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Big documentation update

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Added some js code (thanks melMass) to allow help popups on the nodes, and initial documentation on some nodes.
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kijai 2024-04-06 20:00:34 +03:00
parent 4d3c67ae53
commit 61a264f5ab
6 changed files with 593 additions and 118 deletions
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14
__init__.py
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@ -1,4 +1,16 @@
from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
WEB_DIRECTORY = "./web" WEB_DIRECTORY = "./web"
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
from aiohttp import web
from server import PromptServer
from pathlib import Path
if hasattr(PromptServer, "instance"):
# NOTE: we add an extra static path to avoid comfy mechanism
# that loads every script in web.
PromptServer.instance.app.add_routes(
[web.static("/kjweb_async", (Path(__file__).parent.absolute() / "kjweb_async").as_posix())]
)

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399
nodes.py
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@ -1,6 +1,4 @@
import nodes
import torch import torch
import torch.nn.functional as F import torch.nn.functional as F
from torchvision.transforms import Resize, CenterCrop, InterpolationMode from torchvision.transforms import Resize, CenterCrop, InterpolationMode
from torchvision.transforms import functional as TF from torchvision.transforms import functional as TF
@ -9,7 +7,7 @@ import scipy.ndimage
from scipy.spatial import Voronoi from scipy.spatial import Voronoi
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
from PIL import ImageFilter, Image, ImageDraw, ImageFont, ImageOps from PIL import ImageFilter, Image, ImageDraw, ImageFont
from PIL.PngImagePlugin import PngInfo from PIL.PngImagePlugin import PngInfo
import json import json
import re import re
@ -60,7 +58,6 @@ class FloatConstant:
return (value,) return (value,)
class StringConstant: class StringConstant:
@classmethod @classmethod
def INPUT_TYPES(cls): def INPUT_TYPES(cls):
return { return {
@ -320,6 +317,21 @@ class CreateFadeMaskAdvanced:
RETURN_TYPES = ("MASK",) RETURN_TYPES = ("MASK",)
FUNCTION = "createfademask" FUNCTION = "createfademask"
CATEGORY = "KJNodes/masking/generate" CATEGORY = "KJNodes/masking/generate"
DESCRIPTION = """
Create a batch of masks interpolated between given frames and values.
Uses same syntax as Fizz' BatchValueSchedule.
First value is the frame index (not that this starts from 0, not 1)
and the second value inside the brackets is the float value of the mask in range 0.0 - 1.0
For example the default values:
0:(0.0)
7:(1.0)
15:(0.0)
Would create a mask batch fo 16 frames, starting from black,
interpolating with the chosen curve to fully white at the 8th frame,
and interpolating from that to fully black at the 16th frame.
"""
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
@ -402,6 +414,10 @@ class ScaleBatchPromptSchedule:
RETURN_TYPES = ("STRING",) RETURN_TYPES = ("STRING",)
FUNCTION = "scaleschedule" FUNCTION = "scaleschedule"
CATEGORY = "KJNodes" CATEGORY = "KJNodes"
DESCRIPTION = """
Scales a batch schedule from Fizz' nodes BatchPromptSchedule
to a different frame count.
"""
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
@ -442,7 +458,7 @@ class CrossFadeImages:
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
FUNCTION = "crossfadeimages" FUNCTION = "crossfadeimages"
CATEGORY = "KJNodes" CATEGORY = "KJNodes/image"
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
@ -530,7 +546,7 @@ class GetImageRangeFromBatch:
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
FUNCTION = "imagesfrombatch" FUNCTION = "imagesfrombatch"
CATEGORY = "KJNodes" CATEGORY = "KJNodes/image"
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
@ -557,7 +573,10 @@ class GetImagesFromBatchIndexed:
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
FUNCTION = "indexedimagesfrombatch" FUNCTION = "indexedimagesfrombatch"
CATEGORY = "KJNodes" CATEGORY = "KJNodes/image"
DESCRIPTION = """
Selects and returns the images at the specified indices as an image batch.
"""
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
@ -586,6 +605,9 @@ class GetLatentsFromBatchIndexed:
RETURN_TYPES = ("LATENT",) RETURN_TYPES = ("LATENT",)
FUNCTION = "indexedlatentsfrombatch" FUNCTION = "indexedlatentsfrombatch"
CATEGORY = "KJNodes" CATEGORY = "KJNodes"
DESCRIPTION = """
Selects and returns the latents at the specified indices as an latent batch.
"""
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
@ -617,7 +639,10 @@ class ReplaceImagesInBatch:
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
FUNCTION = "replace" FUNCTION = "replace"
CATEGORY = "KJNodes" CATEGORY = "KJNodes/image"
DESCRIPTION = """
Replaces the images in a batch, starting from the specified start, with the replacement images.
"""
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
@ -647,7 +672,10 @@ class ReverseImageBatch:
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
FUNCTION = "reverseimagebatch" FUNCTION = "reverseimagebatch"
CATEGORY = "KJNodes" CATEGORY = "KJNodes/image"
DESCRIPTION = """
Reverses the order of the images in a batch.
"""
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
@ -667,7 +695,15 @@ class CreateTextMask:
RETURN_TYPES = ("IMAGE", "MASK",) RETURN_TYPES = ("IMAGE", "MASK",)
FUNCTION = "createtextmask" FUNCTION = "createtextmask"
CATEGORY = "KJNodes/masking/generate" CATEGORY = "KJNodes/text"
DESCRIPTION = """
Creates a text image and mask.
Looks for fonts from this folder:
ComfyUI/custom_nodes/ComfyUI-KJNodes/fonts
If start_rotation and/or end_rotation are different values,
creates animation between them.
"""
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
@ -779,10 +815,20 @@ class GrowMaskWithBlur:
} }
CATEGORY = "KJNodes/masking" CATEGORY = "KJNodes/masking"
RETURN_TYPES = ("MASK", "MASK",) RETURN_TYPES = ("MASK", "MASK",)
RETURN_NAMES = ("mask", "mask_inverted",) RETURN_NAMES = ("mask", "mask_inverted",)
FUNCTION = "expand_mask" FUNCTION = "expand_mask"
DESCRIPTION = """
# GrowMaskWithBlur
- mask: Input mask or mask batch
- expand: Expand or contract mask or mask batch by a given amount
- incremental_expandrate: increase expand rate by a given amount per frame
- tapered_corners: use tapered corners
- flip_input: flip input mask
- blur_radius: value higher than 0 will blur the mask
- lerp_alpha: alpha value for interpolation between frames
- decay_factor: decay value for interpolation between frames
- fill_holes: fill holes in the mask (slow)"""
def expand_mask(self, mask, expand, tapered_corners, flip_input, blur_radius, incremental_expandrate, lerp_alpha, decay_factor, fill_holes=False): def expand_mask(self, mask, expand, tapered_corners, flip_input, blur_radius, incremental_expandrate, lerp_alpha, decay_factor, fill_holes=False):
alpha = lerp_alpha alpha = lerp_alpha
@ -837,27 +883,14 @@ class GrowMaskWithBlur:
else: else:
return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),) return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),)
class PlotNode:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"start": ("FLOAT", {"default": 0.5, "min": 0.5, "max": 1.0}),
"max_frames": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
}}
RETURN_TYPES = ("FLOAT", "INT",)
FUNCTION = "plot"
CATEGORY = "KJNodes"
def plot(self, start, max_frames):
result = start + max_frames
return (result,)
class ColorToMask: class ColorToMask:
RETURN_TYPES = ("MASK",) RETURN_TYPES = ("MASK",)
FUNCTION = "clip" FUNCTION = "clip"
CATEGORY = "KJNodes/masking" CATEGORY = "KJNodes/masking"
DESCRIPTION = """
Converts chosen RGB value to a mask
"""
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
@ -915,9 +948,13 @@ class ConditioningMultiCombine:
RETURN_NAMES = ("combined", "inputcount") RETURN_NAMES = ("combined", "inputcount")
FUNCTION = "combine" FUNCTION = "combine"
CATEGORY = "KJNodes/masking/conditioning" CATEGORY = "KJNodes/masking/conditioning"
DESCRIPTION = """
Combines multiple conditioning nodes into one
"""
def combine(self, inputcount, **kwargs): def combine(self, inputcount, **kwargs):
cond_combine_node = nodes.ConditioningCombine() from nodes import ConditioningCombine
cond_combine_node = ConditioningCombine()
cond = kwargs["conditioning_1"] cond = kwargs["conditioning_1"]
for c in range(1, inputcount): for c in range(1, inputcount):
new_cond = kwargs[f"conditioning_{c + 1}"] new_cond = kwargs[f"conditioning_{c + 1}"]
@ -931,14 +968,17 @@ class CondPassThrough:
"required": { "required": {
"positive": ("CONDITIONING", ), "positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ), "negative": ("CONDITIONING", ),
}, },
} }
RETURN_TYPES = ("CONDITIONING", "CONDITIONING",) RETURN_TYPES = ("CONDITIONING", "CONDITIONING",)
RETURN_NAMES = ("positive", "negative") RETURN_NAMES = ("positive", "negative")
FUNCTION = "passthrough" FUNCTION = "passthrough"
CATEGORY = "KJNodes/misc" CATEGORY = "KJNodes/misc"
DESCRIPTION = """
Simply passes through the positive and negative conditioning,
workaround for Set node not allowing bypassed inputs.
"""
def passthrough(self, positive, negative): def passthrough(self, positive, negative):
return (positive, negative,) return (positive, negative,)
@ -972,6 +1012,9 @@ class ConditioningSetMaskAndCombine:
RETURN_NAMES = ("combined_positive", "combined_negative",) RETURN_NAMES = ("combined_positive", "combined_negative",)
FUNCTION = "append" FUNCTION = "append"
CATEGORY = "KJNodes/masking/conditioning" CATEGORY = "KJNodes/masking/conditioning"
DESCRIPTION = """
Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes
"""
def append(self, positive_1, negative_1, positive_2, negative_2, mask_1, mask_2, set_cond_area, mask_1_strength, mask_2_strength): def append(self, positive_1, negative_1, positive_2, negative_2, mask_1, mask_2, set_cond_area, mask_1_strength, mask_2_strength):
c = [] c = []
@ -1018,6 +1061,9 @@ class ConditioningSetMaskAndCombine3:
RETURN_NAMES = ("combined_positive", "combined_negative",) RETURN_NAMES = ("combined_positive", "combined_negative",)
FUNCTION = "append" FUNCTION = "append"
CATEGORY = "KJNodes/masking/conditioning" CATEGORY = "KJNodes/masking/conditioning"
DESCRIPTION = """
Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes
"""
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): 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):
c = [] c = []
@ -1074,6 +1120,9 @@ class ConditioningSetMaskAndCombine4:
RETURN_NAMES = ("combined_positive", "combined_negative",) RETURN_NAMES = ("combined_positive", "combined_negative",)
FUNCTION = "append" FUNCTION = "append"
CATEGORY = "KJNodes/masking/conditioning" CATEGORY = "KJNodes/masking/conditioning"
DESCRIPTION = """
Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes
"""
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): 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):
c = [] c = []
@ -1140,6 +1189,9 @@ class ConditioningSetMaskAndCombine5:
RETURN_NAMES = ("combined_positive", "combined_negative",) RETURN_NAMES = ("combined_positive", "combined_negative",)
FUNCTION = "append" FUNCTION = "append"
CATEGORY = "KJNodes/masking/conditioning" CATEGORY = "KJNodes/masking/conditioning"
DESCRIPTION = """
Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes
"""
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): 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):
c = [] c = []
@ -1187,6 +1239,7 @@ class VRAM_Debug:
return { return {
"required": { "required": {
"empty_cache": ("BOOLEAN", {"default": True}), "empty_cache": ("BOOLEAN", {"default": True}),
"gc_collect": ("BOOLEAN", {"default": True}),
"unload_all_models": ("BOOLEAN", {"default": False}), "unload_all_models": ("BOOLEAN", {"default": False}),
}, },
"optional":{ "optional":{
@ -1198,15 +1251,21 @@ class VRAM_Debug:
RETURN_TYPES = ("IMAGE", "MODEL","INT", "INT",) RETURN_TYPES = ("IMAGE", "MODEL","INT", "INT",)
RETURN_NAMES = ("image_passthrough", "model_passthrough", "freemem_before", "freemem_after") RETURN_NAMES = ("image_passthrough", "model_passthrough", "freemem_before", "freemem_after")
FUNCTION = "VRAMdebug" FUNCTION = "VRAMdebug"
CATEGORY = "KJNodes" CATEGORY = "KJNodes/misc"
DESCRIPTION = """
Placed between model or image chain, performs comfy model management functions and reports free VRAM before and after the functions
"""
def VRAMdebug(self, empty_cache, unload_all_models,image_passthrough=None, model_passthrough=None): def VRAMdebug(self, gc_collect,empty_cache, unload_all_models,image_passthrough=None, model_passthrough=None):
freemem_before = comfy.model_management.get_free_memory() freemem_before = comfy.model_management.get_free_memory()
print("VRAMdebug: free memory before: ", freemem_before) print("VRAMdebug: free memory before: ", freemem_before)
if empty_cache: if empty_cache:
comfy.model_management.soft_empty_cache() comfy.model_management.soft_empty_cache()
if unload_all_models: if unload_all_models:
comfy.model_management.unload_all_models() comfy.model_management.unload_all_models()
if gc_collect:
import gc
gc.collect()
freemem_after = comfy.model_management.get_free_memory() freemem_after = comfy.model_management.get_free_memory()
print("VRAMdebug: free memory after: ", freemem_after) print("VRAMdebug: free memory after: ", freemem_after)
print("VRAMdebug: freed memory: ", freemem_after - freemem_before) print("VRAMdebug: freed memory: ", freemem_after - freemem_before)
@ -1234,7 +1293,10 @@ class SomethingToString:
} }
RETURN_TYPES = ("STRING",) RETURN_TYPES = ("STRING",)
FUNCTION = "stringify" FUNCTION = "stringify"
CATEGORY = "KJNodes" CATEGORY = "KJNodes/text"
DESCRIPTION = """
Converts any type to a string.
"""
def stringify(self, input, prefix="", suffix=""): def stringify(self, input, prefix="", suffix=""):
if isinstance(input, (int, float, bool)): if isinstance(input, (int, float, bool)):
@ -1247,8 +1309,6 @@ class SomethingToString:
return return
return (stringified,) return (stringified,)
from nodes import EmptyLatentImage
class EmptyLatentImagePresets: class EmptyLatentImagePresets:
@classmethod @classmethod
def INPUT_TYPES(cls): def INPUT_TYPES(cls):
@ -1284,6 +1344,7 @@ class EmptyLatentImagePresets:
CATEGORY = "KJNodes" CATEGORY = "KJNodes"
def generate(self, dimensions, invert, batch_size): def generate(self, dimensions, invert, batch_size):
from nodes import EmptyLatentImage
result = [x.strip() for x in dimensions.split('x')] result = [x.strip() for x in dimensions.split('x')]
if invert: if invert:
@ -1319,15 +1380,20 @@ class ColorMatch:
}, },
} }
CATEGORY = "KJNodes/masking" CATEGORY = "KJNodes/image"
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",) RETURN_NAMES = ("image",)
FUNCTION = "colormatch" FUNCTION = "colormatch"
DESCRIPTION = """
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.
https://github.com/hahnec/color-matcher/
"""
def colormatch(self, image_ref, image_target, method): def colormatch(self, image_ref, image_target, method):
try: try:
from color_matcher import ColorMatcher #https://github.com/hahnec/color-matcher/ from color_matcher import ColorMatcher
except: except:
raise Exception("Can't import color-matcher, did you install requirements.txt? Manual install: pip install color-matcher") raise Exception("Can't import color-matcher, did you install requirements.txt? Manual install: pip install color-matcher")
cm = ColorMatcher() cm = ColorMatcher()
@ -1372,10 +1438,11 @@ class SaveImageWithAlpha:
RETURN_TYPES = () RETURN_TYPES = ()
FUNCTION = "save_images_alpha" FUNCTION = "save_images_alpha"
OUTPUT_NODE = True OUTPUT_NODE = True
CATEGORY = "KJNodes/image"
CATEGORY = "image" DESCRIPTION = """
Saves an image and mask as .PNG with the mask as the alpha channel.
"""
def save_images_alpha(self, images, mask, filename_prefix="ComfyUI_image_with_alpha", prompt=None, extra_pnginfo=None): def save_images_alpha(self, images, mask, filename_prefix="ComfyUI_image_with_alpha", prompt=None, extra_pnginfo=None):
filename_prefix += self.prefix_append filename_prefix += self.prefix_append
@ -1447,7 +1514,10 @@ class ImageConcanate:
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
FUNCTION = "concanate" FUNCTION = "concanate"
CATEGORY = "KJNodes" CATEGORY = "KJNodes/image"
DESCRIPTION = """
Concatenates the image2 to image1 in the specified direction.
"""
def concanate(self, image1, image2, direction, match_image_size): def concanate(self, image1, image2, direction, match_image_size):
if match_image_size: if match_image_size:
@ -1474,7 +1544,10 @@ class ImageGridComposite2x2:
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
FUNCTION = "compositegrid" FUNCTION = "compositegrid"
CATEGORY = "KJNodes" CATEGORY = "KJNodes/image"
DESCRIPTION = """
Concatenates the 4 input images into a 2x2 grid.
"""
def compositegrid(self, image1, image2, image3, image4): def compositegrid(self, image1, image2, image3, image4):
top_row = torch.cat((image1, image2), dim=2) top_row = torch.cat((image1, image2), dim=2)
@ -1499,7 +1572,10 @@ class ImageGridComposite3x3:
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
FUNCTION = "compositegrid" FUNCTION = "compositegrid"
CATEGORY = "KJNodes" CATEGORY = "KJNodes/image"
DESCRIPTION = """
Concatenates the 9 input images into a 3x3 grid.
"""
def compositegrid(self, image1, image2, image3, image4, image5, image6, image7, image8, image9): def compositegrid(self, image1, image2, image3, image4, image5, image6, image7, image8, image9):
top_row = torch.cat((image1, image2, image3), dim=2) top_row = torch.cat((image1, image2, image3), dim=2)
@ -1522,7 +1598,7 @@ class ImageBatchTestPattern:
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
FUNCTION = "generatetestpattern" FUNCTION = "generatetestpattern"
CATEGORY = "KJNodes" CATEGORY = "KJNodes/text"
def generatetestpattern(self, batch_size, font, font_size, start_from, width, height): def generatetestpattern(self, batch_size, font, font_size, start_from, width, height):
out = [] out = []
@ -1564,8 +1640,7 @@ class ImageBatchTestPattern:
#based on nodes from mtb https://github.com/melMass/comfy_mtb #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: class BatchCropFromMask:
@ -1640,10 +1715,6 @@ class BatchCropFromMask:
self.max_bbox_height = round(self.max_bbox_height * crop_size_mult) self.max_bbox_height = round(self.max_bbox_height * crop_size_mult)
bbox_aspect_ratio = self.max_bbox_width / self.max_bbox_height 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... # Then, for each mask and corresponding image...
for i, (mask, img) in enumerate(zip(masks, original_images)): for i, (mask, img) in enumerate(zip(masks, original_images)):
_mask = tensor2pil(mask)[0] _mask = tensor2pil(mask)[0]
@ -2001,29 +2072,18 @@ class FilterZeroMasksAndCorrespondingImages:
}, },
} }
RETURN_TYPES = ( RETURN_TYPES = ("MASK", "IMAGE", "IMAGE", "INDEXES",)
"MASK", RETURN_NAMES = ("non_zero_masks_out", "non_zero_mask_images_out", "zero_mask_images_out", "zero_mask_images_out_indexes",)
"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" FUNCTION = "filter"
CATEGORY = "KJNodes/masking" 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): 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_masks = []
non_zero_mask_images = [] non_zero_mask_images = []
zero_mask_images = [] zero_mask_images = []
@ -2071,24 +2131,19 @@ class InsertImageBatchByIndexes:
}, },
} }
RETURN_TYPES = ( RETURN_TYPES = ("IMAGE", )
"IMAGE", RETURN_NAMES = ("images_after_insert", )
)
RETURN_NAMES = (
"images_after_insert",
)
FUNCTION = "insert" FUNCTION = "insert"
CATEGORY = "KJNodes" CATEGORY = "KJNodes/image"
DESCRIPTION = """
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
This node is designed to be use with node FilterZeroMasksAndCorrespondingImages
It inserts the images_to_insert into images according to insert_indexes
Returns: Returns:
images_after_insert: updated original images with origonal sequence order images_after_insert: updated original images with origonal sequence order
""" """
def insert(self, images, images_to_insert, insert_indexes):
images_after_insert = images images_after_insert = images
if images_to_insert is not None and insert_indexes is not None: if images_to_insert is not None and insert_indexes is not None:
@ -2152,9 +2207,9 @@ class BatchUncropAdvanced:
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
FUNCTION = "uncrop" FUNCTION = "uncrop"
CATEGORY = "KJNodes/masking" 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 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)): def inset_border(image, border_width=20, border_color=(0)):
@ -2257,8 +2312,10 @@ class BatchCLIPSeg:
CATEGORY = "KJNodes/masking" CATEGORY = "KJNodes/masking"
RETURN_TYPES = ("MASK",) RETURN_TYPES = ("MASK",)
RETURN_NAMES = ("Mask",) RETURN_NAMES = ("Mask",)
FUNCTION = "segment_image" FUNCTION = "segment_image"
DESCRIPTION = """
Segments an image or batch of images using CLIPSeg.
"""
def segment_image(self, images, text, threshold, binary_mask, combine_mask, use_cuda): def segment_image(self, images, text, threshold, binary_mask, combine_mask, use_cuda):
@ -2318,6 +2375,11 @@ class RoundMask:
RETURN_TYPES = ("MASK",) RETURN_TYPES = ("MASK",)
FUNCTION = "round" FUNCTION = "round"
CATEGORY = "KJNodes/masking" 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): def round(self, mask):
mask = mask.round() mask = mask.round()
@ -2339,6 +2401,9 @@ class ResizeMask:
RETURN_NAMES = ("mask", "width", "height",) RETURN_NAMES = ("mask", "width", "height",)
FUNCTION = "resize" FUNCTION = "resize"
CATEGORY = "KJNodes/masking" 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): def resize(self, mask, width, height, keep_proportions):
if keep_proportions: if keep_proportions:
@ -2383,6 +2448,16 @@ class OffsetMask:
RETURN_NAMES = ("mask",) RETURN_NAMES = ("mask",)
FUNCTION = "offset" FUNCTION = "offset"
CATEGORY = "KJNodes/masking" 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"): def offset(self, mask, x, y, angle, roll=False, incremental=False, duplication_factor=1, padding_mode="empty"):
# Create duplicates of the mask batch # Create duplicates of the mask batch
@ -2468,7 +2543,11 @@ class WidgetToString:
RETURN_TYPES = ("STRING", ) RETURN_TYPES = ("STRING", )
FUNCTION = "get_widget_value" 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): def get_widget_value(self, id, widget_name, extra_pnginfo, prompt, return_all=False):
workflow = extra_pnginfo["workflow"] workflow = extra_pnginfo["workflow"]
@ -2735,6 +2814,57 @@ class BboxToInt:
return (x_min, y_min, width, height, center_x, center_y,) 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: class SplitBboxes:
@classmethod @classmethod
@ -2814,7 +2944,7 @@ class DummyLatentOut:
RETURN_TYPES = ("LATENT",) RETURN_TYPES = ("LATENT",)
FUNCTION = "dummy" FUNCTION = "dummy"
CATEGORY = "KJNodes" CATEGORY = "KJNodes/misc"
OUTPUT_NODE = True OUTPUT_NODE = True
def dummy(self, latent): def dummy(self, latent):
@ -2957,7 +3087,7 @@ class AddLabel:
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
FUNCTION = "addlabel" 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): def addlabel(self, image, text_x, text_y, text, height, font_size, font_color, label_color, font, direction):
batch_size = image.shape[0] batch_size = image.shape[0]
@ -3005,7 +3135,7 @@ class SoundReactive:
RETURN_NAMES =("sound_level", "sound_level_int",) RETURN_NAMES =("sound_level", "sound_level_int",)
FUNCTION = "react" FUNCTION = "react"
CATEGORY = "KJNodes/experimental" CATEGORY = "KJNodes/audio"
def react(self, sound_level, start_range_hz, end_range_hz, smoothing_factor, multiplier, normalize): 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 # Normalize the result to be within the range of -180 to 180
return (interpolated + 180) % 360 - 180 return (interpolated + 180) % 360 - 180
class StableZero123_BatchSchedule: class StableZero123_BatchSchedule:
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
return {"required": { "clip_vision": ("CLIP_VISION",), return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",), "init_image": ("IMAGE",),
"vae": ("VAE",), "vae": ("VAE",),
"width": ("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": nodes.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 256, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],), "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}), "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" 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): 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) output = clip_vision.encode_image(init_image)
@ -3223,8 +3354,8 @@ class SV3D_BatchSchedule:
return {"required": { "clip_vision": ("CLIP_VISION",), return {"required": { "clip_vision": ("CLIP_VISION",),
"init_image": ("IMAGE",), "init_image": ("IMAGE",),
"vae": ("VAE",), "vae": ("VAE",),
"width": ("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": nodes.MAX_RESOLUTION, "step": 8}), "height": ("INT", {"default": 576, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 21, "min": 1, "max": 4096}), "batch_size": ("INT", {"default": 21, "min": 1, "max": 4096}),
"interpolation": (["linear", "ease_in", "ease_out", "ease_in_out"],), "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}), "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_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
RETURN_NAMES = ("positive", "negative", "latent") RETURN_NAMES = ("positive", "negative", "latent")
FUNCTION = "encode" FUNCTION = "encode"
CATEGORY = "KJNodes/experimental"
CATEGORY = "KJNodes" 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): 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) output = clip_vision.encode_image(init_image)
@ -3341,7 +3474,12 @@ class ImageBatchRepeatInterleaving:
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
FUNCTION = "repeat" 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 @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):
@ -3388,10 +3526,14 @@ class NormalizedAmplitudeToMask:
}), }),
},} },}
CATEGORY = "AudioScheduler/Amplitude" CATEGORY = "KJNodes/audio"
RETURN_TYPES = ("MASK",) RETURN_TYPES = ("MASK",)
FUNCTION = "convert" 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): 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] # Ensure normalized_amp is an array and within the range [0, 1]
@ -3460,7 +3602,7 @@ class OffsetMaskByNormalizedAmplitude:
RETURN_TYPES = ("MASK",) RETURN_TYPES = ("MASK",)
RETURN_NAMES = ("mask",) RETURN_NAMES = ("mask",)
FUNCTION = "offset" FUNCTION = "offset"
CATEGORY = "KJNodes/masking" CATEGORY = "KJNodes/audio"
def offset(self, mask, x, y, angle_multiplier, rotate, normalized_amp): def offset(self, mask, x, y, angle_multiplier, rotate, normalized_amp):
@ -3501,7 +3643,7 @@ class ImageTransformByNormalizedAmplitude:
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
FUNCTION = "amptransform" FUNCTION = "amptransform"
CATEGORY = "KJNodes" CATEGORY = "KJNodes/audio"
def amptransform(self, image, normalized_amp, zoom_scale, cumulative, x_offset, y_offset): 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] # Ensure normalized_amp is an array and within the range [0, 1]
@ -3674,9 +3816,10 @@ class GLIGENTextBoxApplyBatch:
}} }}
RETURN_TYPES = ("CONDITIONING", "IMAGE",) RETURN_TYPES = ("CONDITIONING", "IMAGE",)
FUNCTION = "append" FUNCTION = "append"
CATEGORY = "KJNodes/experimental"
CATEGORY = "conditioning/gligen" 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): def append(self, latents, conditioning_to, clip, gligen_textbox_model, text, width, height, coordinates, interpolation):
@ -3725,8 +3868,10 @@ class ImageUpscaleWithModelBatched:
}} }}
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
FUNCTION = "upscale" FUNCTION = "upscale"
CATEGORY = "KJNodes/image"
CATEGORY = "KJNodes" 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): def upscale(self, upscale_model, images, per_batch):
@ -3760,8 +3905,10 @@ class ImageNormalize_Neg1_To_1:
}} }}
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
FUNCTION = "normalize" FUNCTION = "normalize"
CATEGORY = "KJNodes/misc"
CATEGORY = "KJNodes" DESCRIPTION = """
Normalize the images to be in the range [-1, 1]
"""
def normalize(self,images): def normalize(self,images):
@ -3805,6 +3952,9 @@ class Intrinsic_lora_sampling:
RETURN_TYPES = ("IMAGE", "LATENT",) RETURN_TYPES = ("IMAGE", "LATENT",)
FUNCTION = "onestepsample" FUNCTION = "onestepsample"
CATEGORY = "KJNodes" 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): def onestepsample(self, model, lora_name, clip, vae, text, task, per_batch, image=None, optional_latent=None):
pbar = comfy.utils.ProgressBar(3) pbar = comfy.utils.ProgressBar(3)
@ -3887,6 +4037,9 @@ class RemapMaskRange:
RETURN_NAMES = ("mask",) RETURN_NAMES = ("mask",)
FUNCTION = "remap" FUNCTION = "remap"
CATEGORY = "KJNodes/masking" CATEGORY = "KJNodes/masking"
DESCRIPTION = """
Sets new min and max values for the mask.
"""
def remap(self, mask, min, max): def remap(self, mask, min, max):
@ -3917,6 +4070,7 @@ class LoadResAdapterNormalization:
RETURN_TYPES = ("MODEL",) RETURN_TYPES = ("MODEL",)
FUNCTION = "load_res_adapter" FUNCTION = "load_res_adapter"
CATEGORY = "KJNodes/experimental"
def load_res_adapter(self, model, resadapter_path): def load_res_adapter(self, model, resadapter_path):
print("ResAdapter: Checking ResAdapter path") print("ResAdapter: Checking ResAdapter path")
@ -3957,6 +4111,13 @@ class Superprompt:
RETURN_TYPES = ("STRING",) RETURN_TYPES = ("STRING",)
FUNCTION = "process" 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): def process(self, instruction_prompt, prompt, max_new_tokens):
device = comfy.model_management.get_torch_device() device = comfy.model_management.get_torch_device()
@ -3991,8 +4152,10 @@ class RemapImageRange:
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
FUNCTION = "remap" FUNCTION = "remap"
CATEGORY = "KJNodes/image"
CATEGORY = "KJNodes" DESCRIPTION = """
Remaps the image values to the specified range.
"""
def remap(self, image, min, max, clamp): def remap(self, image, min, max, clamp):
if image.dtype == torch.float16: if image.dtype == torch.float16:
@ -4026,8 +4189,10 @@ class CameraPoseVisualizer:
RETURN_TYPES = ("IMAGE",) RETURN_TYPES = ("IMAGE",)
FUNCTION = "plot" FUNCTION = "plot"
CATEGORY = "KJNodes/misc"
CATEGORY = "KJNodes" 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): 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 import matplotlib as mpl
@ -4199,7 +4364,8 @@ NODE_CLASS_MAPPINGS = {
"LoadResAdapterNormalization": LoadResAdapterNormalization, "LoadResAdapterNormalization": LoadResAdapterNormalization,
"Superprompt": Superprompt, "Superprompt": Superprompt,
"RemapImageRange": RemapImageRange, "RemapImageRange": RemapImageRange,
"CameraPoseVisualizer": CameraPoseVisualizer "CameraPoseVisualizer": CameraPoseVisualizer,
"BboxVisualize": BboxVisualize
} }
NODE_DISPLAY_NAME_MAPPINGS = { NODE_DISPLAY_NAME_MAPPINGS = {
"INTConstant": "INT Constant", "INTConstant": "INT Constant",
@ -4273,4 +4439,5 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"Superprompt": "Superprompt", "Superprompt": "Superprompt",
"RemapImageRange": "RemapImageRange", "RemapImageRange": "RemapImageRange",
"CameraPoseVisualizer": "CameraPoseVisualizer", "CameraPoseVisualizer": "CameraPoseVisualizer",
"BboxVisualize": "BboxVisualize",
} }

287
web/js/help_popup.js Normal file
View 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;
}
}

2
web/js/jsnodes.js
View File

@ -1,7 +1,7 @@
import { app } from "../../../scripts/app.js"; import { app } from "../../../scripts/app.js";
app.registerExtension({ app.registerExtension({
name: "KJNodes.ConditioningMultiCombine", name: "KJNodes.jsnodes",
async beforeRegisterNodeDef(nodeType, nodeData, app) { async beforeRegisterNodeDef(nodeType, nodeData, app) {
switch (nodeData.name) { switch (nodeData.name) {
case "ConditioningMultiCombine": case "ConditioningMultiCombine":