mirror of
https://git.datalinker.icu/kijai/ComfyUI-KJNodes.git
synced 2026-01-23 12:44:28 +08:00
Start restructuring and add custom range to spline editor output
This commit is contained in:
kijai
parent
c9813da892
commit
ae0eb9be67
184
__init__.py
184
__init__.py
@ -1,4 +1,186 @@
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from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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from .nodes import *
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from .curve_nodes import *
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NODE_CLASS_MAPPINGS = {
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"INTConstant": INTConstant,
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"FloatConstant": FloatConstant,
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"ImageBatchMulti": ImageBatchMulti,
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"MaskBatchMulti": MaskBatchMulti,
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"ConditioningMultiCombine": ConditioningMultiCombine,
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"ConditioningSetMaskAndCombine": ConditioningSetMaskAndCombine,
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"ConditioningSetMaskAndCombine3": ConditioningSetMaskAndCombine3,
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"ConditioningSetMaskAndCombine4": ConditioningSetMaskAndCombine4,
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"ConditioningSetMaskAndCombine5": ConditioningSetMaskAndCombine5,
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"GrowMaskWithBlur": GrowMaskWithBlur,
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"ColorToMask": ColorToMask,
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"CreateGradientMask": CreateGradientMask,
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"CreateTextMask": CreateTextMask,
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"CreateAudioMask": CreateAudioMask,
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"CreateFadeMask": CreateFadeMask,
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"CreateFadeMaskAdvanced": CreateFadeMaskAdvanced,
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"CreateFluidMask" :CreateFluidMask,
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"VRAM_Debug" : VRAM_Debug,
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"SomethingToString" : SomethingToString,
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"CrossFadeImages": CrossFadeImages,
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"EmptyLatentImagePresets": EmptyLatentImagePresets,
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"ColorMatch": ColorMatch,
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"GetImageRangeFromBatch": GetImageRangeFromBatch,
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"SaveImageWithAlpha": SaveImageWithAlpha,
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"ReverseImageBatch": ReverseImageBatch,
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"ImageGridComposite2x2": ImageGridComposite2x2,
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"ImageGridComposite3x3": ImageGridComposite3x3,
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"ImageConcanate": ImageConcanate,
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"ImageBatchTestPattern": ImageBatchTestPattern,
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"ReplaceImagesInBatch": ReplaceImagesInBatch,
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"BatchCropFromMask": BatchCropFromMask,
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"BatchCropFromMaskAdvanced": BatchCropFromMaskAdvanced,
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"FilterZeroMasksAndCorrespondingImages": FilterZeroMasksAndCorrespondingImages,
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"InsertImageBatchByIndexes": InsertImageBatchByIndexes,
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"BatchUncrop": BatchUncrop,
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"BatchUncropAdvanced": BatchUncropAdvanced,
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"BatchCLIPSeg": BatchCLIPSeg,
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"RoundMask": RoundMask,
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"ResizeMask": ResizeMask,
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"OffsetMask": OffsetMask,
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"WidgetToString": WidgetToString,
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"CreateShapeMask": CreateShapeMask,
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"CreateVoronoiMask": CreateVoronoiMask,
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"CreateMagicMask": CreateMagicMask,
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"BboxToInt": BboxToInt,
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"SplitBboxes": SplitBboxes,
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"ImageGrabPIL": ImageGrabPIL,
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"DummyLatentOut": DummyLatentOut,
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"FlipSigmasAdjusted": FlipSigmasAdjusted,
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"InjectNoiseToLatent": InjectNoiseToLatent,
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"AddLabel": AddLabel,
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"SoundReactive": SoundReactive,
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"GenerateNoise": GenerateNoise,
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"StableZero123_BatchSchedule": StableZero123_BatchSchedule,
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"SV3D_BatchSchedule": SV3D_BatchSchedule,
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"GetImagesFromBatchIndexed": GetImagesFromBatchIndexed,
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"InsertImagesToBatchIndexed": InsertImagesToBatchIndexed,
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"ImageBatchRepeatInterleaving": ImageBatchRepeatInterleaving,
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"NormalizedAmplitudeToMask": NormalizedAmplitudeToMask,
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"OffsetMaskByNormalizedAmplitude": OffsetMaskByNormalizedAmplitude,
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"ImageTransformByNormalizedAmplitude": ImageTransformByNormalizedAmplitude,
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"GetLatentsFromBatchIndexed": GetLatentsFromBatchIndexed,
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"StringConstant": StringConstant,
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"GLIGENTextBoxApplyBatch": GLIGENTextBoxApplyBatch,
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"CondPassThrough": CondPassThrough,
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"ImageUpscaleWithModelBatched": ImageUpscaleWithModelBatched,
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"ScaleBatchPromptSchedule": ScaleBatchPromptSchedule,
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"ImageNormalize_Neg1_To_1": ImageNormalize_Neg1_To_1,
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"Intrinsic_lora_sampling": Intrinsic_lora_sampling,
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"RemapMaskRange": RemapMaskRange,
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"LoadResAdapterNormalization": LoadResAdapterNormalization,
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"Superprompt": Superprompt,
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"RemapImageRange": RemapImageRange,
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"CameraPoseVisualizer": CameraPoseVisualizer,
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"BboxVisualize": BboxVisualize,
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"StringConstantMultiline": StringConstantMultiline,
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"JoinStrings": JoinStrings,
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"Sleep": Sleep,
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"ImagePadForOutpaintMasked": ImagePadForOutpaintMasked,
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"ImageAndMaskPreview": ImageAndMaskPreview,
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"StabilityAPI_SD3": StabilityAPI_SD3,
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"MaskOrImageToWeight": MaskOrImageToWeight,
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"WeightScheduleConvert": WeightScheduleConvert,
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"FloatToMask": FloatToMask,
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"CustomSigmas": CustomSigmas,
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"ImagePass": ImagePass,
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"SplineEditor": SplineEditor,
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"CreateShapeMaskOnPath": CreateShapeMaskOnPath
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"INTConstant": "INT Constant",
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"FloatConstant": "Float Constant",
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"ImageBatchMulti": "Image Batch Multi",
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"MaskBatchMulti": "Mask Batch Multi",
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"ConditioningMultiCombine": "Conditioning Multi Combine",
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"ConditioningSetMaskAndCombine": "ConditioningSetMaskAndCombine",
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"ConditioningSetMaskAndCombine3": "ConditioningSetMaskAndCombine3",
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"ConditioningSetMaskAndCombine4": "ConditioningSetMaskAndCombine4",
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"ConditioningSetMaskAndCombine5": "ConditioningSetMaskAndCombine5",
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"GrowMaskWithBlur": "GrowMaskWithBlur",
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"ColorToMask": "ColorToMask",
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"CreateGradientMask": "CreateGradientMask",
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"CreateTextMask" : "CreateTextMask",
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"CreateFadeMask" : "CreateFadeMask (Deprecated)",
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"CreateFadeMaskAdvanced" : "CreateFadeMaskAdvanced",
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"CreateFluidMask" : "CreateFluidMask",
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"CreateAudioMask" : "CreateAudioMask (Deprecated)",
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"VRAM_Debug" : "VRAM Debug",
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"CrossFadeImages": "CrossFadeImages",
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"SomethingToString": "SomethingToString",
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"EmptyLatentImagePresets": "EmptyLatentImagePresets",
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"ColorMatch": "ColorMatch",
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"GetImageRangeFromBatch": "GetImageRangeFromBatch",
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"InsertImagesToBatchIndexed": "InsertImagesToBatchIndexed",
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"SaveImageWithAlpha": "SaveImageWithAlpha",
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"ReverseImageBatch": "ReverseImageBatch",
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"ImageGridComposite2x2": "ImageGridComposite2x2",
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"ImageGridComposite3x3": "ImageGridComposite3x3",
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"ImageConcanate": "ImageConcatenate",
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"ImageBatchTestPattern": "ImageBatchTestPattern",
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"ReplaceImagesInBatch": "ReplaceImagesInBatch",
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"BatchCropFromMask": "BatchCropFromMask",
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"BatchCropFromMaskAdvanced": "BatchCropFromMaskAdvanced",
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"FilterZeroMasksAndCorrespondingImages": "FilterZeroMasksAndCorrespondingImages",
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"InsertImageBatchByIndexes": "InsertImageBatchByIndexes",
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"BatchUncrop": "BatchUncrop",
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"BatchUncropAdvanced": "BatchUncropAdvanced",
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"BatchCLIPSeg": "BatchCLIPSeg",
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"RoundMask": "RoundMask",
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"ResizeMask": "ResizeMask",
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"OffsetMask": "OffsetMask",
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"WidgetToString": "WidgetToString",
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"CreateShapeMask": "CreateShapeMask",
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"CreateVoronoiMask": "CreateVoronoiMask",
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"CreateMagicMask": "CreateMagicMask",
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"BboxToInt": "BboxToInt",
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"SplitBboxes": "SplitBboxes",
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"ImageGrabPIL": "ImageGrabPIL",
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"DummyLatentOut": "DummyLatentOut",
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"FlipSigmasAdjusted": "FlipSigmasAdjusted",
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"InjectNoiseToLatent": "InjectNoiseToLatent",
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"AddLabel": "AddLabel",
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"SoundReactive": "SoundReactive",
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"GenerateNoise": "GenerateNoise",
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"StableZero123_BatchSchedule": "StableZero123_BatchSchedule",
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"SV3D_BatchSchedule": "SV3D_BatchSchedule",
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"GetImagesFromBatchIndexed": "GetImagesFromBatchIndexed",
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"ImageBatchRepeatInterleaving": "ImageBatchRepeatInterleaving",
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"NormalizedAmplitudeToMask": "NormalizedAmplitudeToMask",
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"OffsetMaskByNormalizedAmplitude": "OffsetMaskByNormalizedAmplitude",
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"ImageTransformByNormalizedAmplitude": "ImageTransformByNormalizedAmplitude",
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"GetLatentsFromBatchIndexed": "GetLatentsFromBatchIndexed",
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"StringConstant": "StringConstant",
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"GLIGENTextBoxApplyBatch": "GLIGENTextBoxApplyBatch",
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"CondPassThrough": "CondPassThrough",
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"ImageUpscaleWithModelBatched": "ImageUpscaleWithModelBatched",
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"ScaleBatchPromptSchedule": "ScaleBatchPromptSchedule",
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"ImageNormalize_Neg1_To_1": "ImageNormalize_Neg1_To_1",
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"Intrinsic_lora_sampling": "Intrinsic_lora_sampling",
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"RemapMaskRange": "RemapMaskRange",
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"LoadResAdapterNormalization": "LoadResAdapterNormalization",
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"Superprompt": "Superprompt",
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"RemapImageRange": "RemapImageRange",
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"CameraPoseVisualizer": "CameraPoseVisualizer",
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"BboxVisualize": "BboxVisualize",
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"StringConstantMultiline": "StringConstantMultiline",
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"JoinStrings": "JoinStrings",
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"Sleep": "🛌 Sleep 🛌",
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"ImagePadForOutpaintMasked": "Pad Image For Outpaint Masked",
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"ImageAndMaskPreview": "Image & Mask Preview",
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"StabilityAPI_SD3": "Stability API SD3",
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"MaskOrImageToWeight": "Mask Or Image To Weight",
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"WeightScheduleConvert": "Weight Schedule Convert",
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"FloatToMask": "Float To Mask",
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"CustomSigmas": "Custom Sigmas",
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"ImagePass": "ImagePass",
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"SplineEditor": "Spline Editor",
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"CreateShapeMaskOnPath": "Create Shape Mask On Path"
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}
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
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WEB_DIRECTORY = "./web"
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399
curve_nodes.py
Normal file
399
curve_nodes.py
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import torch
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import json
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from PIL import Image, ImageDraw
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import numpy as np
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from .utility import pil2tensor
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class SplineEditor:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"points_store": ("STRING", {"multiline": False}),
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"coordinates": ("STRING", {"multiline": False}),
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"mask_width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
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"mask_height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
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"points_to_sample": ("INT", {"default": 16, "min": 2, "max": 1000, "step": 1}),
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"interpolation": (
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[
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'cardinal',
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'monotone',
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'basis',
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'linear',
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'step-before',
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'step-after',
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'polar',
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'polar-reverse',
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],
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{
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"default": 'cardinal'
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}),
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"tension": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"repeat_output": ("INT", {"default": 1, "min": 1, "max": 4096, "step": 1}),
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"float_output_type": (
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[
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'list',
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'list of lists',
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'pandas series',
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'tensor',
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],
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{
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"default": 'list'
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}),
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},
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"optional": {
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"min_value": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
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"max_value": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
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}
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}
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RETURN_TYPES = ("MASK", "STRING", "FLOAT")
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FUNCTION = "splinedata"
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CATEGORY = "KJNodes/experimental"
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DESCRIPTION = """
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# WORK IN PROGRESS
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Do not count on this as part of your workflow yet,
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probably contains lots of bugs and stability is not
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guaranteed!!
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## Graphical editor to create values for various
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## schedules and/or mask batches.
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Shift + click to add control points.
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Right click to delete control points.
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Note that you can't delete from start/end.
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**points_to_sample** value sets the number of samples
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returned from the **drawn spline itself**, this is independent from the
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actual control points, so the interpolation type matters.
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Changing interpolation type and tension value takes effect on
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interaction with the graph.
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output types:
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- mask batch
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example compatible nodes: anything that takes masks
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- list of floats
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example compatible nodes: IPAdapter weights
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- list of lists
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example compatible nodes: unknown
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- pandas series
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example compatible nodes: anything that takes Fizz'
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nodes Batch Value Schedule
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- torch tensor
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example compatible nodes: unknown
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"""
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def splinedata(self, mask_width, mask_height, coordinates, float_output_type, interpolation,
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points_to_sample, points_store, tension, repeat_output, min_value=0.0, max_value=1.0):
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coordinates = json.loads(coordinates)
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for coord in coordinates:
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coord['x'] = int(round(coord['x']))
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coord['y'] = int(round(coord['y']))
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normalized_y_values = [
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(1.0 - (point['y'] / 512) - 0.0) * (max_value - min_value) + min_value
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for point in coordinates
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]
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if float_output_type == 'list':
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out_floats = normalized_y_values * repeat_output
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elif float_output_type == 'list of lists':
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out_floats = ([[value] for value in normalized_y_values] * repeat_output),
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elif float_output_type == 'pandas series':
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try:
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import pandas as pd
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except:
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raise Exception("MaskOrImageToWeight: pandas is not installed. Please install pandas to use this output_type")
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out_floats = pd.Series(normalized_y_values * repeat_output),
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elif float_output_type == 'tensor':
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out_floats = torch.tensor(normalized_y_values * repeat_output, dtype=torch.float32)
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# Create a color map for grayscale intensities
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color_map = lambda y: torch.full((mask_height, mask_width, 3), y, dtype=torch.float32)
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# Create image tensors for each normalized y value
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mask_tensors = [color_map(y) for y in normalized_y_values]
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masks_out = torch.stack(mask_tensors)
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masks_out = masks_out.repeat(repeat_output, 1, 1, 1)
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masks_out = masks_out.mean(dim=-1)
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return (masks_out, str(coordinates), out_floats,)
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class CreateShapeMaskOnPath:
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RETURN_TYPES = ("MASK", "MASK",)
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RETURN_NAMES = ("mask", "mask_inverted",)
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FUNCTION = "createshapemask"
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CATEGORY = "KJNodes/masking/generate"
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DESCRIPTION = """
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Creates a mask or batch of masks with the specified shape.
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Locations are center locations.
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Grow value is the amount to grow the shape on each frame, creating animated masks.
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"""
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"shape": (
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[ 'circle',
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'square',
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'triangle',
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],
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{
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"default": 'circle'
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}),
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"coordinates": ("STRING", {"forceInput": True}),
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"grow": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
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"frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
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"frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
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"shape_width": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}),
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"shape_height": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}),
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},
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}
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def createshapemask(self, coordinates, frame_width, frame_height, shape_width, shape_height, grow, shape):
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# Define the number of images in the batch
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coordinates = coordinates.replace("'", '"')
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coordinates = json.loads(coordinates)
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for coord in coordinates:
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print(coord)
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batch_size = len(coordinates)
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print(batch_size)
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out = []
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color = "white"
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for i, coord in enumerate(coordinates):
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image = Image.new("RGB", (frame_width, frame_height), "black")
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draw = ImageDraw.Draw(image)
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# Calculate the size for this frame and ensure it's not less than 0
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current_width = max(0, shape_width + i*grow)
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current_height = max(0, shape_height + i*grow)
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location_x = coord['x']
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location_y = coord['y']
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if shape == 'circle' or shape == 'square':
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# Define the bounding box for the shape
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left_up_point = (location_x - current_width // 2, location_y - current_height // 2)
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right_down_point = (location_x + current_width // 2, location_y + current_height // 2)
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two_points = [left_up_point, right_down_point]
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if shape == 'circle':
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draw.ellipse(two_points, fill=color)
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elif shape == 'square':
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draw.rectangle(two_points, fill=color)
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elif shape == 'triangle':
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# Define the points for the triangle
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left_up_point = (location_x - current_width // 2, location_y + current_height // 2) # bottom left
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right_down_point = (location_x + current_width // 2, location_y + current_height // 2) # bottom right
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top_point = (location_x, location_y - current_height // 2) # top point
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draw.polygon([top_point, left_up_point, right_down_point], fill=color)
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image = pil2tensor(image)
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mask = image[:, :, :, 0]
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out.append(mask)
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outstack = torch.cat(out, dim=0)
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return (outstack, 1.0 - outstack,)
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class MaskOrImageToWeight:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"output_type": (
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[
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'list',
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'list of lists',
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'pandas series',
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'tensor',
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],
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{
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"default": 'list'
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}),
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},
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"optional": {
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"images": ("IMAGE",),
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"masks": ("MASK",),
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},
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}
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RETURN_TYPES = ("FLOAT",)
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FUNCTION = "execute"
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CATEGORY = "KJNodes"
|
||||
DESCRIPTION = """
|
||||
Gets the mean values from mask or image batch
|
||||
and returns that as the selected output type.
|
||||
"""
|
||||
|
||||
def execute(self, output_type, images=None, masks=None):
|
||||
mean_values = []
|
||||
if masks is not None and images is None:
|
||||
for mask in masks:
|
||||
mean_values.append(mask.mean().item())
|
||||
elif masks is None and images is not None:
|
||||
for image in images:
|
||||
mean_values.append(image.mean().item())
|
||||
elif masks is not None and images is not None:
|
||||
raise Exception("MaskOrImageToWeight: Use either mask or image input only.")
|
||||
|
||||
# Convert mean_values to the specified output_type
|
||||
if output_type == 'list':
|
||||
return mean_values,
|
||||
elif output_type == 'list of lists':
|
||||
return [[value] for value in mean_values],
|
||||
elif output_type == 'pandas series':
|
||||
try:
|
||||
import pandas as pd
|
||||
except:
|
||||
raise Exception("MaskOrImageToWeight: pandas is not installed. Please install pandas to use this output_type")
|
||||
return pd.Series(mean_values),
|
||||
elif output_type == 'tensor':
|
||||
return torch.tensor(mean_values, dtype=torch.float32),
|
||||
else:
|
||||
raise ValueError(f"Unsupported output_type: {output_type}")
|
||||
|
||||
class WeightScheduleConvert:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"input_values": ("FLOAT", {"default": 0.0, "forceInput": True}),
|
||||
"output_type": (
|
||||
[
|
||||
'match_input',
|
||||
'list',
|
||||
'list of lists',
|
||||
'pandas series',
|
||||
'tensor',
|
||||
],
|
||||
{
|
||||
"default": 'list'
|
||||
}),
|
||||
"invert": ("BOOLEAN", {"default": False}),
|
||||
"repeat": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}),
|
||||
},
|
||||
"optional": {
|
||||
"remap_to_frames": ("INT", {"default": 0}),
|
||||
"interpolation_curve": ("FLOAT", {"forceInput": True}),
|
||||
},
|
||||
|
||||
}
|
||||
RETURN_TYPES = ("FLOAT",)
|
||||
FUNCTION = "execute"
|
||||
CATEGORY = "KJNodes"
|
||||
DESCRIPTION = """
|
||||
Converts different value lists/series to another type.
|
||||
"""
|
||||
|
||||
def detect_input_type(self, input_values):
|
||||
import pandas as pd
|
||||
if isinstance(input_values, list):
|
||||
return 'list'
|
||||
elif isinstance(input_values, pd.Series):
|
||||
return 'pandas series'
|
||||
elif isinstance(input_values, torch.Tensor):
|
||||
return 'tensor'
|
||||
elif isinstance(input_values, list) and all(isinstance(sub, list) for sub in input_values):
|
||||
return 'list of lists'
|
||||
else:
|
||||
raise ValueError("Unsupported input type")
|
||||
|
||||
def execute(self, input_values, output_type, invert, repeat, remap_to_frames=0, interpolation_curve=None):
|
||||
import pandas as pd
|
||||
input_type = self.detect_input_type(input_values)
|
||||
|
||||
# Convert input_values to a list of floats
|
||||
if input_type == 'list of lists':
|
||||
float_values = [item for sublist in input_values for item in sublist]
|
||||
elif input_type == 'pandas series':
|
||||
float_values = input_values.tolist()
|
||||
elif input_type == 'tensor':
|
||||
float_values = input_values
|
||||
else:
|
||||
float_values = input_values
|
||||
|
||||
if invert:
|
||||
float_values = [1 - value for value in float_values]
|
||||
|
||||
if interpolation_curve is not None:
|
||||
interpolated_pattern = []
|
||||
orig_float_values = float_values
|
||||
for value in interpolation_curve:
|
||||
min_val = min(orig_float_values)
|
||||
max_val = max(orig_float_values)
|
||||
# Normalize the values to [0, 1]
|
||||
normalized_values = [(value - min_val) / (max_val - min_val) for value in orig_float_values]
|
||||
# Interpolate the normalized values to the new frame count
|
||||
remapped_float_values = np.interp(np.linspace(0, 1, int(remap_to_frames * value)), np.linspace(0, 1, len(normalized_values)), normalized_values).tolist()
|
||||
interpolated_pattern.extend(remapped_float_values)
|
||||
float_values = interpolated_pattern
|
||||
else:
|
||||
# Remap float_values to match target_frame_amount
|
||||
if remap_to_frames > 0 and remap_to_frames != len(float_values):
|
||||
min_val = min(float_values)
|
||||
max_val = max(float_values)
|
||||
# Normalize the values to [0, 1]
|
||||
normalized_values = [(value - min_val) / (max_val - min_val) for value in float_values]
|
||||
# Interpolate the normalized values to the new frame count
|
||||
float_values = np.interp(np.linspace(0, 1, remap_to_frames), np.linspace(0, 1, len(normalized_values)), normalized_values).tolist()
|
||||
|
||||
float_values = float_values * repeat
|
||||
|
||||
if output_type == 'list':
|
||||
return float_values,
|
||||
elif output_type == 'list of lists':
|
||||
return [[value] for value in float_values],
|
||||
elif output_type == 'pandas series':
|
||||
return pd.Series(float_values),
|
||||
elif output_type == 'tensor':
|
||||
if input_type == 'pandas series':
|
||||
return torch.tensor(input_values.values, dtype=torch.float32),
|
||||
elif output_type == 'match_input':
|
||||
return float_values,
|
||||
else:
|
||||
raise ValueError(f"Unsupported output_type: {output_type}")
|
||||
|
||||
class FloatToMask:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"input_values": ("FLOAT", {"forceInput": True, "default": 0}),
|
||||
"width": ("INT", {"default": 100, "min": 1}),
|
||||
"height": ("INT", {"default": 100, "min": 1}),
|
||||
},
|
||||
}
|
||||
RETURN_TYPES = ("MASK",)
|
||||
FUNCTION = "execute"
|
||||
CATEGORY = "KJNodes"
|
||||
DESCRIPTION = """
|
||||
Generates a batch of masks based on the input float values.
|
||||
The batch size is determined by the length of the input float values.
|
||||
Each mask is generated with the specified width and height.
|
||||
"""
|
||||
|
||||
def execute(self, input_values, width, height):
|
||||
import pandas as pd
|
||||
# Ensure input_values is a list
|
||||
if isinstance(input_values, (float, int)):
|
||||
input_values = [input_values]
|
||||
elif isinstance(input_values, pd.Series):
|
||||
input_values = input_values.tolist()
|
||||
elif isinstance(input_values, list) and all(isinstance(item, list) for item in input_values):
|
||||
input_values = [item for sublist in input_values for item in sublist]
|
||||
|
||||
# Generate a batch of masks based on the input_values
|
||||
masks = []
|
||||
for value in input_values:
|
||||
# Assuming value is a float between 0 and 1 representing the mask's intensity
|
||||
mask = torch.ones((height, width), dtype=torch.float32) * value
|
||||
masks.append(mask)
|
||||
masks_out = torch.stack(masks, dim=0)
|
||||
|
||||
return(masks_out,)
|
||||
572
nodes.py
572
nodes.py
@ -4746,194 +4746,7 @@ nodes for example.
|
||||
return(self.save_images(preview, filename_prefix, prompt, extra_pnginfo))
|
||||
|
||||
|
||||
class SplineEditor:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"points_store": ("STRING", {"multiline": False}),
|
||||
"coordinates": ("STRING", {"multiline": False}),
|
||||
"mask_width": ("INT", {"default": 512, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
|
||||
"mask_height": ("INT", {"default": 512, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
|
||||
"points_to_sample": ("INT", {"default": 16, "min": 2, "max": 1000, "step": 1}),
|
||||
"interpolation": (
|
||||
[
|
||||
'cardinal',
|
||||
'monotone',
|
||||
'basis',
|
||||
'linear',
|
||||
'step-before',
|
||||
'step-after',
|
||||
'polar',
|
||||
'polar-reverse',
|
||||
],
|
||||
{
|
||||
"default": 'cardinal'
|
||||
}),
|
||||
"tension": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
|
||||
"repeat_output": ("INT", {"default": 1, "min": 1, "max": 4096, "step": 1}),
|
||||
"float_output_type": (
|
||||
[
|
||||
'list',
|
||||
'list of lists',
|
||||
'pandas series',
|
||||
'tensor',
|
||||
],
|
||||
{
|
||||
"default": 'list'
|
||||
}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("MASK", "STRING", "FLOAT")
|
||||
FUNCTION = "splinedata"
|
||||
CATEGORY = "KJNodes/experimental"
|
||||
DESCRIPTION = """
|
||||
# WORK IN PROGRESS
|
||||
Do not count on this as part of your workflow yet,
|
||||
probably contains lots of bugs and stability is not
|
||||
guaranteed!!
|
||||
|
||||
## Graphical editor to create values for various
|
||||
## schedules and/or mask batches.
|
||||
|
||||
Shift + click to add control points.
|
||||
Right click to delete control points.
|
||||
Note that you can't delete from start/end.
|
||||
|
||||
**points_to_sample** value sets the number of samples
|
||||
returned from the **drawn spline itself**, this is independent from the
|
||||
actual control points, so the interpolation type matters.
|
||||
|
||||
Changing interpolation type and tension value takes effect on
|
||||
interaction with the graph.
|
||||
|
||||
output types:
|
||||
- mask batch
|
||||
example compatible nodes: anything that takes masks
|
||||
- list of floats
|
||||
example compatible nodes: IPAdapter weights
|
||||
- list of lists
|
||||
example compatible nodes: unknown
|
||||
- pandas series
|
||||
example compatible nodes: anything that takes Fizz'
|
||||
nodes Batch Value Schedule
|
||||
- torch tensor
|
||||
example compatible nodes: unknown
|
||||
"""
|
||||
|
||||
def splinedata(self, mask_width, mask_height, coordinates, float_output_type, interpolation, points_to_sample, points_store, tension, repeat_output):
|
||||
|
||||
coordinates = json.loads(coordinates)
|
||||
for coord in coordinates:
|
||||
coord['x'] = int(round(coord['x']))
|
||||
coord['y'] = int(round(coord['y']))
|
||||
normalized_y_values = [
|
||||
1.0 - (point['y'] / 512)
|
||||
for point in coordinates
|
||||
]
|
||||
if float_output_type == 'list':
|
||||
out_floats = normalized_y_values * repeat_output
|
||||
elif float_output_type == 'list of lists':
|
||||
out_floats = ([[value] for value in normalized_y_values] * repeat_output),
|
||||
elif float_output_type == 'pandas series':
|
||||
try:
|
||||
import pandas as pd
|
||||
except:
|
||||
raise Exception("MaskOrImageToWeight: pandas is not installed. Please install pandas to use this output_type")
|
||||
out_floats = pd.Series(normalized_y_values * repeat_output),
|
||||
elif float_output_type == 'tensor':
|
||||
out_floats = torch.tensor(normalized_y_values * repeat_output, dtype=torch.float32)
|
||||
# Create a color map for grayscale intensities
|
||||
color_map = lambda y: torch.full((mask_height, mask_width, 3), y, dtype=torch.float32)
|
||||
|
||||
# Create image tensors for each normalized y value
|
||||
mask_tensors = [color_map(y) for y in normalized_y_values]
|
||||
masks_out = torch.stack(mask_tensors)
|
||||
masks_out = masks_out.repeat(repeat_output, 1, 1, 1)
|
||||
masks_out = masks_out.mean(dim=-1)
|
||||
return (masks_out, str(coordinates), out_floats,)
|
||||
|
||||
class CreateShapeMaskOnPath:
|
||||
|
||||
RETURN_TYPES = ("MASK", "MASK",)
|
||||
RETURN_NAMES = ("mask", "mask_inverted",)
|
||||
FUNCTION = "createshapemask"
|
||||
CATEGORY = "KJNodes/masking/generate"
|
||||
DESCRIPTION = """
|
||||
Creates a mask or batch of masks with the specified shape.
|
||||
Locations are center locations.
|
||||
Grow value is the amount to grow the shape on each frame, creating animated masks.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"shape": (
|
||||
[ 'circle',
|
||||
'square',
|
||||
'triangle',
|
||||
],
|
||||
{
|
||||
"default": 'circle'
|
||||
}),
|
||||
"coordinates": ("STRING", {"forceInput": True}),
|
||||
"grow": ("INT", {"default": 0, "min": -512, "max": 512, "step": 1}),
|
||||
"frame_width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
|
||||
"frame_height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
|
||||
"shape_width": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}),
|
||||
"shape_height": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}),
|
||||
},
|
||||
}
|
||||
|
||||
def createshapemask(self, coordinates, frame_width, frame_height, shape_width, shape_height, grow, shape):
|
||||
# Define the number of images in the batch
|
||||
coordinates = coordinates.replace("'", '"')
|
||||
coordinates = json.loads(coordinates)
|
||||
for coord in coordinates:
|
||||
print(coord)
|
||||
|
||||
batch_size = len(coordinates)
|
||||
print(batch_size)
|
||||
out = []
|
||||
color = "white"
|
||||
|
||||
for i, coord in enumerate(coordinates):
|
||||
image = Image.new("RGB", (frame_width, frame_height), "black")
|
||||
draw = ImageDraw.Draw(image)
|
||||
|
||||
# Calculate the size for this frame and ensure it's not less than 0
|
||||
current_width = max(0, shape_width + i*grow)
|
||||
current_height = max(0, shape_height + i*grow)
|
||||
|
||||
location_x = coord['x']
|
||||
location_y = coord['y']
|
||||
|
||||
if shape == 'circle' or shape == 'square':
|
||||
# Define the bounding box for the shape
|
||||
left_up_point = (location_x - current_width // 2, location_y - current_height // 2)
|
||||
right_down_point = (location_x + current_width // 2, location_y + current_height // 2)
|
||||
two_points = [left_up_point, right_down_point]
|
||||
|
||||
if shape == 'circle':
|
||||
draw.ellipse(two_points, fill=color)
|
||||
elif shape == 'square':
|
||||
draw.rectangle(two_points, fill=color)
|
||||
|
||||
elif shape == 'triangle':
|
||||
# Define the points for the triangle
|
||||
left_up_point = (location_x - current_width // 2, location_y + current_height // 2) # bottom left
|
||||
right_down_point = (location_x + current_width // 2, location_y + current_height // 2) # bottom right
|
||||
top_point = (location_x, location_y - current_height // 2) # top point
|
||||
draw.polygon([top_point, left_up_point, right_down_point], fill=color)
|
||||
|
||||
image = pil2tensor(image)
|
||||
mask = image[:, :, :, 0]
|
||||
out.append(mask)
|
||||
outstack = torch.cat(out, dim=0)
|
||||
return (outstack, 1.0 - outstack,)
|
||||
|
||||
class StabilityAPI_SD3:
|
||||
|
||||
@ -5085,387 +4898,4 @@ If no image is provided, mode is set to text-to-image
|
||||
raise Exception(f"Server error: {error_data}")
|
||||
except json.JSONDecodeError:
|
||||
# If the response is not valid JSON, raise a different exception
|
||||
raise Exception(f"Server error: {response.text}")
|
||||
|
||||
|
||||
class MaskOrImageToWeight:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"output_type": (
|
||||
[
|
||||
'list',
|
||||
'list of lists',
|
||||
'pandas series',
|
||||
'tensor',
|
||||
],
|
||||
{
|
||||
"default": 'list'
|
||||
}),
|
||||
},
|
||||
"optional": {
|
||||
"images": ("IMAGE",),
|
||||
"masks": ("MASK",),
|
||||
},
|
||||
|
||||
}
|
||||
RETURN_TYPES = ("FLOAT",)
|
||||
FUNCTION = "execute"
|
||||
CATEGORY = "KJNodes"
|
||||
DESCRIPTION = """
|
||||
Gets the mean values from mask or image batch
|
||||
and returns that as the selected output type.
|
||||
"""
|
||||
|
||||
def execute(self, output_type, images=None, masks=None):
|
||||
mean_values = []
|
||||
if masks is not None and images is None:
|
||||
for mask in masks:
|
||||
mean_values.append(mask.mean().item())
|
||||
elif masks is None and images is not None:
|
||||
for image in images:
|
||||
mean_values.append(image.mean().item())
|
||||
elif masks is not None and images is not None:
|
||||
raise Exception("MaskOrImageToWeight: Use either mask or image input only.")
|
||||
|
||||
# Convert mean_values to the specified output_type
|
||||
if output_type == 'list':
|
||||
return mean_values,
|
||||
elif output_type == 'list of lists':
|
||||
return [[value] for value in mean_values],
|
||||
elif output_type == 'pandas series':
|
||||
try:
|
||||
import pandas as pd
|
||||
except:
|
||||
raise Exception("MaskOrImageToWeight: pandas is not installed. Please install pandas to use this output_type")
|
||||
return pd.Series(mean_values),
|
||||
elif output_type == 'tensor':
|
||||
return torch.tensor(mean_values, dtype=torch.float32),
|
||||
else:
|
||||
raise ValueError(f"Unsupported output_type: {output_type}")
|
||||
|
||||
class WeightScheduleConvert:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"input_values": ("FLOAT", {"default": 0.0, "forceInput": True}),
|
||||
"output_type": (
|
||||
[
|
||||
'match_input',
|
||||
'list',
|
||||
'list of lists',
|
||||
'pandas series',
|
||||
'tensor',
|
||||
],
|
||||
{
|
||||
"default": 'list'
|
||||
}),
|
||||
"invert": ("BOOLEAN", {"default": False}),
|
||||
"repeat": ("INT", {"default": 1,"min": 1, "max": 255, "step": 1}),
|
||||
},
|
||||
"optional": {
|
||||
"remap_to_frames": ("INT", {"default": 0}),
|
||||
"interpolation_curve": ("FLOAT", {"forceInput": True}),
|
||||
},
|
||||
|
||||
}
|
||||
RETURN_TYPES = ("FLOAT",)
|
||||
FUNCTION = "execute"
|
||||
CATEGORY = "KJNodes"
|
||||
DESCRIPTION = """
|
||||
Converts different value lists/series to another type.
|
||||
"""
|
||||
|
||||
def detect_input_type(self, input_values):
|
||||
import pandas as pd
|
||||
if isinstance(input_values, list):
|
||||
return 'list'
|
||||
elif isinstance(input_values, pd.Series):
|
||||
return 'pandas series'
|
||||
elif isinstance(input_values, torch.Tensor):
|
||||
return 'tensor'
|
||||
elif isinstance(input_values, list) and all(isinstance(sub, list) for sub in input_values):
|
||||
return 'list of lists'
|
||||
else:
|
||||
raise ValueError("Unsupported input type")
|
||||
|
||||
def execute(self, input_values, output_type, invert, repeat, remap_to_frames=0, interpolation_curve=None):
|
||||
import pandas as pd
|
||||
input_type = self.detect_input_type(input_values)
|
||||
|
||||
# Convert input_values to a list of floats
|
||||
if input_type == 'list of lists':
|
||||
float_values = [item for sublist in input_values for item in sublist]
|
||||
elif input_type == 'pandas series':
|
||||
float_values = input_values.tolist()
|
||||
elif input_type == 'tensor':
|
||||
float_values = input_values
|
||||
else:
|
||||
float_values = input_values
|
||||
|
||||
if invert:
|
||||
float_values = [1 - value for value in float_values]
|
||||
|
||||
if interpolation_curve is not None:
|
||||
interpolated_pattern = []
|
||||
orig_float_values = float_values
|
||||
for value in interpolation_curve:
|
||||
min_val = min(orig_float_values)
|
||||
max_val = max(orig_float_values)
|
||||
# Normalize the values to [0, 1]
|
||||
normalized_values = [(value - min_val) / (max_val - min_val) for value in orig_float_values]
|
||||
# Interpolate the normalized values to the new frame count
|
||||
remapped_float_values = np.interp(np.linspace(0, 1, int(remap_to_frames * value)), np.linspace(0, 1, len(normalized_values)), normalized_values).tolist()
|
||||
interpolated_pattern.extend(remapped_float_values)
|
||||
float_values = interpolated_pattern
|
||||
else:
|
||||
# Remap float_values to match target_frame_amount
|
||||
if remap_to_frames > 0 and remap_to_frames != len(float_values):
|
||||
min_val = min(float_values)
|
||||
max_val = max(float_values)
|
||||
# Normalize the values to [0, 1]
|
||||
normalized_values = [(value - min_val) / (max_val - min_val) for value in float_values]
|
||||
# Interpolate the normalized values to the new frame count
|
||||
float_values = np.interp(np.linspace(0, 1, remap_to_frames), np.linspace(0, 1, len(normalized_values)), normalized_values).tolist()
|
||||
|
||||
float_values = float_values * repeat
|
||||
|
||||
if output_type == 'list':
|
||||
return float_values,
|
||||
elif output_type == 'list of lists':
|
||||
return [[value] for value in float_values],
|
||||
elif output_type == 'pandas series':
|
||||
return pd.Series(float_values),
|
||||
elif output_type == 'tensor':
|
||||
if input_type == 'pandas series':
|
||||
return torch.tensor(input_values.values, dtype=torch.float32),
|
||||
elif output_type == 'match_input':
|
||||
return float_values,
|
||||
else:
|
||||
raise ValueError(f"Unsupported output_type: {output_type}")
|
||||
|
||||
class FloatToMask:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"input_values": ("FLOAT", {"forceInput": True, "default": 0}),
|
||||
"width": ("INT", {"default": 100, "min": 1}),
|
||||
"height": ("INT", {"default": 100, "min": 1}),
|
||||
},
|
||||
}
|
||||
RETURN_TYPES = ("MASK",)
|
||||
FUNCTION = "execute"
|
||||
CATEGORY = "KJNodes"
|
||||
DESCRIPTION = """
|
||||
Generates a batch of masks based on the input float values.
|
||||
The batch size is determined by the length of the input float values.
|
||||
Each mask is generated with the specified width and height.
|
||||
"""
|
||||
|
||||
def execute(self, input_values, width, height):
|
||||
import pandas as pd
|
||||
# Ensure input_values is a list
|
||||
if isinstance(input_values, (float, int)):
|
||||
input_values = [input_values]
|
||||
elif isinstance(input_values, pd.Series):
|
||||
input_values = input_values.tolist()
|
||||
elif isinstance(input_values, list) and all(isinstance(item, list) for item in input_values):
|
||||
input_values = [item for sublist in input_values for item in sublist]
|
||||
|
||||
# Generate a batch of masks based on the input_values
|
||||
masks = []
|
||||
for value in input_values:
|
||||
# Assuming value is a float between 0 and 1 representing the mask's intensity
|
||||
mask = torch.ones((height, width), dtype=torch.float32) * value
|
||||
masks.append(mask)
|
||||
masks_out = torch.stack(masks, dim=0)
|
||||
|
||||
return(masks_out,)
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"INTConstant": INTConstant,
|
||||
"FloatConstant": FloatConstant,
|
||||
"ImageBatchMulti": ImageBatchMulti,
|
||||
"MaskBatchMulti": MaskBatchMulti,
|
||||
"ConditioningMultiCombine": ConditioningMultiCombine,
|
||||
"ConditioningSetMaskAndCombine": ConditioningSetMaskAndCombine,
|
||||
"ConditioningSetMaskAndCombine3": ConditioningSetMaskAndCombine3,
|
||||
"ConditioningSetMaskAndCombine4": ConditioningSetMaskAndCombine4,
|
||||
"ConditioningSetMaskAndCombine5": ConditioningSetMaskAndCombine5,
|
||||
"GrowMaskWithBlur": GrowMaskWithBlur,
|
||||
"ColorToMask": ColorToMask,
|
||||
"CreateGradientMask": CreateGradientMask,
|
||||
"CreateTextMask": CreateTextMask,
|
||||
"CreateAudioMask": CreateAudioMask,
|
||||
"CreateFadeMask": CreateFadeMask,
|
||||
"CreateFadeMaskAdvanced": CreateFadeMaskAdvanced,
|
||||
"CreateFluidMask" :CreateFluidMask,
|
||||
"VRAM_Debug" : VRAM_Debug,
|
||||
"SomethingToString" : SomethingToString,
|
||||
"CrossFadeImages": CrossFadeImages,
|
||||
"EmptyLatentImagePresets": EmptyLatentImagePresets,
|
||||
"ColorMatch": ColorMatch,
|
||||
"GetImageRangeFromBatch": GetImageRangeFromBatch,
|
||||
"SaveImageWithAlpha": SaveImageWithAlpha,
|
||||
"ReverseImageBatch": ReverseImageBatch,
|
||||
"ImageGridComposite2x2": ImageGridComposite2x2,
|
||||
"ImageGridComposite3x3": ImageGridComposite3x3,
|
||||
"ImageConcanate": ImageConcanate,
|
||||
"ImageBatchTestPattern": ImageBatchTestPattern,
|
||||
"ReplaceImagesInBatch": ReplaceImagesInBatch,
|
||||
"BatchCropFromMask": BatchCropFromMask,
|
||||
"BatchCropFromMaskAdvanced": BatchCropFromMaskAdvanced,
|
||||
"FilterZeroMasksAndCorrespondingImages": FilterZeroMasksAndCorrespondingImages,
|
||||
"InsertImageBatchByIndexes": InsertImageBatchByIndexes,
|
||||
"BatchUncrop": BatchUncrop,
|
||||
"BatchUncropAdvanced": BatchUncropAdvanced,
|
||||
"BatchCLIPSeg": BatchCLIPSeg,
|
||||
"RoundMask": RoundMask,
|
||||
"ResizeMask": ResizeMask,
|
||||
"OffsetMask": OffsetMask,
|
||||
"WidgetToString": WidgetToString,
|
||||
"CreateShapeMask": CreateShapeMask,
|
||||
"CreateVoronoiMask": CreateVoronoiMask,
|
||||
"CreateMagicMask": CreateMagicMask,
|
||||
"BboxToInt": BboxToInt,
|
||||
"SplitBboxes": SplitBboxes,
|
||||
"ImageGrabPIL": ImageGrabPIL,
|
||||
"DummyLatentOut": DummyLatentOut,
|
||||
"FlipSigmasAdjusted": FlipSigmasAdjusted,
|
||||
"InjectNoiseToLatent": InjectNoiseToLatent,
|
||||
"AddLabel": AddLabel,
|
||||
"SoundReactive": SoundReactive,
|
||||
"GenerateNoise": GenerateNoise,
|
||||
"StableZero123_BatchSchedule": StableZero123_BatchSchedule,
|
||||
"SV3D_BatchSchedule": SV3D_BatchSchedule,
|
||||
"GetImagesFromBatchIndexed": GetImagesFromBatchIndexed,
|
||||
"InsertImagesToBatchIndexed": InsertImagesToBatchIndexed,
|
||||
"ImageBatchRepeatInterleaving": ImageBatchRepeatInterleaving,
|
||||
"NormalizedAmplitudeToMask": NormalizedAmplitudeToMask,
|
||||
"OffsetMaskByNormalizedAmplitude": OffsetMaskByNormalizedAmplitude,
|
||||
"ImageTransformByNormalizedAmplitude": ImageTransformByNormalizedAmplitude,
|
||||
"GetLatentsFromBatchIndexed": GetLatentsFromBatchIndexed,
|
||||
"StringConstant": StringConstant,
|
||||
"GLIGENTextBoxApplyBatch": GLIGENTextBoxApplyBatch,
|
||||
"CondPassThrough": CondPassThrough,
|
||||
"ImageUpscaleWithModelBatched": ImageUpscaleWithModelBatched,
|
||||
"ScaleBatchPromptSchedule": ScaleBatchPromptSchedule,
|
||||
"ImageNormalize_Neg1_To_1": ImageNormalize_Neg1_To_1,
|
||||
"Intrinsic_lora_sampling": Intrinsic_lora_sampling,
|
||||
"RemapMaskRange": RemapMaskRange,
|
||||
"LoadResAdapterNormalization": LoadResAdapterNormalization,
|
||||
"Superprompt": Superprompt,
|
||||
"RemapImageRange": RemapImageRange,
|
||||
"CameraPoseVisualizer": CameraPoseVisualizer,
|
||||
"BboxVisualize": BboxVisualize,
|
||||
"StringConstantMultiline": StringConstantMultiline,
|
||||
"JoinStrings": JoinStrings,
|
||||
"Sleep": Sleep,
|
||||
"ImagePadForOutpaintMasked": ImagePadForOutpaintMasked,
|
||||
"SplineEditor": SplineEditor,
|
||||
"ImageAndMaskPreview": ImageAndMaskPreview,
|
||||
"StabilityAPI_SD3": StabilityAPI_SD3,
|
||||
"MaskOrImageToWeight": MaskOrImageToWeight,
|
||||
"WeightScheduleConvert": WeightScheduleConvert,
|
||||
"FloatToMask": FloatToMask,
|
||||
"CustomSigmas": CustomSigmas,
|
||||
"ImagePass": ImagePass,
|
||||
"CreateShapeMaskOnPath": CreateShapeMaskOnPath
|
||||
}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"INTConstant": "INT Constant",
|
||||
"FloatConstant": "Float Constant",
|
||||
"ImageBatchMulti": "Image Batch Multi",
|
||||
"MaskBatchMulti": "Mask Batch Multi",
|
||||
"ConditioningMultiCombine": "Conditioning Multi Combine",
|
||||
"ConditioningSetMaskAndCombine": "ConditioningSetMaskAndCombine",
|
||||
"ConditioningSetMaskAndCombine3": "ConditioningSetMaskAndCombine3",
|
||||
"ConditioningSetMaskAndCombine4": "ConditioningSetMaskAndCombine4",
|
||||
"ConditioningSetMaskAndCombine5": "ConditioningSetMaskAndCombine5",
|
||||
"GrowMaskWithBlur": "GrowMaskWithBlur",
|
||||
"ColorToMask": "ColorToMask",
|
||||
"CreateGradientMask": "CreateGradientMask",
|
||||
"CreateTextMask" : "CreateTextMask",
|
||||
"CreateFadeMask" : "CreateFadeMask (Deprecated)",
|
||||
"CreateFadeMaskAdvanced" : "CreateFadeMaskAdvanced",
|
||||
"CreateFluidMask" : "CreateFluidMask",
|
||||
"CreateAudioMask" : "CreateAudioMask (Deprecated)",
|
||||
"VRAM_Debug" : "VRAM Debug",
|
||||
"CrossFadeImages": "CrossFadeImages",
|
||||
"SomethingToString": "SomethingToString",
|
||||
"EmptyLatentImagePresets": "EmptyLatentImagePresets",
|
||||
"ColorMatch": "ColorMatch",
|
||||
"GetImageRangeFromBatch": "GetImageRangeFromBatch",
|
||||
"InsertImagesToBatchIndexed": "InsertImagesToBatchIndexed",
|
||||
"SaveImageWithAlpha": "SaveImageWithAlpha",
|
||||
"ReverseImageBatch": "ReverseImageBatch",
|
||||
"ImageGridComposite2x2": "ImageGridComposite2x2",
|
||||
"ImageGridComposite3x3": "ImageGridComposite3x3",
|
||||
"ImageConcanate": "ImageConcatenate",
|
||||
"ImageBatchTestPattern": "ImageBatchTestPattern",
|
||||
"ReplaceImagesInBatch": "ReplaceImagesInBatch",
|
||||
"BatchCropFromMask": "BatchCropFromMask",
|
||||
"BatchCropFromMaskAdvanced": "BatchCropFromMaskAdvanced",
|
||||
"FilterZeroMasksAndCorrespondingImages": "FilterZeroMasksAndCorrespondingImages",
|
||||
"InsertImageBatchByIndexes": "InsertImageBatchByIndexes",
|
||||
"BatchUncrop": "BatchUncrop",
|
||||
"BatchUncropAdvanced": "BatchUncropAdvanced",
|
||||
"BatchCLIPSeg": "BatchCLIPSeg",
|
||||
"RoundMask": "RoundMask",
|
||||
"ResizeMask": "ResizeMask",
|
||||
"OffsetMask": "OffsetMask",
|
||||
"WidgetToString": "WidgetToString",
|
||||
"CreateShapeMask": "CreateShapeMask",
|
||||
"CreateVoronoiMask": "CreateVoronoiMask",
|
||||
"CreateMagicMask": "CreateMagicMask",
|
||||
"BboxToInt": "BboxToInt",
|
||||
"SplitBboxes": "SplitBboxes",
|
||||
"ImageGrabPIL": "ImageGrabPIL",
|
||||
"DummyLatentOut": "DummyLatentOut",
|
||||
"FlipSigmasAdjusted": "FlipSigmasAdjusted",
|
||||
"InjectNoiseToLatent": "InjectNoiseToLatent",
|
||||
"AddLabel": "AddLabel",
|
||||
"SoundReactive": "SoundReactive",
|
||||
"GenerateNoise": "GenerateNoise",
|
||||
"StableZero123_BatchSchedule": "StableZero123_BatchSchedule",
|
||||
"SV3D_BatchSchedule": "SV3D_BatchSchedule",
|
||||
"GetImagesFromBatchIndexed": "GetImagesFromBatchIndexed",
|
||||
"ImageBatchRepeatInterleaving": "ImageBatchRepeatInterleaving",
|
||||
"NormalizedAmplitudeToMask": "NormalizedAmplitudeToMask",
|
||||
"OffsetMaskByNormalizedAmplitude": "OffsetMaskByNormalizedAmplitude",
|
||||
"ImageTransformByNormalizedAmplitude": "ImageTransformByNormalizedAmplitude",
|
||||
"GetLatentsFromBatchIndexed": "GetLatentsFromBatchIndexed",
|
||||
"StringConstant": "StringConstant",
|
||||
"GLIGENTextBoxApplyBatch": "GLIGENTextBoxApplyBatch",
|
||||
"CondPassThrough": "CondPassThrough",
|
||||
"ImageUpscaleWithModelBatched": "ImageUpscaleWithModelBatched",
|
||||
"ScaleBatchPromptSchedule": "ScaleBatchPromptSchedule",
|
||||
"ImageNormalize_Neg1_To_1": "ImageNormalize_Neg1_To_1",
|
||||
"Intrinsic_lora_sampling": "Intrinsic_lora_sampling",
|
||||
"RemapMaskRange": "RemapMaskRange",
|
||||
"LoadResAdapterNormalization": "LoadResAdapterNormalization",
|
||||
"Superprompt": "Superprompt",
|
||||
"RemapImageRange": "RemapImageRange",
|
||||
"CameraPoseVisualizer": "CameraPoseVisualizer",
|
||||
"BboxVisualize": "BboxVisualize",
|
||||
"StringConstantMultiline": "StringConstantMultiline",
|
||||
"JoinStrings": "JoinStrings",
|
||||
"Sleep": "🛌 Sleep 🛌",
|
||||
"ImagePadForOutpaintMasked": "Pad Image For Outpaint Masked",
|
||||
"SplineEditor": "Spline Editor",
|
||||
"ImageAndMaskPreview": "Image & Mask Preview",
|
||||
"StabilityAPI_SD3": "Stability API SD3",
|
||||
"MaskOrImageToWeight": "Mask Or Image To Weight",
|
||||
"WeightScheduleConvert": "Weight Schedule Convert",
|
||||
"FloatToMask": "Float To Mask",
|
||||
"CustomSigmas": "Custom Sigmas",
|
||||
"ImagePass": "ImagePass",
|
||||
"CreateShapeMaskOnPath": "CreateShapeMaskOnPath",
|
||||
}
|
||||
raise Exception(f"Server error: {response.text}")
|
||||
@ -175,7 +175,7 @@ app.registerExtension({
|
||||
createSplineEditor(this, true)
|
||||
}
|
||||
});
|
||||
this.setSize([550, 840])
|
||||
this.setSize([550, 870])
|
||||
this.splineEditor.parentEl = document.createElement("div");
|
||||
this.splineEditor.parentEl.className = "spline-editor";
|
||||
this.splineEditor.parentEl.id = `spline-editor-${this.uuid}`
|
||||
@ -184,7 +184,7 @@ app.registerExtension({
|
||||
chainCallback(this, "onGraphConfigured", function() {
|
||||
console.log('onGraphConfigured');
|
||||
createSplineEditor(this)
|
||||
this.setSize([550, 840])
|
||||
this.setSize([550, 870])
|
||||
});
|
||||
|
||||
//disable context menu on right click
|
||||
@ -292,11 +292,15 @@ function createSplineEditor(context, reset=false) {
|
||||
const pointsWidget = context.widgets.find(w => w.name === "points_to_sample");
|
||||
const pointsStoreWidget = context.widgets.find(w => w.name === "points_store");
|
||||
const tensionWidget = context.widgets.find(w => w.name === "tension");
|
||||
const minValueWidget = context.widgets.find(w => w.name === "min_value");
|
||||
const maxValueWidget = context.widgets.find(w => w.name === "max_value");
|
||||
//const segmentedWidget = context.widgets.find(w => w.name === "segmented");
|
||||
|
||||
var interpolation = interpolationWidget.value
|
||||
var tension = tensionWidget.value
|
||||
var points_to_sample = pointsWidget.value
|
||||
var rangeMin = minValueWidget.value
|
||||
var rangeMax = maxValueWidget.value
|
||||
var pointsLayer = null;
|
||||
|
||||
interpolationWidget.callback = () => {
|
||||
@ -309,7 +313,10 @@ function createSplineEditor(context, reset=false) {
|
||||
updatePath();
|
||||
}
|
||||
|
||||
pointsWidget.callback = () => {
|
||||
minValueWidget.callback = () => {
|
||||
updatePath();
|
||||
}
|
||||
maxValueWidget.callback = () => {
|
||||
updatePath();
|
||||
}
|
||||
|
||||
@ -319,6 +326,7 @@ function createSplineEditor(context, reset=false) {
|
||||
var h = 512
|
||||
var i = 3
|
||||
let points = [];
|
||||
|
||||
if (!reset && pointsStoreWidget.value != "") {
|
||||
points = JSON.parse(pointsStoreWidget.value);
|
||||
} else {
|
||||
@ -426,9 +434,9 @@ function createSplineEditor(context, reset=false) {
|
||||
.font(12 + "px sans-serif")
|
||||
.text(d => {
|
||||
// Normalize y to range 0.0 to 1.0, considering the inverted y-axis
|
||||
var normalizedY = 1.0 - (d.y / h);
|
||||
var normalizedX = (d.x / w);
|
||||
var frame = Math.round((d.x / w) * points_to_sample);
|
||||
let normalizedY = (1.0 - (d.y / h) - 0.0) * (rangeMax - rangeMin) + rangeMin;
|
||||
let normalizedX = (d.x / w);
|
||||
let frame = Math.round((d.x / w) * points_to_sample);
|
||||
return `F: ${frame}, X: ${normalizedX.toFixed(2)}, Y: ${normalizedY.toFixed(2)}`;
|
||||
})
|
||||
.textStyle("orange")
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user