diff --git a/__init__.py b/__init__.py index 5109219..138b9c7 100644 --- a/__init__.py +++ b/__init__.py @@ -1,4 +1,16 @@ from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"] WEB_DIRECTORY = "./web" -__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"] \ No newline at end of file + +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())] + ) \ No newline at end of file diff --git a/kjweb_async/marked.min.js b/kjweb_async/marked.min.js new file mode 100644 index 0000000..2e66c36 --- /dev/null +++ b/kjweb_async/marked.min.js @@ -0,0 +1,6 @@ +/** + * marked v12.0.1 - a markdown parser + * Copyright (c) 2011-2024, Christopher Jeffrey. 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m(le[e])},o.removeHooks=function(e){le[e]&&(le[e]=[])},o.removeAllHooks=function(){le={}},o}();return J})); +//# sourceMappingURL=purify.min.js.map diff --git a/nodes.py b/nodes.py index 6651f3f..301e1ec 100644 --- a/nodes.py +++ b/nodes.py @@ -1,6 +1,4 @@ -import nodes import torch - import torch.nn.functional as F from torchvision.transforms import Resize, CenterCrop, InterpolationMode from torchvision.transforms import functional as TF @@ -9,7 +7,7 @@ import scipy.ndimage from scipy.spatial import Voronoi import matplotlib.pyplot as plt import numpy as np -from PIL import ImageFilter, Image, ImageDraw, ImageFont, ImageOps +from PIL import ImageFilter, Image, ImageDraw, ImageFont from PIL.PngImagePlugin import PngInfo import json import re @@ -60,7 +58,6 @@ class FloatConstant: return (value,) class StringConstant: - @classmethod def INPUT_TYPES(cls): return { @@ -320,6 +317,21 @@ class CreateFadeMaskAdvanced: RETURN_TYPES = ("MASK",) FUNCTION = "createfademask" 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 def INPUT_TYPES(s): @@ -402,6 +414,10 @@ class ScaleBatchPromptSchedule: RETURN_TYPES = ("STRING",) FUNCTION = "scaleschedule" CATEGORY = "KJNodes" + DESCRIPTION = """ +Scales a batch schedule from Fizz' nodes BatchPromptSchedule +to a different frame count. +""" @classmethod def INPUT_TYPES(s): @@ -442,7 +458,7 @@ class CrossFadeImages: RETURN_TYPES = ("IMAGE",) FUNCTION = "crossfadeimages" - CATEGORY = "KJNodes" + CATEGORY = "KJNodes/image" @classmethod def INPUT_TYPES(s): @@ -530,7 +546,7 @@ class GetImageRangeFromBatch: RETURN_TYPES = ("IMAGE",) FUNCTION = "imagesfrombatch" - CATEGORY = "KJNodes" + CATEGORY = "KJNodes/image" @classmethod def INPUT_TYPES(s): @@ -557,7 +573,10 @@ class GetImagesFromBatchIndexed: RETURN_TYPES = ("IMAGE",) FUNCTION = "indexedimagesfrombatch" - CATEGORY = "KJNodes" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Selects and returns the images at the specified indices as an image batch. +""" @classmethod def INPUT_TYPES(s): @@ -586,6 +605,9 @@ class GetLatentsFromBatchIndexed: RETURN_TYPES = ("LATENT",) FUNCTION = "indexedlatentsfrombatch" CATEGORY = "KJNodes" + DESCRIPTION = """ +Selects and returns the latents at the specified indices as an latent batch. +""" @classmethod def INPUT_TYPES(s): @@ -617,7 +639,10 @@ class ReplaceImagesInBatch: RETURN_TYPES = ("IMAGE",) FUNCTION = "replace" - CATEGORY = "KJNodes" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Replaces the images in a batch, starting from the specified start, with the replacement images. +""" @classmethod def INPUT_TYPES(s): @@ -647,7 +672,10 @@ class ReverseImageBatch: RETURN_TYPES = ("IMAGE",) FUNCTION = "reverseimagebatch" - CATEGORY = "KJNodes" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Reverses the order of the images in a batch. +""" @classmethod def INPUT_TYPES(s): @@ -667,7 +695,15 @@ class CreateTextMask: RETURN_TYPES = ("IMAGE", "MASK",) 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 def INPUT_TYPES(s): @@ -779,10 +815,20 @@ class GrowMaskWithBlur: } CATEGORY = "KJNodes/masking" - RETURN_TYPES = ("MASK", "MASK",) RETURN_NAMES = ("mask", "mask_inverted",) 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): alpha = lerp_alpha @@ -837,27 +883,14 @@ class GrowMaskWithBlur: else: 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: RETURN_TYPES = ("MASK",) FUNCTION = "clip" CATEGORY = "KJNodes/masking" + DESCRIPTION = """ +Converts chosen RGB value to a mask +""" @classmethod def INPUT_TYPES(s): @@ -915,9 +948,13 @@ class ConditioningMultiCombine: RETURN_NAMES = ("combined", "inputcount") FUNCTION = "combine" CATEGORY = "KJNodes/masking/conditioning" + DESCRIPTION = """ +Combines multiple conditioning nodes into one +""" def combine(self, inputcount, **kwargs): - cond_combine_node = nodes.ConditioningCombine() + from nodes import ConditioningCombine + cond_combine_node = ConditioningCombine() cond = kwargs["conditioning_1"] for c in range(1, inputcount): new_cond = kwargs[f"conditioning_{c + 1}"] @@ -931,14 +968,17 @@ class CondPassThrough: "required": { "positive": ("CONDITIONING", ), "negative": ("CONDITIONING", ), - }, - + }, } RETURN_TYPES = ("CONDITIONING", "CONDITIONING",) RETURN_NAMES = ("positive", "negative") FUNCTION = "passthrough" 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): return (positive, negative,) @@ -972,6 +1012,9 @@ class ConditioningSetMaskAndCombine: RETURN_NAMES = ("combined_positive", "combined_negative",) FUNCTION = "append" 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): c = [] @@ -1018,6 +1061,9 @@ class ConditioningSetMaskAndCombine3: RETURN_NAMES = ("combined_positive", "combined_negative",) FUNCTION = "append" 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): c = [] @@ -1074,6 +1120,9 @@ class ConditioningSetMaskAndCombine4: RETURN_NAMES = ("combined_positive", "combined_negative",) FUNCTION = "append" 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): c = [] @@ -1140,6 +1189,9 @@ class ConditioningSetMaskAndCombine5: RETURN_NAMES = ("combined_positive", "combined_negative",) FUNCTION = "append" 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): c = [] @@ -1187,6 +1239,7 @@ class VRAM_Debug: return { "required": { "empty_cache": ("BOOLEAN", {"default": True}), + "gc_collect": ("BOOLEAN", {"default": True}), "unload_all_models": ("BOOLEAN", {"default": False}), }, "optional":{ @@ -1198,15 +1251,21 @@ class VRAM_Debug: RETURN_TYPES = ("IMAGE", "MODEL","INT", "INT",) RETURN_NAMES = ("image_passthrough", "model_passthrough", "freemem_before", "freemem_after") 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() print("VRAMdebug: free memory before: ", freemem_before) if empty_cache: comfy.model_management.soft_empty_cache() if unload_all_models: comfy.model_management.unload_all_models() + if gc_collect: + import gc + gc.collect() freemem_after = comfy.model_management.get_free_memory() print("VRAMdebug: free memory after: ", freemem_after) print("VRAMdebug: freed memory: ", freemem_after - freemem_before) @@ -1234,7 +1293,10 @@ class SomethingToString: } RETURN_TYPES = ("STRING",) FUNCTION = "stringify" - CATEGORY = "KJNodes" + CATEGORY = "KJNodes/text" + DESCRIPTION = """ +Converts any type to a string. +""" def stringify(self, input, prefix="", suffix=""): if isinstance(input, (int, float, bool)): @@ -1247,8 +1309,6 @@ class SomethingToString: return return (stringified,) -from nodes import EmptyLatentImage - class EmptyLatentImagePresets: @classmethod def INPUT_TYPES(cls): @@ -1284,6 +1344,7 @@ class EmptyLatentImagePresets: CATEGORY = "KJNodes" def generate(self, dimensions, invert, batch_size): + from nodes import EmptyLatentImage result = [x.strip() for x in dimensions.split('x')] if invert: @@ -1319,15 +1380,20 @@ class ColorMatch: }, } - CATEGORY = "KJNodes/masking" + CATEGORY = "KJNodes/image" RETURN_TYPES = ("IMAGE",) RETURN_NAMES = ("image",) 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): try: - from color_matcher import ColorMatcher #https://github.com/hahnec/color-matcher/ + from color_matcher import ColorMatcher except: raise Exception("Can't import color-matcher, did you install requirements.txt? Manual install: pip install color-matcher") cm = ColorMatcher() @@ -1372,10 +1438,11 @@ class SaveImageWithAlpha: RETURN_TYPES = () FUNCTION = "save_images_alpha" - OUTPUT_NODE = True - - CATEGORY = "image" + CATEGORY = "KJNodes/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): filename_prefix += self.prefix_append @@ -1447,7 +1514,10 @@ class ImageConcanate: RETURN_TYPES = ("IMAGE",) 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): if match_image_size: @@ -1474,7 +1544,10 @@ class ImageGridComposite2x2: RETURN_TYPES = ("IMAGE",) FUNCTION = "compositegrid" - CATEGORY = "KJNodes" + CATEGORY = "KJNodes/image" + DESCRIPTION = """ +Concatenates the 4 input images into a 2x2 grid. +""" def compositegrid(self, image1, image2, image3, image4): top_row = torch.cat((image1, image2), dim=2) @@ -1499,7 +1572,10 @@ class ImageGridComposite3x3: RETURN_TYPES = ("IMAGE",) 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): top_row = torch.cat((image1, image2, image3), dim=2) @@ -1522,7 +1598,7 @@ class ImageBatchTestPattern: RETURN_TYPES = ("IMAGE",) FUNCTION = "generatetestpattern" - CATEGORY = "KJNodes" + CATEGORY = "KJNodes/text" def generatetestpattern(self, batch_size, font, font_size, start_from, width, height): 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. +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", } \ No newline at end of file diff --git a/web/js/help_popup.js b/web/js/help_popup.js new file mode 100644 index 0000000..db1c29a --- /dev/null +++ b/web/js/help_popup.js @@ -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; + } +} \ No newline at end of file diff --git a/web/js/jsnodes.js b/web/js/jsnodes.js index 90e247b..2349636 100644 --- a/web/js/jsnodes.js +++ b/web/js/jsnodes.js @@ -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":