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Added aesthetic scorer and recategorized nodes

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tusharbhutt
2023-09-24 11:14:15 -06:00
committed by GitHub
parent 61f3e8ffd5
commit 5a1ba39288
5 changed files with 511 additions and 262 deletions
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22
__init__.py
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@@ -1,22 +1,28 @@
"""
@author: Endless Sea of Stars
@title: Endless Nodes
@nickname: Endless Nodes
@author: BiffMunky
@title: 🌌 An Endless Sea of Stars Nodes 🌌
@nickname: 🌌 Endless Nodes 🌌
@description: A small set of nodes I created for various numerical and text inputs.
"""
from .endless_nodes import *
NODE_CLASS_MAPPINGS = {
"Endless Nodes Six Input Text Switch": EndlessNode_SixTextInputSwitch,
"Endless Nodes Eight Input Text Switch": EndlessNode_EightTextInputSwitch,
"Endless Nodes Six Integer IO Switch": EndlessNode_SixIntIOSwitch,
"Endless Nodes Six Integer IO Widget": EndlessNode_SixIntIOWidget,
"Endless Nodes Six Input Text Switch": EndlessNode_SixTextInputSwitch,
"Endless Nodes Eight Input Text Switch": EndlessNode_EightTextInputSwitch,
"Endless Nodes Six Integer IO Switch": EndlessNode_SixIntIOSwitch,
"Endless Nodes Six Integer IO Widget": EndlessNode_SixIntIOWidget,
"Endless Nodes Parameterizer": EndlessNode_XLParameterizer,
"Endless Nodes Parameterizer & Prompts": EndlessNode_XLParameterizerPrompt,
"Endless Nodes Combo Parameterizer": EndlessNode_ComboXLParameterizer,
"Endless Nodes Combo Parameterizer & Prompts": EndlessNode_ComboXLParameterizerPrompt,
"Endless Nodes Image Save with Text File": EndlessNode_ImageSaver,
# "Endless Nodes Display String": EndlessNode_DisplayString,
# "Endless Nodes Display Number": EndlessNode_DisplayNumber,
# "Endless Nodes Display Integer": EndlessNode_DisplayInt,
# "Endless Nodes Display Float": EndlessNode_DisplayFloat,
"Endless Nodes Aesthetic Scoring": EndlessNode_Scoring,
}
__all__ = ['NODE_CLASS_MAPPINGS']
print("\033[34mEndelssSeaofStars Custom Nodes: \033[92mLoaded\033[0m")
print("\033[36m 🌌 An Endless Sea of Stars Custom Nodes 🌌 V0.23 \033[34m: \033[92mLoaded\033[0m")

7
changlelog.md
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@@ -1,7 +1,12 @@
Sep 24/23: 0.23 - Rework Aesthetic Score model and integrate it into single node to display score, added a requirements file
Sep 23/23: 0.22 - Unreleased, convert ImageReward output to base ten score
Sep 22/23: 0.21 - Unreleased, recategorized nodes into submenus, added some vanity coding to the node names, changed the ComfyUI manager header text
Sep 21/23: 0.20 - Unreleased, skeleton for save image
Sep 21/23: 0.19 - Unreleased attempt for basic display nodes
Sep 20/23: 0.16 - Added Eight Input Number String
Sep 18/23: 0.15 - Added Combo Parameterizers to reduce number of nodes, allows for common resolution parameters to go to both pos/neg CLIP encode and adds separate pos/neg aesthetic score. Also has a version with pos/neg prompts
Sep 18/23: 0.13 - Fixed Typo, added Paramaterizer with Prompt (unreleased to GitHub)
Sep 18/23: 0.12 - Added "Parameterizer", allows for parameters to be added to CLIP Encode
Sep 15/23: 0.1 - Added Six Input Number Widget, first release to GitHub
Sep 15/23: 0.10 - Added Six Input Number Widget, first release to GitHub
Sep 12/23: 0.05 - Added Six Input Number String
Sep 8/23: Version 0.0 - Basic Flow for Six Input Text Switch

708
endless_nodes.py
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@@ -1,11 +1,15 @@
"""
@author: Endless Sea of Stars
@title: Endless Nodes
@nickname: Endless Nodes
@author: BiffMunky
@title: 🌌 An Endless Sea of Stars Nodes 🌌
@nickname: 🌌 Endless Nodes 🌌
@description: A small set of nodes I created for various numerical and text inputs.
"""
# Version 0.16 - Add Eight Text Input
# Version 0.23 - Aesthetic Scoring TYpe 1 addeded
#0.22 Unreleased - intro'd asestheic score
#0.21 unreleased -- trying for display nodes
#0.20 sorted categories of nodes
#--------------------------------------
# Endless Sea of Stars Custom Node Collection
#https://github.com/tusharbhutt/Endless-Nodes
@@ -13,15 +17,28 @@
#
#import torch
#import numpy as np
import os
import sys
from PIL import Image
from PIL.PngImagePlugin import PngInfo
from os.path import join
from warnings import filterwarnings
import clip
import datetime
import io
import json
import math
import numpy as np
import os
import pytorch_lightning as pl
import re
import sys
import statistics
import torch
import torch.nn as nn
sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
#heh, probably not needed
import comfy.sd
import comfy.utils
@@ -34,318 +51,501 @@ import typing as tg
class EndlessNode_SixTextInputSwitch:
def __init__(self):
pass
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"Input": ("INT", {"default": 1, "min": 1, "max": 6, "step": 1, "display": "slider"}),
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"Input": ("INT", {"default": 1, "min": 1, "max": 6, "step": 1, "display": "slider"}),
#I like the slider idea, it's better for a touch screen
"text1": ("STRING", {"forceInput": True}),
},
"optional": {
"text2": ("STRING", {"forceInput": True}),
"text3": ("STRING", {"forceInput": True}),
"text4": ("STRING", {"forceInput": True}),
"text5": ("STRING", {"forceInput": True}),
"text6": ("STRING", {"forceInput": True}),
}
}
"text1": ("STRING", {"forceInput": True}),
},
"optional": {
"text2": ("STRING", {"forceInput": True}),
"text3": ("STRING", {"forceInput": True}),
"text4": ("STRING", {"forceInput": True}),
"text5": ("STRING", {"forceInput": True}),
"text6": ("STRING", {"forceInput": True}),
}
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("Output",)
FUNCTION = "six_text_switch"
CATEGORY = "Endless"
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("Output",)
FUNCTION = "six_text_switch"
CATEGORY = "Endless 🌌/Switches"
def six_text_switch(self, Input, text1=None,text2=None,text3=None,text4=None,text5=None,text6=None):
def six_text_switch(self, Input, text1=None,text2=None,text3=None,text4=None,text5=None,text6=None):
if Input == 1:
return (text1,)
elif Input == 2:
return (text2,)
elif Input == 3:
return (text3,)
elif Input == 4:
return (text4,)
elif Input == 5:
return (text5,)
else:
return (text6,)
if Input == 1:
return (text1,)
elif Input == 2:
return (text2,)
elif Input == 3:
return (text3,)
elif Input == 4:
return (text4,)
elif Input == 5:
return (text5,)
else:
return (text6,)
#Eight Text Input Node for selection (needed more slots, what can I say)
class EndlessNode_EightTextInputSwitch:
def __init__(self):
pass
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"Input": ("INT", {"default": 1, "min": 1, "max": 8, "step": 1, "display": "slider"}),
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"Input": ("INT", {"default": 1, "min": 1, "max": 8, "step": 1, "display": "slider"}),
#I like the slider idea, it's better for a touch screen
"text1": ("STRING", {"forceInput": True}),
},
"optional": {
"text2": ("STRING", {"forceInput": True}),
"text3": ("STRING", {"forceInput": True}),
"text4": ("STRING", {"forceInput": True}),
"text5": ("STRING", {"forceInput": True}),
"text6": ("STRING", {"forceInput": True}),
"text7": ("STRING", {"forceInput": True}),
"text8": ("STRING", {"forceInput": True}),
}
}
"text1": ("STRING", {"forceInput": True}),
},
"optional": {
"text2": ("STRING", {"forceInput": True}),
"text3": ("STRING", {"forceInput": True}),
"text4": ("STRING", {"forceInput": True}),
"text5": ("STRING", {"forceInput": True}),
"text6": ("STRING", {"forceInput": True}),
"text7": ("STRING", {"forceInput": True}),
"text8": ("STRING", {"forceInput": True}),
}
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("Output",)
FUNCTION = "eight_text_switch"
CATEGORY = "Endless"
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("Output",)
FUNCTION = "eight_text_switch"
CATEGORY = "Endless 🌌/Switches"
def eight_text_switch(self,Input,text1=None,text2=None,text3=None,text4=None,text5=None,text6=None,text7=None,text8=None,):
def eight_text_switch(self,Input,text1=None,text2=None,text3=None,text4=None,text5=None,text6=None,text7=None,text8=None,):
if Input == 1:
return (text1,)
elif Input == 2:
return (text2,)
elif Input == 3:
return (text3,)
elif Input == 4:
return (text4,)
elif Input == 5:
return (text5,)
elif Input == 6:
return (text6,)
elif Input == 7:
return (text7,)
else:
return (text8,)
if Input == 1:
return (text1,)
elif Input == 2:
return (text2,)
elif Input == 3:
return (text3,)
elif Input == 4:
return (text4,)
elif Input == 5:
return (text5,)
elif Input == 6:
return (text6,)
elif Input == 7:
return (text7,)
else:
return (text8,)
#--------------------------------------
##Six Integer Input and Output via connectors
class EndlessNode_SixIntIOSwitch:
def __init__(self):
pass
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"INT1": ("INT", {"forceInput": True}),
},
"optional": {
"INT2": ("INT", {"forceInput": True}),
"INT3": ("INT", {"forceInput": True}),
"INT4": ("INT", {"forceInput": True}),
"INT5": ("INT", {"forceInput": True}),
"INT6": ("INT", {"forceInput": True}),
}
}
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"INT1": ("INT", {"forceInput": True}),
},
"optional": {
"INT2": ("INT", {"forceInput": True}),
"INT3": ("INT", {"forceInput": True}),
"INT4": ("INT", {"forceInput": True}),
"INT5": ("INT", {"forceInput": True}),
"INT6": ("INT", {"forceInput": True}),
}
}
RETURN_TYPES = ("INT","INT","INT","INT","INT","INT",)
RETURN_NAMES = ("INT1","INT2","INT3","INT4","INT5","INT6",)
FUNCTION = "six_intIO_switch"
CATEGORY = "Endless"
RETURN_TYPES = ("INT","INT","INT","INT","INT","INT",)
RETURN_NAMES = ("INT1","INT2","INT3","INT4","INT5","INT6",)
FUNCTION = "six_intIO_switch"
CATEGORY = "Endless 🌌/Switches"
def six_intIO_switch(self, Input, INT1=0, INT2=0, INT3=0, INT4=0, INT5=0, INT6=0):
def six_intIO_switch(self, Input, INT1=0, INT2=0, INT3=0, INT4=0, INT5=0, INT6=0):
if Input == 1:
return (INT1, )
elif Input == 2:
return (INT2, )
elif Input == 3:
return (INT3, )
elif Input == 4:
return (INT4, )
elif Input == 5:
return (INT5, )
else:
return (INT6, )
if Input == 1:
return (INT1, )
elif Input == 2:
return (INT2, )
elif Input == 3:
return (INT3, )
elif Input == 4:
return (INT4, )
elif Input == 5:
return (INT5, )
else:
return (INT6, )
#--------------------------------------
##Six Integer Input and Output by Widget
class EndlessNode_SixIntIOWidget:
def __init__(self):
pass
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"int1": ("INT", {"default": 0,}),
},
"optional": {
"int2": ("INT", {"default": 0,}),
"int3": ("INT", {"default": 0,}),
"int4": ("INT", {"default": 0,}),
"int5": ("INT", {"default": 0,}),
"int6": ("INT", {"default": 0,}),
}
}
RETURN_TYPES = ("INT","INT","INT","INT","INT","INT",)
RETURN_NAMES = ("INT1","INT2","INT3","INT4","INT5","INT6",)
FUNCTION = "six_int_widget"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"int1": ("INT", {"default": 0,}),
},
"optional": {
"int2": ("INT", {"default": 0,}),
"int3": ("INT", {"default": 0,}),
"int4": ("INT", {"default": 0,}),
"int5": ("INT", {"default": 0,}),
"int6": ("INT", {"default": 0,}),
}
}
RETURN_TYPES = ("INT","INT","INT","INT","INT","INT",)
RETURN_NAMES = ("INT1","INT2","INT3","INT4","INT5","INT6",)
FUNCTION = "six_int_widget"
CATEGORY="Endless"
CATEGORY = "Endless 🌌/Switches"
def six_int_widget(self,int1,int2,int3,int4,int5,int6):
return(int1,int2,int3,int4,int5,int6)
def six_int_widget(self,int1,int2,int3,int4,int5,int6):
return(int1,int2,int3,int4,int5,int6)
#Text Encode Combo Box with prompt
class EndlessNode_XLParameterizerPrompt:
def __init__(self):
pass
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"base_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_crop_w": ("INT", {"default": 0, "min": 0, "max": 1024, "step": 8}),
"base_crop_h": ("INT", {"default": 0, "min": 0, "max": 1024, "step": 8}),
"base_target_w": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_target_h": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_ascore": ("FLOAT", {"default": 6, "min": 0, "max": 0xffffffffffffffff}),
},
"optional": {
"endlessG": ("STRING", {"default": "TEXT_G,acts as main prompt and connects to refiner text input", "multiline": True}),
"endlessL": ("STRING", {"default": "TEXT_L, acts as supporting prompt", "multiline": True}),
}
}
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"base_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_crop_w": ("INT", {"default": 0, "min": 0, "max": 1024, "step": 8}),
"base_crop_h": ("INT", {"default": 0, "min": 0, "max": 1024, "step": 8}),
"base_target_w": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_target_h": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_ascore": ("FLOAT", {"default": 6, "min": 0, "max": 0xffffffffffffffff}),
},
"optional": {
"endlessG": ("STRING", {"default": "TEXT_G,acts as main prompt and connects to refiner text input", "multiline": True}),
"endlessL": ("STRING", {"default": "TEXT_L, acts as supporting prompt", "multiline": True}),
}
}
RETURN_TYPES = ("INT","INT","INT","INT","INT","INT","INT","INT","FLOAT","STRING","STRING",)
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",)
RETURN_TYPES = ("INT","INT","INT","INT","INT","INT","INT","INT","FLOAT","STRING","STRING",)
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",)
FUNCTION = "ParameterizerPrompt"
FUNCTION = "ParameterizerPrompt"
CATEGORY="Endless"
CATEGORY = "Endless 🌌/Parameters"
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):
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)
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):
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)
# CLIP tect encodee box without prompt
class EndlessNode_XLParameterizer:
def __init__(self):
pass
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"base_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_crop_w": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 16}),
"base_crop_h": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 16}),
"base_target_w": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_target_h": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_ascore": ("FLOAT", {"default": 6, "min": 0, "max": 0xffffffffffffffff}),
}
}
RETURN_TYPES = ("INT","INT","INT","INT","INT","INT","INT","INT","FLOAT",)
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",)
FUNCTION = "Parameterizer"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"base_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_crop_w": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 16}),
"base_crop_h": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 16}),
"base_target_w": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_target_h": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_ascore": ("FLOAT", {"default": 6, "min": 0, "max": 0xffffffffffffffff}),
}
}
RETURN_TYPES = ("INT","INT","INT","INT","INT","INT","INT","INT","FLOAT",)
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",)
FUNCTION = "Parameterizer"
CATEGORY="Endless"
CATEGORY = "Endless 🌌/Parameters"
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):
return(base_width,base_height,base_crop_w,base_crop_h,base_target_w,base_target_h,refiner_width,refiner_height,refiner_ascore)
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):
return(base_width,base_height,base_crop_w,base_crop_h,base_target_w,base_target_h,refiner_width,refiner_height,refiner_ascore)
#Text Encode Combo Box with prompt
class EndlessNode_ComboXLParameterizerPrompt:
def __init__(self):
pass
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"base_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_crop_w": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 16}),
"base_crop_h": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 16}),
"base_target_w": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_target_h": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_pascore": ("FLOAT", {"default": 6.5, "min": 0, "max": 0xffffffffffffffff}),
"refiner_nascore": ("FLOAT", {"default": 2.5, "min": 0, "max": 0xffffffffffffffff}),
},
"optional": {
"PendlessG": ("STRING", {"default": "Positive TEXT_G,acts as main prompt and connects to refiner text input", "multiline": True}),
"PendlessL": ("STRING", {"default": "Positive TEXT_L, acts as supporting prompt", "multiline": True}),
"NendlessG": ("STRING", {"default": "Negative TEXT_G, acts as main prompt and connects to refiner text input", "multiline": True}),
"NendlessL": ("STRING", {"default": "Negative TEXT_L, acts as supporting prompt", "multiline": True}),
}
}
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"base_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_crop_w": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 16}),
"base_crop_h": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 16}),
"base_target_w": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_target_h": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_pascore": ("FLOAT", {"default": 6.5, "min": 0, "max": 0xffffffffffffffff}),
"refiner_nascore": ("FLOAT", {"default": 2.5, "min": 0, "max": 0xffffffffffffffff}),
},
"optional": {
"PendlessG": ("STRING", {"default": "Positive TEXT_G,acts as main prompt and connects to refiner text input", "multiline": True}),
"PendlessL": ("STRING", {"default": "Positive TEXT_L, acts as supporting prompt", "multiline": True}),
"NendlessG": ("STRING", {"default": "Negative TEXT_G, acts as main prompt and connects to refiner text input", "multiline": True}),
"NendlessL": ("STRING", {"default": "Negative TEXT_L, acts as supporting prompt", "multiline": True}),
}
}
RETURN_TYPES = ("INT","INT","INT","INT","INT","INT","INT","INT","FLOAT","FLOAT","STRING","STRING", "STRING","STRING",)
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",)
RETURN_TYPES = ("INT","INT","INT","INT","INT","INT","INT","INT","FLOAT","FLOAT","STRING","STRING", "STRING","STRING",)
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",)
FUNCTION = "ComboParameterizerPrompt"
FUNCTION = "ComboParameterizerPrompt"
CATEGORY="Endless"
CATEGORY = "Endless 🌌/Parameters"
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):
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)
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):
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)
# 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
class EndlessNode_ComboXLParameterizer:
def __init__(self):
pass
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"base_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_crop_w": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 16}),
"base_crop_h": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 16}),
"base_target_w": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_target_h": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_pascore": ("FLOAT", {"default": 6.5, "min": 0, "max": 0xffffffffffffffff}),
"refiner_nascore": ("FLOAT", {"default": 2.5, "min": 0, "max": 0xffffffffffffffff}),
}
}
RETURN_TYPES = ("INT","INT","INT","INT","INT","INT","INT","INT","FLOAT","FLOAT")
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",)
FUNCTION = "ComboParameterizer"
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"base_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_crop_w": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 16}),
"base_crop_h": ("INT", {"default": 0, "min": 0, "max": 8192, "step": 16}),
"base_target_w": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"base_target_h": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_width": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_height": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 16}),
"refiner_pascore": ("FLOAT", {"default": 6.5, "min": 0, "max": 0xffffffffffffffff}),
"refiner_nascore": ("FLOAT", {"default": 2.5, "min": 0, "max": 0xffffffffffffffff}),
}
}
RETURN_TYPES = ("INT","INT","INT","INT","INT","INT","INT","INT","FLOAT","FLOAT")
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",)
FUNCTION = "ComboParameterizer"
CATEGORY="Endless"
CATEGORY = "Endless 🌌/Parameters"
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):
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)
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):
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)
# FUTURE NODE IDEAS
#--------------------------------------
## Aesthetic Scoring Type One
folder_paths.folder_names_and_paths["aesthetic"] = ([os.path.join(folder_paths.models_dir,"aesthetic")], folder_paths.supported_pt_extensions)
class MLP(pl.LightningModule):
def __init__(self, input_size, xcol='emb', ycol='avg_rating'):
super().__init__()
self.input_size = input_size
self.xcol = xcol
self.ycol = ycol
self.layers = nn.Sequential(
nn.Linear(self.input_size, 1024),
#nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(1024, 128),
#nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(128, 64),
#nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(64, 16),
#nn.ReLU(),
nn.Linear(16, 1)
)
def forward(self, x):
return self.layers(x)
def training_step(self, batch, batch_idx):
x = batch[self.xcol]
y = batch[self.ycol].reshape(-1, 1)
x_hat = self.layers(x)
loss = F.mse_loss(x_hat, y)
return loss
def validation_step(self, batch, batch_idx):
x = batch[self.xcol]
y = batch[self.ycol].reshape(-1, 1)
x_hat = self.layers(x)
loss = F.mse_loss(x_hat, y)
return loss
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
return optimizer
def normalized(a, axis=-1, order=2):
import numpy as np # pylint: disable=import-outside-toplevel
l2 = np.atleast_1d(np.linalg.norm(a, order, axis))
l2[l2 == 0] = 1
return a / np.expand_dims(l2, axis)
class EndlessNode_Scoring:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model_name": (folder_paths.get_filename_list("aesthetic"), ),
"image": ("IMAGE",),
}
}
RETURN_TYPES = ("NUM",)
FUNCTION = "calc_score"
CATEGORY = "Endless 🌌/Scoring"
def calc_score(self, model_name, image):
m_path = folder_paths.folder_names_and_paths["aesthetic"][0]
m_path2 = os.path.join(m_path[0], model_name)
model = MLP(768) # CLIP embedding dim is 768 for CLIP ViT L 14
s = torch.load(m_path2)
model.load_state_dict(s)
model.to("cuda")
model.eval()
device = "cuda"
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))
# Dimension Flipper: flip dimensions
# WhatThePrompt: add prompt data to sidecar text file
# ???
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
#--------------------------------------
@@ -360,4 +560,14 @@ class EndlessNode_ComboXLParameterizer:
#
# ComfyUI Interface for the basic ideas of what nodes I wanted
#
# https://github.com/comfyanonymous/ComfyUI
# 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
#
#--------------------------------------

34
readme.md
View File

@@ -5,9 +5,14 @@ When using the [ComfyUI](https://github.com/comfyanonymous/ComfyUI) interface fo
Rightly or wrongly, I am pretending to teach myself a bit of Python to get some nodes up and running to do what I'd like. There are no promises that these nodes will work for you or that I will maintain them. Feel free to do with them as you wish, according to the license model.
**UPDATE: Sep 24, 2023**
+ Took the node from https://github.com/strimmlarn that does aesthetic scoring and re-purposed it
**UPDATE: Sep 20, 2023**
+ Added and eight input number switch because I needed it
+ Added an eight input number switch because I needed it
**UPDATE: Sep 18, 2023**
@@ -32,6 +37,8 @@ In Windows, you can then right-click to start a command prompt and type:
You can also get the nodes via the [ComfyUI Manager](https://github.com/ltdrdata/ComfyUI-Manager)
**NOTE: Requires CLIP and Pytorch-Lightning for the Aesthetic Scorer! I've added them in the requirement file but if it doesn't work, yo will need to download manually**
## Node List
### Six Text Input Switch
@@ -88,6 +95,23 @@ After making the Parameterizer, I realized having two separate ones for both the
![comboparameterizerprompt](./img/comboparameterizerprompt.png)
## Aesthetic Scoring Output ##
This node will output a predicted aestheic score as a number and dispaly it with the appropriate node (e.g., rgthree's "Any" node). I took the node from https://github.com/strimmlarn that does aesthetic scoring and repurposed it so that it is simpler and outputs the score as a number. I combined the model loader and score calculator into one, and removed the Aesthetic Score Sorter.
![aestheticone](./img/aestheticone.png)
You can load a number of scoring models, I use the "chadscorer" model found here:
https://github.com/grexzen/SD-Chad/blob/main/chadscorer.pth
As for the original node from strimmlarn, please refer to this GitHub if you would like to examine it:
https://github.com/strimmlarn/ComfyUI-Strimmlarns-Aesthetic-Score
The scorer adds about 7-10 seconds to a workflow on my Nvidia 3060 12 GB card, your mileage may vary
## Usage License and Restrictions
See GPL Licensing V3 for usage. You may modify this code as long as you keep the credits for this repository and for those noted in the credit section below. **YOU ARE EXPRESSLY FORBIDDEN FROM USING THIS NODE TO CREATE ANY IMAGES OR ARTWORK THAT VIOLATES THE STABLE DIFFUSION USAGE NOTES [HERE](https://huggingface.co/stabilityai/stable-diffusion-2#misuse-malicious-use-and-out-of-scope-use) AND [HERE](https://huggingface.co/stabilityai/stable-diffusion-2#misuse-and-malicious-use).**
@@ -104,8 +128,10 @@ These nodes may or may not be maintained. They work on my system, but may not o
[Comfyroll Custom Nodes](https://github.com/RockOfFire/ComfyUI_Comfyroll_CustomNode) for the overall node code layout, coding snippets, and inspiration for the text input and number switches
[WLSH Nodes](https://github.com/wallish77/wlsh_nodes) for some coding for the Integer Widget
[ComfyUI](https://github.com/comfyanonymous/ComfyUI) Interface for the basic ideas of what nodes I wanted
[ComfyUI](https://github.com/comfyanonymous/ComfyUI) Interface for the basic ideas of what nodes I wanted
[ComfyUI-Strimmlarns-Aesthetic-Score](https://github.com/strimmlarn/ComfyUI-Strimmlarns-Aesthetic-Score) for the original coding for Aesthetic Scoring Type One
The orginal scorer, and therefore my derivative too, use the [MLP class code](https://github.com/christophschuhmann/improved-aesthetic-predictor) from Christoph Schuhmann

2
requirements.txt Normal file
View File

@@ -0,0 +1,2 @@
clip
pytorch-lightning