mirror of
https://git.datalinker.icu/kijai/ComfyUI-KJNodes.git
synced 2026-05-16 06:29:09 +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 @@
|
|||||||
from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
|
from .nodes import *
|
||||||
|
from .curve_nodes import *
|
||||||
|
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,
|
||||||
|
"ImageAndMaskPreview": ImageAndMaskPreview,
|
||||||
|
"StabilityAPI_SD3": StabilityAPI_SD3,
|
||||||
|
"MaskOrImageToWeight": MaskOrImageToWeight,
|
||||||
|
"WeightScheduleConvert": WeightScheduleConvert,
|
||||||
|
"FloatToMask": FloatToMask,
|
||||||
|
"CustomSigmas": CustomSigmas,
|
||||||
|
"ImagePass": ImagePass,
|
||||||
|
"SplineEditor": SplineEditor,
|
||||||
|
"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",
|
||||||
|
"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",
|
||||||
|
"SplineEditor": "Spline Editor",
|
||||||
|
"CreateShapeMaskOnPath": "Create Shape Mask On Path"
|
||||||
|
}
|
||||||
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
|
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
|
||||||
|
|
||||||
WEB_DIRECTORY = "./web"
|
WEB_DIRECTORY = "./web"
|
||||||
|
|||||||
399
curve_nodes.py
Normal file
399
curve_nodes.py
Normal file
@ -0,0 +1,399 @@
|
|||||||
|
import torch
|
||||||
|
import json
|
||||||
|
from PIL import Image, ImageDraw
|
||||||
|
import numpy as np
|
||||||
|
from .utility import pil2tensor
|
||||||
|
|
||||||
|
class SplineEditor:
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def INPUT_TYPES(cls):
|
||||||
|
return {
|
||||||
|
"required": {
|
||||||
|
"points_store": ("STRING", {"multiline": False}),
|
||||||
|
"coordinates": ("STRING", {"multiline": False}),
|
||||||
|
"mask_width": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
|
||||||
|
"mask_height": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
|
||||||
|
"points_to_sample": ("INT", {"default": 16, "min": 2, "max": 1000, "step": 1}),
|
||||||
|
"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'
|
||||||
|
}),
|
||||||
|
},
|
||||||
|
"optional": {
|
||||||
|
"min_value": ("FLOAT", {"default": 0.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
|
||||||
|
"max_value": ("FLOAT", {"default": 1.0, "min": -10000.0, "max": 10000.0, "step": 0.01}),
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
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, min_value=0.0, max_value=1.0):
|
||||||
|
|
||||||
|
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) - 0.0) * (max_value - min_value) + min_value
|
||||||
|
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 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,)
|
||||||
570
nodes.py
570
nodes.py
@ -4746,194 +4746,7 @@ nodes for example.
|
|||||||
return(self.save_images(preview, filename_prefix, prompt, extra_pnginfo))
|
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:
|
class StabilityAPI_SD3:
|
||||||
|
|
||||||
@ -5086,386 +4899,3 @@ If no image is provided, mode is set to text-to-image
|
|||||||
except json.JSONDecodeError:
|
except json.JSONDecodeError:
|
||||||
# If the response is not valid JSON, raise a different exception
|
# If the response is not valid JSON, raise a different exception
|
||||||
raise Exception(f"Server error: {response.text}")
|
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",
|
|
||||||
}
|
|
||||||
@ -175,7 +175,7 @@ app.registerExtension({
|
|||||||
createSplineEditor(this, true)
|
createSplineEditor(this, true)
|
||||||
}
|
}
|
||||||
});
|
});
|
||||||
this.setSize([550, 840])
|
this.setSize([550, 870])
|
||||||
this.splineEditor.parentEl = document.createElement("div");
|
this.splineEditor.parentEl = document.createElement("div");
|
||||||
this.splineEditor.parentEl.className = "spline-editor";
|
this.splineEditor.parentEl.className = "spline-editor";
|
||||||
this.splineEditor.parentEl.id = `spline-editor-${this.uuid}`
|
this.splineEditor.parentEl.id = `spline-editor-${this.uuid}`
|
||||||
@ -184,7 +184,7 @@ app.registerExtension({
|
|||||||
chainCallback(this, "onGraphConfigured", function() {
|
chainCallback(this, "onGraphConfigured", function() {
|
||||||
console.log('onGraphConfigured');
|
console.log('onGraphConfigured');
|
||||||
createSplineEditor(this)
|
createSplineEditor(this)
|
||||||
this.setSize([550, 840])
|
this.setSize([550, 870])
|
||||||
});
|
});
|
||||||
|
|
||||||
//disable context menu on right click
|
//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 pointsWidget = context.widgets.find(w => w.name === "points_to_sample");
|
||||||
const pointsStoreWidget = context.widgets.find(w => w.name === "points_store");
|
const pointsStoreWidget = context.widgets.find(w => w.name === "points_store");
|
||||||
const tensionWidget = context.widgets.find(w => w.name === "tension");
|
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");
|
//const segmentedWidget = context.widgets.find(w => w.name === "segmented");
|
||||||
|
|
||||||
var interpolation = interpolationWidget.value
|
var interpolation = interpolationWidget.value
|
||||||
var tension = tensionWidget.value
|
var tension = tensionWidget.value
|
||||||
var points_to_sample = pointsWidget.value
|
var points_to_sample = pointsWidget.value
|
||||||
|
var rangeMin = minValueWidget.value
|
||||||
|
var rangeMax = maxValueWidget.value
|
||||||
var pointsLayer = null;
|
var pointsLayer = null;
|
||||||
|
|
||||||
interpolationWidget.callback = () => {
|
interpolationWidget.callback = () => {
|
||||||
@ -309,7 +313,10 @@ function createSplineEditor(context, reset=false) {
|
|||||||
updatePath();
|
updatePath();
|
||||||
}
|
}
|
||||||
|
|
||||||
pointsWidget.callback = () => {
|
minValueWidget.callback = () => {
|
||||||
|
updatePath();
|
||||||
|
}
|
||||||
|
maxValueWidget.callback = () => {
|
||||||
updatePath();
|
updatePath();
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -319,6 +326,7 @@ function createSplineEditor(context, reset=false) {
|
|||||||
var h = 512
|
var h = 512
|
||||||
var i = 3
|
var i = 3
|
||||||
let points = [];
|
let points = [];
|
||||||
|
|
||||||
if (!reset && pointsStoreWidget.value != "") {
|
if (!reset && pointsStoreWidget.value != "") {
|
||||||
points = JSON.parse(pointsStoreWidget.value);
|
points = JSON.parse(pointsStoreWidget.value);
|
||||||
} else {
|
} else {
|
||||||
@ -426,9 +434,9 @@ function createSplineEditor(context, reset=false) {
|
|||||||
.font(12 + "px sans-serif")
|
.font(12 + "px sans-serif")
|
||||||
.text(d => {
|
.text(d => {
|
||||||
// Normalize y to range 0.0 to 1.0, considering the inverted y-axis
|
// Normalize y to range 0.0 to 1.0, considering the inverted y-axis
|
||||||
var normalizedY = 1.0 - (d.y / h);
|
let normalizedY = (1.0 - (d.y / h) - 0.0) * (rangeMax - rangeMin) + rangeMin;
|
||||||
var normalizedX = (d.x / w);
|
let normalizedX = (d.x / w);
|
||||||
var frame = Math.round((d.x / w) * points_to_sample);
|
let frame = Math.round((d.x / w) * points_to_sample);
|
||||||
return `F: ${frame}, X: ${normalizedX.toFixed(2)}, Y: ${normalizedY.toFixed(2)}`;
|
return `F: ${frame}, X: ${normalizedX.toFixed(2)}, Y: ${normalizedY.toFixed(2)}`;
|
||||||
})
|
})
|
||||||
.textStyle("orange")
|
.textStyle("orange")
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user