lora_up @ lora_down
+    del lora_up, lora_down
+    return weight
+
+
+# Calculate new rank
+
+
+def rank_resize(S, rank, dynamic_method, dynamic_param, scale=1):
+    param_dict = {}
+
+    if dynamic_method == "sv_ratio":
+        # Calculate new dim and alpha based off ratio
+        new_rank = index_sv_ratio(S, dynamic_param) + 1
+        new_alpha = float(scale * new_rank)
+
+    elif dynamic_method == "sv_cumulative":
+        # Calculate new dim and alpha based off cumulative sum
+        new_rank = index_sv_cumulative(S, dynamic_param) + 1
+        new_alpha = float(scale * new_rank)
+
+    elif dynamic_method == "sv_fro":
+        # Calculate new dim and alpha based off sqrt sum of squares
+        new_rank = index_sv_fro(S, dynamic_param) + 1
+        new_alpha = float(scale * new_rank)
+    else:
+        new_rank = rank
+        new_alpha = float(scale * new_rank)
+
+    if S[0] <= MIN_SV:  # Zero matrix, set dim to 1
+        new_rank = 1
+        new_alpha = float(scale * new_rank)
+    elif new_rank > rank:  # cap max rank at rank
+        new_rank = rank
+        new_alpha = float(scale * new_rank)
+
+    # Calculate resize info
+    s_sum = torch.sum(torch.abs(S))
+    s_rank = torch.sum(torch.abs(S[:new_rank]))
+
+    S_squared = S.pow(2)
+    s_fro = torch.sqrt(torch.sum(S_squared))
+    s_red_fro = torch.sqrt(torch.sum(S_squared[:new_rank]))
+    fro_percent = float(s_red_fro / s_fro)
+
+    param_dict["new_rank"] = new_rank
+    param_dict["new_alpha"] = new_alpha
+    param_dict["sum_retained"] = (s_rank) / s_sum
+    param_dict["fro_retained"] = fro_percent
+    param_dict["max_ratio"] = S[0] / S[new_rank - 1]
+
+    return param_dict
+
+
+def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dynamic_param, verbose):
+    max_old_rank = None
+    new_alpha = None
+    verbose_str = "\n"
+    fro_list = []
+    rank_list = []
+
+    if dynamic_method:
+        print(f"Dynamically determining new alphas and dims based off {dynamic_method}: {dynamic_param}, max rank is {new_rank}")
+
+    lora_down_weight = None
+    lora_up_weight = None
+
+    o_lora_sd = lora_sd.copy()
+    block_down_name = None
+    block_up_name = None
+
+    total_keys = len([k for k in lora_sd if k.endswith(".weight")])
+
+    pbar = comfy.utils.ProgressBar(total_keys)
+    for key, value in tqdm(lora_sd.items(), leave=True, desc="Resizing LoRA weights"):
+        key_parts = key.split(".")
+        block_down_name = None
+        for _format in LORA_DOWN_UP_FORMATS:
+            # Currently we only match lora_down_name in the last two parts of key
+            # because ("down", "up") are general words and may appear in block_down_name
+            if len(key_parts) >= 2 and _format[0] == key_parts[-2]:
+                block_down_name = ".".join(key_parts[:-2])
+                lora_down_name = "." + _format[0]
+                lora_up_name = "." + _format[1]
+                weight_name = "." + key_parts[-1]
+                break
+            if len(key_parts) >= 1 and _format[0] == key_parts[-1]:
+                block_down_name = ".".join(key_parts[:-1])
+                lora_down_name = "." + _format[0]
+                lora_up_name = "." + _format[1]
+                weight_name = ""
+                break
+
+        if block_down_name is None:
+            # This parameter is not lora_down
+            continue
+
+        # Now weight_name can be ".weight" or ""
+        # Find corresponding lora_up and alpha
+        block_up_name = block_down_name
+        lora_down_weight = value
+        lora_up_weight = lora_sd.get(block_up_name + lora_up_name + weight_name, None)
+        lora_alpha = lora_sd.get(block_down_name + ".alpha", None)
+
+        weights_loaded = lora_down_weight is not None and lora_up_weight is not None
+
+        if weights_loaded:
+
+            conv2d = len(lora_down_weight.size()) == 4
+            old_rank = lora_down_weight.size()[0]
+            max_old_rank = max(max_old_rank or 0, old_rank)
+
+            # Skip if merged weight would be too large (>100k elements in any dimension)
+            if conv2d:
+                in_rank, in_size, kernel_size, _ = lora_down_weight.shape
+                out_size, out_rank, _, _ = lora_up_weight.shape
+                merged_size = out_size * in_size * kernel_size * kernel_size
+            else:
+                in_rank, in_size = lora_down_weight.shape
+                out_size, out_rank = lora_up_weight.shape
+                merged_size = out_size * in_size
+
+            if merged_size > 100_000_000:  # Skip if >100M elements
+                logging.warning(f"Skipping {block_down_name}: merged weight too large ({merged_size:,} elements)")
+                tqdm.write(f"SKIPPED: {block_down_name} - too large ({merged_size:,} elements)")
+                pbar.update(1)
+                continue
+
+            if lora_alpha is None:
+                scale = 1.0
+            else:
+                scale = lora_alpha / old_rank
+
+            if conv2d:
+                full_weight_matrix = merge_conv(lora_down_weight, lora_up_weight, device)
+                param_dict = extract_conv(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale)
+            else:
+                full_weight_matrix = merge_linear(lora_down_weight, lora_up_weight, device)
+                param_dict = extract_linear(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale)
+
+            if verbose:
+                max_ratio = param_dict["max_ratio"]
+                sum_retained = param_dict["sum_retained"]
+                fro_retained = param_dict["fro_retained"]
+                if not np.isnan(fro_retained):
+                    fro_list.append(float(fro_retained))
+                log_str = f"{block_down_name:75} | sum(S) retained: {sum_retained:.1%}, fro retained: {fro_retained:.1%}, max(S) ratio: {max_ratio:0.1f}, new dim: {param_dict['new_rank']}"
+                tqdm.write(log_str)
+                verbose_str += log_str
+            
+            if verbose and dynamic_method:
+                verbose_str += f", dynamic | dim: {param_dict['new_rank']}, alpha: {param_dict['new_alpha']}\n"
+            else:
+                verbose_str += "\n"
+
+            new_alpha = param_dict["new_alpha"]
+            o_lora_sd[block_down_name + lora_down_name + weight_name] = param_dict["lora_down"].to(save_dtype).contiguous()
+            o_lora_sd[block_up_name + lora_up_name + weight_name] = param_dict["lora_up"].to(save_dtype).contiguous()
+            o_lora_sd[block_down_name + ".alpha"] = torch.tensor(param_dict["new_alpha"]).to(save_dtype)
+
+            block_down_name = None
+            block_up_name = None
+            lora_down_weight = None
+            lora_up_weight = None
+            weights_loaded = False
+            rank_list.append(param_dict["new_rank"])
+            del param_dict
+        pbar.update(1)
+
+    if verbose:
+        print(f"Average Frobenius norm retention: {np.mean(fro_list):.2%} | std: {np.std(fro_list):0.3f}")
+    return o_lora_sd, max_old_rank, new_alpha, rank_list

custom_nodes/ComfyUI-KJNodes/pyproject.toml

@@ -0,0 +1,15 @@
+[project]
+name = "comfyui-kjnodes"
+description = "Various quality of life -nodes for ComfyUI, mostly just visual stuff to improve usability."
+version = "1.2.9"
+license = {file = "LICENSE"}
+dependencies = ["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 = "https://avatars.githubusercontent.com/u/40791699"

custom_nodes/ComfyUI-KJNodes/requirements.txt

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

custom_nodes/ComfyUI-KJNodes/utility/fluid.py

@@ -0,0 +1,67 @@
+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/utility/magictex.py

@@ -0,0 +1,95 @@
+"""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/utility/numerical.py

@@ -0,0 +1,25 @@
+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/utility/utility.py

@@ -0,0 +1,88 @@
+import torch
+import numpy as np
+from PIL import Image, ImageColor
+from typing import Union, List
+import logging
+
+# 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)
+        )
+    ]
+
+def string_to_color(color_string: str) -> List[int]:
+    color_list = [0, 0, 0]  # Default fallback (black)
+
+    if ',' in color_string:
+        # Handle CSV format (e.g., "255, 0, 0" or "255, 0, 0, 128" or "1.0, 0.5, 0.0")
+        try:
+            values = [float(channel.strip()) for channel in color_string.split(',')]
+            # Convert to 0-255 range if values are in 0-1 range
+            if all(0 <= v <= 1 for v in values):
+                color_list = [int(v * 255) for v in values]
+            else:
+                color_list = [int(v) for v in values]
+        except ValueError:
+            logging.warning(f"Invalid color format: {color_string}. Using default black.")
+    elif color_string.lstrip('#').isalnum() and not color_string.lstrip('#').replace('.', '', 1).isdigit():
+        # Could be Hex format or color name
+        color_string_stripped = color_string.lstrip('#')
+        # Try hex first
+        if len(color_string_stripped) in [6, 8] and all(c in '0123456789ABCDEFabcdef' for c in color_string_stripped):
+            if len(color_string_stripped) == 6:  # #RRGGBB
+                color_list = [int(color_string_stripped[i:i+2], 16) for i in (0, 2, 4)]
+            elif len(color_string_stripped) == 8:  # #RRGGBBAA
+                color_list = [int(color_string_stripped[i:i+2], 16) for i in (0, 2, 4, 6)]
+        else:
+            # Try color name (e.g., "red", "blue", "cyan")
+            try:
+                rgb = ImageColor.getrgb(color_string)
+                color_list = list(rgb)
+            except ValueError:
+                logging.warning(f"Invalid color name or hex format: {color_string}. Using default black.")
+    else:
+        # Handle single value (grayscale) - can be int or float
+        try:
+            value = float(color_string.strip())
+            # Convert to 0-255 range if it's a float between 0-1
+            if 0 <= value <= 1:
+                value = int(value * 255)
+            else:
+                value = int(value)
+            color_list = [value, value, value]
+        except ValueError:
+            logging.warning(f"Invalid color format: {color_string}. Using default black.")
+
+    # Clip values to valid range
+    color_list = np.clip(color_list, 0, 255).tolist()
+
+    return color_list

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

@@ -0,0 +1,23 @@
+const { app } = window.comfyAPI.app;
+
+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/web/js/browserstatus.js

@@ -0,0 +1,55 @@
+const { api } = window.comfyAPI.api;
+const { app } = window.comfyAPI.app;
+
+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/web/js/contextmenu.js

@@ -0,0 +1,175 @@
+const { app } = window.comfyAPI.app;
+
+// 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,
+	];
+	
+	// Automatically connect nodes
+	if (options.side === "left") {
+		// New node on left: connect new node's output to nextTo's first available input
+		if (node.outputs && node.outputs.length > 0 && nextTo.inputs && nextTo.inputs.length > 0) {
+			for (let i = 0; i < nextTo.inputs.length; i++) {
+				if (!nextTo.inputs[i].link) {
+					node.connect(0, nextTo, i);
+					break;
+				}
+			}
+		}
+	} else {
+		// New node on right: connect nextTo's output to new node's first available input
+		if (nextTo.outputs && nextTo.outputs.length > 0 && node.inputs && node.inputs.length > 0) {
+			for (let i = 0; i < node.inputs.length; i++) {
+				if (!node.inputs[i].link) {
+					nextTo.connect(0, node, i);
+					break;
+				}
+			}
+		}
+	}
+	
+	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 });}
+					},
+					{
+					content: "Add PreviewAsTextNode",
+					callback: () => {addNode("PreviewAny", 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/web/js/fast_preview.js

@@ -0,0 +1,95 @@
+const { app } = window.comfyAPI.app;
+
+//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/web/js/help_popup.js

@@ -0,0 +1,326 @@
+const { app } = window.comfyAPI.app;
+
+// 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/web/js/jsnodes.js

@@ -0,0 +1,416 @@
+const { app } = window.comfyAPI.app;
+const { applyTextReplacements } = window.comfyAPI.utils;
+
+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._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"];
+					const num_inputs = this.inputs.filter(input => input.type === this._type).length
+					if(target_number_of_inputs===num_inputs)return; // already set, do nothing
+
+					if(target_number_of_inputs < num_inputs){
+						const inputs_to_remove = num_inputs - target_number_of_inputs;
+						for(let i = 0; i < inputs_to_remove; i++) {
+							this.removeInput(this.inputs.length - 1);
+						}
+					}
+					else{
+						for(let i = num_inputs+1; i <= target_number_of_inputs; ++i)
+							this.addInput(`conditioning_${i}`, this._type)
+					}
+					});
+				}
+				break;
+			case "ImageBatchMulti":
+			case "ImageAddMulti":
+			case "ImageConcatMulti":
+			case "CrossFadeImagesMulti":
+			case "TransitionImagesMulti":
+				nodeType.prototype.onNodeCreated = function () {
+				this._type = "IMAGE"
+				this.addWidget("button", "Update inputs", null, () => {
+					if (!this.inputs) {
+						this.inputs = [];
+					}
+					const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"];
+					const num_inputs = this.inputs.filter(input => input.type === this._type).length
+					if(target_number_of_inputs===num_inputs)return; // already set, do nothing
+
+					if(target_number_of_inputs < num_inputs){
+						const inputs_to_remove = num_inputs - target_number_of_inputs;
+						for(let i = 0; i < inputs_to_remove; i++) {
+							this.removeInput(this.inputs.length - 1);
+						}
+					}
+					else{
+						for(let i = num_inputs+1; i <= target_number_of_inputs; ++i)
+							this.addInput(`image_${i}`, this._type, {shape: 7});						
+					}
+					
+					});
+				}
+				break;
+			case "MaskBatchMulti":
+				nodeType.prototype.onNodeCreated = function () {
+				this._type = "MASK"
+				this.addWidget("button", "Update inputs", null, () => {
+					if (!this.inputs) {
+						this.inputs = [];
+					}
+					const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"];
+					const num_inputs = this.inputs.filter(input => input.type === this._type).length
+					if(target_number_of_inputs===num_inputs)return; // already set, do nothing
+
+					if(target_number_of_inputs < num_inputs){
+						const inputs_to_remove = num_inputs - target_number_of_inputs;
+						for(let i = 0; i < inputs_to_remove; i++) {
+							this.removeInput(this.inputs.length - 1);
+						}
+					}
+					else{
+						for(let i = num_inputs+1; i <= target_number_of_inputs; ++i)
+							this.addInput(`mask_${i}`, this._type)
+					}
+					});
+					}
+					break;
+			
+			case "FluxBlockLoraSelect":
+			case "HunyuanVideoBlockLoraSelect":
+			case "Wan21BlockLoraSelect":
+			case "LTX2BlockLoraSelect":
+				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 "GetLatentSizeAndCount":
+				const onGetLatentConnectInput = nodeType.prototype.onConnectInput;
+				nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) {
+					console.log(this)
+					const v = onGetLatentConnectInput? onGetLatentConnectInput.apply(this, arguments): undefined
+					//console.log(this)
+					this.outputs[1]["label"] = "batch_size"
+					this.outputs[2]["label"] = "channels" 
+					this.outputs[3]["label"] = "frames" 
+					this.outputs[4]["label"] = "height" 
+					this.outputs[5]["label"] = "width"
+					return v;
+				}
+				//const onGetImageSizeExecuted = nodeType.prototype.onExecuted;
+				const onGetLatentSizeExecuted = nodeType.prototype.onAfterExecuteNode;
+				nodeType.prototype.onExecuted = function(message) {
+					console.log(this)
+					const r = onGetLatentSizeExecuted? onGetLatentSizeExecuted.apply(this,arguments): undefined
+					let values = message["text"].toString().split('x').map(Number);
+					console.log(values)
+					this.outputs[1]["label"] = values[0] + " batch"
+					this.outputs[2]["label"] = values[1] + " channels" 
+					this.outputs[3]["label"] = values[2] + " frames" 
+					this.outputs[4]["label"] = values[3] + " height" 
+					this.outputs[5]["label"] = values[4] + " width" 
+					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.addWidget("button", "Update inputs", null, () => {
+						if (!this.inputs) {
+							this.inputs = [];
+						}
+						const target_number_of_inputs = this.widgets.find(w => w.name === "inputcount")["value"];
+						const num_inputs = this.inputs.filter(input => input.name && input.name.toLowerCase().includes("string_")).length
+						if (target_number_of_inputs === num_inputs) return; // already set, do nothing
+			
+						if(target_number_of_inputs < num_inputs){
+							const inputs_to_remove = num_inputs - target_number_of_inputs;
+							for(let i = 0; i < inputs_to_remove; i++) {
+								this.removeInput(this.inputs.length - 1);
+							}
+						}
+						else{
+							for(let i = num_inputs+1; i <= target_number_of_inputs; ++i)
+								this.addInput(`string_${i}`, this._type, {shape: 7});
+						}
+					});
+				}
+				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/web/js/point_editor.js

@@ -0,0 +1,744 @@
+const { app } = window.comfyAPI.app;
+import { getLocalMouse } from './protovisUtil.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}`
+
+        this.previewMediaType = 'image'
+
+        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 () {
+        let mouse = getLocalMouse(this);
+        if (pv.event.shiftKey && pv.event.button === 2) { // Use pv.event to access the event object
+          let scaledMouse = {
+            x: mouse.x / app.canvas.ds.scale,
+            y: 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: mouse.x / app.canvas.ds.scale,
+            y: 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 " + mouse.x / app.canvas.ds.scale + ", " + mouse.y / app.canvas.ds.scale);
+          drawing = true;
+          self.bbox[0].startX = mouse.x / app.canvas.ds.scale;
+          self.bbox[0].startY = 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) {
+          let mouse = getLocalMouse(this);
+          self.bbox[0].endX = mouse.x / app.canvas.ds.scale;
+          self.bbox[0].endY = mouse.y / app.canvas.ds.scale;
+          self.vis.render();
+        }
+      })
+      .event("mouseup", function () {
+        let mouse = getLocalMouse(this);
+        console.log("end drawing at " + mouse.x / app.canvas.ds.scale + ", " + 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 mouse = getLocalMouse(this);
+          let adjustedX = mouse.x / app.canvas.ds.scale; // Adjust the new position by the inverse of the scale factor
+          let adjustedY = 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 = adjustedX;
+          self.bbox[0].endY = adjustedY;
+          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 mouse = getLocalMouse(this);
+        let adjustedX = mouse.x / app.canvas.ds.scale; // Adjust the new X position by the inverse of the scale factor
+        let adjustedY = 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 mouse = getLocalMouse(this);
+        let adjustedX = mouse.x / app.canvas.ds.scale; // Adjust the new X position by the inverse of the scale factor
+        let adjustedY = 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) {
+    if (e.target.closest(`#points-editor-${self.node.uuid}`) || 
+        e.target.closest('#context-menu')) {
+      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/web/js/protovisUtil.js

@@ -0,0 +1,25 @@
+/**
+ * Utility functions for protovis in ComfyUI.
+ */
+
+/**
+ * Get correct local coordinates for protovis in transformed containers.
+ * Uses getBoundingClientRect() which properly accounts for CSS transforms.
+ *
+ * This fixes coordinate calculation issues when protovis widgets are rendered
+ * inside ComfyUI's vueNodes mode, which uses CSS transforms for panning/zooming.
+ *
+ * @param {pv.Mark} mark - The protovis mark instance
+ * @returns {{x: number, y: number}} Local coordinates relative to the canvas
+ */
+export function getLocalMouse(mark) {
+  const e = pv.event
+  if (!e) return { x: 0, y: 0 }
+  const canvas = mark.root.canvas()
+  if (!canvas) return { x: 0, y: 0 }
+  const rect = canvas.getBoundingClientRect()
+  return {
+    x: e.clientX - rect.left,
+    y: e.clientY - rect.top
+  }
+}

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

@@ -0,0 +1,567 @@
+const { app } = window.comfyAPI.app;
+
+//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 = {
+		"DEFAULT": LGraphCanvas.node_colors.gray,
+        "MODEL": LGraphCanvas.node_colors.blue,
+        "LATENT": LGraphCanvas.node_colors.purple,
+        "VAE": LGraphCanvas.node_colors.red,
+		"WANVAE": 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"},
+
+    };
+	console.log("Setting color for type:", colorMap[type]);
+    const colors = colorMap[type];
+    if (colors) {
+        this.color = colors.color;
+        this.bgcolor = colors.bgcolor;
+    }
+	else{
+		// Default color
+		this.color = LGraphCanvas.node_colors.gray;
+		this.bgcolor = LGraphCanvas.node_colors.gray;
+	}
+}
+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) {
+						if (this.outputs && this.outputs[slot]) {
+							this.outputs[slot].type = '*';
+							this.outputs[slot].name = '*';
+						}
+					}
+					//On Connect
+					if (link_info && node.graph && slotType == 1 && isChangeConnect) {
+						const resolve = link_info.resolve(node.graph)
+						const type = (resolve?.subgraphInput ?? resolve?.output)?.type
+						if (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() {
+					this.canvas.centerOnNode(this.currentSetter);
+					this.canvas.selectNode(this.currentSetter, 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";
+				this.currentSetter = this.findSetter(this.graph)
+				if (!this.currentSetter) return
+				options.unshift(
+					{
+						content: "Go to setter",
+						callback: () => {
+							this.goToSetter();
+						},
+					},
+					{
+						content: menuEntry,
+						callback: () => {
+							let linkType = (this.currentSetter.inputs[0].type);
+							this.drawConnection = !this.drawConnection;
+							this.slotColor = this.canvas.default_connection_color_byType[linkType]
+							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";
+	},
+});