Merge pull request #10 from SmirkingKitsune/patch-2
Additional Interrogator, AlwaysVisible and Experimental (Fun) Tools Updatepull/11/head
Santiago Alvarez
GitHub
commit
627c565ceb
109
README.md
109
README.md
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@ -3,7 +3,112 @@ script that runs the interrogator before processing each image
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[idea](https://www.reddit.com/r/StableDiffusion/comments/122w8u2/batch_img2img_with_automatic_interrogate_clip_in/)
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# To Do
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## Help Doc
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For the script to run, make sure that script checkbox is checked and a interrogator is selected.
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[`Interrogation Model(s)`]: The interrogators will run in the order of user selection.
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[`CLIP (EXT)`]: If user does not have a installed, and enabled version of `clip-interrogator-ext`, then `CLIP (EXT)` will not appear in the interrogator selection dropdown menu.
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[`WD (EXT)`]: If user does not have a installed, and enabled version of `stable-diffusion-webui-wd14-tagger`, then `WD (EXT)` will not appear in the interrogator selection dropdown menu.
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[`Interrogator results position`]: User can determine if the interrogation result is positioned at the beginning or end of the prompt.
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### CLIP EXT Options
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This menu will only appear if `CLIP (EXT)` is selected.
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[`CLIP Extension Model(s)`]: Users may select multiple interrogation models, interrogators will run in the order of user selection.
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[`CLIP Extension Mode`]: User may select what mode the CLIP extention interrogator will run in: `best`, `fast`, `classic`, or `negative`
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[`Unload CLIP Interrogator After Use`]: User has the option to keep interrogators loaded or have interrogators unloaded at the end of each interrogation.
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- It is advisable to unload models because of memory usage, however, keeping model loaded will be faster
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[`Unload All CLIP Interrogators`]: User has the ability to unload all CLIP interrogation models by pressing the `Unload All CLIP Interrogators` button.
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- User can also unload CLIP interrogators from the CLIP interrogator extention tab.
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### WD EXT Options
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This menu will only appear if `WD (EXT)` is selected.
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[`WD Extension Model(s)`]: Users may select multiple tagger models, taggers will run in the order of user selection.
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[`Tag Sensitivity Threshold`]: Tagger models will use `threshold` to determine if a suspected tag should be applied. Tags that do not meet the threshold will not be applied.
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[`Remove Underscores from Tags`]: User has the option to remove underscores from tags. The models inherently have underscores between words instead of spaces, this option replaces underscores with spaces.
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- To ensure that this option does not mutilate text emojis, underscores are compared against a list of underscore emojis to determine if replacement is nessassary.
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[`Append Interpreted Rating(s)`]: User has the option to append the rating to each interrogation.
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- [`Rating(s) Sensitivity Threshold`]: If `Append Interpreted Rating(s)` is disabled this slider will be hidden.
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- Note, setting the rating sensitivity to zero will result in all ratings being appended.
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[`Unload Tagger After Use`]: User has the option to keep taggers loaded or have taggers unloaded at the end of each interrogation.
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- It is advisable to unload models because of memory usage, however, keeping model loaded will be faster
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[`Unload All Tagger Models`]: User has the ability to unload all tagger models by pressing the `Unload All Tagger Models` button.
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- User can also unload CLIP interrogators from the CLIP interrogator extention tab.
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### Filtering Tools
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Users have the ability to filter content out of the interrogation prompts.
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- [`Filter Duplicate Positive Prompt Entries from Interrogation`]: Users can remove content from interrogations that was already added by the user in the prompt, this prevents exageration of prompt entries.
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- [`Filter Duplicate Negative Prompt Entries from Interrogation`]: Users can remove content from interrogations that is in the negative prompt, this prevents contradicting the negative prompt.
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- [`Filter Custom Prompt Entries from Interrogation`]: Users can create their own custom filter to remove content from interrogations without modifying the prompt or negative prompt.
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- Note, if `Filter Custom Prompt Entries from Interrogation` is not enabled, options associated with it will be hidden
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- [`Custom Filter Prompt`]: Users can add their custom filter to the provided textbox. Please note, any attention syntax will be ignored, as any entry matching added entries are filtered.
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- [`Optimize Custom FIlter`]: User can optimize custom filter, optimize will remove duplicate entries, extra spaces, and empty entries.
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- [`Load Custom Filter`]: User can load custom filter from the previous save
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- [`Save Custom Filter`]: User can scae custom filter for future use
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**WARNING: Saving the custom filter will overwrite previous custom filter save.**
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- [`Find & Replace User Defined Pairs in the Interrogation`]: Users can create their own custom find and replace lists to replace words and phrases in the interrogations without modifying the prompt or negative prompt.
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- Note, if `Find & Replace User Defined Pairs in the Interrogation` is not enabled, options associated with it will be hidden
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- [`Find`]: Users can add their custom phrases and words, seperated by comma, to find in the provided textbox.
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- [`Replace`]: Users can add their custom phrases and words replacements, seperated by comma, in the provided textbox. Leaving an entry blank will result in deleting the word from the interrogation prompt.
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- [`Parsed Pairs Visualizer`]: These two lists of find phrases and words will be paired with their replace counterparts. Pairing is based on order in the lists.
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- [`Load Custom Replace`]: User can load custom replace from the previous save
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- [`Save Custom Replace`]: User can scae custom replace for future use
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**WARNING: Saving the custom replace lists will overwrite previous custom replace lists save.**
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### Experimental Tools
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A bunch of tools that were added that are helpful with understanding the script, or offer greater variety with interrogation output.
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- [`Enable Debug Mode`]: DEBUG statements will be printed to console log.
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- [`Enable Reverse Mode`]: Interrogation will be added to the negative prompt.
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- [`Enable No Puncuation Mode`]: Interrogation will be filtered of all puncuations (except for a variety of emoji art).
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- [`Enable Exaggeration Mode`]: Interrogators will be permitted to add depulicate responses.
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- [`Enable Interrogator Prompt Weight`]: Use attention syntax on interrogation.
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- [`Interrogator Prompt Weight`]: This slider will specify the attention weight.
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- This option is hidden if `Enable Interrogator Prompt Weight` is not enabled.
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- [`Enable Prompt Output`]: Prompt statements will be printed to console log after every interrogation.
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## To Do
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- [x] ~~Use native A1111 interrogator~~
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- [x] ~~Use CLIP extension interrogator~~
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- [x] ~~Use WD14 extention taggers~~
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- [x] ~~Add the option to append ratings to interrogation~~
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- [x] ~~Use e621 tagger~~ (User should be able to use e621 by adding e621 to their WD14 extention)
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- [ ] Make extention options dynamically added, instead of static visibility modification (so options could be applied to models individually)
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- [ ] Add the option to add model output to either prompt or negative prompt individually (Helpful for CLIP negative mode)
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- [x] ~~Add a find and remove option to the custom filter~~
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- [x] ~~Add a find and replace option to the custom filter~~
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- [ ] Add the option to insert interrogation to user specified point in the prompt
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@ -1,9 +1,13 @@
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import gradio as gr
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import re #For remove_attention regular expressions
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from modules import scripts, deepbooru
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import re
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from modules import scripts, deepbooru, script_callbacks, shared
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from modules.ui_components import InputAccordion
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from modules.processing import process_images
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import modules.shared as shared
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import os # Used for saving previous custom prompt
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from modules.shared import state
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import sys
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import importlib.util
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NAME = "Img2img Batch Interrogator"
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"""
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@ -12,52 +16,223 @@ Thanks to Smirking Kitsune.
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"""
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# Extention List Crawler
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def get_extensions_list():
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from modules import extensions
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extensions.list_extensions()
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ext_list = []
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for ext in extensions.extensions:
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ext: extensions.Extension
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ext.read_info_from_repo()
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if ext.remote is not None:
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ext_list.append({
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"name": ext.name,
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"enabled":ext.enabled
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})
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return ext_list
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class Script(scripts.Script):
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# Extention Checker
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def is_interrogator_enabled(interrogator):
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for ext in get_extensions_list():
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if ext["name"] == interrogator:
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return ext["enabled"]
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return False
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# EXT Importer
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def import_module(module_name, file_path):
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spec = importlib.util.spec_from_file_location(module_name, file_path)
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module = importlib.util.module_from_spec(spec)
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sys.modules[module_name] = module
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spec.loader.exec_module(module)
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return module
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class Script(scripts.ScriptBuiltinUI):
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wd_ext_utils = None
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clip_ext = None
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first = True
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prompt_contamination = ""
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def title(self):
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return "Img2img batch interrogator"
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# "Img2img Batch Interrogator"
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return NAME
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def show(self, is_img2img):
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return is_img2img
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return scripts.AlwaysVisible if is_img2img else False
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# Checks for CLIP EXT to see if it is installed and enabled
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@classmethod
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def load_clip_ext_module(cls):
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if is_interrogator_enabled('clip-interrogator-ext'):
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cls.clip_ext = import_module("clip-interrogator-ext", "extensions/clip-interrogator-ext/scripts/clip_interrogator_ext.py")
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print(f"[{NAME} LOADER]: `clip-interrogator-ext` found...")
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return cls.clip_ext
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print(f"[{NAME} LOADER]: `clip-interrogator-ext` NOT found!")
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return None
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# Initiates extenion check at startup for CLIP EXT
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@classmethod
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def load_clip_ext_module_wrapper(cls, *args, **kwargs):
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return cls.load_clip_ext_module()
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# Checks for WD EXT to see if it is installed and enabled
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@classmethod
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def load_wd_ext_module(cls):
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if is_interrogator_enabled('stable-diffusion-webui-wd14-tagger'):
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sys.path.append('extensions/stable-diffusion-webui-wd14-tagger')
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cls.wd_ext_utils = import_module("utils", "extensions/stable-diffusion-webui-wd14-tagger/tagger/utils.py")
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print(f"[{NAME} LOADER]: `stable-diffusion-webui-wd14-tagger` found...")
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return cls.wd_ext_utils
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print(f"[{NAME} LOADER]: `stable-diffusion-webui-wd14-tagger` NOT found!")
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return None
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# Initiates extenion check at startup for WD EXT
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@classmethod
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def load_wd_ext_module_wrapper(cls, *args, **kwargs):
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return cls.load_wd_ext_module()
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# Initiates prompt reset on image save
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@classmethod
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def load_custom_filter_module_wrapper(cls, *args, **kwargs):
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return cls.load_custom_filter()
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# Button interaction handler
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def b_clicked(o):
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return gr.Button.update(interactive=True)
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def ui(self, is_img2img):
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# Function to load custom filter from file
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def load_custom_filter(custom_filter):
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with open("extensions\sd-Img2img-batch-interrogator\custom_filter.txt", "r") as file:
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#Experimental Tool, prints dev statements to the console
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def debug_print(self, debug_mode, message):
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if debug_mode:
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print(f"[{NAME} DEBUG]: {message}")
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# Function to clean the custom_filter
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def clean_string(self, input_string):
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# Split the string into a list
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items = input_string.split(',')
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# Clean up each item: strip whitespace and convert to lowercase
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cleaned_items = [item.strip() for item in items if item.strip()]
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# Remove duplicates while preserving order
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unique_items = []
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seen = set()
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for item in cleaned_items:
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if item not in seen:
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seen.add(item)
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unique_items.append(item)
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# Join the cleaned, unique items back into a string
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return ', '.join(unique_items)
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# Custom replace function to replace phrases with associated pair
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def custom_replace(self, text, replace_pairs):
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for old, new in replace_pairs.items():
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text = re.sub(r'\b' + re.escape(old) + r'\b', new, text)
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return text
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# Tag filtering, removes negative tags from prompt
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def filter_words(self, prompt, negative):
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# Corrects a potential error where negative is nonetype
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if negative is None:
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negative = ""
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# Split prompt and negative strings into lists of words
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prompt_words = [word.strip() for word in prompt.split(",")]
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negative_words = [self.remove_attention(word.strip()) for word in negative.split(",")]
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# Filter out words from prompt that are in negative
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filtered_words = [word for word in prompt_words if self.remove_attention(word) not in negative_words]
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# Join filtered words back into a string
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filtered_prompt = ", ".join(filtered_words)
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return filtered_prompt
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# Initial Model Options generator, only add supported interrogators, support may vary depending on client
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def get_initial_model_options(self):
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options = ["CLIP (Native)", "Deepbooru (Native)"]
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if is_interrogator_enabled('clip-interrogator-ext'):
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options.insert(0, "CLIP (EXT)")
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if is_interrogator_enabled('stable-diffusion-webui-wd14-tagger'):
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options.append("WD (EXT)")
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return options
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# Gets a list of WD models from WD EXT
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def get_WD_EXT_models(self):
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if self.wd_ext_utils is not None:
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try:
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self.wd_ext_utils.refresh_interrogators()
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models = list(self.wd_ext_utils.interrogators.keys())
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if not models:
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raise Exception("[{NAME} DEBUG]: No WD Tagger models found.")
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return models
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except Exception as errorrror:
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print(f"[{NAME} ERROR]: Error accessing WD Tagger: {error}")
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return []
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# Function to load CLIP models list into CLIP model selector
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def load_clip_models(self):
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if self.clip_ext is not None:
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models = self.clip_ext.get_models()
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return gr.Dropdown.update(choices=models if models else None)
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return gr.Dropdown.update(choices=None)
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# Function to load custom filter from file
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def load_custom_filter(self):
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try:
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with open("extensions/sd-Img2img-batch-interrogator/custom_filter.txt", "r", encoding="utf-8") as file:
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custom_filter = file.read()
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return custom_filter
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model_options = ["CLIP", "Deepbooru"]
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model_selection = gr.Dropdown(choices=model_options, label="Select Interrogation Model(s)", multiselect=True, value="Deepbooru")
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in_front = gr.Checkbox(label="Prompt in front", value=True)
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use_weight = gr.Checkbox(label="Use Interrogator Prompt Weight", value=True)
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prompt_weight = gr.Slider(
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0.0, 1.0, value=0.5, step=0.1, label="Interrogator Prompt Weight"
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)
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with gr.Accordion("Filtering tools:"):
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no_duplicates = gr.Checkbox(label="Filter Duplicate Prompt Content from Interrogation", value=False)
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use_negatives = gr.Checkbox(label="Filter Negative Prompt Content from Interrogation", value=False)
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use_custom_filter = gr.Checkbox(label="Filter Custom Prompt Content from Interrogation", value=False)
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custom_filter = gr.Textbox(
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label="Custom Filter Prompt",
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placeholder="Prompt content seperated by commas. Warning ignores attention syntax, parentheses '()' and colon suffix ':XX.XX' are discarded.",
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show_copy_button=True
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)
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# Button to load custom filter from file
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load_custom_filter_button = gr.Button(value="Load Last Custom Filter")
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except Exception as error:
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print(f"[{NAME} ERROR]: Error loading custom filter: {error}")
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# This should be resolved by generating a blank file
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if error == "[Errno 2] No such file or directory: 'extensions/sd-Img2img-batch-interrogator/custom_filter.txt'":
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self.save_custom_filter("")
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return ""
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# Listeners
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load_custom_filter_button.click(load_custom_filter, inputs=custom_filter, outputs=custom_filter)
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# Function used to prep custom filter environment with previously saved configuration
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def load_custom_filter_on_start(self):
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return self.load_custom_filter()
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# Function to load custom replace from file
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def load_custom_replace(self):
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try:
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with open("extensions/sd-Img2img-batch-interrogator/custom_replace.txt", "r", encoding="utf-8") as file:
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content = file.read().strip().split('\n')
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if len(content) >= 2:
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return content[0], content[1]
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else:
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print(f"[{NAME} ERROR]: Invalid custom replace file format.")
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return "", ""
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except Exception as error:
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print(f"[{NAME} ERROR]: Error loading custom replace: {error}")
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# This should be resolved by generating a blank file
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if error == "[Errno 2] No such file or directory: 'extensions/sd-Img2img-batch-interrogator/custom_replace.txt'":
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self.save_custom_replace("", "")
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return "", ""
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# Function used to prep find and replace environment with previously saved configuration
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def load_custom_replace_on_start(self):
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old, new = self.load_custom_replace()
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return old, new
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return [in_front, prompt_weight, model_selection, use_weight, no_duplicates, use_negatives, use_custom_filter, custom_filter]
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# Function to load WD models list into WD model selector
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def load_wd_models(self):
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if self.wd_ext_utils is not None:
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models = self.get_WD_EXT_models()
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return gr.Dropdown.update(choices=models if models else None)
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return gr.Dropdown.update(choices=None)
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# Parse two strings to display pairs
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def parse_replace_pairs(self, custom_replace_find, custom_replace_replacements):
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old_list = [phrase.strip() for phrase in custom_replace_find.split(',')]
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new_list = [phrase.strip() for phrase in custom_replace_replacements.split(',')]
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# Ensure both lists have the same length
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min_length = min(len(old_list), len(new_list))
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return {old_list[i]: new_list[i] for i in range(min_length)}
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# Refresh the model_selection dropdown
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def refresh_model_options(self):
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new_options = self.get_initial_model_options()
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return gr.Dropdown.update(choices=new_options)
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# Required to parse information from a string that is between () or has :##.## suffix
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def remove_attention(self, words):
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# Define a regular expression pattern to match attention-related suffixes
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@ -79,77 +254,460 @@ class Script(scripts.Script):
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words = re.sub(r"TEMP_RIGHT_PLACEHOLDER", r"\\)", words)
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return words.strip()
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|
||||
# Experimental Tool, removes puncutation, but tries to keep a variety of known emojis
|
||||
def remove_punctuation(self, text):
|
||||
# List of text emojis to preserve
|
||||
skipables = ["'s", "...", ":-)", ":)", ":-]", ":]", ":->", ":>", "8-)", "8)", ":-}", ":}", ":^)", "=]", "=)", ":-D", ":D", "8-D", "8D", "=D", "=3", "B^D",
|
||||
"c:", "C:", "x-D", "X-D", ":-))", ":))", ":-(", ":(", ":-c", ":c", ":-<", ":<", ":-[", ":[", ":-||", ":{", ":@", ":(", ";(", ":'-(", ":'(", ":=(", ":'-)",
|
||||
":')", ">:(", ">:[", "D-':", "D:<", "D:", "D;", "D=", ":-O", ":O", ":-o", ":o", ":-0", ":0", "8-0", ">:O", "=O", "=o", "=0", ":-3", ":3", "=3", ">:3",
|
||||
":-*", ":*", ":x", ";-)", ";)", "*-)", "*)", ";-]", ";]", ";^)", ";>", ":-,", ";D", ";3", ":-P", ":P", "X-P", "x-p", ":-p", ":p", ":-Þ", ":Þ", ":-þ",
|
||||
":þ", ":-b", ":b", "d:", "=p", ">:P", ":-/", ":/", ":-.", ">:/", "=/", ":L", "=L", ":S", ":-|", ":|", ":$", "://)", "://3", ":-X", ":X", ":-#", ":#",
|
||||
":-&", ":&", "O:-)", "O:)", "0:-3", "0:3", "0:-)", "0:)", "0;^)", ">:-)", ">:)", "}:-)", "}:)", "3:-)", "3:)", ">;-)", ">;)", ">:3", ">;3", "|;-)", "|-O",
|
||||
"B-)", ":-J", "#-)", "%-)", "%)", ":-###..", ":###..", "<:-|", "',:-|", "',:-l", ":E", "8-X", "8=X", "x-3", "x=3", "~:>", "@};-", "@}->--", "@}-;-'---",
|
||||
"@>-->--", "8====D", "8===D", "8=D", "3=D", "8=>", "8===D~~~", "*<|:-)", "</3", "<\3", "<3", "><>", "<><", "<*)))-{", "><(((*>", "\o/", "*\0/*", "o7",
|
||||
"v.v", "._.", "._.;", "X_X", "x_x", "+_+", "X_x", "x_X", "<_<", ">_>", "<.<", ">.>", "O_O", "o_o", "O-O", "o-o", "O_o", "o_O", ">.<", ">_<", "^5", "o/\o",
|
||||
">_>^ ^<_<", "V.v.V"] # Maybe I should remove emojis with parenthesis () in them...
|
||||
# Temporarily replace text emojis with placeholders
|
||||
for i, noticables in enumerate(skipables):
|
||||
text = text.replace(noticables, f"SKIP_PLACEHOLDER_{i}")
|
||||
# Remove punctuation except commas
|
||||
text = re.sub(r'[^\w\s,]', '', text)
|
||||
# Split the text into tags
|
||||
tags = [tag.strip() for tag in text.split(',')]
|
||||
# Remove empty tags
|
||||
tags = [tag for tag in tags if tag]
|
||||
# Rejoin the tags
|
||||
text = ', '.join(tags)
|
||||
# Restore text emojis
|
||||
for i, noticables in enumerate(skipables):
|
||||
text = text.replace(f"SKIP_PLACEHOLDER_{i}", noticables)
|
||||
return text
|
||||
|
||||
# For WD Tagger, removes underscores from tags that should have spaces
|
||||
def replace_underscores(self, tag):
|
||||
skipable = [
|
||||
"0_0", "(o)_(o)", "+_+", "+_-", "._.", "<o>_<o>", "<|>_<|>", "=_=", ">_<",
|
||||
"3_3", "6_9", ">_o", "@_@", "^_^", "o_o", "u_u", "x_x", "|_|", "||_||"
|
||||
]
|
||||
if tag in skipable:
|
||||
return tag
|
||||
return tag.replace('_', ' ')
|
||||
|
||||
def filter_words(self, prompt, negative):
|
||||
# Corrects a potential error where negative is nonetype
|
||||
if negative is None:
|
||||
negative = ""
|
||||
|
||||
# Split prompt and negative strings into lists of words
|
||||
prompt_words = [word.strip() for word in prompt.split(",")]
|
||||
negative_words = [self.remove_attention(word.strip()) for word in negative.split(",")]
|
||||
|
||||
# Filter out words from prompt that are in negative
|
||||
filtered_words = [word for word in prompt_words if self.remove_attention(word) not in negative_words]
|
||||
|
||||
# Join filtered words back into a string
|
||||
filtered_prompt = ", ".join(filtered_words)
|
||||
|
||||
return filtered_prompt
|
||||
|
||||
def run(self, p, in_front, prompt_weight, model_selection, use_weight, no_duplicates, use_negatives, use_custom_filter, custom_filter):
|
||||
|
||||
raw_prompt = p.prompt
|
||||
interrogator = ""
|
||||
|
||||
# fix alpha channel
|
||||
p.init_images[0] = p.init_images[0].convert("RGB")
|
||||
|
||||
first = True # Two interrogator concatination correction boolean
|
||||
for model in model_selection:
|
||||
# This prevents two interrogators from being incorrectly concatinated
|
||||
if first == False:
|
||||
interrogator += ", "
|
||||
first = False
|
||||
# Should add the interrogators in the order determined by the model_selection list
|
||||
if model == "Deepbooru":
|
||||
interrogator += deepbooru.model.tag(p.init_images[0])
|
||||
elif model == "CLIP":
|
||||
interrogator += shared.interrogator.interrogate(p.init_images[0])
|
||||
|
||||
# Remove duplicate prompt content from interrogator prompt
|
||||
if no_duplicates:
|
||||
interrogator = self.filter_words(interrogator, raw_prompt)
|
||||
# Remove negative prompt content from interrogator prompt
|
||||
if use_negatives:
|
||||
interrogator = self.filter_words(interrogator, p.negative_prompt)
|
||||
# Remove custom prompt content from interrogator prompt
|
||||
if use_custom_filter:
|
||||
interrogator = self.filter_words(interrogator, custom_filter)
|
||||
# Save custom filter to text file
|
||||
with open("extensions\sd-Img2img-batch-interrogator\custom_filter.txt", "w") as file:
|
||||
# Resets the prompt_contamination string, prompt_contamination is used to clean the p.prompt after it has been modified by a previous batch job
|
||||
def reset_prompt_contamination(self, debug_mode):
|
||||
"""
|
||||
Note: prompt_contamination
|
||||
During the course of a process_batch, the p.prompt and p.all_prompts[0]
|
||||
is going to become contaminated with previous interrogation in the batch, to
|
||||
mitigate this problem, prompt_contamination is used to identify and remove contamination
|
||||
"""
|
||||
self.debug_print(debug_mode, f"Reset was Called! The following prompt will be removed from the prompt_contamination cleaner: {self.prompt_contamination}")
|
||||
self.prompt_contamination = ""
|
||||
|
||||
# Function to save custom filter from file
|
||||
def save_custom_filter(self, custom_filter):
|
||||
try:
|
||||
with open("extensions/sd-Img2img-batch-interrogator/custom_filter.txt", "w", encoding="utf-8") as file:
|
||||
file.write(custom_filter)
|
||||
print(f"[{NAME}]: Custom filter saved successfully.")
|
||||
except Exception as error:
|
||||
print(f"[{NAME} ERROR]: Error saving custom filter: {error}")
|
||||
return self.update_save_confirmation_row_false()
|
||||
|
||||
if use_weight:
|
||||
if p.prompt == "":
|
||||
p.prompt = interrogator
|
||||
elif in_front:
|
||||
p.prompt = f"{p.prompt}, ({interrogator}:{prompt_weight})"
|
||||
else:
|
||||
p.prompt = f"({interrogator}:{prompt_weight}), {p.prompt}"
|
||||
# Function to save custom replace from file
|
||||
def save_custom_replace(self, custom_replace_find, custom_replace_replacements):
|
||||
try:
|
||||
with open("extensions/sd-Img2img-batch-interrogator/custom_replace.txt", "w", encoding="utf-8") as file:
|
||||
file.write(f"{custom_replace_find}\n{custom_replace_replacements}")
|
||||
print(f"[{NAME}]: Custom replace saved successfully.")
|
||||
except Exception as error:
|
||||
print(f"[{NAME} ERROR]: Error saving custom replace: {error}")
|
||||
return self.update_save_confirmation_row_false()
|
||||
|
||||
# depending on if CLIP (EXT) is present, CLIP (EXT) could be removed from model selector
|
||||
def update_clip_ext_visibility(self, model_selection):
|
||||
is_visible = "CLIP (EXT)" in model_selection
|
||||
if is_visible:
|
||||
clip_models = self.load_clip_models()
|
||||
return gr.Accordion.update(visible=True), clip_models
|
||||
else:
|
||||
if p.prompt == "":
|
||||
p.prompt = interrogator
|
||||
elif in_front:
|
||||
p.prompt = f"{p.prompt}, {interrogator}"
|
||||
return gr.Accordion.update(visible=False), gr.Dropdown.update()
|
||||
|
||||
# Updates the visibility of group with input bool making it dynamically visible
|
||||
def update_group_visibility(self, user_defined_visibility):
|
||||
return gr.Group.update(visible=user_defined_visibility)
|
||||
|
||||
# Updates the visibility of slider with input bool making it dynamically visible
|
||||
def update_slider_visibility(self, user_defined_visibility):
|
||||
return gr.Slider.update(visible=user_defined_visibility)
|
||||
|
||||
# Makes save confirmation dialague invisible
|
||||
def update_save_confirmation_row_false(self):
|
||||
return gr.Accordion.update(visible=False)
|
||||
|
||||
# Makes save confirmation dialague visible
|
||||
def update_save_confirmation_row_true(self):
|
||||
return gr.Accordion.update(visible=True)
|
||||
|
||||
# Used for user visualization, (no longer used for parsing pairs)
|
||||
def update_parsed_pairs(self, custom_replace_find, custom_replace_replacements):
|
||||
old_list = [phrase.strip() for phrase in custom_replace_find.split(',')]
|
||||
new_list = [phrase.strip() for phrase in custom_replace_replacements.split(',')]
|
||||
|
||||
# Ensure both lists have the same length
|
||||
min_length = min(len(old_list), len(new_list))
|
||||
pairs = [f"{old_list[i]}:{new_list[i]}" for i in range(min_length)]
|
||||
|
||||
return ", ".join(pairs)
|
||||
|
||||
# depending on if WD (EXT) is present, WD (EXT) could be removed from model selector
|
||||
def update_wd_ext_visibility(self, model_selection):
|
||||
is_visible = "WD (EXT)" in model_selection
|
||||
if is_visible:
|
||||
wd_models = self.load_wd_models()
|
||||
return gr.Accordion.update(visible=True), wd_models
|
||||
else:
|
||||
return gr.Accordion.update(visible=False), gr.Dropdown.update()
|
||||
|
||||
#Unloads CLIP Models
|
||||
def unload_clip_models(self):
|
||||
if self.clip_ext is not None:
|
||||
self.clip_ext.unload()
|
||||
|
||||
#Unloads WD Models
|
||||
def unload_wd_models(self):
|
||||
unloaded_models = 0
|
||||
if self.wd_ext_utils is not None:
|
||||
for interrogator in self.wd_ext_utils.interrogators.values():
|
||||
if interrogator.unload():
|
||||
unloaded_models = unloaded_models + 1
|
||||
print(f"Unloaded {unloaded_models} Tagger Model(s).")
|
||||
|
||||
def ui(self, is_img2img):
|
||||
if not is_img2img:
|
||||
return
|
||||
|
||||
with InputAccordion(False, label=NAME, elem_id="tag_batch_enabled") as tag_batch_enabled:
|
||||
with gr.Row():
|
||||
model_selection = gr.Dropdown(
|
||||
choices=self.get_initial_model_options(),
|
||||
label="Interrogation Model(s):",
|
||||
multiselect=True
|
||||
)
|
||||
refresh_models_button = gr.Button("🔄", elem_classes="tool")
|
||||
|
||||
in_front = gr.Radio(
|
||||
choices=["Prepend to prompt", "Append to prompt"],
|
||||
value="Prepend to prompt",
|
||||
label="Interrogator result position")
|
||||
|
||||
# CLIP EXT Options
|
||||
clip_ext_accordion = gr.Accordion("CLIP EXT Options:", visible=False)
|
||||
with clip_ext_accordion:
|
||||
clip_ext_model = gr.Dropdown(choices=[], value='ViT-L-14/openai', label="CLIP Extension Model(s):", multiselect=True)
|
||||
clip_ext_mode = gr.Radio(choices=["best", "fast", "classic", "negative"], value='best', label="CLIP Extension Mode")
|
||||
unload_clip_models_afterwords = gr.Checkbox(label="Unload CLIP Interrogator After Use", value=True)
|
||||
unload_clip_models_button = gr.Button(value="Unload All CLIP Interrogators")
|
||||
|
||||
# WD EXT Options
|
||||
wd_ext_accordion = gr.Accordion("WD EXT Options:", visible=False)
|
||||
with wd_ext_accordion:
|
||||
wd_ext_model = gr.Dropdown(choices=[], value='wd-v1-4-moat-tagger.v2', label="WD Extension Model(s):", multiselect=True)
|
||||
wd_threshold = gr.Slider(0.0, 1.0, value=0.35, step=0.01, label="Tag Sensitivity Threshold")
|
||||
wd_underscore_fix = gr.Checkbox(label="Remove Underscores from Tags", value=True)
|
||||
wd_append_ratings = gr.Checkbox(label="Append Interpreted Rating(s)", value=False)
|
||||
wd_ratings = gr.Slider(0.0, 1.0, value=0.5, step=0.01, label="Rating(s) Sensitivity Threshold", visible=False)
|
||||
unload_wd_models_afterwords = gr.Checkbox(label="Unload Tagger After Use", value=True)
|
||||
unload_wd_models_button = gr.Button(value="Unload All Tagger Models")
|
||||
|
||||
filtering_tools = gr.Accordion("Filtering tools:")
|
||||
with filtering_tools:
|
||||
use_positive_filter = gr.Checkbox(label="Filter Duplicate Positive Prompt Entries from Interrogation")
|
||||
use_negative_filter = gr.Checkbox(label="Filter Duplicate Negative Prompt Entries from Interrogation")
|
||||
use_custom_filter = gr.Checkbox(label="Filter Custom Prompt Entries from Interrogation")
|
||||
custom_filter_group = gr.Group(visible=False)
|
||||
with custom_filter_group:
|
||||
custom_filter = gr.Textbox(value=self.load_custom_filter_on_start(),
|
||||
label="Custom Filter Prompt",
|
||||
placeholder="Prompt content separated by commas. Warning ignores attention syntax, parentheses '()' and colon suffix ':XX.XX' are discarded.",
|
||||
show_copy_button=True
|
||||
)
|
||||
# Button to remove duplicates and strip strange spacing
|
||||
clean_custom_filter_button = gr.Button(value="Optimize Custom Filter")
|
||||
# Button to load/save custom filter from file
|
||||
with gr.Row():
|
||||
load_custom_filter_button = gr.Button(value="Load Custom Filter")
|
||||
save_custom_filter_button = gr.Button(value="Save Custom Filter")
|
||||
save_confirmation_custom_filter = gr.Accordion("Are You Sure You Want to Save?", visible=False)
|
||||
with save_confirmation_custom_filter:
|
||||
with gr.Row():
|
||||
cancel_save_custom_filter_button = gr.Button(value="Cancel")
|
||||
confirm_save_custom_filter_button = gr.Button(value="Save", variant="stop")
|
||||
|
||||
# Find and Replace
|
||||
use_custom_replace = gr.Checkbox(label="Find & Replace User Defined Pairs in the Interrogation")
|
||||
custom_replace_group = gr.Group(visible=False)
|
||||
with custom_replace_group:
|
||||
with gr.Row():
|
||||
custom_replace_find = gr.Textbox(
|
||||
value=self.load_custom_replace_on_start()[0],
|
||||
label="Find:",
|
||||
placeholder="Enter phrases to replace, separated by commas",
|
||||
show_copy_button=True
|
||||
)
|
||||
custom_replace_replacements = gr.Textbox(
|
||||
value=self.load_custom_replace_on_start()[1],
|
||||
label="Replace:",
|
||||
placeholder="Enter replacement phrases, separated by commas",
|
||||
show_copy_button=True
|
||||
)
|
||||
with gr.Row():
|
||||
parsed_pairs = gr.Textbox(
|
||||
label="Parsed Pairs",
|
||||
placeholder="Parsed pairs will be shown here",
|
||||
interactive=False
|
||||
)
|
||||
update_parsed_pairs_button = gr.Button("🔄", elem_classes="tool")
|
||||
with gr.Row():
|
||||
load_custom_replace_button = gr.Button("Load Custom Replace")
|
||||
save_custom_replace_button = gr.Button("Save Custom Replace")
|
||||
save_confirmation_custom_replace = gr.Accordion("Are You Sure You Want to Save?", visible=False)
|
||||
with save_confirmation_custom_replace:
|
||||
with gr.Row():
|
||||
cancel_save_custom_replace_button = gr.Button(value="Cancel")
|
||||
confirm_save_custom_replace_button = gr.Button(value="Save", variant="stop")
|
||||
|
||||
|
||||
experimental_tools = gr.Accordion("Experamental tools:", open=False)
|
||||
with experimental_tools:
|
||||
debug_mode = gr.Checkbox(label="Enable Debug Mode", info="[Debug Mode]: DEBUG statements will be printed to console log.")
|
||||
reverse_mode = gr.Checkbox(label="Enable Reverse Mode", info="[Reverse Mode]: Interrogation will be added to the negative prompt.")
|
||||
no_puncuation_mode = gr.Checkbox(label="Enable No Puncuation Mode", info="[No Puncuation Mode]: Interrogation will be filtered of all puncuations (except for a variety of emoji art).")
|
||||
exaggeration_mode = gr.Checkbox(label="Enable Exaggeration Mode", info="[Exaggeration Mode]: Interrogators will be permitted to add depulicate responses.")
|
||||
prompt_weight_mode = gr.Checkbox(label="Enable Interrogator Prompt Weight Mode", info="[Interrogator Prompt Weight]: Use attention syntax on interrogation.")
|
||||
prompt_weight = gr.Slider(0.0, 1.0, value=0.5, step=0.01, label="Interrogator Prompt Weight", visible=False)
|
||||
prompt_output = gr.Checkbox(label="Enable Prompt Output", value=True, info="[Prompt Output]: Prompt statements will be printed to console log after every interrogation.")
|
||||
|
||||
# Listeners
|
||||
model_selection.change(fn=self.update_clip_ext_visibility, inputs=[model_selection], outputs=[clip_ext_accordion, clip_ext_model])
|
||||
model_selection.change(fn=self.update_wd_ext_visibility, inputs=[model_selection], outputs=[wd_ext_accordion, wd_ext_model])
|
||||
unload_clip_models_button.click(self.unload_clip_models, inputs=None, outputs=None)
|
||||
unload_wd_models_button.click(self.unload_wd_models, inputs=None, outputs=None)
|
||||
prompt_weight_mode.change(fn=self.update_slider_visibility, inputs=[prompt_weight_mode], outputs=[prompt_weight])
|
||||
wd_append_ratings.change(fn=self.update_slider_visibility, inputs=[wd_append_ratings], outputs=[wd_ratings])
|
||||
clean_custom_filter_button.click(self.clean_string, inputs=custom_filter, outputs=custom_filter)
|
||||
load_custom_filter_button.click(self.load_custom_filter, inputs=None, outputs=custom_filter)
|
||||
custom_replace_find.change(fn=self.update_parsed_pairs, inputs=[custom_replace_find, custom_replace_replacements], outputs=[parsed_pairs])
|
||||
custom_replace_replacements.change(fn=self.update_parsed_pairs, inputs=[custom_replace_find, custom_replace_replacements], outputs=[parsed_pairs])
|
||||
update_parsed_pairs_button.click(fn=self.update_parsed_pairs, inputs=[custom_replace_find, custom_replace_replacements], outputs=[parsed_pairs])
|
||||
save_custom_filter_button.click(self.update_save_confirmation_row_true, inputs=None, outputs=[save_confirmation_custom_filter])
|
||||
cancel_save_custom_filter_button.click(self.update_save_confirmation_row_false, inputs=None, outputs=[save_confirmation_custom_filter])
|
||||
confirm_save_custom_filter_button.click(self.save_custom_filter, inputs=custom_filter, outputs=[save_confirmation_custom_filter])
|
||||
load_custom_replace_button.click(fn=self.load_custom_replace, inputs=[],outputs=[custom_replace_find, custom_replace_replacements])
|
||||
save_custom_replace_button.click(fn=self.update_save_confirmation_row_true, inputs=[], outputs=[save_confirmation_custom_replace])
|
||||
cancel_save_custom_replace_button.click(fn=self.update_save_confirmation_row_false, inputs=[], outputs=[save_confirmation_custom_replace])
|
||||
confirm_save_custom_replace_button.click(fn=self.save_custom_replace, inputs=[custom_replace_find, custom_replace_replacements], outputs=[save_confirmation_custom_replace])
|
||||
refresh_models_button.click(fn=self.refresh_model_options, inputs=[], outputs=[model_selection])
|
||||
use_custom_filter.change(fn=self.update_group_visibility, inputs=[use_custom_filter], outputs=[custom_filter_group])
|
||||
use_custom_replace.change(fn=self.update_group_visibility, inputs=[use_custom_replace], outputs=[custom_replace_group])
|
||||
|
||||
ui = [
|
||||
tag_batch_enabled, model_selection, debug_mode, in_front, prompt_weight_mode, prompt_weight, reverse_mode, exaggeration_mode, prompt_output, use_positive_filter, use_negative_filter,
|
||||
use_custom_filter, custom_filter, use_custom_replace, custom_replace_find, custom_replace_replacements, clip_ext_model, clip_ext_mode, wd_ext_model, wd_threshold, wd_underscore_fix, wd_append_ratings, wd_ratings,
|
||||
unload_clip_models_afterwords, unload_wd_models_afterwords, no_puncuation_mode
|
||||
]
|
||||
return ui
|
||||
|
||||
def process_batch(
|
||||
self, p, tag_batch_enabled, model_selection, debug_mode, in_front, prompt_weight_mode, prompt_weight, reverse_mode, exaggeration_mode, prompt_output, use_positive_filter, use_negative_filter,
|
||||
use_custom_filter, custom_filter, use_custom_replace, custom_replace_find, custom_replace_replacements, clip_ext_model, clip_ext_mode, wd_ext_model, wd_threshold, wd_underscore_fix, wd_append_ratings, wd_ratings,
|
||||
unload_clip_models_afterwords, unload_wd_models_afterwords, no_puncuation_mode, batch_number, prompts, seeds, subseeds):
|
||||
|
||||
if not tag_batch_enabled:
|
||||
return
|
||||
|
||||
self.debug_print(debug_mode, f"process_batch called. batch_number={batch_number}, state.job_no={state.job_no}, state.job_count={state.job_count}, state.job_count={state.job}")
|
||||
if model_selection and not batch_number:
|
||||
# Calls reset_prompt_contamination to prep for multiple p.prompts
|
||||
if state.job_no <= 0:
|
||||
self.debug_print(debug_mode, f"Condition met for reset, calling reset_prompt_contamination")
|
||||
self.reset_prompt_contamination(debug_mode)
|
||||
#self.debug_print(debug_mode, f"prompt_contamination: {self.prompt_contamination}")
|
||||
# Experimental reverse mode cleaner
|
||||
if not reverse_mode:
|
||||
# Remove contamination from previous batch job from negative prompt
|
||||
p.prompt = p.prompt.replace(self.prompt_contamination, "")
|
||||
else:
|
||||
p.prompt = f"{interrogator}, {p.prompt}"
|
||||
# Remove contamination from previous batch job from negative prompt
|
||||
p.negative_prompt = p.prompt.replace(self.prompt_contamination, "")
|
||||
|
||||
# local variable preperations
|
||||
self.debug_print(debug_mode, f"Initial p.prompt: {p.prompt}")
|
||||
preliminary_interrogation = ""
|
||||
interrogation = ""
|
||||
|
||||
# fix alpha channel
|
||||
init_image = p.init_images[0]
|
||||
p.init_images[0] = p.init_images[0].convert("RGB")
|
||||
|
||||
# Interrogator interrogation loop
|
||||
for model in model_selection:
|
||||
# Check for skipped job
|
||||
if state.skipped:
|
||||
print("Job skipped.")
|
||||
state.skipped = False
|
||||
continue
|
||||
|
||||
# Check for interruption
|
||||
if state.interrupted:
|
||||
print("Job interrupted. Ending process.")
|
||||
state.interrupted = False
|
||||
break
|
||||
|
||||
# Should add the interrogators in the order determined by the model_selection list
|
||||
if model == "Deepbooru (Native)":
|
||||
preliminary_interrogation = deepbooru.model.tag(p.init_images[0])
|
||||
self.debug_print(debug_mode, f"[Deepbooru (Native)]: [Result]: {preliminary_interrogation}")
|
||||
interrogation += f"{preliminary_interrogation}, "
|
||||
elif model == "CLIP (Native)":
|
||||
preliminary_interrogation = shared.interrogator.interrogate(p.init_images[0])
|
||||
self.debug_print(debug_mode, f"[CLIP (Native)]: [Result]: {preliminary_interrogation}")
|
||||
interrogation += f"{preliminary_interrogation}, "
|
||||
elif model == "CLIP (EXT)":
|
||||
if self.clip_ext is not None:
|
||||
for clip_model in clip_ext_model:
|
||||
# Clip-Ext resets state.job system during runtime...
|
||||
job = state.job
|
||||
job_no = state.job_no
|
||||
job_count = state.job_count
|
||||
# Check for skipped job
|
||||
if state.skipped:
|
||||
print("Job skipped.")
|
||||
state.skipped = False
|
||||
continue
|
||||
# Check for interruption
|
||||
if state.interrupted:
|
||||
print("Job interrupted. Ending process.")
|
||||
state.interrupted = False
|
||||
break
|
||||
preliminary_interrogation = self.clip_ext.image_to_prompt(p.init_images[0], clip_ext_mode, clip_model)
|
||||
if unload_clip_models_afterwords:
|
||||
self.clip_ext.unload()
|
||||
self.debug_print(debug_mode, f"[CLIP ({clip_model}:{clip_ext_mode})]: [Result]: {preliminary_interrogation}")
|
||||
interrogation += f"{preliminary_interrogation}, "
|
||||
# Redeclare variables for state.job system
|
||||
state.job = job
|
||||
state.job_no = job_no
|
||||
state.job_count = job_count
|
||||
elif model == "WD (EXT)":
|
||||
if self.wd_ext_utils is not None:
|
||||
for wd_model in wd_ext_model:
|
||||
# Check for skipped job
|
||||
if state.skipped:
|
||||
print("Job skipped.")
|
||||
state.skipped = False
|
||||
continue
|
||||
# Check for interruption
|
||||
if state.interrupted:
|
||||
print("Job interrupted. Ending process.")
|
||||
state.interrupted = False
|
||||
break
|
||||
rating, tags = self.wd_ext_utils.interrogators[wd_model].interrogate(p.init_images[0])
|
||||
tags_list = [tag for tag, conf in tags.items() if conf > wd_threshold]
|
||||
if wd_underscore_fix:
|
||||
tags_spaced = [self.replace_underscores(tag) for tag in tags_list]
|
||||
preliminary_interrogation = ", ".join(tags_spaced)
|
||||
else:
|
||||
preliminary_interrogation = ", ".join(tags_list)
|
||||
if unload_wd_models_afterwords:
|
||||
self.wd_ext_utils.interrogators[wd_model].unload()
|
||||
self.debug_print(debug_mode, f"[WD ({wd_model}:{wd_threshold})]: [Result]: {preliminary_interrogation}")
|
||||
self.debug_print(debug_mode, f"[WD ({wd_model}:{wd_threshold})]: [Ratings]: {rating}")
|
||||
if wd_append_ratings:
|
||||
qualifying_ratings = [key for key, value in rating.items() if value >= wd_ratings]
|
||||
if qualifying_ratings:
|
||||
self.debug_print(wd_append_ratings, f"[WD ({wd_model}:{wd_threshold})]: Rating sensitivity set to {wd_ratings}, therefore rating is: {qualifying_ratings}")
|
||||
preliminary_interrogation += ", " + ", ".join(qualifying_ratings)
|
||||
else:
|
||||
self.debug_print(wd_append_ratings, f"[WD ({wd_model}:{wd_threshold})]: Rating sensitivity set to {wd_ratings}, unable to determine a rating! Perhaps the rating sensitivity is set too high.")
|
||||
interrogation += f"{preliminary_interrogation}, "
|
||||
|
||||
# Filter prevents overexaggeration of tags due to interrogation models having similar results
|
||||
if not exaggeration_mode:
|
||||
interrogation = self.clean_string(interrogation)
|
||||
|
||||
# Find and Replace user defined words in the interrogation prompt
|
||||
if use_custom_replace:
|
||||
replace_pairs = self.parse_replace_pairs(custom_replace_find, custom_replace_replacements)
|
||||
interrogation = self.custom_replace(interrogation, replace_pairs)
|
||||
|
||||
# Remove duplicate prompt content from interrogator prompt
|
||||
if use_positive_filter:
|
||||
interrogation = self.filter_words(interrogation, p.prompt)
|
||||
# Remove negative prompt content from interrogator prompt
|
||||
if use_negative_filter:
|
||||
interrogation = self.filter_words(interrogation, p.negative_prompt)
|
||||
# Remove custom prompt content from interrogator prompt
|
||||
if use_custom_filter:
|
||||
interrogation = self.filter_words(interrogation, custom_filter)
|
||||
|
||||
print(f"Prompt: {p.prompt}")
|
||||
|
||||
processed = process_images(p)
|
||||
|
||||
# Restore the UI elements we modified
|
||||
p.prompt = raw_prompt
|
||||
|
||||
return processed
|
||||
# Experimental tool for removing puncuations, but commas and a variety of emojis
|
||||
if no_puncuation_mode:
|
||||
interrogation = self.remove_punctuation(interrogation)
|
||||
|
||||
# This will weight the interrogation, and also ensure that trailing commas to the interrogation are correctly placed.
|
||||
if prompt_weight_mode:
|
||||
interrogation = f"({interrogation.rstrip(', ')}:{prompt_weight}), "
|
||||
else:
|
||||
interrogation = f"{interrogation.rstrip(', ')}, "
|
||||
|
||||
# Experimental reverse mode prep
|
||||
if not reverse_mode:
|
||||
prompt = p.prompt
|
||||
else:
|
||||
prompt = p.negative_prompt
|
||||
|
||||
# This will construct the prompt
|
||||
if prompt == "":
|
||||
prompt = interrogation
|
||||
elif in_front == "Append to prompt":
|
||||
prompt = f"{prompt.rstrip(', ')}, {interrogation}"
|
||||
else:
|
||||
prompt = f"{interrogation}{prompt}"
|
||||
|
||||
# Experimental reverse mode assignment
|
||||
if not reverse_mode:
|
||||
"""
|
||||
Note: p.prompt, p.all_prompts[0], and prompts[0]
|
||||
To get A1111 to record the updated prompt, p.all_prompts needs to be updated.
|
||||
But, in process_batch to update the stable diffusion prompt, prompts[0] needs to be updated.
|
||||
prompts[0] are already parsed for extra network syntax,
|
||||
"""
|
||||
p.prompt = prompt
|
||||
for i in range(len(p.all_prompts)):
|
||||
p.all_prompts[i] = prompt
|
||||
# As far as I can tell, prompts is a list that is always 1,
|
||||
# as it is just p.prompt without the extra network syntax
|
||||
# But since it is a list, I think it should be iterated through to future proof
|
||||
for i in range(len(prompts)):
|
||||
prompts[i] = re.sub("[<].*[>]", "", prompt)
|
||||
else:
|
||||
p.negative_prompt = prompt
|
||||
for i in range(len(p.all_negative_prompts)):
|
||||
p.all_negative_prompts[i] = prompt
|
||||
|
||||
# Restore Alpha Channel
|
||||
p.init_images[0] = init_image
|
||||
|
||||
# Prep for reset
|
||||
self.prompt_contamination = interrogation
|
||||
|
||||
# Prompt Output default is True
|
||||
self.debug_print(prompt_output or debug_mode, f"[Prompt]: {prompt}")
|
||||
|
||||
self.debug_print(debug_mode, f"End of {NAME} Process ({state.job_no+1}/{state.job_count})...")
|
||||
|
||||
#Startup Callbacks
|
||||
script_callbacks.on_app_started(Script.load_clip_ext_module_wrapper)
|
||||
script_callbacks.on_app_started(Script.load_wd_ext_module_wrapper)
|
||||
|
|
|
|||
Loading…
Reference in New Issue