mirror of
https://git.datalinker.icu/kijai/ComfyUI-KJNodes.git
synced 2025-12-08 20:34:35 +08:00
Big documentation update
Added some js code (thanks melMass) to allow help popups on the nodes, and initial documentation on some nodes.
This commit is contained in:
kijai
parent
4d3c67ae53
commit
61a264f5ab
14
__init__.py
14
__init__.py
@ -1,4 +1,16 @@
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from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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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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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
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from aiohttp import web
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from server import PromptServer
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from pathlib import Path
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if hasattr(PromptServer, "instance"):
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# NOTE: we add an extra static path to avoid comfy mechanism
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# that loads every script in web.
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PromptServer.instance.app.add_routes(
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[web.static("/kjweb_async", (Path(__file__).parent.absolute() / "kjweb_async").as_posix())]
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)
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6
kjweb_async/marked.min.js
vendored
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6
kjweb_async/marked.min.js
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File diff suppressed because one or more lines are too long
3
kjweb_async/purify.min.js
vendored
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3
kjweb_async/purify.min.js
vendored
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File diff suppressed because one or more lines are too long
399
nodes.py
399
nodes.py
@ -1,6 +1,4 @@
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import nodes
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import torch
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import torch.nn.functional as F
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from torchvision.transforms import Resize, CenterCrop, InterpolationMode
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from torchvision.transforms import functional as TF
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@ -9,7 +7,7 @@ import scipy.ndimage
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from scipy.spatial import Voronoi
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import matplotlib.pyplot as plt
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import numpy as np
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from PIL import ImageFilter, Image, ImageDraw, ImageFont, ImageOps
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from PIL import ImageFilter, Image, ImageDraw, ImageFont
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from PIL.PngImagePlugin import PngInfo
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import json
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import re
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@ -60,7 +58,6 @@ class FloatConstant:
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return (value,)
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class StringConstant:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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@ -320,6 +317,21 @@ class CreateFadeMaskAdvanced:
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RETURN_TYPES = ("MASK",)
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FUNCTION = "createfademask"
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CATEGORY = "KJNodes/masking/generate"
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DESCRIPTION = """
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Create a batch of masks interpolated between given frames and values.
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Uses same syntax as Fizz' BatchValueSchedule.
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First value is the frame index (not that this starts from 0, not 1)
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and the second value inside the brackets is the float value of the mask in range 0.0 - 1.0
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For example the default values:
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0:(0.0)
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7:(1.0)
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15:(0.0)
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Would create a mask batch fo 16 frames, starting from black,
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interpolating with the chosen curve to fully white at the 8th frame,
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and interpolating from that to fully black at the 16th frame.
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"""
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@classmethod
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def INPUT_TYPES(s):
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@ -402,6 +414,10 @@ class ScaleBatchPromptSchedule:
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RETURN_TYPES = ("STRING",)
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FUNCTION = "scaleschedule"
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CATEGORY = "KJNodes"
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DESCRIPTION = """
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Scales a batch schedule from Fizz' nodes BatchPromptSchedule
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to a different frame count.
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"""
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@classmethod
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def INPUT_TYPES(s):
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@ -442,7 +458,7 @@ class CrossFadeImages:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "crossfadeimages"
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CATEGORY = "KJNodes"
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CATEGORY = "KJNodes/image"
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@classmethod
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def INPUT_TYPES(s):
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@ -530,7 +546,7 @@ class GetImageRangeFromBatch:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "imagesfrombatch"
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CATEGORY = "KJNodes"
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CATEGORY = "KJNodes/image"
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@classmethod
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def INPUT_TYPES(s):
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@ -557,7 +573,10 @@ class GetImagesFromBatchIndexed:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "indexedimagesfrombatch"
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CATEGORY = "KJNodes"
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CATEGORY = "KJNodes/image"
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DESCRIPTION = """
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Selects and returns the images at the specified indices as an image batch.
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"""
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@classmethod
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def INPUT_TYPES(s):
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@ -586,6 +605,9 @@ class GetLatentsFromBatchIndexed:
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "indexedlatentsfrombatch"
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CATEGORY = "KJNodes"
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DESCRIPTION = """
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Selects and returns the latents at the specified indices as an latent batch.
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"""
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@classmethod
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def INPUT_TYPES(s):
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@ -617,7 +639,10 @@ class ReplaceImagesInBatch:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "replace"
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CATEGORY = "KJNodes"
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CATEGORY = "KJNodes/image"
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DESCRIPTION = """
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Replaces the images in a batch, starting from the specified start, with the replacement images.
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"""
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@classmethod
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def INPUT_TYPES(s):
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@ -647,7 +672,10 @@ class ReverseImageBatch:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "reverseimagebatch"
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CATEGORY = "KJNodes"
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CATEGORY = "KJNodes/image"
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DESCRIPTION = """
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Reverses the order of the images in a batch.
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"""
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@classmethod
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def INPUT_TYPES(s):
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@ -667,7 +695,15 @@ class CreateTextMask:
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RETURN_TYPES = ("IMAGE", "MASK",)
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FUNCTION = "createtextmask"
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CATEGORY = "KJNodes/masking/generate"
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CATEGORY = "KJNodes/text"
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DESCRIPTION = """
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Creates a text image and mask.
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Looks for fonts from this folder:
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ComfyUI/custom_nodes/ComfyUI-KJNodes/fonts
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If start_rotation and/or end_rotation are different values,
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creates animation between them.
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"""
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@classmethod
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def INPUT_TYPES(s):
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@ -779,10 +815,20 @@ class GrowMaskWithBlur:
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}
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CATEGORY = "KJNodes/masking"
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RETURN_TYPES = ("MASK", "MASK",)
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RETURN_NAMES = ("mask", "mask_inverted",)
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FUNCTION = "expand_mask"
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DESCRIPTION = """
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# GrowMaskWithBlur
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- mask: Input mask or mask batch
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- expand: Expand or contract mask or mask batch by a given amount
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- incremental_expandrate: increase expand rate by a given amount per frame
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- tapered_corners: use tapered corners
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- flip_input: flip input mask
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- blur_radius: value higher than 0 will blur the mask
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- lerp_alpha: alpha value for interpolation between frames
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- decay_factor: decay value for interpolation between frames
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- fill_holes: fill holes in the mask (slow)"""
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def expand_mask(self, mask, expand, tapered_corners, flip_input, blur_radius, incremental_expandrate, lerp_alpha, decay_factor, fill_holes=False):
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alpha = lerp_alpha
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@ -837,27 +883,14 @@ class GrowMaskWithBlur:
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else:
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return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),)
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class PlotNode:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"start": ("FLOAT", {"default": 0.5, "min": 0.5, "max": 1.0}),
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"max_frames": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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}}
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RETURN_TYPES = ("FLOAT", "INT",)
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FUNCTION = "plot"
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CATEGORY = "KJNodes"
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def plot(self, start, max_frames):
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result = start + max_frames
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return (result,)
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class ColorToMask:
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RETURN_TYPES = ("MASK",)
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FUNCTION = "clip"
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CATEGORY = "KJNodes/masking"
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DESCRIPTION = """
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Converts chosen RGB value to a mask
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"""
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@classmethod
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def INPUT_TYPES(s):
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@ -915,9 +948,13 @@ class ConditioningMultiCombine:
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RETURN_NAMES = ("combined", "inputcount")
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FUNCTION = "combine"
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CATEGORY = "KJNodes/masking/conditioning"
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DESCRIPTION = """
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Combines multiple conditioning nodes into one
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"""
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def combine(self, inputcount, **kwargs):
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cond_combine_node = nodes.ConditioningCombine()
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from nodes import ConditioningCombine
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cond_combine_node = ConditioningCombine()
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cond = kwargs["conditioning_1"]
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for c in range(1, inputcount):
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new_cond = kwargs[f"conditioning_{c + 1}"]
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@ -931,14 +968,17 @@ class CondPassThrough:
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"required": {
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"positive": ("CONDITIONING", ),
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"negative": ("CONDITIONING", ),
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},
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},
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}
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RETURN_TYPES = ("CONDITIONING", "CONDITIONING",)
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RETURN_NAMES = ("positive", "negative")
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FUNCTION = "passthrough"
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CATEGORY = "KJNodes/misc"
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DESCRIPTION = """
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Simply passes through the positive and negative conditioning,
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workaround for Set node not allowing bypassed inputs.
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"""
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def passthrough(self, positive, negative):
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return (positive, negative,)
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@ -972,6 +1012,9 @@ class ConditioningSetMaskAndCombine:
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RETURN_NAMES = ("combined_positive", "combined_negative",)
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FUNCTION = "append"
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CATEGORY = "KJNodes/masking/conditioning"
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DESCRIPTION = """
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Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes
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"""
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def append(self, positive_1, negative_1, positive_2, negative_2, mask_1, mask_2, set_cond_area, mask_1_strength, mask_2_strength):
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c = []
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@ -1018,6 +1061,9 @@ class ConditioningSetMaskAndCombine3:
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RETURN_NAMES = ("combined_positive", "combined_negative",)
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FUNCTION = "append"
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CATEGORY = "KJNodes/masking/conditioning"
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DESCRIPTION = """
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Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes
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"""
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def append(self, positive_1, negative_1, positive_2, positive_3, negative_2, negative_3, mask_1, mask_2, mask_3, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength):
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c = []
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@ -1074,6 +1120,9 @@ class ConditioningSetMaskAndCombine4:
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RETURN_NAMES = ("combined_positive", "combined_negative",)
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FUNCTION = "append"
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CATEGORY = "KJNodes/masking/conditioning"
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DESCRIPTION = """
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Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes
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"""
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def append(self, positive_1, negative_1, positive_2, positive_3, positive_4, negative_2, negative_3, negative_4, mask_1, mask_2, mask_3, mask_4, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength, mask_4_strength):
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c = []
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@ -1140,6 +1189,9 @@ class ConditioningSetMaskAndCombine5:
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RETURN_NAMES = ("combined_positive", "combined_negative",)
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FUNCTION = "append"
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CATEGORY = "KJNodes/masking/conditioning"
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DESCRIPTION = """
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Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes
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"""
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def append(self, positive_1, negative_1, positive_2, positive_3, positive_4, positive_5, negative_2, negative_3, negative_4, negative_5, mask_1, mask_2, mask_3, mask_4, mask_5, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength, mask_4_strength, mask_5_strength):
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c = []
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@ -1187,6 +1239,7 @@ class VRAM_Debug:
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return {
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"required": {
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"empty_cache": ("BOOLEAN", {"default": True}),
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"gc_collect": ("BOOLEAN", {"default": True}),
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"unload_all_models": ("BOOLEAN", {"default": False}),
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},
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"optional":{
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@ -1198,15 +1251,21 @@ class VRAM_Debug:
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RETURN_TYPES = ("IMAGE", "MODEL","INT", "INT",)
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RETURN_NAMES = ("image_passthrough", "model_passthrough", "freemem_before", "freemem_after")
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FUNCTION = "VRAMdebug"
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CATEGORY = "KJNodes"
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CATEGORY = "KJNodes/misc"
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DESCRIPTION = """
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Placed between model or image chain, performs comfy model management functions and reports free VRAM before and after the functions
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"""
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def VRAMdebug(self, empty_cache, unload_all_models,image_passthrough=None, model_passthrough=None):
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def VRAMdebug(self, gc_collect,empty_cache, unload_all_models,image_passthrough=None, model_passthrough=None):
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freemem_before = comfy.model_management.get_free_memory()
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print("VRAMdebug: free memory before: ", freemem_before)
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if empty_cache:
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comfy.model_management.soft_empty_cache()
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if unload_all_models:
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comfy.model_management.unload_all_models()
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if gc_collect:
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import gc
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gc.collect()
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freemem_after = comfy.model_management.get_free_memory()
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print("VRAMdebug: free memory after: ", freemem_after)
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print("VRAMdebug: freed memory: ", freemem_after - freemem_before)
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@ -1234,7 +1293,10 @@ class SomethingToString:
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}
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RETURN_TYPES = ("STRING",)
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FUNCTION = "stringify"
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CATEGORY = "KJNodes"
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CATEGORY = "KJNodes/text"
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DESCRIPTION = """
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Converts any type to a string.
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"""
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def stringify(self, input, prefix="", suffix=""):
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if isinstance(input, (int, float, bool)):
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@ -1247,8 +1309,6 @@ class SomethingToString:
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return
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return (stringified,)
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from nodes import EmptyLatentImage
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class EmptyLatentImagePresets:
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@classmethod
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def INPUT_TYPES(cls):
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@ -1284,6 +1344,7 @@ class EmptyLatentImagePresets:
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CATEGORY = "KJNodes"
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def generate(self, dimensions, invert, batch_size):
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from nodes import EmptyLatentImage
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result = [x.strip() for x in dimensions.split('x')]
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if invert:
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@ -1319,15 +1380,20 @@ class ColorMatch:
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},
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}
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CATEGORY = "KJNodes/masking"
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CATEGORY = "KJNodes/image"
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = "colormatch"
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DESCRIPTION = """
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color-matcher enables color transfer across images which comes in handy for automatic color-grading of photographs, paintings and film sequences as well as light-field and stopmotion corrections. The methods behind the mappings are based on the approach from Reinhard et al., the Monge-Kantorovich Linearization (MKL) as proposed by Pitie et al. and our analytical solution to a Multi-Variate Gaussian Distribution (MVGD) transfer in conjunction with classical histogram matching. As shown below our HM-MVGD-HM compound outperforms existing methods.
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https://github.com/hahnec/color-matcher/
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"""
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def colormatch(self, image_ref, image_target, method):
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try:
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from color_matcher import ColorMatcher #https://github.com/hahnec/color-matcher/
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from color_matcher import ColorMatcher
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except:
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raise Exception("Can't import color-matcher, did you install requirements.txt? Manual install: pip install color-matcher")
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cm = ColorMatcher()
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@ -1372,10 +1438,11 @@ class SaveImageWithAlpha:
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RETURN_TYPES = ()
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FUNCTION = "save_images_alpha"
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OUTPUT_NODE = True
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CATEGORY = "image"
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CATEGORY = "KJNodes/image"
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DESCRIPTION = """
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Saves an image and mask as .PNG with the mask as the alpha channel.
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"""
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def save_images_alpha(self, images, mask, filename_prefix="ComfyUI_image_with_alpha", prompt=None, extra_pnginfo=None):
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filename_prefix += self.prefix_append
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@ -1447,7 +1514,10 @@ class ImageConcanate:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "concanate"
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CATEGORY = "KJNodes"
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CATEGORY = "KJNodes/image"
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DESCRIPTION = """
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Concatenates the image2 to image1 in the specified direction.
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"""
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def concanate(self, image1, image2, direction, match_image_size):
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if match_image_size:
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@ -1474,7 +1544,10 @@ class ImageGridComposite2x2:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "compositegrid"
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CATEGORY = "KJNodes"
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CATEGORY = "KJNodes/image"
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DESCRIPTION = """
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Concatenates the 4 input images into a 2x2 grid.
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"""
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def compositegrid(self, image1, image2, image3, image4):
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top_row = torch.cat((image1, image2), dim=2)
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@ -1499,7 +1572,10 @@ class ImageGridComposite3x3:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "compositegrid"
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CATEGORY = "KJNodes"
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CATEGORY = "KJNodes/image"
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DESCRIPTION = """
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Concatenates the 9 input images into a 3x3 grid.
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"""
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def compositegrid(self, image1, image2, image3, image4, image5, image6, image7, image8, image9):
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top_row = torch.cat((image1, image2, image3), dim=2)
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@ -1522,7 +1598,7 @@ class ImageBatchTestPattern:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "generatetestpattern"
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CATEGORY = "KJNodes"
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CATEGORY = "KJNodes/text"
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def generatetestpattern(self, batch_size, font, font_size, start_from, width, height):
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out = []
|
||||
@ -1564,8 +1640,7 @@ class ImageBatchTestPattern:
|
||||
|
||||
#based on nodes from mtb https://github.com/melMass/comfy_mtb
|
||||
|
||||
from .utility import tensor2pil, pil2tensor, tensor2np, np2tensor
|
||||
|
||||
from .utility import tensor2pil, pil2tensor
|
||||
|
||||
class BatchCropFromMask:
|
||||
|
||||
@ -1640,10 +1715,6 @@ class BatchCropFromMask:
|
||||
self.max_bbox_height = round(self.max_bbox_height * crop_size_mult)
|
||||
bbox_aspect_ratio = self.max_bbox_width / self.max_bbox_height
|
||||
|
||||
# # Make sure max_bbox_size is divisible by 32, if not, round it upwards so it is
|
||||
# self.max_bbox_width = math.ceil(self.max_bbox_width / 32) * 32
|
||||
# self.max_bbox_height = math.ceil(self.max_bbox_height / 32) * 32
|
||||
|
||||
# Then, for each mask and corresponding image...
|
||||
for i, (mask, img) in enumerate(zip(masks, original_images)):
|
||||
_mask = tensor2pil(mask)[0]
|
||||
@ -2001,29 +2072,18 @@ class FilterZeroMasksAndCorrespondingImages:
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = (
|
||||
"MASK",
|
||||
"IMAGE",
|
||||
"IMAGE",
|
||||
"INDEXES"
|
||||
)
|
||||
RETURN_NAMES = (
|
||||
"non_zero_masks_out",
|
||||
"non_zero_mask_images_out",
|
||||
"zero_mask_images_out",
|
||||
"zero_mask_images_out_indexes"
|
||||
)
|
||||
RETURN_TYPES = ("MASK", "IMAGE", "IMAGE", "INDEXES",)
|
||||
RETURN_NAMES = ("non_zero_masks_out", "non_zero_mask_images_out", "zero_mask_images_out", "zero_mask_images_out_indexes",)
|
||||
FUNCTION = "filter"
|
||||
CATEGORY = "KJNodes/masking"
|
||||
DESCRIPTION = """
|
||||
Filter out all the empty (i.e. all zero) mask in masks
|
||||
Also filter out all the corresponding images in original_images by indexes if provide
|
||||
|
||||
original_images (optional): If provided, need have same length as masks.
|
||||
"""
|
||||
|
||||
def filter(self, masks, original_images=None):
|
||||
"""
|
||||
Filter out all the empty (i.e. all zero) mask in masks
|
||||
Also filter out all the corresponding images in original_images by indexes if provide
|
||||
|
||||
Args:
|
||||
original_images (optional): If provide, it need have same length as masks.
|
||||
"""
|
||||
non_zero_masks = []
|
||||
non_zero_mask_images = []
|
||||
zero_mask_images = []
|
||||
@ -2071,24 +2131,19 @@ class InsertImageBatchByIndexes:
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = (
|
||||
"IMAGE",
|
||||
)
|
||||
RETURN_NAMES = (
|
||||
"images_after_insert",
|
||||
)
|
||||
RETURN_TYPES = ("IMAGE", )
|
||||
RETURN_NAMES = ("images_after_insert", )
|
||||
FUNCTION = "insert"
|
||||
CATEGORY = "KJNodes"
|
||||
|
||||
def insert(self, images, images_to_insert, insert_indexes):
|
||||
"""
|
||||
This node is designed to be use with node FilterZeroMasksAndCorrespondingImages
|
||||
It inserts the images_to_insert into images according to insert_indexes
|
||||
CATEGORY = "KJNodes/image"
|
||||
DESCRIPTION = """
|
||||
This node is designed to be use with node FilterZeroMasksAndCorrespondingImages
|
||||
It inserts the images_to_insert into images according to insert_indexes
|
||||
|
||||
Returns:
|
||||
images_after_insert: updated original images with origonal sequence order
|
||||
"""
|
||||
|
||||
Returns:
|
||||
images_after_insert: updated original images with origonal sequence order
|
||||
"""
|
||||
|
||||
def insert(self, images, images_to_insert, insert_indexes):
|
||||
images_after_insert = images
|
||||
|
||||
if images_to_insert is not None and insert_indexes is not None:
|
||||
@ -2152,9 +2207,9 @@ class BatchUncropAdvanced:
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "uncrop"
|
||||
|
||||
CATEGORY = "KJNodes/masking"
|
||||
|
||||
|
||||
def uncrop(self, original_images, cropped_images, cropped_masks, combined_crop_mask, bboxes, border_blending, crop_rescale, use_combined_mask, use_square_mask, combined_bounding_box = None):
|
||||
|
||||
def inset_border(image, border_width=20, border_color=(0)):
|
||||
@ -2257,8 +2312,10 @@ class BatchCLIPSeg:
|
||||
CATEGORY = "KJNodes/masking"
|
||||
RETURN_TYPES = ("MASK",)
|
||||
RETURN_NAMES = ("Mask",)
|
||||
|
||||
FUNCTION = "segment_image"
|
||||
DESCRIPTION = """
|
||||
Segments an image or batch of images using CLIPSeg.
|
||||
"""
|
||||
|
||||
def segment_image(self, images, text, threshold, binary_mask, combine_mask, use_cuda):
|
||||
|
||||
@ -2318,6 +2375,11 @@ class RoundMask:
|
||||
RETURN_TYPES = ("MASK",)
|
||||
FUNCTION = "round"
|
||||
CATEGORY = "KJNodes/masking"
|
||||
DESCRIPTION = """
|
||||
Rounds the mask or batch of masks to a binary mask.
|
||||
<img src="https://github.com/kijai/ComfyUI-KJNodes/assets/40791699/52c85202-f74e-4b96-9dac-c8bda5ddcc40" width="300" height="250" alt="RoundMask example">
|
||||
|
||||
"""
|
||||
|
||||
def round(self, mask):
|
||||
mask = mask.round()
|
||||
@ -2339,6 +2401,9 @@ class ResizeMask:
|
||||
RETURN_NAMES = ("mask", "width", "height",)
|
||||
FUNCTION = "resize"
|
||||
CATEGORY = "KJNodes/masking"
|
||||
DESCRIPTION = """
|
||||
Resizes the mask or batch of masks to the specified width and height.
|
||||
"""
|
||||
|
||||
def resize(self, mask, width, height, keep_proportions):
|
||||
if keep_proportions:
|
||||
@ -2383,6 +2448,16 @@ class OffsetMask:
|
||||
RETURN_NAMES = ("mask",)
|
||||
FUNCTION = "offset"
|
||||
CATEGORY = "KJNodes/masking"
|
||||
DESCRIPTION = """
|
||||
Offsets the mask by the specified amount.
|
||||
- mask: Input mask or mask batch
|
||||
- x: Horizontal offset
|
||||
- y: Vertical offset
|
||||
- angle: Angle in degrees
|
||||
- roll: roll edge wrapping
|
||||
- duplication_factor: Number of times to duplicate the mask to form a batch
|
||||
- border padding_mode: Padding mode for the mask
|
||||
"""
|
||||
|
||||
def offset(self, mask, x, y, angle, roll=False, incremental=False, duplication_factor=1, padding_mode="empty"):
|
||||
# Create duplicates of the mask batch
|
||||
@ -2468,7 +2543,11 @@ class WidgetToString:
|
||||
|
||||
RETURN_TYPES = ("STRING", )
|
||||
FUNCTION = "get_widget_value"
|
||||
CATEGORY = "KJNodes"
|
||||
CATEGORY = "KJNodes/text"
|
||||
DESCRIPTION = """
|
||||
Selects a node and it's specified widget and outputs the value as a string.
|
||||
To see node id's, enable node id display from Manager badge menu.
|
||||
"""
|
||||
|
||||
def get_widget_value(self, id, widget_name, extra_pnginfo, prompt, return_all=False):
|
||||
workflow = extra_pnginfo["workflow"]
|
||||
@ -2735,6 +2814,57 @@ class BboxToInt:
|
||||
|
||||
return (x_min, y_min, width, height, center_x, center_y,)
|
||||
|
||||
class BboxVisualize:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"images": ("IMAGE",),
|
||||
"bboxes": ("BBOX",),
|
||||
"line_width": ("INT", {"default": 1,"min": 1, "max": 10, "step": 1}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
RETURN_NAMES = ("images",)
|
||||
FUNCTION = "visualizebbox"
|
||||
DESCRIPTION = """
|
||||
Visualizes the specified bbox on the image.
|
||||
"""
|
||||
|
||||
CATEGORY = "KJNodes/masking"
|
||||
|
||||
def visualizebbox(self, bboxes, images, line_width):
|
||||
image_list = []
|
||||
for image, bbox in zip(images, bboxes):
|
||||
x_min, y_min, width, height = bbox
|
||||
image = image.permute(2, 0, 1)
|
||||
|
||||
img_with_bbox = image.clone()
|
||||
|
||||
# Define the color for the bbox, e.g., red
|
||||
color = torch.tensor([1, 0, 0], dtype=torch.float32)
|
||||
|
||||
# Draw lines for each side of the bbox with the specified line width
|
||||
for lw in range(line_width):
|
||||
# Top horizontal line
|
||||
img_with_bbox[:, y_min + lw, x_min:x_min + width] = color[:, None]
|
||||
|
||||
# Bottom horizontal line
|
||||
img_with_bbox[:, y_min + height - lw, x_min:x_min + width] = color[:, None]
|
||||
|
||||
# Left vertical line
|
||||
img_with_bbox[:, y_min:y_min + height, x_min + lw] = color[:, None]
|
||||
|
||||
# Right vertical line
|
||||
img_with_bbox[:, y_min:y_min + height, x_min + width - lw] = color[:, None]
|
||||
|
||||
img_with_bbox = img_with_bbox.permute(1, 2, 0).unsqueeze(0)
|
||||
image_list.append(img_with_bbox)
|
||||
|
||||
return (torch.cat(image_list, dim=0),)
|
||||
|
||||
class SplitBboxes:
|
||||
|
||||
@classmethod
|
||||
@ -2814,7 +2944,7 @@ class DummyLatentOut:
|
||||
|
||||
RETURN_TYPES = ("LATENT",)
|
||||
FUNCTION = "dummy"
|
||||
CATEGORY = "KJNodes"
|
||||
CATEGORY = "KJNodes/misc"
|
||||
OUTPUT_NODE = True
|
||||
|
||||
def dummy(self, latent):
|
||||
@ -2957,7 +3087,7 @@ class AddLabel:
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "addlabel"
|
||||
|
||||
CATEGORY = "KJNodes"
|
||||
CATEGORY = "KJNodes/text"
|
||||
|
||||
def addlabel(self, image, text_x, text_y, text, height, font_size, font_color, label_color, font, direction):
|
||||
batch_size = image.shape[0]
|
||||
@ -3005,7 +3135,7 @@ class SoundReactive:
|
||||
RETURN_NAMES =("sound_level", "sound_level_int",)
|
||||
FUNCTION = "react"
|
||||
|
||||
CATEGORY = "KJNodes/experimental"
|
||||
CATEGORY = "KJNodes/audio"
|
||||
|
||||
def react(self, sound_level, start_range_hz, end_range_hz, smoothing_factor, multiplier, normalize):
|
||||
|
||||
@ -3082,14 +3212,15 @@ def interpolate_angle(start, end, fraction):
|
||||
# Normalize the result to be within the range of -180 to 180
|
||||
return (interpolated + 180) % 360 - 180
|
||||
|
||||
|
||||
class StableZero123_BatchSchedule:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": { "clip_vision": ("CLIP_VISION",),
|
||||
"init_image": ("IMAGE",),
|
||||
"vae": ("VAE",),
|
||||
"width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
|
||||
"height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
|
||||
"width": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
|
||||
"height": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
|
||||
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
|
||||
"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],),
|
||||
"azimuth_points_string": ("STRING", {"default": "0:(0.0),\n7:(1.0),\n15:(0.0)\n", "multiline": True}),
|
||||
@ -3101,7 +3232,7 @@ class StableZero123_BatchSchedule:
|
||||
|
||||
FUNCTION = "encode"
|
||||
|
||||
CATEGORY = "KJNodes"
|
||||
CATEGORY = "KJNodes/experimental"
|
||||
|
||||
def encode(self, clip_vision, init_image, vae, width, height, batch_size, azimuth_points_string, elevation_points_string, interpolation):
|
||||
output = clip_vision.encode_image(init_image)
|
||||
@ -3223,8 +3354,8 @@ class SV3D_BatchSchedule:
|
||||
return {"required": { "clip_vision": ("CLIP_VISION",),
|
||||
"init_image": ("IMAGE",),
|
||||
"vae": ("VAE",),
|
||||
"width": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
|
||||
"height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
|
||||
"width": ("INT", {"default": 576, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
|
||||
"height": ("INT", {"default": 576, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
|
||||
"batch_size": ("INT", {"default": 21, "min": 1, "max": 4096}),
|
||||
"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],),
|
||||
"azimuth_points_string": ("STRING", {"default": "0:(0.0),\n9:(180.0),\n20:(360.0)\n", "multiline": True}),
|
||||
@ -3233,10 +3364,12 @@ class SV3D_BatchSchedule:
|
||||
|
||||
RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
|
||||
RETURN_NAMES = ("positive", "negative", "latent")
|
||||
|
||||
FUNCTION = "encode"
|
||||
|
||||
CATEGORY = "KJNodes"
|
||||
CATEGORY = "KJNodes/experimental"
|
||||
DESCRIPTION = """
|
||||
Allow scheduling of the azimuth and elevation conditions for SV3D.
|
||||
Note that SV3D is still a video model and the schedule needs to always go forward
|
||||
"""
|
||||
|
||||
def encode(self, clip_vision, init_image, vae, width, height, batch_size, azimuth_points_string, elevation_points_string, interpolation):
|
||||
output = clip_vision.encode_image(init_image)
|
||||
@ -3341,7 +3474,12 @@ class ImageBatchRepeatInterleaving:
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "repeat"
|
||||
CATEGORY = "KJNodes"
|
||||
CATEGORY = "KJNodes/image"
|
||||
DESCRIPTION = """
|
||||
Repeats each image in a batch by the specified number of times.
|
||||
Example batch of 5 images: 0, 1 ,2, 3, 4
|
||||
with repeats 2 becomes batch of 10 images: 0, 0, 1, 1, 2, 2, 3, 3, 4, 4
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
@ -3388,10 +3526,14 @@ class NormalizedAmplitudeToMask:
|
||||
}),
|
||||
},}
|
||||
|
||||
CATEGORY = "AudioScheduler/Amplitude"
|
||||
|
||||
CATEGORY = "KJNodes/audio"
|
||||
RETURN_TYPES = ("MASK",)
|
||||
FUNCTION = "convert"
|
||||
DESCRIPTION = """
|
||||
Works as a bridge to the AudioScheduler -nodes:
|
||||
https://github.com/a1lazydog/ComfyUI-AudioScheduler
|
||||
Creates masks based on the normalized amplitude.
|
||||
"""
|
||||
|
||||
def convert(self, normalized_amp, width, height, frame_offset, shape, location_x, location_y, size, color):
|
||||
# Ensure normalized_amp is an array and within the range [0, 1]
|
||||
@ -3460,7 +3602,7 @@ class OffsetMaskByNormalizedAmplitude:
|
||||
RETURN_TYPES = ("MASK",)
|
||||
RETURN_NAMES = ("mask",)
|
||||
FUNCTION = "offset"
|
||||
CATEGORY = "KJNodes/masking"
|
||||
CATEGORY = "KJNodes/audio"
|
||||
|
||||
def offset(self, mask, x, y, angle_multiplier, rotate, normalized_amp):
|
||||
|
||||
@ -3501,7 +3643,7 @@ class ImageTransformByNormalizedAmplitude:
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "amptransform"
|
||||
CATEGORY = "KJNodes"
|
||||
CATEGORY = "KJNodes/audio"
|
||||
|
||||
def amptransform(self, image, normalized_amp, zoom_scale, cumulative, x_offset, y_offset):
|
||||
# Ensure normalized_amp is an array and within the range [0, 1]
|
||||
@ -3674,9 +3816,10 @@ class GLIGENTextBoxApplyBatch:
|
||||
}}
|
||||
RETURN_TYPES = ("CONDITIONING", "IMAGE",)
|
||||
FUNCTION = "append"
|
||||
|
||||
CATEGORY = "conditioning/gligen"
|
||||
|
||||
CATEGORY = "KJNodes/experimental"
|
||||
DESCRIPTION = """
|
||||
Experimental, does not function yet as ComfyUI base changes are needed
|
||||
"""
|
||||
|
||||
|
||||
def append(self, latents, conditioning_to, clip, gligen_textbox_model, text, width, height, coordinates, interpolation):
|
||||
@ -3725,8 +3868,10 @@ class ImageUpscaleWithModelBatched:
|
||||
}}
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "upscale"
|
||||
|
||||
CATEGORY = "KJNodes"
|
||||
CATEGORY = "KJNodes/image"
|
||||
DESCRIPTION = """
|
||||
Same as ComfyUI native model upscaling node, but allows setting sub-batches for reduced VRAM usage.
|
||||
"""
|
||||
|
||||
def upscale(self, upscale_model, images, per_batch):
|
||||
|
||||
@ -3760,8 +3905,10 @@ class ImageNormalize_Neg1_To_1:
|
||||
}}
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "normalize"
|
||||
|
||||
CATEGORY = "KJNodes"
|
||||
CATEGORY = "KJNodes/misc"
|
||||
DESCRIPTION = """
|
||||
Normalize the images to be in the range [-1, 1]
|
||||
"""
|
||||
|
||||
def normalize(self,images):
|
||||
|
||||
@ -3805,6 +3952,9 @@ class Intrinsic_lora_sampling:
|
||||
RETURN_TYPES = ("IMAGE", "LATENT",)
|
||||
FUNCTION = "onestepsample"
|
||||
CATEGORY = "KJNodes"
|
||||
DESCRIPTION = """
|
||||
https://github.com/duxiaodan/intrinsic-lora
|
||||
"""
|
||||
|
||||
def onestepsample(self, model, lora_name, clip, vae, text, task, per_batch, image=None, optional_latent=None):
|
||||
pbar = comfy.utils.ProgressBar(3)
|
||||
@ -3887,6 +4037,9 @@ class RemapMaskRange:
|
||||
RETURN_NAMES = ("mask",)
|
||||
FUNCTION = "remap"
|
||||
CATEGORY = "KJNodes/masking"
|
||||
DESCRIPTION = """
|
||||
Sets new min and max values for the mask.
|
||||
"""
|
||||
|
||||
def remap(self, mask, min, max):
|
||||
|
||||
@ -3917,6 +4070,7 @@ class LoadResAdapterNormalization:
|
||||
|
||||
RETURN_TYPES = ("MODEL",)
|
||||
FUNCTION = "load_res_adapter"
|
||||
CATEGORY = "KJNodes/experimental"
|
||||
|
||||
def load_res_adapter(self, model, resadapter_path):
|
||||
print("ResAdapter: Checking ResAdapter path")
|
||||
@ -3957,6 +4111,13 @@ class Superprompt:
|
||||
|
||||
RETURN_TYPES = ("STRING",)
|
||||
FUNCTION = "process"
|
||||
CATEGORY = "KJNodes/text"
|
||||
DESCRIPTION = """
|
||||
SuperPrompt
|
||||
A T5 model fine-tuned on the SuperPrompt dataset for upsampling text prompts to more detailed descriptions.
|
||||
Meant to be used as a pre-generation step for text-to-image models that benefit from more detailed prompts.
|
||||
https://huggingface.co/roborovski/superprompt-v1
|
||||
"""
|
||||
|
||||
def process(self, instruction_prompt, prompt, max_new_tokens):
|
||||
device = comfy.model_management.get_torch_device()
|
||||
@ -3991,8 +4152,10 @@ class RemapImageRange:
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "remap"
|
||||
|
||||
CATEGORY = "KJNodes"
|
||||
CATEGORY = "KJNodes/image"
|
||||
DESCRIPTION = """
|
||||
Remaps the image values to the specified range.
|
||||
"""
|
||||
|
||||
def remap(self, image, min, max, clamp):
|
||||
if image.dtype == torch.float16:
|
||||
@ -4026,8 +4189,10 @@ class CameraPoseVisualizer:
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "plot"
|
||||
|
||||
CATEGORY = "KJNodes"
|
||||
CATEGORY = "KJNodes/misc"
|
||||
DESCRIPTION = """
|
||||
Visualizes the camera poses from a .txt file with RealEstate camera intrinsics and coordinates in a 3D plot.
|
||||
"""
|
||||
|
||||
def plot(self, pose_file_path, sample_stride, frames, base_xval, zval, use_exact_fx, relative_c2w, x_min, x_max, y_min, y_max, z_min, z_max, use_viewer):
|
||||
import matplotlib as mpl
|
||||
@ -4199,7 +4364,8 @@ NODE_CLASS_MAPPINGS = {
|
||||
"LoadResAdapterNormalization": LoadResAdapterNormalization,
|
||||
"Superprompt": Superprompt,
|
||||
"RemapImageRange": RemapImageRange,
|
||||
"CameraPoseVisualizer": CameraPoseVisualizer
|
||||
"CameraPoseVisualizer": CameraPoseVisualizer,
|
||||
"BboxVisualize": BboxVisualize
|
||||
}
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"INTConstant": "INT Constant",
|
||||
@ -4273,4 +4439,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"Superprompt": "Superprompt",
|
||||
"RemapImageRange": "RemapImageRange",
|
||||
"CameraPoseVisualizer": "CameraPoseVisualizer",
|
||||
"BboxVisualize": "BboxVisualize",
|
||||
}
|
||||
287
web/js/help_popup.js
Normal file
287
web/js/help_popup.js
Normal file
@ -0,0 +1,287 @@
|
||||
import { app } from "../../../scripts/app.js";
|
||||
|
||||
// code based on mtb nodes by Mel Massadian https://github.com/melMass/comfy_mtb/
|
||||
export const loadScript = (
|
||||
FILE_URL,
|
||||
async = true,
|
||||
type = 'text/javascript',
|
||||
) => {
|
||||
return new Promise((resolve, reject) => {
|
||||
try {
|
||||
// Check if the script already exists
|
||||
const existingScript = document.querySelector(`script[src="${FILE_URL}"]`)
|
||||
if (existingScript) {
|
||||
resolve({ status: true, message: 'Script already loaded' })
|
||||
return
|
||||
}
|
||||
|
||||
const scriptEle = document.createElement('script')
|
||||
scriptEle.type = type
|
||||
scriptEle.async = async
|
||||
scriptEle.src = FILE_URL
|
||||
|
||||
scriptEle.addEventListener('load', (ev) => {
|
||||
resolve({ status: true })
|
||||
})
|
||||
|
||||
scriptEle.addEventListener('error', (ev) => {
|
||||
reject({
|
||||
status: false,
|
||||
message: `Failed to load the script ＄{FILE_URL}`,
|
||||
})
|
||||
})
|
||||
|
||||
document.body.appendChild(scriptEle)
|
||||
} catch (error) {
|
||||
reject(error)
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
loadScript('/kjweb_async/marked.min.js').catch((e) => {
|
||||
console.log(e)
|
||||
})
|
||||
loadScript('/kjweb_async/purify.min.js').catch((e) => {
|
||||
console.log(e)
|
||||
})
|
||||
|
||||
app.registerExtension({
|
||||
name: "KJNodes.HelpPopup",
|
||||
async beforeRegisterNodeDef(nodeType, nodeData) {
|
||||
try {
|
||||
if (nodeData?.category?.startsWith("KJNodes")) {
|
||||
addDocumentation(nodeData, nodeType);
|
||||
}
|
||||
} catch (error) {
|
||||
console.error("Error in registering KJNodes.HelpPopup", error);
|
||||
}
|
||||
},
|
||||
});
|
||||
|
||||
const create_documentation_stylesheet = () => {
|
||||
const tag = 'kj-documentation-stylesheet'
|
||||
|
||||
let styleTag = document.head.querySelector(tag)
|
||||
|
||||
if (!styleTag) {
|
||||
styleTag = document.createElement('style')
|
||||
styleTag.type = 'text/css'
|
||||
styleTag.id = tag
|
||||
styleTag.innerHTML = `
|
||||
.kj-documentation-popup {
|
||||
background: var(--comfy-menu-bg);
|
||||
position: absolute;
|
||||
color: var(--fg-color);
|
||||
font: 12px monospace;
|
||||
line-height: 1.5em;
|
||||
padding: 10px;
|
||||
border-radius: 10px;
|
||||
border-style: solid;
|
||||
border-width: medium;
|
||||
border-color: var(--border-color);
|
||||
z-index: 5;
|
||||
overflow: hidden;
|
||||
}
|
||||
.content-wrapper {
|
||||
overflow: auto;
|
||||
max-height: 100%;
|
||||
/* Scrollbar styling for Chrome */
|
||||
&::-webkit-scrollbar {
|
||||
width: 6px;
|
||||
}
|
||||
&::-webkit-scrollbar-track {
|
||||
background: var(--bg-color);
|
||||
}
|
||||
&::-webkit-scrollbar-thumb {
|
||||
background-color: var(--fg-color);
|
||||
border-radius: 6px;
|
||||
border: 3px solid var(--bg-color);
|
||||
}
|
||||
|
||||
/* Scrollbar styling for Firefox */
|
||||
scrollbar-width: thin;
|
||||
scrollbar-color: var(--fg-color) var(--bg-color);
|
||||
a:visited {
|
||||
color: orange;
|
||||
}
|
||||
a:hover {
|
||||
color: red;
|
||||
}
|
||||
}
|
||||
`
|
||||
document.head.appendChild(styleTag)
|
||||
}
|
||||
}
|
||||
|
||||
/** Add documentation widget to the selected node */
|
||||
export const addDocumentation = (
|
||||
nodeData,
|
||||
nodeType,
|
||||
opts = { icon_size: 14, icon_margin: 4 },) => {
|
||||
|
||||
opts = opts || {}
|
||||
const iconSize = opts.icon_size ? opts.icon_size : 14
|
||||
const iconMargin = opts.icon_margin ? opts.icon_margin : 4
|
||||
let docElement = null
|
||||
let contentWrapper = null
|
||||
//if no description in the node python code, don't do anything
|
||||
if (!nodeData.description) {
|
||||
return
|
||||
}
|
||||
let hasResized = false //track if the popup has been resized manually
|
||||
|
||||
const drawFg = nodeType.prototype.onDrawForeground
|
||||
nodeType.prototype.onDrawForeground = function (ctx) {
|
||||
const r = drawFg ? drawFg.apply(this, arguments) : undefined
|
||||
if (this.flags.collapsed) return r
|
||||
|
||||
// icon position
|
||||
const x = this.size[0] - iconSize - iconMargin
|
||||
|
||||
// create the popup
|
||||
if (this.show_doc && docElement === null) {
|
||||
docElement = document.createElement('div')
|
||||
contentWrapper = document.createElement('div');
|
||||
docElement.appendChild(contentWrapper);
|
||||
|
||||
create_documentation_stylesheet()
|
||||
contentWrapper.classList.add('content-wrapper');
|
||||
docElement.classList.add('kj-documentation-popup')
|
||||
|
||||
//parse the string from the python node code to html with marked, and sanitize the html with DOMPurify
|
||||
contentWrapper.innerHTML = DOMPurify.sanitize(marked.parse(nodeData.description,))
|
||||
|
||||
// resize handle
|
||||
const resizeHandle = document.createElement('div');
|
||||
resizeHandle.style.width = '0';
|
||||
resizeHandle.style.height = '0';
|
||||
resizeHandle.style.position = 'absolute';
|
||||
resizeHandle.style.bottom = '0';
|
||||
resizeHandle.style.right = '0';
|
||||
resizeHandle.style.cursor = 'se-resize';
|
||||
|
||||
// Add pseudo-elements to create a triangle shape
|
||||
const borderColor = getComputedStyle(document.documentElement).getPropertyValue('--border-color').trim();
|
||||
resizeHandle.style.borderTop = '10px solid transparent';
|
||||
resizeHandle.style.borderLeft = '10px solid transparent';
|
||||
resizeHandle.style.borderBottom = `10px solid ${borderColor}`;
|
||||
resizeHandle.style.borderRight = `10px solid ${borderColor}`;
|
||||
|
||||
docElement.appendChild(resizeHandle)
|
||||
let isResizing = false
|
||||
let startX, startY, startWidth, startHeight
|
||||
|
||||
resizeHandle.addEventListener('mousedown', function (e) {
|
||||
e.stopPropagation();
|
||||
isResizing = true;
|
||||
startX = e.clientX;
|
||||
startY = e.clientY;
|
||||
startWidth = parseInt(document.defaultView.getComputedStyle(docElement).width, 10);
|
||||
startHeight = parseInt(document.defaultView.getComputedStyle(docElement).height, 10);
|
||||
});
|
||||
|
||||
// close button
|
||||
const closeButton = document.createElement('div');
|
||||
closeButton.textContent = '❌'; // Use an emoji or text for the close button
|
||||
closeButton.style.position = 'absolute';
|
||||
closeButton.style.top = '0';
|
||||
closeButton.style.right = '0';
|
||||
closeButton.style.cursor = 'pointer';
|
||||
closeButton.style.padding = '5px'; // Add some padding around the text
|
||||
closeButton.style.color = 'red'; // Set the text color
|
||||
closeButton.style.fontSize = '16px'; // Adjust the font size as needed
|
||||
|
||||
docElement.appendChild(closeButton)
|
||||
|
||||
closeButton.addEventListener('mousedown', (e) => {
|
||||
e.stopPropagation();
|
||||
this.show_doc = !this.show_doc
|
||||
docElement.parentNode.removeChild(docElement)
|
||||
docElement = null
|
||||
});
|
||||
|
||||
document.addEventListener('mousemove', function (e) {
|
||||
if (!isResizing) return;
|
||||
const newWidth = startWidth + e.clientX - startX;
|
||||
const newHeight = startHeight + e.clientY - startY;
|
||||
docElement.style.width = `${newWidth}px`;
|
||||
docElement.style.height = `${newHeight}px`;
|
||||
});
|
||||
|
||||
document.addEventListener('mouseup', function () {
|
||||
isResizing = false
|
||||
hasResized = true
|
||||
})
|
||||
|
||||
document.body.appendChild(docElement)
|
||||
}
|
||||
// close the popup
|
||||
else if (!this.show_doc && docElement !== null) {
|
||||
docElement.parentNode.removeChild(docElement)
|
||||
docElement = null
|
||||
}
|
||||
// update position of the popup
|
||||
if (this.show_doc && docElement !== null) {
|
||||
const rect = ctx.canvas.getBoundingClientRect()
|
||||
const scaleX = rect.width / ctx.canvas.width
|
||||
const scaleY = rect.height / ctx.canvas.height
|
||||
|
||||
const transform = new DOMMatrix()
|
||||
.scaleSelf(scaleX, scaleY)
|
||||
.multiplySelf(ctx.getTransform())
|
||||
.translateSelf(this.size[0] * scaleX, 0)
|
||||
.translateSelf(10, -32)
|
||||
|
||||
const scale = new DOMMatrix()
|
||||
.scaleSelf(transform.a, transform.d);
|
||||
|
||||
const styleObject = {
|
||||
transformOrigin: '0 0',
|
||||
transform: scale,
|
||||
left: `${transform.a + transform.e}px`,
|
||||
top: `${transform.d + transform.f}px`,
|
||||
};
|
||||
// keep possible manual resize
|
||||
if (!hasResized) {
|
||||
//styleObject.height = `${this.size[1] || this.parent?.inputHeight || 32}px`;
|
||||
//styleObject.height = `${docElement.offsetHeight || 32}px`;
|
||||
styleObject.width = `${this.size[0] * 1.5}px`;
|
||||
}
|
||||
Object.assign(docElement.style, styleObject);
|
||||
}
|
||||
|
||||
ctx.save()
|
||||
ctx.translate(x - 2, iconSize - 34)
|
||||
ctx.scale(iconSize / 32, iconSize / 32)
|
||||
ctx.strokeStyle = 'rgba(255,255,255,0.3)'
|
||||
ctx.lineCap = 'round'
|
||||
ctx.lineJoin = 'round'
|
||||
ctx.lineWidth = 2.4
|
||||
ctx.font = 'bold 36px monospace'
|
||||
ctx.fillStyle = 'orange';
|
||||
ctx.fillText('?', 0, 24)
|
||||
ctx.restore()
|
||||
return r
|
||||
}
|
||||
// handle clicking of the icon
|
||||
const mouseDown = nodeType.prototype.onMouseDown
|
||||
nodeType.prototype.onMouseDown = function (e, localPos, canvas) {
|
||||
const r = mouseDown ? mouseDown.apply(this, arguments) : undefined
|
||||
const iconX = this.size[0] - iconSize - iconMargin
|
||||
const iconY = iconSize - 34
|
||||
if (
|
||||
localPos[0] > iconX &&
|
||||
localPos[0] < iconX + iconSize &&
|
||||
localPos[1] > iconY &&
|
||||
localPos[1] < iconY + iconSize
|
||||
) {
|
||||
if (this.show_doc === undefined) {
|
||||
this.show_doc = true
|
||||
} else {
|
||||
this.show_doc = !this.show_doc
|
||||
}
|
||||
return true;
|
||||
}
|
||||
return r;
|
||||
}
|
||||
}
|
||||
@ -1,7 +1,7 @@
|
||||
import { app } from "../../../scripts/app.js";
|
||||
|
||||
app.registerExtension({
|
||||
name: "KJNodes.ConditioningMultiCombine",
|
||||
name: "KJNodes.jsnodes",
|
||||
async beforeRegisterNodeDef(nodeType, nodeData, app) {
|
||||
switch (nodeData.name) {
|
||||
case "ConditioningMultiCombine":
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user