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cleanup
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kijai
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152
nodes.py
152
nodes.py
@ -2,27 +2,23 @@ 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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from scipy.interpolate import CubicSpline
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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
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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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import os
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import random
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from scipy.special import erf
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from .fluid import Fluid
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import comfy.model_management
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import math
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import model_management
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from nodes import MAX_RESOLUTION
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import folder_paths
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from comfy.cli_args import args
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script_dir = os.path.dirname(os.path.abspath(__file__))
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folder_paths.add_model_folder_path("kjnodes_fonts", os.path.join(script_dir, "fonts"))
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script_directory = os.path.dirname(os.path.abspath(__file__))
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folder_paths.add_model_folder_path("kjnodes_fonts", os.path.join(script_directory, "fonts"))
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class INTConstant:
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@classmethod
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@ -34,7 +30,6 @@ class INTConstant:
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RETURN_TYPES = ("INT",)
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RETURN_NAMES = ("value",)
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FUNCTION = "get_value"
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CATEGORY = "KJNodes/constants"
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def get_value(self, value):
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@ -51,7 +46,6 @@ class FloatConstant:
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RETURN_TYPES = ("FLOAT",)
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RETURN_NAMES = ("value",)
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FUNCTION = "get_value"
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CATEGORY = "KJNodes/constants"
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def get_value(self, value):
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@ -67,7 +61,6 @@ class StringConstant:
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}
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RETURN_TYPES = ("STRING",)
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FUNCTION = "passtring"
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CATEGORY = "KJNodes/constants"
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def passtring(self, string):
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@ -92,11 +85,12 @@ class CreateFluidMask:
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"inflow_radius": ("INT", {"default": 8,"min": 0, "max": 255, "step": 1}),
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"inflow_padding": ("INT", {"default": 50,"min": 0, "max": 255, "step": 1}),
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"inflow_duration": ("INT", {"default": 60,"min": 0, "max": 255, "step": 1}),
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},
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}
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#using code from https://github.com/GregTJ/stable-fluids
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def createfluidmask(self, frames, width, height, invert, inflow_count, inflow_velocity, inflow_radius, inflow_padding, inflow_duration):
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from .fluid import Fluid
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from scipy.spatial import erf
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out = []
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masks = []
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RESOLUTION = width, height
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@ -159,7 +153,7 @@ class CreateAudioMask:
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print("Can not import librosa. Install it with 'pip install librosa'")
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "createaudiomask"
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CATEGORY = "KJNodes/masking/generate"
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CATEGORY = "KJNodes/deprecated"
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@classmethod
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def INPUT_TYPES(s):
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@ -180,7 +174,7 @@ class CreateAudioMask:
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out = []
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masks = []
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if audio_path == "audio.wav": #I don't know why relative path won't work otherwise...
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audio_path = os.path.join(script_dir, audio_path)
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audio_path = os.path.join(script_directory, audio_path)
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audio, sr = self.librosa.load(audio_path)
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spectrogram = np.abs(self.librosa.stft(audio))
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@ -245,7 +239,7 @@ class CreateFadeMask:
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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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CATEGORY = "KJNodes/deprecated"
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@classmethod
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def INPUT_TYPES(s):
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@ -321,13 +315,13 @@ class CreateFadeMaskAdvanced:
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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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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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@ -941,7 +935,6 @@ class ConditioningMultiCombine:
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"conditioning_1": ("CONDITIONING", ),
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"conditioning_2": ("CONDITIONING", ),
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},
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}
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RETURN_TYPES = ("CONDITIONING", "INT")
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@ -1257,16 +1250,16 @@ Placed between model or image chain, performs comfy model management functions a
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"""
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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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freemem_before = 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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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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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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freemem_after = 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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return (image_passthrough, model_passthrough, freemem_before, freemem_after)
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@ -1386,7 +1379,14 @@ class ColorMatch:
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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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color-matcher enables color transfer across images which comes in handy for automatic
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color-grading of photographs, paintings and film sequences as well as light-field
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and stopmotion corrections.
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The methods behind the mappings are based on the approach from Reinhard et al.,
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the Monge-Kantorovich Linearization (MKL) as proposed by Pitie et al. and our analytical solution
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to a Multi-Variate Gaussian Distribution (MVGD) transfer in conjunction with classical histogram
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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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@ -1445,6 +1445,8 @@ 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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from comfy.cli_args import args
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from PIL.PngImagePlugin import PngInfo
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filename_prefix += self.prefix_append
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full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
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results = list()
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@ -2287,9 +2289,6 @@ class BatchUncropAdvanced:
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return (pil2tensor(out_images),)
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from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
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class BatchCLIPSeg:
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def __init__(self):
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@ -2318,7 +2317,7 @@ Segments an image or batch of images using CLIPSeg.
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"""
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def segment_image(self, images, text, threshold, binary_mask, combine_mask, use_cuda):
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from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
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out = []
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height, width, _ = images[0].shape
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if use_cuda and torch.cuda.is_available():
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@ -2659,6 +2658,7 @@ class CreateVoronoiMask:
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}
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def createvoronoi(self, frames, num_points, line_width, speed, frame_width, frame_height):
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from scipy.spatial import Voronoi
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# Define the number of images in the batch
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batch_size = frames
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out = []
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@ -2710,10 +2710,6 @@ class CreateVoronoiMask:
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out.append(mask)
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return (torch.stack(out, dim=0), 1.0 - torch.stack(out, dim=0),)
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from mpl_toolkits.axes_grid1 import ImageGrid
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from .magictex import *
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class CreateMagicMask:
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@ -2737,6 +2733,7 @@ class CreateMagicMask:
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}
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def createmagicmask(self, frames, transitions, depth, distortion, seed, frame_width, frame_height):
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from .magictex import coordinate_grid, random_transform, magic
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rng = np.random.default_rng(seed)
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out = []
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coords = coordinate_grid((frame_width, frame_height))
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@ -2804,9 +2801,10 @@ class BboxToInt:
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RETURN_TYPES = ("INT","INT","INT","INT","INT","INT",)
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RETURN_NAMES = ("x_min","y_min","width","height", "center_x","center_y",)
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FUNCTION = "bboxtoint"
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CATEGORY = "KJNodes/masking"
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DESCRIPTION = """
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Returns selected index from bounding box list as integers.
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"""
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def bboxtoint(self, bboxes, index):
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x_min, y_min, width, height = bboxes[index]
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center_x = int(x_min + width / 2)
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@ -2879,8 +2877,10 @@ class SplitBboxes:
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RETURN_TYPES = ("BBOX","BBOX",)
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RETURN_NAMES = ("bboxes_a","bboxes_b",)
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FUNCTION = "splitbbox"
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CATEGORY = "KJNodes/masking"
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DESCRIPTION = """
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Splits the specified bbox list at the given index into two lists.
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"""
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def splitbbox(self, bboxes, index):
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bboxes_a = bboxes[:index] # Sub-list from the start of bboxes up to (but not including) the index
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@ -2901,6 +2901,10 @@ class ImageGrabPIL:
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RETURN_NAMES = ("image",)
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FUNCTION = "screencap"
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CATEGORY = "KJNodes/experimental"
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DESCRIPTION = """
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Captures an area specified by screen coordinates.
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Can be used for realtime diffusion with autoqueue.
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"""
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@classmethod
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def INPUT_TYPES(s):
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@ -2946,32 +2950,13 @@ class DummyLatentOut:
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FUNCTION = "dummy"
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CATEGORY = "KJNodes/misc"
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OUTPUT_NODE = True
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DESCRIPTION = """
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Does nothing, used to trigger generic workflow output.
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A way to get previews in the UI without saving anything to disk.
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"""
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def dummy(self, latent):
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return (latent,)
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class NormalizeLatent:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"latent": ("LATENT",),
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}
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}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "normalize"
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CATEGORY = "KJNodes/noise"
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OUTPUT_NODE = True
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def normalize(self, latent):
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samples = latent["samples"]
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samples /= samples.std()
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out = latent.copy()
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out["samples"] = samples
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return (out,)
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class FlipSigmasAdjusted:
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@classmethod
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@ -3094,7 +3079,7 @@ class AddLabel:
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width = image.shape[2]
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if font == "TTNorms-Black.otf":
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font_path = os.path.join(script_dir, "fonts", "TTNorms-Black.otf")
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font_path = os.path.join(script_directory, "fonts", "TTNorms-Black.otf")
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else:
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font_path = folder_paths.get_full_path("kjnodes_fonts", font)
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label_image = Image.new("RGB", (width, height), label_color)
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@ -3134,7 +3119,6 @@ class SoundReactive:
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RETURN_TYPES = ("FLOAT","INT",)
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RETURN_NAMES =("sound_level", "sound_level_int",)
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FUNCTION = "react"
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CATEGORY = "KJNodes/audio"
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def react(self, sound_level, start_range_hz, end_range_hz, smoothing_factor, multiplier, normalize):
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@ -3167,8 +3151,10 @@ class GenerateNoise:
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "generatenoise"
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CATEGORY = "KJNodes/noise"
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DESCRIPTION = """
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Generates noise for injection or to be used as empty latents on samplers with add_noise off.
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"""
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def generatenoise(self, batch_size, width, height, seed, multiplier, constant_batch_noise, normalize, sigmas=None, model=None):
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@ -3229,9 +3215,7 @@ class StableZero123_BatchSchedule:
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RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
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RETURN_NAMES = ("positive", "negative", "latent")
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FUNCTION = "encode"
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CATEGORY = "KJNodes/experimental"
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def encode(self, clip_vision, init_image, vae, width, height, batch_size, azimuth_points_string, elevation_points_string, interpolation):
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@ -3530,8 +3514,8 @@ class NormalizedAmplitudeToMask:
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RETURN_TYPES = ("MASK",)
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FUNCTION = "convert"
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DESCRIPTION = """
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Works as a bridge to the AudioScheduler -nodes:
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https://github.com/a1lazydog/ComfyUI-AudioScheduler
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Works as a bridge to the AudioScheduler -nodes:
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https://github.com/a1lazydog/ComfyUI-AudioScheduler
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Creates masks based on the normalized amplitude.
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"""
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@ -3603,6 +3587,11 @@ class OffsetMaskByNormalizedAmplitude:
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RETURN_NAMES = ("mask",)
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FUNCTION = "offset"
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CATEGORY = "KJNodes/audio"
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DESCRIPTION = """
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Works as a bridge to the AudioScheduler -nodes:
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https://github.com/a1lazydog/ComfyUI-AudioScheduler
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Offsets masks based on the normalized amplitude.
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"""
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def offset(self, mask, x, y, angle_multiplier, rotate, normalized_amp):
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@ -3644,6 +3633,11 @@ class ImageTransformByNormalizedAmplitude:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "amptransform"
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CATEGORY = "KJNodes/audio"
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DESCRIPTION = """
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Works as a bridge to the AudioScheduler -nodes:
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https://github.com/a1lazydog/ComfyUI-AudioScheduler
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Transforms image based on the normalized amplitude.
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"""
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def amptransform(self, image, normalized_amp, zoom_scale, cumulative, x_offset, y_offset):
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# Ensure normalized_amp is an array and within the range [0, 1]
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@ -3739,8 +3733,8 @@ def interpolate_coordinates(coordinates_dict, batch_size):
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return interpolated
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def interpolate_coordinates_with_curves(coordinates_dict, batch_size):
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from scipy.interpolate import CubicSpline
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sorted_coords = sorted(coordinates_dict.items())
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x_coords, y_coords = zip(*[coord for index, coord in sorted_coords])
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@ -3872,10 +3866,9 @@ class ImageUpscaleWithModelBatched:
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DESCRIPTION = """
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Same as ComfyUI native model upscaling node, but allows setting sub-batches for reduced VRAM usage.
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"""
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def upscale(self, upscale_model, images, per_batch):
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device = comfy.model_management.get_torch_device()
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device = model_management.get_torch_device()
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upscale_model.to(device)
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in_img = images.movedim(-1,-3).to(device)
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@ -3911,14 +3904,12 @@ Normalize the images to be in the range [-1, 1]
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"""
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def normalize(self,images):
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images = images * 2.0 - 1.0
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return (images,)
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import comfy.sample
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from nodes import CLIPTextEncode
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folder_paths.add_model_folder_path("intristic_loras", os.path.join(script_dir, "intristic_loras"))
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folder_paths.add_model_folder_path("intristic_loras", os.path.join(script_directory, "intristic_loras"))
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class Intrinsic_lora_sampling:
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def __init__(self):
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@ -4120,10 +4111,10 @@ https://huggingface.co/roborovski/superprompt-v1
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"""
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def process(self, instruction_prompt, prompt, max_new_tokens):
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device = comfy.model_management.get_torch_device()
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device = model_management.get_torch_device()
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from transformers import T5Tokenizer, T5ForConditionalGeneration
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checkpoint_path = os.path.join(script_dir, "models","superprompt-v1")
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checkpoint_path = os.path.join(script_directory, "models","superprompt-v1")
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tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small", legacy=False)
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model = T5ForConditionalGeneration.from_pretrained(checkpoint_path, device_map=device)
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@ -4339,7 +4330,6 @@ NODE_CLASS_MAPPINGS = {
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"SplitBboxes": SplitBboxes,
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"ImageGrabPIL": ImageGrabPIL,
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"DummyLatentOut": DummyLatentOut,
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"NormalizeLatent": NormalizeLatent,
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"FlipSigmasAdjusted": FlipSigmasAdjusted,
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"InjectNoiseToLatent": InjectNoiseToLatent,
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"AddLabel": AddLabel,
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||||
@ -4379,9 +4369,10 @@ NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"ColorToMask": "ColorToMask",
|
||||
"CreateGradientMask": "CreateGradientMask",
|
||||
"CreateTextMask" : "CreateTextMask",
|
||||
"CreateFadeMask" : "CreateFadeMask",
|
||||
"CreateFadeMask" : "CreateFadeMask (Deprecated)",
|
||||
"CreateFadeMaskAdvanced" : "CreateFadeMaskAdvanced",
|
||||
"CreateFluidMask" : "CreateFluidMask",
|
||||
"CreateAudioMask" : "CreateAudioMask (Deprecated)",
|
||||
"VRAM_Debug" : "VRAM Debug",
|
||||
"CrossFadeImages": "CrossFadeImages",
|
||||
"SomethingToString": "SomethingToString",
|
||||
@ -4413,7 +4404,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"SplitBboxes": "SplitBboxes",
|
||||
"ImageGrabPIL": "ImageGrabPIL",
|
||||
"DummyLatentOut": "DummyLatentOut",
|
||||
"NormalizeLatent": "NormalizeLatent",
|
||||
"FlipSigmasAdjusted": "FlipSigmasAdjusted",
|
||||
"InjectNoiseToLatent": "InjectNoiseToLatent",
|
||||
"AddLabel": "AddLabel",
|
||||
|
||||
@ -52,6 +52,9 @@ app.registerExtension({
|
||||
if (nodeData?.category?.startsWith("KJNodes")) {
|
||||
addDocumentation(nodeData, nodeType);
|
||||
}
|
||||
if (nodeData?.category?.includes("SUPIR")) {
|
||||
addDocumentation(nodeData, nodeType);
|
||||
}
|
||||
} catch (error) {
|
||||
console.error("Error in registering KJNodes.HelpPopup", error);
|
||||
}
|
||||
@ -128,7 +131,6 @@ const create_documentation_stylesheet = () => {
|
||||
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) {
|
||||
@ -210,7 +212,6 @@ const create_documentation_stylesheet = () => {
|
||||
|
||||
document.addEventListener('mouseup', function () {
|
||||
isResizing = false
|
||||
hasResized = true
|
||||
})
|
||||
|
||||
document.body.appendChild(docElement)
|
||||
@ -241,12 +242,6 @@ const create_documentation_stylesheet = () => {
|
||||
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);
|
||||
}
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user