mirror of
https://github.com/tusharbhutt/Endless-Nodes.git
synced 2026-03-21 20:42:12 -03:00
Removed scorers, reverted file saver
This commit is contained in:
tusharbhutt
@@ -12,16 +12,20 @@ When using the [ComfyUI](https://github.com/comfyanonymous/ComfyUI) interface fo
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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. Yes, I am using ChatGPT, and yes I am a glutton for punishment. 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.
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**UPDATE: Oct 7, 2023**
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+ ***REMOVED THE AESTHERIC SCORERS, TOO MANY PEOPLE CAN'T GET CLIP LOADED. WILL REVISIT AFTER VACATION***
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**UPDATE: Oct 4, 2023**
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+ Squished the bugs in the numeric to numerical and string nodes. Special thanks to [chrisgoringe](https://github.com/chrisgoringe) for some vital insight into correcting messy commas in the tuples for the converter nodes, much appreciated!
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+ Squished the bugs in the numeric to numeric and string nodes. Special thanks to [chrisgoringe](https://github.com/chrisgoringe) for some vital insight into correcting messy commas in the tuples for the converter nodes, much appreciated!
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+ Added nodes to convert from string to numeric values, with some basic error checking.
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**UPDATE: Oct 3, 2023**
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+ Added an Image Saver that can place JSON files ***in separate folders***
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+ Added nodes to convert from one numeric type to another, and to string.
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+ Added nodes to convert from one numeric type to another, and to string. **Still in beta**
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**UPDATE: Sep 24, 2023**
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120
__init__.py
120
__init__.py
@@ -7,69 +7,71 @@
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from .endless_nodes import *
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NODE_CLASS_MAPPINGS = {
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"Endless Nodes Six Input Text Switch": EndlessNode_SixTextInputSwitch,
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"Endless Nodes Eight Input Text Switch": EndlessNode_EightTextInputSwitch,
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"Endless Nodes Six Integer IO Switch": EndlessNode_SixIntIOSwitch,
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"Endless Nodes Six Integer IO Widget": EndlessNode_SixIntIOWidget,
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"Endless Nodes Parameterizer": EndlessNode_XLParameterizer,
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"Endless Nodes Parameterizer & Prompts": EndlessNode_XLParameterizerPrompt,
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"Endless Nodes Combo Parameterizer": EndlessNode_ComboXLParameterizer,
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"Endless Nodes Combo Parameterizer & Prompts": EndlessNode_ComboXLParameterizerPrompt,
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"Endless Nodes Image Saver with JSON": EndlessNode_ImageSaver,
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"Endless Nodes Aesthetic Scoring": EndlessNode_Scoring,
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# "Endless Nodes Aesthetic Scoring Auto": EndlessNode_ScoringAutoScore,
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"Endless Nodes Image Reward": EndlessNode_ImageReward,
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# "Endless Nodes Image Reward Auto": EndlessNode_ImageRewardAutoScore,
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"Endless Nodes Float to Integer": EndlessNode_FloattoInt,
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"Endless Nodes Float to Number": EndlessNode_FloattoNum,
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"Endless Nodes Float to String": EndlessNode_FloattoString,
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"Endless Nodes Float to X": EndlessNode_FloattoX,
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"Endless Nodes Integer to Float": EndlessNode_InttoFloat,
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"Endless Nodes Integer to Number": EndlessNode_InttoNum,
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"Endless Nodes Integer to String": EndlessNode_InttoString,
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"Endless Nodes Integer to X": EndlessNode_InttoX,
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"Endless Nodes Number to Float": EndlessNode_NumtoFloat,
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"Endless Nodes Number to Integer": EndlessNode_NumtoInt,
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"Endless Nodes Number to String": EndlessNode_NumtoString,
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"Endless Nodes Number to X": EndlessNode_NumtoX,
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"Endless Nodes String to Float": EndlessNode_StringtoFloat,
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"Endless Nodes String to Integer": EndlessNode_StringtoInt,
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"Endless Nodes String to Num": EndlessNode_StringtoNum,
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"Endless Nodes String to X": EndlessNode_StringtoX,
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"ESS Six Input Text Switch": EndlessNode_SixTextInputSwitch,
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"ESS Eight Input Text Switch": EndlessNode_EightTextInputSwitch,
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"ESS Six Input Random": EndlessNode_SixTextRandomSwitch,
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"ESS Eight Input Random": EndlessNode_EightTextRandomSwitch,
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"ESS Six Integer IO Switch": EndlessNode_SixIntIOSwitch,
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"ESS Six Integer IO Widget": EndlessNode_SixIntIOWidget,
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"ESS Parameterizer": EndlessNode_XLParameterizer,
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"ESS Parameterizer & Prompts": EndlessNode_XLParameterizerPrompt,
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"ESS Combo Parameterizer": EndlessNode_ComboXLParameterizer,
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"ESS Combo Parameterizer & Prompts": EndlessNode_ComboXLParameterizerPrompt,
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"ESS Image Saver with JSON": EndlessNode_ImageSaver,
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# "ESS Aesthetic Scoring": EndlessNode_Scoring,
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# "ESS Aesthetic Scoring Auto": EndlessNode_ScoringAutoScore,
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# "ESS Image Reward": EndlessNode_ImageReward,
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# "ESS Image Reward Auto": EndlessNode_ImageRewardAutoScore,
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"ESS Float to Integer": EndlessNode_FloattoInt,
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"ESS Float to Number": EndlessNode_FloattoNum,
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"ESS Float to String": EndlessNode_FloattoString,
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"ESS Float to X": EndlessNode_FloattoX,
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"ESS Integer to Float": EndlessNode_InttoFloat,
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"ESS Integer to Number": EndlessNode_InttoNum,
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"ESS Integer to String": EndlessNode_InttoString,
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"ESS Integer to X": EndlessNode_InttoX,
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"ESS Number to Float": EndlessNode_NumtoFloat,
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"ESS Number to Integer": EndlessNode_NumtoInt,
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"ESS Number to String": EndlessNode_NumtoString,
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"ESS Number to X": EndlessNode_NumtoX,
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"ESS String to Float": EndlessNode_StringtoFloat,
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"ESS String to Integer": EndlessNode_StringtoInt,
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"ESS String to Num": EndlessNode_StringtoNum,
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"ESS String to X": EndlessNode_StringtoX,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"Endless Nodes Six Input Text Switch" : "Endless 🌊✨ Six Input Text Switch",
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"Endless Nodes Eight Input Text Switch": "Endless 🌊✨ Eight Input Text Switch",
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"Endless Nodes Six Integer IO Switch": "Endless 🌊✨ Six Integer IO Switch",
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"Endless Nodes Six Integer IO Widget": "Endless 🌊✨ Six Integer IO Widget",
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"Endless Nodes Parameterizer": "Endless 🌊✨ Parameterizer",
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"Endless Nodes Parameterizer & Prompts": "Endless 🌊✨ Parameterizer & Prompts",
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"Endless Nodes Combo Parameterizer": "Endless 🌊✨ Combo Parameterizer",
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"Endless Nodes Combo Parameterizer & Prompts": "Endless 🌊✨ Combo Parameterizer & Prompts",
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"Endless Nodes Image Saver with JSON": "Endless 🌊✨ Image Saver with JSON",
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"Endless Nodes Aesthetic Scoring": "Endless 🌊✨ Aesthetic Scoring",
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# "Endless Nodes Aesthetic Scoring Auto": "Endless 🌊✨ Aesthetic Scoring Auto",
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"Endless Nodes Image Reward": "Endless 🌊✨ Image Reward",
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# "Endless Nodes Image Reward Auto": "Endless 🌊✨ Image Reward Auto",
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"Endless Nodes Float to Integer": "Endless 🌊✨ Float to Integer",
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"Endless Nodes Float to Number": "Endless 🌊✨ Float to Number",
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"Endless Nodes Float to String": "Endless 🌊✨ Float to String",
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"Endless Nodes Float to X": "Endless 🌊✨ Float to X",
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"Endless Nodes Integer to Float": "Endless 🌊✨ Integer to Float",
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"Endless Nodes Integer to Number": "Endless 🌊✨ Integer to Number",
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"Endless Nodes Integer to String": "Endless 🌊✨ Integer to String",
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"Endless Nodes Integer to X": "Endless 🌊✨ Integer to X",
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"Endless Nodes Number to Float": "Endless 🌊✨ Number to Float",
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"Endless Nodes Number to Integer": "Endless 🌊✨ Number to Integer",
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"Endless Nodes Number to String": "Endless 🌊✨ Number to String",
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"Endless Nodes Number to X": "Endless 🌊✨ Number to X",
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"Endless Nodes String to Float": "Endless 🌊✨ String to Float",
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"Endless Nodes String to Integer": "Endless 🌊✨ String to Integer",
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"Endless Nodes String to Num": "Endless 🌊✨ String to Num",
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"Endless Nodes String to X": "Endless 🌊✨ String to X",
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"ESS Six Input Text Switch" : "♾️🌊✨ Six Input Text Switch",
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"ESS Eight Input Text Switch": "♾️🌊✨ Eight Input Text Switch",
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"ESS Six Integer IO Switch": "♾️🌊✨ Six Integer IO Switch",
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"ESS Six Integer IO Widget": "♾️🌊✨ Six Integer IO Widget",
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"ESS Parameterizer": "♾️🌊✨ Parameterizer",
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"ESS Parameterizer & Prompts": "♾️🌊✨ Parameterizer & Prompts",
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"ESS Combo Parameterizer": "♾️🌊✨ Combo Parameterizer",
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"ESS Combo Parameterizer & Prompts": "♾️🌊✨ Combo Parameterizer & Prompts",
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"ESS Image Saver with JSON": "♾️🌊✨ Image Saver with JSON",
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"ESS Aesthetic Scoring": "♾️🌊✨ Aesthetic Scoring",
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# "ESS Aesthetic Scoring Auto": "♾️🌊✨ Aesthetic Scoring Auto",
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"ESS Image Reward": "♾️🌊✨ Image Reward",
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# "ESS Image Reward Auto": "♾️🌊✨ Image Reward Auto",
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"ESS Float to Integer": "♾️🌊✨ Float to Integer",
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"ESS Float to Number": "♾️🌊✨ Float to Number",
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"ESS Float to String": "♾️🌊✨ Float to String",
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"ESS Float to X": "♾️🌊✨ Float to X",
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"ESS Integer to Float": "♾️🌊✨ Integer to Float",
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"ESS Integer to Number": "♾️🌊✨ Integer to Number",
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"ESS Integer to String": "♾️🌊✨ Integer to String",
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"ESS Integer to X": "♾️🌊✨ Integer to X",
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"ESS Number to Float": "♾️🌊✨ Number to Float",
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"ESS Number to Integer": "♾️🌊✨ Number to Integer",
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"ESS Number to String": "♾️🌊✨ Number to String",
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"ESS Number to X": "♾️🌊✨ Number to X",
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"ESS String to Float": "♾️🌊✨ String to Float",
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"ESS String to Integer": "♾️🌊✨ String to Integer",
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"ESS String to Num": "♾️🌊✨ String to Num",
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"ESS String to X": "♾️🌊✨ String to X",
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}
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#Heh, doesn't seem to work :(
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__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
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@@ -1,4 +1,5 @@
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Oct 05/23, V0.32: Set rules for image saver so paths + filename length do not exceed 248 (leaves room for extension)
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Oct 07/23, V0.33: Removed Aesthetic Scorer and ImageReward until I can figure out why the CLIP module isn't working for a few people
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Oct 05/23, V0.32: (UNRELEASED)Set rules for image saver so paths + filename length do not exceed 248 (leaves room for extension)
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Oct 04/23, V0.31: Release of V0.28 functionality (int, float, num to X), added String to X, code cleanup, vanity node renaming and recategorization
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Oct 04/23, V0.30: Squished bugs in the various X to X nodes
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Oct 03/23, V0.29: Save Image module added, saves images and JSON to separate folder if requested
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437
endless_nodes.py
437
endless_nodes.py
@@ -10,6 +10,9 @@
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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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# Oct 06/23, V0.35: Reverted the Image Saver module as I had inadvertently removed the ability to add date and time to the filenames
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# Oct 05/23, V0.34: Renamed nodes to make them shorter and easier to search for, breaks names of previous workflows though
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# Oct 05/23, V0.33: Added random text input choice for six and eight nodes inputs
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# Oct 05/23, V0.32: Set rules for image saver so paths + filename length do not exceed 248 (leaves room for extension)
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# Oct 04/23, V0.31: Release of V0.28 functionality (int, float, num to X), added String to X, code cleanup, vanity node renaming and recategorization
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# Oct 04/23, V0.30: Squished bugs in the various X to X nodes
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@@ -41,8 +44,8 @@ from PIL.PngImagePlugin import PngInfo
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from colorama import init, Fore, Back, Style
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from os.path import join
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from warnings import filterwarnings
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import ImageReward as RM
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import clip
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# import ImageReward as RM
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# import clip
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import colorama
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import datetime
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import folder_paths
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@@ -59,7 +62,7 @@ import sys
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import time
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import torch
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import torch.nn as nn
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# import random
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sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
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@@ -103,7 +106,7 @@ class EndlessNode_SixTextInputSwitch:
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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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CATEGORY = "Endless 🌊✨/Switches/Fixed"
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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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@@ -151,7 +154,7 @@ class EndlessNode_EightTextInputSwitch:
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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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CATEGORY = "Endless 🌊✨/Switches/Fixed"
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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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@@ -199,7 +202,7 @@ class EndlessNode_SixIntIOSwitch:
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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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CATEGORY = "Endless 🌊✨/Switches/Fixed"
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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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@@ -241,7 +244,7 @@ class EndlessNode_SixIntIOWidget:
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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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CATEGORY = "Endless 🌊✨/Switches/Fixed"
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def six_int_widget(self,int1,int2,int3,int4,int5,int6):
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@@ -394,96 +397,96 @@ class EndlessNode_ComboXLParameterizer:
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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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# IMAGE SCORING BLOCK #
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# IMAGE SCORING BLOCK # IT'S DEAD JIM, WHY CAN'T WE HAVE NICE THINGS?
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#----------------------------------------------
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# Aesthetic Scoring Node
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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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# 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):
|
||||
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 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)
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||||
# 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
|
||||
# class EndlessNode_Scoring:
|
||||
# def __init__(self):
|
||||
# pass
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"model_name": (folder_paths.get_filename_list("aesthetic"), {"multiline": False, "default": "chadscorer.pth"}),
|
||||
"image": ("IMAGE",),
|
||||
}
|
||||
}
|
||||
# @classmethod
|
||||
# def INPUT_TYPES(cls):
|
||||
# return {
|
||||
# "required": {
|
||||
# "model_name": (folder_paths.get_filename_list("aesthetic"), {"multiline": False, "default": "chadscorer.pth"}),
|
||||
# "image": ("IMAGE",),
|
||||
# }
|
||||
# }
|
||||
|
||||
RETURN_TYPES = ("NUMBER","IMAGE")
|
||||
FUNCTION = "calc_score"
|
||||
CATEGORY = "Endless 🌊✨/Scoring"
|
||||
# RETURN_TYPES = ("NUMBER","IMAGE")
|
||||
# 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,)
|
||||
# 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,)
|
||||
|
||||
# #---------------------------------------------- NOT WORKING, NEED TO LOOK AT IT
|
||||
# # Aesthetic Scoring Node with Scoring passed to image
|
||||
@@ -533,86 +536,86 @@ class EndlessNode_Scoring:
|
||||
#----------------------------------------------
|
||||
# Image Reward Scoring
|
||||
|
||||
class EndlessNode_ImageReward:
|
||||
def __init__(self):
|
||||
self.model = None
|
||||
# class EndlessNode_ImageReward:
|
||||
# def __init__(self):
|
||||
# self.model = None
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"model": ("STRING", {"multiline": False, "default": "ImageReward-v1.0"}),
|
||||
"prompt": ("STRING", {"multiline": True, "forceInput": True}),
|
||||
"images": ("IMAGE",),
|
||||
},
|
||||
}
|
||||
# @classmethod
|
||||
# def INPUT_TYPES(cls):
|
||||
# return {
|
||||
# "required": {
|
||||
# "model": ("STRING", {"multiline": False, "default": "ImageReward-v1.0"}),
|
||||
# "prompt": ("STRING", {"multiline": True, "forceInput": True}),
|
||||
# "images": ("IMAGE",),
|
||||
# },
|
||||
# }
|
||||
|
||||
RETURN_TYPES = ("FLOAT", "STRING", "FLOAT", "STRING")
|
||||
RETURN_NAMES = ("SCORE_FLOAT", "SCORE_STRING", "VALUE_FLOAT", "VALUE_STRING")
|
||||
# RETURN_TYPES = ("FLOAT", "STRING", "FLOAT", "STRING")
|
||||
# RETURN_NAMES = ("SCORE_FLOAT", "SCORE_STRING", "VALUE_FLOAT", "VALUE_STRING")
|
||||
|
||||
CATEGORY = "Endless 🌊✨/Scoring"
|
||||
# CATEGORY = "Endless 🌊✨/Scoring"
|
||||
|
||||
FUNCTION = "process_images"
|
||||
# FUNCTION = "process_images"
|
||||
|
||||
def process_images(self, model, prompt, images,): #rounded):
|
||||
if self.model is None:
|
||||
self.model = RM.load(model)
|
||||
# def process_images(self, model, prompt, images,): #rounded):
|
||||
# if self.model is None:
|
||||
# self.model = RM.load(model)
|
||||
|
||||
score = 0.0
|
||||
for image in images:
|
||||
# convert to PIL image
|
||||
i = 255.0 * image.cpu().numpy()
|
||||
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
|
||||
score += self.model.score(prompt, [img])
|
||||
score /= len(images)
|
||||
# assume std dev follows normal distribution curve
|
||||
valuescale = 0.5 * (1 + math.erf(score / math.sqrt(2))) * 10 # *10 to get a value between -10
|
||||
return (score, str(score), valuescale, str(valuescale))
|
||||
# score = 0.0
|
||||
# for image in images:
|
||||
# # convert to PIL image
|
||||
# i = 255.0 * image.cpu().numpy()
|
||||
# img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
|
||||
# score += self.model.score(prompt, [img])
|
||||
# score /= len(images)
|
||||
# # assume std dev follows normal distribution curve
|
||||
# valuescale = 0.5 * (1 + math.erf(score / math.sqrt(2))) * 10 # *10 to get a value between -10
|
||||
# return (score, str(score), valuescale, str(valuescale))
|
||||
|
||||
|
||||
#---------------------------------------------- NOT WORKING, NEED TO LOOK AT
|
||||
# Image Reward Scoring with score passed to image
|
||||
# #---------------------------------------------- NOT WORKING, NEED TO LOOK AT
|
||||
# # Image Reward Scoring with score passed to image
|
||||
|
||||
class EndlessNode_ImageRewardAutoScore:
|
||||
def __init__(self):
|
||||
self.model = None
|
||||
# class EndlessNode_ImageRewardAutoScore:
|
||||
# def __init__(self):
|
||||
# self.model = None
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"model": ("STRING", {"multiline": False, "default": "ImageReward-v1.0"}),
|
||||
"prompt": ("STRING", {"multiline": True, "forceInput": True}),
|
||||
"images": ("IMAGE",),
|
||||
},
|
||||
}
|
||||
# @classmethod
|
||||
# def INPUT_TYPES(cls):
|
||||
# return {
|
||||
# "required": {
|
||||
# "model": ("STRING", {"multiline": False, "default": "ImageReward-v1.0"}),
|
||||
# "prompt": ("STRING", {"multiline": True, "forceInput": True}),
|
||||
# "images": ("IMAGE",),
|
||||
# },
|
||||
# }
|
||||
|
||||
RETURN_TYPES = ("FLOAT", "STRING", "FLOAT", "STRING", "IMAGE")
|
||||
RETURN_NAMES = ("SCORE_FLOAT", "SCORE_STRING", "VALUE_FLOAT", "VALUE_STRING", "TO_IMAGE")
|
||||
OUTPUT_NODE = True
|
||||
# RETURN_TYPES = ("FLOAT", "STRING", "FLOAT", "STRING", "IMAGE")
|
||||
# RETURN_NAMES = ("SCORE_FLOAT", "SCORE_STRING", "VALUE_FLOAT", "VALUE_STRING", "TO_IMAGE")
|
||||
# OUTPUT_NODE = True
|
||||
|
||||
CATEGORY = "Endless 🌊✨/Scoring"
|
||||
# CATEGORY = "Endless 🌊✨/Scoring"
|
||||
|
||||
FUNCTION = "score_meta"
|
||||
# FUNCTION = "score_meta"
|
||||
|
||||
def score_meta(self, model, prompt, images):
|
||||
if self.model is None:
|
||||
self.model = RM.load(model)
|
||||
# def score_meta(self, model, prompt, images):
|
||||
# if self.model is None:
|
||||
# self.model = RM.load(model)
|
||||
|
||||
# Scoring part
|
||||
score = 0.0
|
||||
for image in images:
|
||||
i = 255.0 * image.cpu().numpy()
|
||||
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
|
||||
score += self.model.score(prompt, [img])
|
||||
score /= len(images)
|
||||
valuescale = 0.5 * (1 + math.erf(score / math.sqrt(2))) * 10
|
||||
# # Scoring part
|
||||
# score = 0.0
|
||||
# for image in images:
|
||||
# i = 255.0 * image.cpu().numpy()
|
||||
# img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
|
||||
# score += self.model.score(prompt, [img])
|
||||
# score /= len(images)
|
||||
# valuescale = 0.5 * (1 + math.erf(score / math.sqrt(2))) * 10
|
||||
|
||||
# Metadata part
|
||||
extra_pnginfo = {"SCORE": str(score)}
|
||||
# # Metadata part
|
||||
# extra_pnginfo = {"SCORE": str(score)}
|
||||
|
||||
# Returning both the score and the modified image
|
||||
return (score, str(score), valuescale, str(valuescale), images)
|
||||
# # Returning both the score and the modified image
|
||||
# return (score, str(score), valuescale, str(valuescale), images)
|
||||
|
||||
# ______________________________________________________________________________________________________________________________________________________________
|
||||
# IMAGE SAVERS BLOCK #
|
||||
@@ -642,52 +645,12 @@ class EndlessNode_ImageSaver:
|
||||
"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ()
|
||||
FUNCTION = "save_images"
|
||||
OUTPUT_NODE = True
|
||||
CATEGORY = "Endless 🌊✨/IO"
|
||||
|
||||
def generate_filenames(self, filename_prefix, delimiter, counter, filename_number_padding, filename_number_start, img_extension, img_folder, json_folder):
|
||||
# Determine the maximum allowed length for the complete path and subtract eight for the filename extensino
|
||||
max_path_length = 248
|
||||
|
||||
# Calculate the length of the parts that are constant (excluding the variable counter part)
|
||||
constant_part_length = len(filename_prefix) + len(delimiter) + len(img_extension) + 1 # +1 for the counter itself
|
||||
|
||||
# Calculate the available space for the variable counter part
|
||||
available_space = max_path_length - constant_part_length - len(str(filename_number_padding))
|
||||
|
||||
# Truncate the filename_prefix if it exceeds the available space
|
||||
if len(filename_prefix) > available_space:
|
||||
filename_prefix = filename_prefix[:available_space]
|
||||
|
||||
# Generate the filename with the truncated prefix
|
||||
if filename_number_start == 'true':
|
||||
img_file = f"{filename_prefix}{delimiter}{counter:0{filename_number_padding}}{img_extension}"
|
||||
json_file = f"{filename_prefix}{delimiter}{counter:0{filename_number_padding}}.json"
|
||||
else:
|
||||
img_file = f"{filename_prefix}{delimiter}{counter:0{filename_number_padding}}{img_extension}"
|
||||
json_file = f"{filename_prefix}{delimiter}{counter:0{filename_number_padding}}.json"
|
||||
|
||||
# Calculate the maximum allowed length for image and JSON folders
|
||||
max_folder_length = max_path_length - len(os.path.basename(img_file)) - len(img_extension)
|
||||
|
||||
# Check and truncate img_folder length if necessary
|
||||
if img_folder and len(img_folder) > max_folder_length:
|
||||
img_folder = img_folder[:max_folder_length]
|
||||
|
||||
# Check and truncate json_folder length if necessary
|
||||
if json_folder and len(json_folder) > max_folder_length:
|
||||
json_folder = json_folder[:max_folder_length]
|
||||
|
||||
# Construct full paths for image and text files based on folders provided
|
||||
img_file = os.path.join(img_folder, os.path.basename(img_file)) if img_folder else os.path.join(self.output_dir, img_file)
|
||||
json_file = os.path.join(json_folder, os.path.basename(json_file)) if json_folder else os.path.join(os.path.dirname(img_file), os.path.basename(json_file))
|
||||
|
||||
# ...
|
||||
|
||||
return img_file, json_file
|
||||
|
||||
def save_images(self, images, filename_prefix="ComfyUI", delimiter="_",
|
||||
filename_number_padding=4, filename_number_start='false',
|
||||
img_folder=None, json_folder=None, prompt=None, extra_pnginfo=None):
|
||||
@@ -713,7 +676,9 @@ class EndlessNode_ImageSaver:
|
||||
for x in extra_pnginfo:
|
||||
metadata.add_text(x, json.dumps(extra_pnginfo[x]))
|
||||
|
||||
img_file, json_file = self.generate_filenames(filename_prefix, delimiter, counter,filename_number_padding, filename_number_start,img_extension, img_folder, json_folder)
|
||||
img_file, json_file = self.generate_filenames(filename_prefix, delimiter, counter,
|
||||
filename_number_padding, filename_number_start,
|
||||
img_extension, img_folder, json_folder)
|
||||
|
||||
try:
|
||||
if img_extension == '.png':
|
||||
@@ -746,6 +711,90 @@ class EndlessNode_ImageSaver:
|
||||
counter += 1
|
||||
|
||||
return {"ui": {"results": results}}
|
||||
|
||||
def generate_filenames(self, filename_prefix, delimiter, counter,
|
||||
filename_number_padding, filename_number_start, img_extension,
|
||||
img_folder, json_folder):
|
||||
if filename_number_start == 'true':
|
||||
img_file = f"{filename_prefix}{delimiter}{counter:0{filename_number_padding}}{img_extension}"
|
||||
json_file = f"{filename_prefix}{delimiter}{counter:0{filename_number_padding}}.json"
|
||||
else:
|
||||
img_file = f"{filename_prefix}{delimiter}{counter:0{filename_number_padding}}{img_extension}"
|
||||
json_file = f"{filename_prefix}{delimiter}{counter:0{filename_number_padding}}.json"
|
||||
|
||||
# Construct full paths for image and text files based on folders provided
|
||||
|
||||
if img_folder:
|
||||
img_folder = self.replace_date_time_placeholders(img_folder)
|
||||
os.makedirs(img_folder, exist_ok=True) # Create the image folder if it doesn't exist
|
||||
img_file = os.path.join(img_folder, img_file)
|
||||
else:
|
||||
img_file = os.path.join(self.output_dir, img_file)
|
||||
|
||||
if json_folder:
|
||||
json_folder = self.replace_date_time_placeholders(json_folder)
|
||||
os.makedirs(json_folder, exist_ok=True) # Create the image folder if it doesn't exist
|
||||
json_file = os.path.join(json_folder, json_file)
|
||||
else:
|
||||
json_file = os.path.join(os.path.dirname(img_file), json_file)
|
||||
|
||||
# Apply placeholders for date and time in filenames and folder
|
||||
img_file = self.replace_date_time_placeholders(img_file)
|
||||
json_file = self.replace_date_time_placeholders(json_file)
|
||||
|
||||
|
||||
|
||||
# Check if files with the same name exist, increment counter if necessary
|
||||
while os.path.exists(img_file) or os.path.exists(json_file):
|
||||
counter += 1
|
||||
if filename_number_start == 'true':
|
||||
img_file = f"{filename_prefix}{delimiter}{counter:0{filename_number_padding}}{img_extension}"
|
||||
json_file = f"{filename_prefix}{delimiter}{counter:0{filename_number_padding}}.json"
|
||||
else:
|
||||
img_file = f"{filename_prefix}{delimiter}{counter:0{filename_number_padding}}{img_extension}"
|
||||
json_file = f"{filename_prefix}{delimiter}{counter:0{filename_number_padding}}.json"
|
||||
|
||||
# Construct full paths for image and text files based on folders provided
|
||||
|
||||
if img_folder:
|
||||
img_folder = self.replace_date_time_placeholders(img_folder)
|
||||
os.makedirs(img_folder, exist_ok=True) # Create the image folder if it doesn't exist
|
||||
img_file = os.path.join(img_folder, img_file)
|
||||
else:
|
||||
img_file = os.path.join(self.output_dir, img_file)
|
||||
|
||||
if json_folder:
|
||||
json_folder = self.replace_date_time_placeholders(json_folder)
|
||||
os.makedirs(json_folder, exist_ok=True) # Create the image folder if it doesn't exist
|
||||
json_file = os.path.join(json_folder, json_file)
|
||||
else:
|
||||
json_file = os.path.join(os.path.dirname(img_file), json_file)
|
||||
|
||||
# Apply placeholders for date and time in filenames and folder
|
||||
img_file = self.replace_date_time_placeholders(img_file)
|
||||
json_file = self.replace_date_time_placeholders(json_file)
|
||||
|
||||
return img_file, json_file
|
||||
|
||||
def replace_date_time_placeholders(self, filename):
|
||||
# Replace date and time placeholders with actual date and time strings
|
||||
now = datetime.datetime.now()
|
||||
placeholders = {
|
||||
'%Y': now.strftime('%Y'), # Year with century as a decimal number
|
||||
'%y': now.strftime('%y'), # Year without century as a zero-padded decimal number
|
||||
'%m': now.strftime('%m'), # Month as a zero-padded decimal number
|
||||
'%d': now.strftime('%d'), # Day of the month as a zero-padded decimal number
|
||||
'%H': now.strftime('%H'), # Hour (24-hour clock) as a zero-padded decimal number
|
||||
'%M': now.strftime('%M'), # Minute as a zero-padded decimal number
|
||||
'%S': now.strftime('%S'), # Second as a zero-padded decimal number
|
||||
}
|
||||
|
||||
for placeholder, replacement in placeholders.items():
|
||||
filename = filename.replace(placeholder, replacement)
|
||||
|
||||
return filename
|
||||
|
||||
|
||||
# ______________________________________________________________________________________________________________________________________________________________
|
||||
# CONVERTER NODES BLOCK #
|
||||
#
|
||||
@@ -771,7 +820,7 @@ class EndlessNode_FloattoInt:
|
||||
return (int(FloatValue),)
|
||||
|
||||
# ----------------------------------------------
|
||||
# Float value to Number. There is no real "Number" function in Python, this is here so that nodes that need a NUMBER can take the FLOAT value
|
||||
# Float to Number. There is no real "Number" function in Python, this is here so that nodes that need a NUMBER can take the FLOAT value
|
||||
|
||||
class EndlessNode_FloattoNum:
|
||||
CATEGORY = "Endless 🌊✨/Converters/Float"
|
||||
@@ -814,7 +863,7 @@ class EndlessNode_FloattoString:
|
||||
return(str(FloatValue),)
|
||||
|
||||
# ----------------------------------------------
|
||||
# Float value to X
|
||||
# Float to X
|
||||
|
||||
class EndlessNode_FloattoX:
|
||||
CATEGORY = "Endless 🌊✨/Converters/Float"
|
||||
@@ -1098,6 +1147,8 @@ class EndlessNode_StringtoX:
|
||||
except (ValueError, TypeError): # Handle non-numerical input here by returning default value of 0
|
||||
return 0, 0.0, 0.0
|
||||
|
||||
|
||||
|
||||
#______________________________________________________________________________________________________________________________________________________________
|
||||
# CREDITS #
|
||||
#
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
clip @ git+https://github.com/openai/CLIP.git
|
||||
# clip @ git+https://github.com/openai/CLIP.git
|
||||
pytorch-lightning
|
||||
image-reward==1.4
|
||||
# image-reward==1.4
|
||||
colorama
|
||||
ftfy
|
||||
regex
|
||||
tqdm
|
||||
# ftfy
|
||||
# regex
|
||||
# tqdm
|
||||
Reference in New Issue
Block a user