mirror of
https://github.com/tusharbhutt/Endless-Nodes.git
synced 2026-03-23 13:22:11 -03:00
574 lines
20 KiB
Python
574 lines
20 KiB
Python
"""
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@author: BiffMunky
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@title: 🌌 An Endless Sea of Stars Nodes 🌌
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@nickname: 🌌 Endless Nodes 🌌
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@description: A small set of nodes I created for various numerical and text inputs.
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"""
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# Version 0.23 - Aesthetic Scoring TYpe 1 addeded
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#0.22 Unreleased - intro'd asestheic score
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#0.21 unreleased -- trying for display nodes
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#0.20 sorted categories of nodes
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#--------------------------------------
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# Endless Sea of Stars Custom Node Collection
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#https://github.com/tusharbhutt/Endless-Nodes
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#
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#
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#import torch
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from PIL import Image
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from PIL.PngImagePlugin import PngInfo
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from os.path import join
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from warnings import filterwarnings
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import clip
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import datetime
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import io
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import json
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import math
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import numpy as np
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import os
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import pytorch_lightning as pl
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import re
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import sys
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import statistics
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import torch
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import torch.nn as nn
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sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
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import comfy.sd
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import comfy.utils
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import folder_paths
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import typing as tg
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#--------------------------------------
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#Six Text Input Node for selection
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class EndlessNode_SixTextInputSwitch:
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def __init__(self):
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pass
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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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"Input": ("INT", {"default": 1, "min": 1, "max": 6, "step": 1, "display": "slider"}),
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#I like the slider idea, it's better for a touch screen
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"text1": ("STRING", {"forceInput": True}),
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},
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"optional": {
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"text2": ("STRING", {"forceInput": True}),
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"text3": ("STRING", {"forceInput": True}),
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"text4": ("STRING", {"forceInput": True}),
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"text5": ("STRING", {"forceInput": True}),
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"text6": ("STRING", {"forceInput": True}),
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}
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("Output",)
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FUNCTION = "six_text_switch"
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CATEGORY = "Endless 🌌/Switches"
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def six_text_switch(self, Input, text1=None,text2=None,text3=None,text4=None,text5=None,text6=None):
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if Input == 1:
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return (text1,)
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elif Input == 2:
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return (text2,)
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elif Input == 3:
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return (text3,)
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elif Input == 4:
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return (text4,)
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elif Input == 5:
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return (text5,)
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else:
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return (text6,)
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#Eight Text Input Node for selection (needed more slots, what can I say)
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class EndlessNode_EightTextInputSwitch:
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def __init__(self):
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pass
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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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"Input": ("INT", {"default": 1, "min": 1, "max": 8, "step": 1, "display": "slider"}),
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#I like the slider idea, it's better for a touch screen
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"text1": ("STRING", {"forceInput": True}),
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},
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"optional": {
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"text2": ("STRING", {"forceInput": True}),
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"text3": ("STRING", {"forceInput": True}),
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"text4": ("STRING", {"forceInput": True}),
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"text5": ("STRING", {"forceInput": True}),
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"text6": ("STRING", {"forceInput": True}),
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"text7": ("STRING", {"forceInput": True}),
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"text8": ("STRING", {"forceInput": True}),
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}
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("Output",)
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FUNCTION = "eight_text_switch"
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CATEGORY = "Endless 🌌/Switches"
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def eight_text_switch(self,Input,text1=None,text2=None,text3=None,text4=None,text5=None,text6=None,text7=None,text8=None,):
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if Input == 1:
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return (text1,)
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elif Input == 2:
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return (text2,)
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elif Input == 3:
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return (text3,)
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elif Input == 4:
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return (text4,)
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elif Input == 5:
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return (text5,)
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elif Input == 6:
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return (text6,)
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elif Input == 7:
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return (text7,)
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else:
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return (text8,)
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#--------------------------------------
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##Six Integer Input and Output via connectors
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class EndlessNode_SixIntIOSwitch:
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def __init__(self):
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pass
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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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"INT1": ("INT", {"forceInput": True}),
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},
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"optional": {
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"INT2": ("INT", {"forceInput": True}),
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"INT3": ("INT", {"forceInput": True}),
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"INT4": ("INT", {"forceInput": True}),
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"INT5": ("INT", {"forceInput": True}),
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"INT6": ("INT", {"forceInput": True}),
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}
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}
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RETURN_TYPES = ("INT","INT","INT","INT","INT","INT",)
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RETURN_NAMES = ("INT1","INT2","INT3","INT4","INT5","INT6",)
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FUNCTION = "six_intIO_switch"
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CATEGORY = "Endless 🌌/Switches"
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def six_intIO_switch(self, Input, INT1=0, INT2=0, INT3=0, INT4=0, INT5=0, INT6=0):
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if Input == 1:
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return (INT1, )
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elif Input == 2:
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return (INT2, )
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elif Input == 3:
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return (INT3, )
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elif Input == 4:
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return (INT4, )
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elif Input == 5:
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return (INT5, )
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else:
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return (INT6, )
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#--------------------------------------
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##Six Integer Input and Output by Widget
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class EndlessNode_SixIntIOWidget:
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def __init__(self):
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pass
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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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"int1": ("INT", {"default": 0,}),
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},
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"optional": {
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"int2": ("INT", {"default": 0,}),
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"int3": ("INT", {"default": 0,}),
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"int4": ("INT", {"default": 0,}),
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"int5": ("INT", {"default": 0,}),
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"int6": ("INT", {"default": 0,}),
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}
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}
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RETURN_TYPES = ("INT","INT","INT","INT","INT","INT",)
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RETURN_NAMES = ("INT1","INT2","INT3","INT4","INT5","INT6",)
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FUNCTION = "six_int_widget"
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CATEGORY = "Endless 🌌/Switches"
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def six_int_widget(self,int1,int2,int3,int4,int5,int6):
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return(int1,int2,int3,int4,int5,int6)
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#Text Encode Combo Box with prompt
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class EndlessNode_XLParameterizerPrompt:
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def __init__(self):
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pass
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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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"base_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"base_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"base_crop_w": ("INT", {"default": 0, "min": 0, "max": 1024, "step": 8}),
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"base_crop_h": ("INT", {"default": 0, "min": 0, "max": 1024, "step": 8}),
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"base_target_w": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"base_target_h": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"refiner_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"refiner_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"refiner_ascore": ("FLOAT", {"default": 6, "min": 0, "max": 0xffffffffffffffff}),
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},
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"optional": {
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"endlessG": ("STRING", {"default": "TEXT_G,acts as main prompt and connects to refiner text input", "multiline": True}),
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"endlessL": ("STRING", {"default": "TEXT_L, acts as supporting prompt", "multiline": True}),
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}
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}
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RETURN_TYPES = ("INT","INT","INT","INT","INT","INT","INT","INT","FLOAT","STRING","STRING",)
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RETURN_NAMES = ("Base Width","Base Height","Base Cropped Width","Base Cropped Height","Base Target Width","Base Target Height","Refiner Width","Refiner Height","Refiner Aesthetic Score","Text_G/Refiner Prompt","Text_L Prompt",)
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FUNCTION = "ParameterizerPrompt"
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CATEGORY = "Endless 🌌/Parameters"
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def ParameterizerPrompt(self,base_width,base_height,base_crop_w,base_crop_h,base_target_w,base_target_h,refiner_width,refiner_height,refiner_ascore,endlessG,endlessL):
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return(base_width,base_height,base_crop_w,base_crop_h,base_target_w,base_target_h,refiner_width,refiner_height,refiner_ascore,endlessG,endlessL)
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# CLIP tect encodee box without prompt
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class EndlessNode_XLParameterizer:
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def __init__(self):
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pass
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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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"base_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"base_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"base_crop_w": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 16}),
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"base_crop_h": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 16}),
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"base_target_w": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"base_target_h": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"refiner_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"refiner_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"refiner_ascore": ("FLOAT", {"default": 6, "min": 0, "max": 0xffffffffffffffff}),
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}
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}
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RETURN_TYPES = ("INT","INT","INT","INT","INT","INT","INT","INT","FLOAT",)
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RETURN_NAMES = ("Base Width","Base Height","Base Cropped Width","Base Cropped Height","Base Target Width","Base Target Height","Refiner Width","Refiner Height","Refiner Aesthetic Score",)
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FUNCTION = "Parameterizer"
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CATEGORY = "Endless 🌌/Parameters"
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def Parameterizer(self,base_width,base_height,base_crop_w,base_crop_h,base_target_w,base_target_h,refiner_width,refiner_height,refiner_ascore):
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return(base_width,base_height,base_crop_w,base_crop_h,base_target_w,base_target_h,refiner_width,refiner_height,refiner_ascore)
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#Text Encode Combo Box with prompt
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class EndlessNode_ComboXLParameterizerPrompt:
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def __init__(self):
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pass
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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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"base_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"base_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"base_crop_w": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 16}),
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"base_crop_h": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 16}),
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"base_target_w": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"base_target_h": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"refiner_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"refiner_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"refiner_pascore": ("FLOAT", {"default": 6.5, "min": 0, "max": 0xffffffffffffffff}),
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"refiner_nascore": ("FLOAT", {"default": 2.5, "min": 0, "max": 0xffffffffffffffff}),
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},
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"optional": {
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"PendlessG": ("STRING", {"default": "Positive TEXT_G,acts as main prompt and connects to refiner text input", "multiline": True}),
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"PendlessL": ("STRING", {"default": "Positive TEXT_L, acts as supporting prompt", "multiline": True}),
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"NendlessG": ("STRING", {"default": "Negative TEXT_G, acts as main prompt and connects to refiner text input", "multiline": True}),
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"NendlessL": ("STRING", {"default": "Negative TEXT_L, acts as supporting prompt", "multiline": True}),
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}
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}
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RETURN_TYPES = ("INT","INT","INT","INT","INT","INT","INT","INT","FLOAT","FLOAT","STRING","STRING", "STRING","STRING",)
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RETURN_NAMES = ("Base Width","Base Height","Base Cropped Width","Base Cropped Height","Base Target Width","Base Target Height","Refiner Width","Refiner Height","Positive Refiner Aesthetic Score","Negative Refiner Aesthetic Score","Positive Text_G and Refiner Text Prompt","Postive Text_L Prompt","Negative Text_G and Refiner Text Prompt","Negative Text_L Prompt",)
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FUNCTION = "ComboParameterizerPrompt"
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CATEGORY = "Endless 🌌/Parameters"
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def ComboParameterizerPrompt(self,base_width,base_height,base_crop_w,base_crop_h,base_target_w,base_target_h,refiner_width,refiner_height,refiner_pascore,refiner_nascore,PendlessG,PendlessL,NendlessG,NendlessL):
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return(base_width,base_height,base_crop_w,base_crop_h,base_target_w,base_target_h,refiner_width,refiner_height,refiner_pascore,refiner_nascore,PendlessG,PendlessL,NendlessG,NendlessL)
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# CLIP text encode box without prompt, COMBO that allows one box for both pos/neg parameters to be fed to CLIP text, with separate POS/NEG Aestheticscore
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class EndlessNode_ComboXLParameterizer:
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def __init__(self):
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pass
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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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"base_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"base_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"base_crop_w": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 16}),
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"base_crop_h": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 16}),
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"base_target_w": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"base_target_h": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"refiner_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"refiner_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
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"refiner_pascore": ("FLOAT", {"default": 6.5, "min": 0, "max": 0xffffffffffffffff}),
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"refiner_nascore": ("FLOAT", {"default": 2.5, "min": 0, "max": 0xffffffffffffffff}),
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}
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}
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RETURN_TYPES = ("INT","INT","INT","INT","INT","INT","INT","INT","FLOAT","FLOAT")
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RETURN_NAMES = ("Base Width","Base Height","Base Cropped Width","Base Cropped Height","Base Target Width","Base Target Height","Refiner Width","Refiner Height","Positive Refiner Aesthetic Score","Negative Refiner Aesthetic Score",)
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FUNCTION = "ComboParameterizer"
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CATEGORY = "Endless 🌌/Parameters"
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def ComboParameterizer(self,base_width,base_height,base_crop_w,base_crop_h,base_target_w,base_target_h,refiner_width,refiner_height,refiner_pascore, refiner_nascore):
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return(base_width,base_height,base_crop_w,base_crop_h,base_target_w,base_target_h,refiner_width,refiner_height,refiner_pascore, refiner_nascore)
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#--------------------------------------
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## Aesthetic Scoring Type One
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folder_paths.folder_names_and_paths["aesthetic"] = ([os.path.join(folder_paths.models_dir,"aesthetic")], folder_paths.supported_pt_extensions)
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class MLP(pl.LightningModule):
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def __init__(self, input_size, xcol='emb', ycol='avg_rating'):
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super().__init__()
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self.input_size = input_size
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self.xcol = xcol
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self.ycol = ycol
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self.layers = nn.Sequential(
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nn.Linear(self.input_size, 1024),
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#nn.ReLU(),
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nn.Dropout(0.2),
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nn.Linear(1024, 128),
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#nn.ReLU(),
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nn.Dropout(0.2),
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nn.Linear(128, 64),
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#nn.ReLU(),
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nn.Dropout(0.1),
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nn.Linear(64, 16),
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#nn.ReLU(),
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nn.Linear(16, 1)
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)
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def forward(self, x):
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return self.layers(x)
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def training_step(self, batch, batch_idx):
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x = batch[self.xcol]
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y = batch[self.ycol].reshape(-1, 1)
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x_hat = self.layers(x)
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loss = F.mse_loss(x_hat, y)
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return loss
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def validation_step(self, batch, batch_idx):
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x = batch[self.xcol]
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y = batch[self.ycol].reshape(-1, 1)
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x_hat = self.layers(x)
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loss = F.mse_loss(x_hat, y)
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return loss
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def configure_optimizers(self):
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optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
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return optimizer
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def normalized(a, axis=-1, order=2):
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import numpy as np # pylint: disable=import-outside-toplevel
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l2 = np.atleast_1d(np.linalg.norm(a, order, axis))
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l2[l2 == 0] = 1
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return a / np.expand_dims(l2, axis)
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class EndlessNode_Scoring:
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def __init__(self):
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pass
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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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"model_name": (folder_paths.get_filename_list("aesthetic"), ),
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"image": ("IMAGE",),
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}
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}
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RETURN_TYPES = ("NUM",)
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FUNCTION = "calc_score"
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CATEGORY = "Endless 🌌/Scoring"
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def calc_score(self, model_name, image):
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m_path = folder_paths.folder_names_and_paths["aesthetic"][0]
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m_path2 = os.path.join(m_path[0], model_name)
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model = MLP(768) # CLIP embedding dim is 768 for CLIP ViT L 14
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s = torch.load(m_path2)
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model.load_state_dict(s)
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model.to("cuda")
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model.eval()
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device = "cuda"
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model2, preprocess = clip.load("ViT-L/14", device=device) # RN50x64
|
|
tensor_image = image[0]
|
|
img = (tensor_image * 255).to(torch.uint8).numpy()
|
|
pil_image = Image.fromarray(img, mode='RGB')
|
|
image2 = preprocess(pil_image).unsqueeze(0).to(device)
|
|
with torch.no_grad():
|
|
image_features = model2.encode_image(image2)
|
|
im_emb_arr = normalized(image_features.cpu().detach().numpy() )
|
|
prediction = model(torch.from_numpy(im_emb_arr).to(device).type(torch.cuda.FloatTensor))
|
|
final_prediction = round(float(prediction[0]), 2)
|
|
del model
|
|
return (final_prediction,)
|
|
|
|
|
|
##test of image saver ##
|
|
|
|
|
|
class EndlessNode_ImageSaver:
|
|
def __init__(self):
|
|
self.output_dir = folder_paths.get_output_directory()
|
|
self.type = "output"
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"images": ("IMAGE",),
|
|
"filename_prefix": ("STRING", {"default": "ComfyUI"}),
|
|
"subfolder": ("STRING", {"default": None}), # Add subfolder input
|
|
},
|
|
"hidden": {
|
|
"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ()
|
|
FUNCTION = "save_images"
|
|
|
|
OUTPUT_NODE = True
|
|
|
|
CATEGORY = "Endless 🌌/IO"
|
|
|
|
def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None, subfolder=None):
|
|
|
|
# Replace illegal characters in the filename prefix with dashes
|
|
filename_prefix = re.sub(r'[<>:"\/\\|?*]', '-', filename_prefix)
|
|
|
|
# Get the current date in Y-m-d format
|
|
today = datetime.datetime.now().strftime("%Y-%m-%d")
|
|
|
|
# If a custom subfolder is provided, use it; otherwise, use the date
|
|
if subfolder is not None:
|
|
full_output_folder = os.path.join(self.output_dir, subfolder)
|
|
else:
|
|
full_output_folder = os.path.join(self.output_dir, today)
|
|
|
|
# Create the subfolder if it doesn't exist
|
|
os.makedirs(full_output_folder, exist_ok=True)
|
|
|
|
counter = self.get_next_number(full_output_folder)
|
|
|
|
results = list()
|
|
for image in images:
|
|
i = 255. * image.cpu().numpy()
|
|
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
|
|
|
|
metadata = PngInfo()
|
|
if prompt is not None:
|
|
metadata.add_text("prompt", json.dumps(prompt))
|
|
if extra_pnginfo is not None:
|
|
for x in extra_pnginfo:
|
|
metadata.add_text(x, json.dumps(extra_pnginfo[x]))
|
|
|
|
file = f"{counter:05}-c-{filename_prefix}.png"
|
|
img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4)
|
|
results.append({
|
|
"filename": file,
|
|
"subfolder": full_output_folder,
|
|
"type": self.type
|
|
})
|
|
|
|
# Check if a user-specified folder for TEXT files is provided
|
|
if subfolder is not None:
|
|
# Create the full path for the TEXT file using the same name as the PNG
|
|
text_file = os.path.join(subfolder, f"{counter:05}-c-{filename_prefix}.txt")
|
|
else:
|
|
# Use the same folder as the image if no custom subfolder is provided
|
|
text_file = os.path.join(full_output_folder, f"{counter:05}-c-{filename_prefix}.txt")
|
|
|
|
# Save some example text content to the TEXT file (you can modify this)
|
|
with open(text_file, 'w') as text:
|
|
text.write("This is an example text file.")
|
|
|
|
counter += 1
|
|
|
|
return {"ui": {"images": results}}
|
|
|
|
def get_next_number(self, directory):
|
|
files = os.listdir(directory)
|
|
highest_number = 0
|
|
for file in files:
|
|
parts = file.split('-')
|
|
try:
|
|
num = int(parts[0])
|
|
if num > highest_number:
|
|
highest_number = num
|
|
except ValueError:
|
|
# If it's not a number, skip this file
|
|
continue
|
|
|
|
# Return the next number
|
|
return highest_number + 1
|
|
|
|
|
|
#--------------------------------------
|
|
# CREDITS
|
|
#
|
|
# Comfyroll Custom Nodes for the overall node code layout, coding snippets, and inspiration for the text input and number switches
|
|
#
|
|
# https://github.com/RockOfFire/ComfyUI_Comfyroll_CustomNode
|
|
#
|
|
# WLSH Nodes for some coding for the Integer Widget
|
|
# https://github.com/wallish77/wlsh_nodes
|
|
#
|
|
# ComfyUI Interface for the basic ideas of what nodes I wanted
|
|
#
|
|
# https://github.com/comfyanonymous/ComfyUI
|
|
#
|
|
# ComfyUI-Strimmlarns-Aesthetic-Score for the original coding for Aesthetic Scoring Type One
|
|
#
|
|
# https://github.com/strimmlarn/ComfyUI-Strimmlarns-Aesthetic-Score
|
|
#
|
|
# The scorer uses the MLP class code from Christoph Schuhmann
|
|
#
|
|
#https://github.com/christophschuhmann/improved-aesthetic-predictor
|
|
#
|
|
#--------------------------------------
|