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Prompt_Generator/.github/ISSUE_TEMPLATE/bug_report.md

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+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**What fork of Webui are you using (Eg: Automatic1111, vladmandic):**
+
+
+**Additional context**
+Add any other context about the problem here.

Prompt_Generator/.github/ISSUE_TEMPLATE/custom.md

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+---
+name: Custom issue template
+about: Describe this issue template's purpose here.
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+

Prompt_Generator/.github/ISSUE_TEMPLATE/feature_request.md

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+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

Prompt_Generator/.gitignore

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+/style.css

Prompt_Generator/README.md

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+# Prompt Generator
+
+Adds a tab to the webui that allows the user to generate a prompt from a small base prompt. Based on [FredZhang7/distilgpt2-stable-diffusion-v2](https://huggingface.co/FredZhang7/distilgpt2-stable-diffusion-v2) and [Gustavosta/MagicPrompt-Stable-Diffusion](https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion). I did nothing apart from porting it to [AUTOMATIC1111 WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
+
+
+
+![Screenshot 2023-04-29 000027](https://user-images.githubusercontent.com/8998556/235261664-2c92689d-9915-4543-8d6a-57a8ecd0f484.png)
+
+
+## Installation
+
+1. Install [AUTOMATIC1111's Stable Diffusion Webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
+2. Clone this repository into the `extensions` folder inside the webui
+
+## Usage
+
+1. Write in the prompt in the *Start of the prompt* text box
+2. Select which model you want to use
+3. Click Generate and wait
+
+The initial use of the model may take longer as it needs to be downloaded to your machine for offline use. The model will be used on your device and will be stored in the default location of `*username*/.cache/huggingface/hub/models`. The entire process of generating results will be done on your local machine and not require internet access.
+
+## Parameters Explanation
+
+- **Start of the prompt**: As the name suggests, the start of the prompt that the generator should start with
+- **Temperature**: A higher temperature will produce more diverse results, but with a higher risk of less coherent text
+- **Top K**: Strategy is to sample from a shortlist of the top K tokens. This approach allows the other high-scoring tokens a chance of being picked.
+- **Max Length**: the maximum number of tokens for the output of the model
+- **Repetition Penalty**: The parameter for repetition penalty. 1.0 means no penalty. See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details. Default setting is 1.2
+- **How Many To Generate**: The number of results to generate
+- **Use blacklist?**: Using `.\extensions\stable-diffusion-webui-Prompt_Generator\blacklist.txt`. It will delete any matches to the generated result (case insensitive). Each item to be filtered out should be on a new line. *Be aware that it simply deletes it and doesn't generate more to make up for the lost words*
+- **Use punctuation**: Allows the use of commas in the output
+
+## Models
+
+There are two 'default' models provided:
+
+### FredZhang7
+
+Made by [FredZhang7](https://huggingface.co/FredZhang7) under creativeml-openrail-m license. 
+
+Useful to get styles for a prompt. Eg: "A cat sitting" -> "A cat sitting on a chair, digital art. The room is made of clay and metal with the sun shining through in front trending at Artstation 4k uhd..."
+
+### MagicPrompt
+
+Made by [Gustavosta](https://huggingface.co/Gustavosta) under the MIT license. 
+
+Useful to get more natural language prompts. Eg: "A cat sitting" -> "A cat sitting in a chair, wearing pair of sunglasses"
+
+*Be aware that sometimes the model fails to produce anything or less than the wanted amount, either try again or use a new prompt in that case*
+
+## Install more models
+
+To install more model to use, ensure that the models are hosted on [huggingface.co](https://huggingface.co) and edit the json file at `.\extensions\stable-diffusion-webui-Prompt_Generator\models.json` with the relevant information. Use the models in the file as an example
+
+You might need to restart the extension/reload the UI if new items are added onto the list
+
+## Credits
+
+Credits to both [FredZhang7](https://huggingface.co/FredZhang7) and [Gustavosta](https://huggingface.co/Gustavosta)

Prompt_Generator/install.py

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+import launch
+if not launch.is_installed("transformers"):
+    launch.run_pip("install --upgrade transformers", "Requirement of Prompt-Maker")
+if not launch.is_installed("torch"):
+    launch.run_pip("install --upgrade torch", "Requirement of Prompt-Maker")

Prompt_Generator/javascript/prompt_generator_hints.js

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+//Basically copied and adapted from AUTOMATIC1111 implementation of the main UI
+// mouseover tooltips for various UI elements in the form of "UI element label"="Tooltip text".
+
+prompt_generator_titles = {
+    "Temperature": "A higher temperature will produce more diverse results, but with a higher risk of less coherent text",
+    "Max Length": "The maximum number of tokens for the output of the model",
+    "Top K": "Strategy is to sample from a shortlist of the top K tokens. This approach allows the other high-scoring tokens a chance of being picked.",
+    "Repetition Penalty": "The parameter for repetition penalty. 1.0 means no penalty. Default setting is 1.2. Paper explaining it is linked to Github's readme",
+    "How Many To Generate":"The number of results to generate. Not guaranteed if models fails to create them",
+    "Generate Using Magic Prompt":"Be aware that sometimes the model fails to produce anything or less than the wanted amount, either try again or use a new prompt in that case"
+}
+
+onUiUpdate(function(){
+	gradioApp().querySelectorAll('span, button, select, p').forEach(function(span){
+		tooltip = prompt_generator_titles[span.textContent];
+
+		if(!tooltip){
+		    tooltip = prompt_generator_titles[span.value];
+		}
+
+		if(!tooltip){
+			for (const c of span.classList) {
+				if (c in prompt_generator_titles) {
+					tooltip = prompt_generator_titles[c];
+					break;
+				}
+			}
+		}
+
+		if(tooltip){
+			span.title = tooltip;
+		}
+	})
+
+	gradioApp().querySelectorAll('select').forEach(function(select){
+	    if (select.onchange != null) return;
+
+	    select.onchange = function(){
+            select.title = prompt_generator_titles[select.value] || "";
+	    }
+	})
+})

Prompt_Generator/licence

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+MIT License
+
+Copyright (c) 2023 imrayya
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Prompt_Generator/models.json

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+[
+    {
+        "Title":"Gustavosta",
+        "Tokenizer":"gpt2",
+        "Model":"Gustavosta/MagicPrompt-Dalle"
+    },
+    {
+        "Title":"FredZhang7",
+        "Tokenizer":"distilgpt2",
+        "Model":"FredZhang7/distilgpt2-stable-diffusion-v2"
+    }
+]

Prompt_Generator/scripts/prompt_generator.py

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+"""
+Copyright 2023 Imrayya
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+"""
+
+
+import json
+import re
+
+import gradio as gr
+import modules
+from pathlib import Path
+from modules import script_callbacks
+import modules.scripts as scripts
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+result_prompt = ""
+models = {}
+max_no_results = 20  # TODO move to setting panel
+base_dir = scripts.basedir()
+model_file = Path(base_dir, "models.json")
+
+
+class Model:
+    '''
+    Small strut to hold data for the text generator
+    '''
+
+    def __init__(self, name, model, tokenizer) -> None:
+        self.name = name
+        self.model = model
+        self.tokenizer = tokenizer
+        pass
+
+
+def populate_models():
+    """Get the models that this extension can use via models.json
+    """
+    # TODO add button to refresh and update model list
+    path = model_file
+    with open(path, 'r') as f:
+        data = json.load(f)
+    for item in data:
+        name = item["Title"]
+        model = item["Model"]
+        tokenizer = item["Tokenizer"]
+        models[name] = Model(name, model, tokenizer)
+
+
+def add_to_prompt(prompt):  # A holder TODO figure out how to get rid of it
+    return prompt
+
+
+def get_list_blacklist():
+    # Set the directory you want to start from
+    file_path = './extensions/stable-diffusion-webui-Prompt_Generator/blacklist.txt'
+    things_to_black_list = []
+    with open(file_path, 'r') as f:
+        # Read each line in the file and append it to the list
+        for line in f:
+            things_to_black_list.append(line.rstrip())
+
+    return things_to_black_list
+
+
+def on_ui_tabs():
+    # Method to create the extended prompt
+    def generate_longer_generic(prompt, temperature, top_k,
+                                max_length, repetition_penalty,
+                                num_return_sequences, name, use_punctuation=False,
+                                use_blacklist=False):  # TODO make the progress bar work
+        """Generates a longer string from the input
+
+        Args:
+            prompt (str): As the name suggests, the start of the prompt that the generator should start with.
+
+            temperature (float): A higher temperature will produce more diverse results, but with a higher risk of less coherent text
+
+            top_k (float): Strategy is to sample from a shortlist of the top K tokens. This approach allows the other high-scoring tokens a chance of being picked.
+
+            max_length (int): the maximum number of tokens for the output of the model
+
+            repetition_penalty (float): The parameter for repetition penalty. 1.0 means no penalty. Default setting is 1.2
+
+            num_return_sequences (int): The number of results to generate
+
+            name (str): Which Model to use
+
+            use_punctuation (bool): Allows the use of commas in the output. Defaults to False.
+
+            use_blacklist (bool): It will delete any matches to the generated result (case insensitive). Each item to be filtered out should be on a new line. Defaults to False.
+
+        Returns:
+            Returns only an error otherwise saves it in result_prompt
+        """
+        try:
+            print("[Prompt_Generator]:","Loading Tokenizer")
+            tokenizer = GPT2Tokenizer.from_pretrained(models[name].tokenizer)
+            tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+            print("[Prompt_Generator]:","Loading Model")
+            model = GPT2LMHeadModel.from_pretrained(models[name].model)
+        except Exception as e:
+            print("[Prompt_Generator]:",f"Exception encountered while attempting to install tokenizer")
+            return gr.update(), f"Error: {e}"
+        try:
+            print("[Prompt_Generator]:",f"Generate new prompt from: \"{prompt}\" with {name}")
+            input_ids = tokenizer(prompt, return_tensors='pt').input_ids
+            if (use_punctuation):
+                output = model.generate(input_ids, do_sample=True, temperature=temperature,
+                                        top_k=round(top_k), max_length=max_length,
+                                        num_return_sequences=num_return_sequences,
+                                        repetition_penalty=float(
+                                            repetition_penalty),
+                                        early_stopping=True)
+            else:
+                output = model.generate(input_ids, do_sample=True, temperature=temperature,
+                                        top_k=round(top_k), max_length=max_length,
+                                        num_return_sequences=num_return_sequences,
+                                        repetition_penalty=float(
+                                            repetition_penalty),
+                                        penalty_alpha=0.6, no_repeat_ngram_size=1,
+                                        early_stopping=True)
+            print("[Prompt_Generator]:","Generation complete!")
+            tempString = ""
+            if (use_blacklist):
+                blacklist = get_list_blacklist()
+            for i in range(len(output)):
+
+                tempString += tokenizer.decode(
+                    output[i], skip_special_tokens=True) + "\n"
+
+                if (use_blacklist):
+                    for to_check in blacklist:
+                        tempString = re.sub(
+                            to_check, "", tempString, flags=re.IGNORECASE)
+                if (i == 0):
+                    global result_prompt
+
+            result_prompt = tempString
+            # print(result_prompt)
+        except Exception as e:
+            print("[Prompt_Generator]:",
+                f"Exception encountered while attempting to generate prompt: {e}")
+            return gr.update(), f"Error: {e}"
+
+    def ui_dynamic_result_visible(num):
+        """Makes the results visible"""
+        k = int(num)
+        return [gr.Row.update(visible=True)]*k + [gr.Row.update(visible=False)]*(max_no_results-k)
+
+    def ui_dynamic_result_prompts():
+        """Populates the results with the prompts"""
+
+        lines = result_prompt.splitlines()
+        num = len(lines)
+        result_list = []
+        for i in range(int(max_no_results)):
+            if (i < num):
+                result_list.append(lines[i])
+            else:
+                result_list.append("")
+        return result_list
+
+    def ui_dynamic_result_batch():
+        return result_prompt
+
+    def save_prompt_to_file(path, append: bool):
+        if len(result_prompt) == 0:
+            print("[Prompt_Generator]:","Prompt is empty")
+            return
+        with open(path, encoding="utf-8", mode="a" if append else "w") as f:
+            f.write(result_prompt)
+        print("[Prompt_Generator]:","Prompt written to: ", path)
+
+    # ----------------------------------------------------------------------------
+    # UI structure
+    txt2img_prompt = modules.ui.txt2img_paste_fields[0][0]
+    img2img_prompt = modules.ui.img2img_paste_fields[0][0]
+
+    with gr.Blocks(analytics_enabled=False) as prompt_generator:
+        # Handles UI for prompt creation
+        with gr.Column():
+            with gr.Row():
+                prompt_textbox = gr.Textbox(
+                    lines=2, elem_id="promptTxt", label="Start of the prompt")
+        with gr.Column():
+            gr.HTML(
+                "Mouse over the labels to access tooltips that provide explanations for the parameters.")
+            with gr.Row():
+                temp_slider = gr.Slider(
+                    elem_id="temp_slider", label="Temperature", interactive=True, minimum=0, maximum=1, value=0.9)
+                maxLength_slider = gr.Slider(
+                    elem_id="max_length_slider", label="Max Length", interactive=True, minimum=1, maximum=200, step=1, value=90)
+                topK_slider = gr.Slider(
+                    elem_id="top_k_slider", label="Top K", value=8, minimum=1, maximum=20, step=1, interactive=True)
+        with gr.Column():
+            with gr.Row():
+                repetitionPenalty_slider = gr.Slider(
+                    elem_id="repetition_penalty_slider", label="Repetition Penalty", value=1.2, minimum=0.1, maximum=10, interactive=True)
+                numReturnSequences_slider = gr.Slider(
+                    elem_id="num_return_sequences_slider", label="How Many To Generate", value=5, minimum=1, maximum=max_no_results, interactive=True, step=1)
+        with gr.Column():
+            with gr.Row():
+                useBlacklist_checkbox = gr.Checkbox(label="Use blacklist?")
+                gr.HTML(value="<center>Using <code>\".\extensions\stable-diffusion-webui-Prompt_Generator\\blacklist.txt</code>\".<br>It will delete any matches to the generated result (case insensitive).</center>")
+        with gr.Column():
+            with gr.Row():
+                populate_models()
+                generate_dropdown = gr.Dropdown(choices=list(models.keys()), value=list(models.keys())[
+                                                1 if len(models) > 0 else 0], label="Which model to use?", show_label=True)  # TODO Add default to setting page
+                use_punctuation_check = gr.Checkbox(label="Use punctuation?")
+                generate_button = gr.Button(
+                    value="Generate", elem_id="generate_button")  # TODO Add element to show that it is working in the background so users don't think nothing is happening
+
+        # Handles UI for results
+        results_vis = []
+        results_txt_list = []
+        with gr.Tab("Results"):
+            with gr.Column():
+                for i in range(max_no_results):
+                    with gr.Row(visible=False) as row:
+                        # Doesn't seem to do anything
+                        row.style(equal_height=True)
+                        with gr.Column(scale=3):  # Guessing at the scale
+                            textBox = gr.Textbox(label="", lines=3)
+                        with gr.Column(scale=1):
+                            txt2img = gr.Button("send to txt2img")
+                            img2img = gr.Button("send to img2img")
+                        # Handles ___2img buttons
+                        txt2img.click(add_to_prompt, inputs=[
+                            textBox], outputs=[txt2img_prompt]).then(None, _js='switch_to_txt2img',
+                                                                     inputs=None, outputs=None)
+                        img2img.click(add_to_prompt, inputs=[
+                            textBox], outputs=[img2img_prompt]).then(None, _js='switch_to_img2img',
+                                                                     inputs=None, outputs=None)
+                        results_txt_list.append(textBox)
+                    results_vis.append(row)
+        with gr.Tab("Batch"):
+            with gr.Column():
+                batch_texbox = gr.Textbox("", label="Results")
+                with gr.Row():
+                    with gr.Column(scale=4):
+                        savePathText = gr.Textbox(
+                            Path(base_dir, "batch_prompt.txt"), label="Path", interactive=True)
+                    with gr.Column(scale=1):
+                        append_checkBox = gr.Checkbox(label="Append")
+                        save_button = gr.Button("Save To file")
+
+        # ----------------------------------------------------------------------------------
+        # Handle buttons
+        save_button.click(fn=save_prompt_to_file, inputs=[
+            savePathText, append_checkBox])
+        # Please note that we use `.then()` to run other ui elements after the generation is done
+        generate_button.click(fn=generate_longer_generic, inputs=[
+            prompt_textbox, temp_slider, topK_slider, maxLength_slider,
+            repetitionPenalty_slider, numReturnSequences_slider,
+            generate_dropdown, use_punctuation_check, useBlacklist_checkbox]).then(
+            fn=ui_dynamic_result_visible, inputs=numReturnSequences_slider,
+            outputs=results_vis).then(
+            fn=ui_dynamic_result_prompts, outputs=results_txt_list).then(fn=ui_dynamic_result_batch, outputs=batch_texbox)
+    return (prompt_generator, "Prompt Generator", "Prompt Generator"),
+
+
+script_callbacks.on_ui_tabs(on_ui_tabs)

adetailer/.github/ISSUE_TEMPLATE/bug_report.yaml

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+name: Bug report
+description: Create a report
+title: "[Bug]: "
+labels:
+  - bug
+
+body:
+  - type: textarea
+    attributes:
+      label: Describe the bug
+      description: A clear and concise description of what the bug is.
+      placeholder: |
+        Any language accepted
+        아무 언어 사용가능
+        すべての言語に対応
+        接受所有语言
+        Se aceptan todos los idiomas
+        Alle Sprachen werden akzeptiert
+        Toutes les langues sont acceptées
+        Принимаются все языки
+
+  - type: textarea
+    attributes:
+      label: Screenshots
+      description: Screenshots related to the issue.
+
+  - type: textarea
+    attributes:
+      label: Console logs, from start to end.
+      description: |
+        The full console log of your terminal.
+      placeholder: |
+        Python ...
+        Version: ...
+        Commit hash: ...
+        Installing requirements
+        ...
+
+        Launching Web UI with arguments: ...
+        [-] ADetailer initialized. version: ...
+        ...
+        ...
+
+        Traceback (most recent call last):
+        ...
+        ...
+      render: Shell
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: List of installed extensions

adetailer/.github/ISSUE_TEMPLATE/feature_request.yaml

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+name: Feature request
+description: Suggest an idea for this project
+title: "[Feature Request]: "
+
+body:
+  - type: textarea
+    attributes:
+      label: Is your feature request related to a problem? Please describe.
+      description: A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+  - type: textarea
+    attributes:
+      label: Describe the solution you'd like
+      description: A clear and concise description of what you want to happen.
+
+  - type: textarea
+    attributes:
+      label: Describe alternatives you've considered
+      description: A clear and concise description of any alternative solutions or features you've considered.
+
+  - type: textarea
+    attributes:
+      label: Additional context
+      description: Add any other context or screenshots about the feature request here.

adetailer/.github/ISSUE_TEMPLATE/question.yaml

@@ -0,0 +1,10 @@
+name: Question
+description: Write a question
+labels:
+  - question
+
+body:
+  - type: textarea
+    attributes:
+      label: Question
+      description: Please do not write bug reports or feature requests here.

adetailer/.github/workflows/stale.yml

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+name: 'Close stale issues and PRs'
+on:
+  schedule:
+    - cron: '30 1 * * *'
+
+jobs:
+  stale:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/stale@v8
+        with:
+          days-before-stale: 23
+          days-before-close: 3

adetailer/.gitignore

@@ -0,0 +1,196 @@
+# Created by https://www.toptal.com/developers/gitignore/api/python,visualstudiocode
+# Edit at https://www.toptal.com/developers/gitignore?templates=python,visualstudiocode
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+### Python Patch ###
+# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
+poetry.toml
+
+# ruff
+.ruff_cache/
+
+# LSP config files
+pyrightconfig.json
+
+### VisualStudioCode ###
+.vscode/*
+!.vscode/settings.json
+!.vscode/tasks.json
+!.vscode/launch.json
+!.vscode/extensions.json
+!.vscode/*.code-snippets
+
+# Local History for Visual Studio Code
+.history/
+
+# Built Visual Studio Code Extensions
+*.vsix
+
+### VisualStudioCode Patch ###
+# Ignore all local history of files
+.history
+.ionide
+
+# End of https://www.toptal.com/developers/gitignore/api/python,visualstudiocode
+*.ipynb

adetailer/.pre-commit-config.yaml

@@ -0,0 +1,19 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.4.0
+    hooks:
+      - id: trailing-whitespace
+        args: [--markdown-linebreak-ext=md]
+      - id: end-of-file-fixer
+      - id: mixed-line-ending
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: "v0.0.280"
+    hooks:
+      - id: ruff
+        args: [--fix, --exit-non-zero-on-fix]
+
+  - repo: https://github.com/psf/black
+    rev: 23.7.0
+    hooks:
+      - id: black

adetailer/CHANGELOG.md

@@ -0,0 +1,275 @@
+# Changelog
+
+## 2023-07-31
+
+- v23.7.11
+- separate clip skip 옵션 추가
+- install requirements 정리 (ultralytics 새 버전, mediapipe~=3.20)
+
+## 2023-07-28
+
+- v23.7.10
+- ultralytics, mediapipe import문 정리
+- traceback에서 컬러를 없앰 (api 때문), 라이브러리 버전도 보여주게 설정.
+- huggingface_hub, pydantic을 install.py에서 없앰
+- 안쓰는 컨트롤넷 관련 코드 삭제
+
+
+## 2023-07-23
+
+- v23.7.9
+- `ultralytics.utils` ModuleNotFoundError 해결 (https://github.com/ultralytics/ultralytics/issues/3856)
+- `pydantic` 2.0 이상 버전 설치안되도록 함
+- `controlnet_dir` cmd args 문제 수정 (PR #107)
+
+## 2023-07-20
+
+- v23.7.8
+- `paste_field_names` 추가했던 것을 되돌림
+
+## 2023-07-19
+
+- v23.7.7
+- 인페인팅 단계에서 별도의 샘플러를 선택할 수 있게 옵션을 추가함 (xyz그리드에도 추가)
+- webui 1.0.0-pre 이하 버전에서 batch index 문제 수정
+- 스크립트에 `paste_field_names`을 추가함. 사용되는지는 모르겠음
+
+## 2023-07-16
+
+- v23.7.6
+- `ultralytics 8.0.135`에 추가된 cpuinfo 기능을 위해 `py-cpuinfo`를 미리 설치하게 함. (미리 설치 안하면 cpu나 mps사용할 때 재시작해야함)
+- init_image가 RGB 모드가 아닐 때 RGB로 변경.
+
+## 2023-07-07
+
+- v23.7.4
+- batch count > 1일때 프롬프트의 인덱스 문제 수정
+
+- v23.7.5
+- i2i의 `cached_uc`와 `cached_c`가 p의 `cached_uc`와 `cached_c`가 다른 인스턴스가 되도록 수정
+
+## 2023-07-05
+
+- v23.7.3
+- 버그 수정
+  - `object()`가 json 직렬화 안되는 문제
+  - `process`를 호출함에 따라 배치 카운트가 2이상일 때, all_prompts가 고정되는 문제
+  - `ad-before`와 `ad-preview` 이미지 파일명이 실제 파일명과 다른 문제
+  - pydantic 2.0 호환성 문제
+
+## 2023-07-04
+
+- v23.7.2
+- `mediapipe_face_mesh_eyes_only` 모델 추가: `mediapipe_face_mesh`로 감지한 뒤 눈만 사용함.
+- 매 배치 시작 전에 `scripts.postprocess`를, 후에 `scripts.process`를 호출함.
+  - 컨트롤넷을 사용하면 소요 시간이 조금 늘어나지만 몇몇 문제 해결에 도움이 됨.
+- `lora_block_weight`를 스크립트 화이트리스트에 추가함.
+  - 한번이라도 ADetailer를 사용한 사람은 수동으로 추가해야함.
+
+## 2023-07-03
+
+- v23.7.1
+- `process_images`를 진행한 뒤 `StableDiffusionProcessing` 오브젝트의 close를 호출함
+- api 호출로 사용했는지 확인하는 속성 추가
+- `NansException`이 발생했을 때 중지하지 않고 남은 과정 계속 진행함
+
+## 2023-07-02
+
+- v23.7.0
+- `NansException`이 발생하면 로그에 표시하고 원본 이미지를 반환하게 설정
+- `rich`를 사용한 에러 트레이싱
+  - install.py에 `rich` 추가
+- 생성 중에 컴포넌트의 값을 변경하면 args의 값도 함께 변경되는 문제 수정 (issue #180)
+- 터미널 로그로 ad_prompt와 ad_negative_prompt에 적용된 실제 프롬프트 확인할 수 있음 (입력과 다를 경우에만)
+
+## 2023-06-28
+
+- v23.6.4
+- 최대 모델 수 5 -> 10개
+- ad_prompt와 ad_negative_prompt에 빈칸으로 놔두면 입력 프롬프트가 사용된다는 문구 추가
+- huggingface 모델 다운로드 실패시 로깅
+- 1st 모델이 `None`일 경우 나머지 입력을 무시하던 문제 수정
+- `--use-cpu` 에 `adetailer` 입력 시 cpu로 yolo모델을 사용함
+
+## 2023-06-20
+
+- v23.6.3
+- 컨트롤넷 inpaint 모델에 대해, 3가지 모듈을 사용할 수 있도록 함
+- Noise Multiplier 옵션 추가 (PR #149)
+- pydantic 최소 버전 1.10.8로 설정 (Issue #146)
+
+## 2023-06-05
+
+- v23.6.2
+- xyz_grid에서 ADetailer를 사용할 수 있게함.
+  - 8가지 옵션만 1st 탭에 적용되도록 함.
+
+## 2023-06-01
+
+- v23.6.1
+- `inpaint, scribble, lineart, openpose, tile` 5가지 컨트롤넷 모델 지원 (PR #107)
+- controlnet guidance start, end 인자 추가 (PR #107)
+- `modules.extensions`를 사용하여 컨트롤넷 확장을 불러오고 경로를 알아내로록 변경
+- ui에서 컨트롤넷을 별도 함수로 분리
+
+## 2023-05-30
+
+- v23.6.0
+- 스크립트의 이름을 `After Detailer`에서 `ADetailer`로 변경
+  - API 사용자는 변경 필요함
+- 몇몇 설정 변경
+  - `ad_conf` → `ad_confidence`. 0~100 사이의 int → 0.0~1.0 사이의 float
+  - `ad_inpaint_full_res` → `ad_inpaint_only_masked`
+  - `ad_inpaint_full_res_padding` → `ad_inpaint_only_masked_padding`
+- mediapipe face mesh 모델 추가
+  - mediapipe 최소 버전 `0.10.0`
+
+- rich traceback 제거함
+- huggingface 다운로드 실패할 때 에러가 나지 않게 하고 해당 모델을 제거함
+
+## 2023-05-26
+
+- v23.5.19
+- 1번째 탭에도 `None` 옵션을 추가함
+- api로 ad controlnet model에 inpaint가 아닌 다른 컨트롤넷 모델을 사용하지 못하도록 막음
+- adetailer 진행중에 total tqdm 진행바 업데이트를 멈춤
+- state.inturrupted 상태에서 adetailer 과정을 중지��
+- 컨트롤넷 process를 각 batch가 끝난 순간에만 호출하도록 변경
+
+### 2023-05-25
+
+- v23.5.18
+- 컨트롤넷 관련 수정
+  - unit의 `input_mode`를 `SIMPLE`로 모두 변경
+  - 컨트롤넷 유넷 훅과 하이잭 함수들을 adetailer를 실행할 때에만 되돌리는 기능 추가
+  - adetailer 처리가 끝난 뒤 컨트롤넷 스크립트의 process를 다시 진행함. (batch count 2 이상일때의 문제 해결)
+- 기본 활성 스크립트 목록에서 컨트롤넷을 뺌
+
+### 2023-05-22
+
+- v23.5.17
+- 컨트롤넷 확장이 있으면 컨트롤넷 스크립트를 활성화함. (컨트롤넷 관련 문제 해결)
+- 모든 컴포넌트에 elem_id 설정
+- ui에 버전을 표시함
+
+
+### 2023-05-19
+
+- v23.5.16
+- 추가한 옵션
+  - Mask min/max ratio
+  - Mask merge mode
+  - Restore faces after ADetailer
+- 옵션들을 Accordion으로 묶음
+
+### 2023-05-18
+
+- v23.5.15
+- 필요한 것만 임포트하도록 변경 (vae 로딩 오류 없어짐. 로딩 속도 빨라짐)
+
+### 2023-05-17
+
+- v23.5.14
+- `[SKIP]`으로 ad prompt 일부를 건너뛰는 기능 추가
+- bbox 정렬 옵션 추가
+- sd_webui 타입힌트를 만들어냄
+- enable checker와 관련된 api 오류 수정?
+
+### 2023-05-15
+
+- v23.5.13
+- `[SEP]`으로 ad prompt를 분리하여 적용하는 기능 추가
+- enable checker를 다시 pydantic으로 변경함
+- ui 관련 함수를 adetailer.ui 폴더로 분리함
+- controlnet을 사용할 때 모든 controlnet unit 비활성화
+- adetailer 폴더가 없으면 만들게 함
+
+### 2023-05-13
+
+- v23.5.12
+- `ad_enable`을 제외한 입력이 dict타입으로 들어오도록 변경
+  - web api로 사용할 때에 특히 사용하기 쉬움
+  - web api breaking change
+- `mask_preprocess` 인자를 넣지 않았던 오류 수정 (PR #47)
+- huggingface에서 모델을 다운로드하지 않는 옵션 추가 `--ad-no-huggingface`
+
+### 2023-05-12
+
+- v23.5.11
+- `ultralytics` 알람 제거
+- 필요없는 exif 인자 더 제거함
+- `use separate steps` 옵션 추가
+- ui 배치를 조정함
+
+### 2023-05-09
+
+- v23.5.10
+- 선택한 스크립트만 ADetailer에 적용하는 옵션 추가, 기본값 `True`. 설정 탭에서 지정가능.
+  - 기본값: `dynamic_prompting,dynamic_thresholding,wildcards,wildcard_recursive`
+- `person_yolov8s-seg.pt` 모델 추가
+- `ultralytics`의 최소 버전을 `8.0.97`로 설정 (C:\\ 문제 해결된 버전)
+
+### 2023-05-08
+
+- v23.5.9
+- 2가지 이상의 모델을 사용할 수 있음. 기본값: 2, 최대: 5
+- segment 모델을 사용할 수 있게 함. `person_yolov8n-seg.pt` 추가
+
+### 2023-05-07
+
+- v23.5.8
+- 프롬프트와 네거티브 프롬프트에 방향키 지원 (PR #24)
+- `mask_preprocess`를 추가함. 이전 버전과 시드값이 달라질 가능성 있음!
+- 이미지 처리가 일어났을 때에만 before이미지를 저장함
+- 설정창의 레이블을 ADetailer 대신 더 적절하게 수정함
+
+### 2023-05-06
+
+- v23.5.7
+- `ad_use_cfg_scale` 옵션 추가. cfg 스케일을 따로 사용할지 말지 결정함.
+- `ad_enable` 기본값을 `True`에서 `False`로 변경
+- `ad_model`의 기본값을 `None`에서 첫번째 모델로 변경
+- 최소 2개의 입력(ad_enable, ad_model)만 들어오면 작동하게 변경.
+
+- v23.5.7.post0
+- `init_controlnet_ext`을 controlnet_exists == True일때에만 실행
+- webui를 C드라이브 바로 밑에 설치한 사람들에게 `ultralytics` 경고 표시
+
+### 2023-05-05 (어린이날)
+
+- v23.5.5
+- `Save images before ADetailer` 옵션 추가
+- 입력으로 들어온 인자와 ALL_ARGS의 길이가 다르면 에러메세지
+- README.md에 설치방법 추가
+
+- v23.5.6
+- get_args에서 IndexError가 발생하면 자세한 에러메세지를 볼 수 있음
+- AdetailerArgs에 extra_params 내장
+- scripts_args를 딥카피함
+- postprocess_image를 약간 분리함
+
+- v23.5.6.post0
+- `init_controlnet_ext`에서 에러메세지를 자세히 볼 수 있음
+
+### 2023-05-04
+
+- v23.5.4
+- use pydantic for arguments validation
+- revert: ad_model to `None` as default
+- revert: `__future__` imports
+- lazily import yolo and mediapipe
+
+### 2023-05-03
+
+- v23.5.3.post0
+- remove `__future__` imports
+- change to copy scripts and scripts args
+
+- v23.5.3.post1
+- change default ad_model from `None`
+
+### 2023-05-02
+
+- v23.5.3
+- Remove `None` from model list and add `Enable ADetailer` checkbox.
+- install.py `skip_install` fix.

adetailer/LICENSE.md

@@ -0,0 +1,662 @@
+
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU Affero General Public License is a free, copyleft license for
+software and other kinds of works, specifically designed to ensure
+cooperation with the community in the case of network server software.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+our General Public Licenses are intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  Developers that use our General Public Licenses protect your rights
+with two steps: (1) assert copyright on the software, and (2) offer
+you this License which gives you legal permission to copy, distribute
+and/or modify the software.
+
+  A secondary benefit of defending all users' freedom is that
+improvements made in alternate versions of the program, if they
+receive widespread use, become available for other developers to
+incorporate.  Many developers of free software are heartened and
+encouraged by the resulting cooperation.  However, in the case of
+software used on network servers, this result may fail to come about.
+The GNU General Public License permits making a modified version and
+letting the public access it on a server without ever releasing its
+source code to the public.
+
+  The GNU Affero General Public License is designed specifically to
+ensure that, in such cases, the modified source code becomes available
+to the community.  It requires the operator of a network server to
+provide the source code of the modified version running there to the
+users of that server.  Therefore, public use of a modified version, on
+a publicly accessible server, gives the public access to the source
+code of the modified version.
+
+  An older license, called the Affero General Public License and
+published by Affero, was designed to accomplish similar goals.  This is
+a different license, not a version of the Affero GPL, but Affero has
+released a new version of the Affero GPL which permits relicensing under
+this license.
+
+  The precise terms and conditions for copying, distribution and
+modification follow.
+
+                       TERMS AND CONDITIONS
+
+  0. Definitions.
+
+  "This License" refers to version 3 of the GNU Affero General Public License.
+
+  "Copyright" also means copyright-like laws that apply to other kinds of
+works, such as semiconductor masks.
+
+  "The Program" refers to any copyrightable work licensed under this
+License.  Each licensee is addressed as "you".  "Licensees" and
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+                     END OF TERMS AND CONDITIONS
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+<http://www.gnu.org/licenses/>.

adetailer/README.md

@@ -0,0 +1,94 @@
+# !After Detailer
+
+!After Detailer is a extension for stable diffusion webui, similar to Detection Detailer, except it uses ultralytics instead of the mmdet.
+
+## Install
+
+(from Mikubill/sd-webui-controlnet)
+
+1. Open "Extensions" tab.
+2. Open "Install from URL" tab in the tab.
+3. Enter `https://github.com/Bing-su/adetailer.git` to "URL for extension's git repository".
+4. Press "Install" button.
+5. Wait 5 seconds, and you will see the message "Installed into stable-diffusion-webui\extensions\adetailer. Use Installed tab to restart".
+6. Go to "Installed" tab, click "Check for updates", and then click "Apply and restart UI". (The next time you can also use this method to update extensions.)
+7. Completely restart A1111 webui including your terminal. (If you do not know what is a "terminal", you can reboot your computer: turn your computer off and turn it on again.)
+
+You can now install it directly from the Extensions tab.
+
+![image](https://i.imgur.com/g6GdRBT.png)
+
+You **DON'T** need to download any model from huggingface.
+
+## Options
+
+| Model, Prompts                    |                                       |                                                   |
+| --------------------------------- | ------------------------------------- | ------------------------------------------------- |
+| ADetailer model                   | Determine what to detect.             | `None` = disable                                  |
+| ADetailer prompt, negative prompt | Prompts and negative prompts to apply | If left blank, it will use the same as the input. |
+
+| Detection                            |                                                                                              |     |
+| ------------------------------------ | -------------------------------------------------------------------------------------------- | --- |
+| Detection model confidence threshold | Only objects with a detection model confidence above this threshold are used for inpainting. |     |
+| Mask min/max ratio                   | Only use masks whose area is between those ratios for the area of the entire image.          |     |
+
+If you want to exclude objects in the background, try setting the min ratio to around `0.01`.
+
+| Mask Preprocessing              |                                                                                                                                     |                                                                                         |
+| ------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- |
+| Mask x, y offset                | Moves the mask horizontally and vertically by                                                                                       |                                                                                         |
+| Mask erosion (-) / dilation (+) | Enlarge or reduce the detected mask.                                                                                                | [opencv example](https://docs.opencv.org/4.7.0/db/df6/tutorial_erosion_dilatation.html) |
+| Mask merge mode                 | `None`: Inpaint each mask<br/>`Merge`: Merge all masks and inpaint<br/>`Merge and Invert`: Merge all masks and Invert, then inpaint |                                                                                         |
+
+Applied in this order: x, y offset → erosion/dilation → merge/invert.
+
+#### Inpainting
+
+Each option corresponds to a corresponding option on the inpaint tab. Therefore, please refer to the inpaint tab for usage details on how to use each option.
+
+## ControlNet Inpainting
+
+You can use the ControlNet extension if you have ControlNet installed and ControlNet models.
+
+Support `inpaint, scribble, lineart, openpose, tile` controlnet models. Once you choose a model, the preprocessor is set automatically. It works separately from the model set by the Controlnet extension.
+
+## Advanced Options
+
+API request example: [wiki/API](https://github.com/Bing-su/adetailer/wiki/API)
+
+`ui-config.json` entries: [wiki/ui-config.json](https://github.com/Bing-su/adetailer/wiki/ui-config.json)
+
+`[SEP], [SKIP]` tokens: [wiki/Advanced](https://github.com/Bing-su/adetailer/wiki/Advanced)
+
+## Media
+
+- 🎥 [どこよりも詳しいAfter Detailer (adetailer)の使い方① 【Stable Diffusion】](https://youtu.be/sF3POwPUWCE)
+- 🎥 [どこよりも詳しいAfter Detailer (adetailer)の使い方② 【Stable Diffusion】](https://youtu.be/urNISRdbIEg)
+
+## Model
+
+| Model                 | Target                | mAP 50                        | mAP 50-95                     |
+| --------------------- | --------------------- | ----------------------------- | ----------------------------- |
+| face_yolov8n.pt       | 2D / realistic face   | 0.660                         | 0.366                         |
+| face_yolov8s.pt       | 2D / realistic face   | 0.713                         | 0.404                         |
+| hand_yolov8n.pt       | 2D / realistic hand   | 0.767                         | 0.505                         |
+| person_yolov8n-seg.pt | 2D / realistic person | 0.782 (bbox)<br/>0.761 (mask) | 0.555 (bbox)<br/>0.460 (mask) |
+| person_yolov8s-seg.pt | 2D / realistic person | 0.824 (bbox)<br/>0.809 (mask) | 0.605 (bbox)<br/>0.508 (mask) |
+| mediapipe_face_full   | realistic face        | -                             | -                             |
+| mediapipe_face_short  | realistic face        | -                             | -                             |
+| mediapipe_face_mesh   | realistic face        | -                             | -                             |
+
+The yolo models can be found on huggingface [Bingsu/adetailer](https://huggingface.co/Bingsu/adetailer).
+
+### Additional Model
+
+Put your [ultralytics](https://github.com/ultralytics/ultralytics) yolo model in `webui/models/adetailer`. The model name should end with `.pt` or `.pth`.
+
+It must be a bbox detection or segment model and use all label.
+
+## Example
+
+![image](https://i.imgur.com/38RSxSO.png)
+![image](https://i.imgur.com/2CYgjLx.png)
+
+[![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/F1F1L7V2N)

adetailer/Taskfile.yml

@@ -0,0 +1,25 @@
+# https://taskfile.dev
+
+version: "3"
+
+dotenv:
+  - .env
+
+vars:
+  SHELL: '{{if eq .OS "Windows_NT"}}powershell{{end}}'
+
+tasks:
+  default:
+    cmds:
+      - echo "$PYTHON"
+      - echo "$WEBUI"
+    silent: true
+
+  launch:
+    dir: "{{.WEBUI}}"
+    cmds:
+      - "{{.PYTHON}} launch.py --xformers --api --autolaunch"
+
+  lint:
+    cmds:
+      - pre-commit run -a

adetailer/adetailer/__init__.py

@@ -0,0 +1,20 @@
+from .__version__ import __version__
+from .args import AD_ENABLE, ALL_ARGS, ADetailerArgs, EnableChecker
+from .common import PredictOutput, get_models
+from .mediapipe import mediapipe_predict
+from .ultralytics import ultralytics_predict
+
+AFTER_DETAILER = "ADetailer"
+
+__all__ = [
+    "__version__",
+    "AD_ENABLE",
+    "ADetailerArgs",
+    "AFTER_DETAILER",
+    "ALL_ARGS",
+    "EnableChecker",
+    "PredictOutput",
+    "get_models",
+    "mediapipe_predict",
+    "ultralytics_predict",
+]

adetailer/adetailer/__version__.py

@@ -0,0 +1 @@
+__version__ = "23.7.11"

adetailer/adetailer/args.py

@@ -0,0 +1,232 @@
+from __future__ import annotations
+
+from collections import UserList
+from functools import cached_property, partial
+from typing import Any, Literal, NamedTuple, Optional, Union
+
+import pydantic
+from pydantic import (
+    BaseModel,
+    Extra,
+    NonNegativeFloat,
+    NonNegativeInt,
+    PositiveInt,
+    confloat,
+    conint,
+    constr,
+    root_validator,
+    validator,
+)
+
+cn_model_regex = r".*(inpaint|tile|scribble|lineart|openpose).*|^None$"
+
+
+class Arg(NamedTuple):
+    attr: str
+    name: str
+
+
+class ArgsList(UserList):
+    @cached_property
+    def attrs(self) -> tuple[str]:
+        return tuple(attr for attr, _ in self)
+
+    @cached_property
+    def names(self) -> tuple[str]:
+        return tuple(name for _, name in self)
+
+
+class ADetailerArgs(BaseModel, extra=Extra.forbid):
+    ad_model: str = "None"
+    ad_prompt: str = ""
+    ad_negative_prompt: str = ""
+    ad_confidence: confloat(ge=0.0, le=1.0) = 0.3
+    ad_mask_min_ratio: confloat(ge=0.0, le=1.0) = 0.0
+    ad_mask_max_ratio: confloat(ge=0.0, le=1.0) = 1.0
+    ad_dilate_erode: int = 4
+    ad_x_offset: int = 0
+    ad_y_offset: int = 0
+    ad_mask_merge_invert: Literal["None", "Merge", "Merge and Invert"] = "None"
+    ad_mask_blur: NonNegativeInt = 4
+    ad_denoising_strength: confloat(ge=0.0, le=1.0) = 0.4
+    ad_inpaint_only_masked: bool = True
+    ad_inpaint_only_masked_padding: NonNegativeInt = 32
+    ad_use_inpaint_width_height: bool = False
+    ad_inpaint_width: PositiveInt = 512
+    ad_inpaint_height: PositiveInt = 512
+    ad_use_steps: bool = False
+    ad_steps: PositiveInt = 28
+    ad_use_cfg_scale: bool = False
+    ad_cfg_scale: NonNegativeFloat = 7.0
+    ad_use_sampler: bool = False
+    ad_sampler: str = "DPM++ 2M Karras"
+    ad_use_noise_multiplier: bool = False
+    ad_noise_multiplier: confloat(ge=0.5, le=1.5) = 1.0
+    ad_use_clip_skip: bool = False
+    ad_clip_skip: conint(ge=1, le=12) = 1
+    ad_restore_face: bool = False
+    ad_controlnet_model: constr(regex=cn_model_regex) = "None"
+    ad_controlnet_module: Optional[constr(regex=r".*inpaint.*|^None$")] = None
+    ad_controlnet_weight: confloat(ge=0.0, le=1.0) = 1.0
+    ad_controlnet_guidance_start: confloat(ge=0.0, le=1.0) = 0.0
+    ad_controlnet_guidance_end: confloat(ge=0.0, le=1.0) = 1.0
+    is_api: bool = True
+
+    @root_validator(skip_on_failure=True)
+    def ad_controlnt_module_validator(cls, values):  # noqa: N805
+        cn_model = values.get("ad_controlnet_model", "None")
+        cn_module = values.get("ad_controlnet_module", None)
+        if "inpaint" not in cn_model or cn_module == "None":
+            values["ad_controlnet_module"] = None
+        return values
+
+    @validator("is_api", pre=True)
+    def is_api_validator(cls, v: Any):  # noqa: N805
+        "tuple is json serializable but cannot be made with json deserialize."
+        return type(v) is not tuple
+
+    @staticmethod
+    def ppop(
+        p: dict[str, Any],
+        key: str,
+        pops: list[str] | None = None,
+        cond: Any = None,
+    ) -> None:
+        if pops is None:
+            pops = [key]
+        if key not in p:
+            return
+        value = p[key]
+        cond = (not bool(value)) if cond is None else value == cond
+
+        if cond:
+            for k in pops:
+                p.pop(k, None)
+
+    def extra_params(self, suffix: str = "") -> dict[str, Any]:
+        if self.ad_model == "None":
+            return {}
+
+        p = {name: getattr(self, attr) for attr, name in ALL_ARGS}
+        ppop = partial(self.ppop, p)
+
+        ppop("ADetailer prompt")
+        ppop("ADetailer negative prompt")
+        ppop("ADetailer mask min ratio", cond=0.0)
+        ppop("ADetailer mask max ratio", cond=1.0)
+        ppop("ADetailer x offset", cond=0)
+        ppop("ADetailer y offset", cond=0)
+        ppop("ADetailer mask merge/invert", cond="None")
+        ppop("ADetailer inpaint only masked", ["ADetailer inpaint padding"])
+        ppop(
+            "ADetailer use inpaint width/height",
+            [
+                "ADetailer use inpaint width/height",
+                "ADetailer inpaint width",
+                "ADetailer inpaint height",
+            ],
+        )
+        ppop(
+            "ADetailer use separate steps",
+            ["ADetailer use separate steps", "ADetailer steps"],
+        )
+        ppop(
+            "ADetailer use separate CFG scale",
+            ["ADetailer use separate CFG scale", "ADetailer CFG scale"],
+        )
+        ppop(
+            "ADetailer use separate sampler",
+            ["ADetailer use separate sampler", "ADetailer sampler"],
+        )
+        ppop(
+            "ADetailer use separate noise multiplier",
+            ["ADetailer use separate noise multiplier", "ADetailer noise multiplier"],
+        )
+
+        ppop(
+            "ADetailer use separate CLIP skip",
+            ["ADetailer use separate CLIP skip", "ADetailer CLIP skip"],
+        )
+
+        ppop("ADetailer restore face")
+        ppop(
+            "ADetailer ControlNet model",
+            [
+                "ADetailer ControlNet model",
+                "ADetailer ControlNet module",
+                "ADetailer ControlNet weight",
+                "ADetailer ControlNet guidance start",
+                "ADetailer ControlNet guidance end",
+            ],
+            cond="None",
+        )
+        ppop("ADetailer ControlNet module")
+        ppop("ADetailer ControlNet weight", cond=1.0)
+        ppop("ADetailer ControlNet guidance start", cond=0.0)
+        ppop("ADetailer ControlNet guidance end", cond=1.0)
+
+        if suffix:
+            p = {k + suffix: v for k, v in p.items()}
+
+        return p
+
+
+class EnableChecker(BaseModel):
+    enable: bool
+    arg_list: list
+
+    def is_enabled(self) -> bool:
+        ad_model = ALL_ARGS[0].attr
+        if not self.enable:
+            return False
+        return any(arg.get(ad_model, "None") != "None" for arg in self.arg_list)
+
+
+_all_args = [
+    ("ad_enable", "ADetailer enable"),
+    ("ad_model", "ADetailer model"),
+    ("ad_prompt", "ADetailer prompt"),
+    ("ad_negative_prompt", "ADetailer negative prompt"),
+    ("ad_confidence", "ADetailer confidence"),
+    ("ad_mask_min_ratio", "ADetailer mask min ratio"),
+    ("ad_mask_max_ratio", "ADetailer mask max ratio"),
+    ("ad_x_offset", "ADetailer x offset"),
+    ("ad_y_offset", "ADetailer y offset"),
+    ("ad_dilate_erode", "ADetailer dilate/erode"),
+    ("ad_mask_merge_invert", "ADetailer mask merge/invert"),
+    ("ad_mask_blur", "ADetailer mask blur"),
+    ("ad_denoising_strength", "ADetailer denoising strength"),
+    ("ad_inpaint_only_masked", "ADetailer inpaint only masked"),
+    ("ad_inpaint_only_masked_padding", "ADetailer inpaint padding"),
+    ("ad_use_inpaint_width_height", "ADetailer use inpaint width/height"),
+    ("ad_inpaint_width", "ADetailer inpaint width"),
+    ("ad_inpaint_height", "ADetailer inpaint height"),
+    ("ad_use_steps", "ADetailer use separate steps"),
+    ("ad_steps", "ADetailer steps"),
+    ("ad_use_cfg_scale", "ADetailer use separate CFG scale"),
+    ("ad_cfg_scale", "ADetailer CFG scale"),
+    ("ad_use_sampler", "ADetailer use separate sampler"),
+    ("ad_sampler", "ADetailer sampler"),
+    ("ad_use_noise_multiplier", "ADetailer use separate noise multiplier"),
+    ("ad_noise_multiplier", "ADetailer noise multiplier"),
+    ("ad_use_clip_skip", "ADetailer use separate CLIP skip"),
+    ("ad_clip_skip", "ADetailer CLIP skip"),
+    ("ad_restore_face", "ADetailer restore face"),
+    ("ad_controlnet_model", "ADetailer ControlNet model"),
+    ("ad_controlnet_module", "ADetailer ControlNet module"),
+    ("ad_controlnet_weight", "ADetailer ControlNet weight"),
+    ("ad_controlnet_guidance_start", "ADetailer ControlNet guidance start"),
+    ("ad_controlnet_guidance_end", "ADetailer ControlNet guidance end"),
+]
+
+AD_ENABLE = Arg(*_all_args[0])
+_args = [Arg(*args) for args in _all_args[1:]]
+ALL_ARGS = ArgsList(_args)
+
+BBOX_SORTBY = [
+    "None",
+    "Position (left to right)",
+    "Position (center to edge)",
+    "Area (large to small)",
+]
+MASK_MERGE_INVERT = ["None", "Merge", "Merge and Invert"]

adetailer/adetailer/common.py

@@ -0,0 +1,127 @@
+from __future__ import annotations
+
+from collections import OrderedDict
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Optional, Union
+
+from huggingface_hub import hf_hub_download
+from PIL import Image, ImageDraw
+from rich import print
+
+repo_id = "Bingsu/adetailer"
+
+
+@dataclass
+class PredictOutput:
+    bboxes: list[list[int | float]] = field(default_factory=list)
+    masks: list[Image.Image] = field(default_factory=list)
+    preview: Optional[Image.Image] = None
+
+
+def hf_download(file: str):
+    try:
+        path = hf_hub_download(repo_id, file)
+    except Exception:
+        msg = f"[-] ADetailer: Failed to load model {file!r} from huggingface"
+        print(msg)
+        path = "INVALID"
+    return path
+
+
+def get_models(
+    model_dir: Union[str, Path], huggingface: bool = True
+) -> OrderedDict[str, Optional[str]]:
+    model_dir = Path(model_dir)
+    if model_dir.is_dir():
+        model_paths = [
+            p
+            for p in model_dir.rglob("*")
+            if p.is_file() and p.suffix in (".pt", ".pth")
+        ]
+    else:
+        model_paths = []
+
+    models = OrderedDict()
+    if huggingface:
+        models.update(
+            {
+                "face_yolov8n.pt": hf_download("face_yolov8n.pt"),
+                "face_yolov8s.pt": hf_download("face_yolov8s.pt"),
+                "hand_yolov8n.pt": hf_download("hand_yolov8n.pt"),
+                "person_yolov8n-seg.pt": hf_download("person_yolov8n-seg.pt"),
+                "person_yolov8s-seg.pt": hf_download("person_yolov8s-seg.pt"),
+            }
+        )
+    models.update(
+        {
+            "mediapipe_face_full": None,
+            "mediapipe_face_short": None,
+            "mediapipe_face_mesh": None,
+            "mediapipe_face_mesh_eyes_only": None,
+        }
+    )
+
+    invalid_keys = [k for k, v in models.items() if v == "INVALID"]
+    for key in invalid_keys:
+        models.pop(key)
+
+    for path in model_paths:
+        if path.name in models:
+            continue
+        models[path.name] = str(path)
+
+    return models
+
+
+def create_mask_from_bbox(
+    bboxes: list[list[float]], shape: tuple[int, int]
+) -> list[Image.Image]:
+    """
+    Parameters
+    ----------
+        bboxes: list[list[float]]
+            list of [x1, y1, x2, y2]
+            bounding boxes
+        shape: tuple[int, int]
+            shape of the image (width, height)
+
+    Returns
+    -------
+        masks: list[Image.Image]
+        A list of masks
+
+    """
+    masks = []
+    for bbox in bboxes:
+        mask = Image.new("L", shape, 0)
+        mask_draw = ImageDraw.Draw(mask)
+        mask_draw.rectangle(bbox, fill=255)
+        masks.append(mask)
+    return masks
+
+
+def create_bbox_from_mask(
+    masks: list[Image.Image], shape: tuple[int, int]
+) -> list[list[int]]:
+    """
+    Parameters
+    ----------
+        masks: list[Image.Image]
+            A list of masks
+        shape: tuple[int, int]
+            shape of the image (width, height)
+
+    Returns
+    -------
+        bboxes: list[list[float]]
+        A list of bounding boxes
+
+    """
+    bboxes = []
+    for mask in masks:
+        mask = mask.resize(shape)
+        bbox = mask.getbbox()
+        if bbox is not None:
+            bboxes.append(list(bbox))
+    return bboxes

adetailer/adetailer/mask.py

@@ -0,0 +1,245 @@
+from __future__ import annotations
+
+from enum import IntEnum
+from functools import partial, reduce
+from math import dist
+
+import cv2
+import numpy as np
+from PIL import Image, ImageChops
+
+from adetailer.args import MASK_MERGE_INVERT
+from adetailer.common import PredictOutput
+
+
+class SortBy(IntEnum):
+    NONE = 0
+    LEFT_TO_RIGHT = 1
+    CENTER_TO_EDGE = 2
+    AREA = 3
+
+
+class MergeInvert(IntEnum):
+    NONE = 0
+    MERGE = 1
+    MERGE_INVERT = 2
+
+
+def _dilate(arr: np.ndarray, value: int) -> np.ndarray:
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (value, value))
+    return cv2.dilate(arr, kernel, iterations=1)
+
+
+def _erode(arr: np.ndarray, value: int) -> np.ndarray:
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (value, value))
+    return cv2.erode(arr, kernel, iterations=1)
+
+
+def dilate_erode(img: Image.Image, value: int) -> Image.Image:
+    """
+    The dilate_erode function takes an image and a value.
+    If the value is positive, it dilates the image by that amount.
+    If the value is negative, it erodes the image by that amount.
+
+    Parameters
+    ----------
+        img: PIL.Image.Image
+            the image to be processed
+        value: int
+            kernel size of dilation or erosion
+
+    Returns
+    -------
+        PIL.Image.Image
+            The image that has been dilated or eroded
+    """
+    if value == 0:
+        return img
+
+    arr = np.array(img)
+    arr = _dilate(arr, value) if value > 0 else _erode(arr, -value)
+
+    return Image.fromarray(arr)
+
+
+def offset(img: Image.Image, x: int = 0, y: int = 0) -> Image.Image:
+    """
+    The offset function takes an image and offsets it by a given x(→) and y(↑) value.
+
+    Parameters
+    ----------
+        mask: Image.Image
+            Pass the mask image to the function
+        x: int
+            →
+        y: int
+            ↑
+
+    Returns
+    -------
+        PIL.Image.Image
+            A new image that is offset by x and y
+    """
+    return ImageChops.offset(img, x, -y)
+
+
+def is_all_black(img: Image.Image) -> bool:
+    arr = np.array(img)
+    return cv2.countNonZero(arr) == 0
+
+
+def bbox_area(bbox: list[float]):
+    return (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
+
+
+def mask_preprocess(
+    masks: list[Image.Image],
+    kernel: int = 0,
+    x_offset: int = 0,
+    y_offset: int = 0,
+    merge_invert: int | MergeInvert | str = MergeInvert.NONE,
+) -> list[Image.Image]:
+    """
+    The mask_preprocess function takes a list of masks and preprocesses them.
+    It dilates and erodes the masks, and offsets them by x_offset and y_offset.
+
+    Parameters
+    ----------
+        masks: list[Image.Image]
+            A list of masks
+        kernel: int
+            kernel size of dilation or erosion
+        x_offset: int
+            →
+        y_offset: int
+            ↑
+
+    Returns
+    -------
+        list[Image.Image]
+            A list of processed masks
+    """
+    if not masks:
+        return []
+
+    if x_offset != 0 or y_offset != 0:
+        masks = [offset(m, x_offset, y_offset) for m in masks]
+
+    if kernel != 0:
+        masks = [dilate_erode(m, kernel) for m in masks]
+        masks = [m for m in masks if not is_all_black(m)]
+
+    return mask_merge_invert(masks, mode=merge_invert)
+
+
+# Bbox sorting
+def _key_left_to_right(bbox: list[float]) -> float:
+    """
+    Left to right
+
+    Parameters
+    ----------
+    bbox: list[float]
+        list of [x1, y1, x2, y2]
+    """
+    return bbox[0]
+
+
+def _key_center_to_edge(bbox: list[float], *, center: tuple[float, float]) -> float:
+    """
+    Center to edge
+
+    Parameters
+    ----------
+    bbox: list[float]
+        list of [x1, y1, x2, y2]
+    image: Image.Image
+        the image
+    """
+    bbox_center = ((bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2)
+    return dist(center, bbox_center)
+
+
+def _key_area(bbox: list[float]) -> float:
+    """
+    Large to small
+
+    Parameters
+    ----------
+    bbox: list[float]
+        list of [x1, y1, x2, y2]
+    """
+    return -bbox_area(bbox)
+
+
+def sort_bboxes(
+    pred: PredictOutput, order: int | SortBy = SortBy.NONE
+) -> PredictOutput:
+    if order == SortBy.NONE or len(pred.bboxes) <= 1:
+        return pred
+
+    if order == SortBy.LEFT_TO_RIGHT:
+        key = _key_left_to_right
+    elif order == SortBy.CENTER_TO_EDGE:
+        width, height = pred.preview.size
+        center = (width / 2, height / 2)
+        key = partial(_key_center_to_edge, center=center)
+    elif order == SortBy.AREA:
+        key = _key_area
+    else:
+        raise RuntimeError
+
+    items = len(pred.bboxes)
+    idx = sorted(range(items), key=lambda i: key(pred.bboxes[i]))
+    pred.bboxes = [pred.bboxes[i] for i in idx]
+    pred.masks = [pred.masks[i] for i in idx]
+    return pred
+
+
+# Filter by ratio
+def is_in_ratio(bbox: list[float], low: float, high: float, orig_area: int) -> bool:
+    area = bbox_area(bbox)
+    return low <= area / orig_area <= high
+
+
+def filter_by_ratio(pred: PredictOutput, low: float, high: float) -> PredictOutput:
+    if not pred.bboxes:
+        return pred
+
+    w, h = pred.preview.size
+    orig_area = w * h
+    items = len(pred.bboxes)
+    idx = [i for i in range(items) if is_in_ratio(pred.bboxes[i], low, high, orig_area)]
+    pred.bboxes = [pred.bboxes[i] for i in idx]
+    pred.masks = [pred.masks[i] for i in idx]
+    return pred
+
+
+# Merge / Invert
+def mask_merge(masks: list[Image.Image]) -> list[Image.Image]:
+    arrs = [np.array(m) for m in masks]
+    arr = reduce(cv2.bitwise_or, arrs)
+    return [Image.fromarray(arr)]
+
+
+def mask_invert(masks: list[Image.Image]) -> list[Image.Image]:
+    return [ImageChops.invert(m) for m in masks]
+
+
+def mask_merge_invert(
+    masks: list[Image.Image], mode: int | MergeInvert | str
+) -> list[Image.Image]:
+    if isinstance(mode, str):
+        mode = MASK_MERGE_INVERT.index(mode)
+
+    if mode == MergeInvert.NONE or not masks:
+        return masks
+
+    if mode == MergeInvert.MERGE:
+        return mask_merge(masks)
+
+    if mode == MergeInvert.MERGE_INVERT:
+        merged = mask_merge(masks)
+        return mask_invert(merged)
+
+    raise RuntimeError

adetailer/adetailer/mediapipe.py

@@ -0,0 +1,179 @@
+from __future__ import annotations
+
+from functools import partial
+
+import mediapipe as mp
+import numpy as np
+from PIL import Image, ImageDraw
+
+from adetailer import PredictOutput
+from adetailer.common import create_bbox_from_mask, create_mask_from_bbox
+
+
+def mediapipe_predict(
+    model_type: str, image: Image.Image, confidence: float = 0.3
+) -> PredictOutput:
+    mapping = {
+        "mediapipe_face_short": partial(mediapipe_face_detection, 0),
+        "mediapipe_face_full": partial(mediapipe_face_detection, 1),
+        "mediapipe_face_mesh": mediapipe_face_mesh,
+        "mediapipe_face_mesh_eyes_only": mediapipe_face_mesh_eyes_only,
+    }
+    if model_type in mapping:
+        func = mapping[model_type]
+        return func(image, confidence)
+    msg = f"[-] ADetailer: Invalid mediapipe model type: {model_type}, Available: {list(mapping.keys())!r}"
+    raise RuntimeError(msg)
+
+
+def mediapipe_face_detection(
+    model_type: int, image: Image.Image, confidence: float = 0.3
+) -> PredictOutput:
+    img_width, img_height = image.size
+
+    mp_face_detection = mp.solutions.face_detection
+    draw_util = mp.solutions.drawing_utils
+
+    img_array = np.array(image)
+
+    with mp_face_detection.FaceDetection(
+        model_selection=model_type, min_detection_confidence=confidence
+    ) as face_detector:
+        pred = face_detector.process(img_array)
+
+    if pred.detections is None:
+        return PredictOutput()
+
+    preview_array = img_array.copy()
+
+    bboxes = []
+    for detection in pred.detections:
+        draw_util.draw_detection(preview_array, detection)
+
+        bbox = detection.location_data.relative_bounding_box
+        x1 = bbox.xmin * img_width
+        y1 = bbox.ymin * img_height
+        w = bbox.width * img_width
+        h = bbox.height * img_height
+        x2 = x1 + w
+        y2 = y1 + h
+
+        bboxes.append([x1, y1, x2, y2])
+
+    masks = create_mask_from_bbox(bboxes, image.size)
+    preview = Image.fromarray(preview_array)
+
+    return PredictOutput(bboxes=bboxes, masks=masks, preview=preview)
+
+
+def get_convexhull(points: np.ndarray) -> list[tuple[int, int]]:
+    """
+    Parameters
+    ----------
+      points: An ndarray of shape (n, 2) containing the 2D points.
+
+    Returns
+    -------
+      list[tuple[int, int]]: Input for the draw.polygon function
+    """
+    from scipy.spatial import ConvexHull
+
+    hull = ConvexHull(points)
+    vertices = hull.vertices
+    return list(zip(points[vertices, 0], points[vertices, 1]))
+
+
+def mediapipe_face_mesh(image: Image.Image, confidence: float = 0.3) -> PredictOutput:
+    mp_face_mesh = mp.solutions.face_mesh
+    draw_util = mp.solutions.drawing_utils
+    drawing_styles = mp.solutions.drawing_styles
+
+    w, h = image.size
+
+    with mp_face_mesh.FaceMesh(
+        static_image_mode=True, max_num_faces=20, min_detection_confidence=confidence
+    ) as face_mesh:
+        arr = np.array(image)
+        pred = face_mesh.process(arr)
+
+        if pred.multi_face_landmarks is None:
+            return PredictOutput()
+
+        preview = arr.copy()
+        masks = []
+
+        for landmarks in pred.multi_face_landmarks:
+            draw_util.draw_landmarks(
+                image=preview,
+                landmark_list=landmarks,
+                connections=mp_face_mesh.FACEMESH_TESSELATION,
+                landmark_drawing_spec=None,
+                connection_drawing_spec=drawing_styles.get_default_face_mesh_tesselation_style(),
+            )
+
+            points = np.array([(land.x * w, land.y * h) for land in landmarks.landmark])
+            outline = get_convexhull(points)
+
+            mask = Image.new("L", image.size, "black")
+            draw = ImageDraw.Draw(mask)
+            draw.polygon(outline, fill="white")
+            masks.append(mask)
+
+        bboxes = create_bbox_from_mask(masks, image.size)
+        preview = Image.fromarray(preview)
+        return PredictOutput(bboxes=bboxes, masks=masks, preview=preview)
+
+
+def mediapipe_face_mesh_eyes_only(
+    image: Image.Image, confidence: float = 0.3
+) -> PredictOutput:
+    mp_face_mesh = mp.solutions.face_mesh
+
+    left_idx = np.array(list(mp_face_mesh.FACEMESH_LEFT_EYE)).flatten()
+    right_idx = np.array(list(mp_face_mesh.FACEMESH_RIGHT_EYE)).flatten()
+
+    w, h = image.size
+
+    with mp_face_mesh.FaceMesh(
+        static_image_mode=True, max_num_faces=20, min_detection_confidence=confidence
+    ) as face_mesh:
+        arr = np.array(image)
+        pred = face_mesh.process(arr)
+
+        if pred.multi_face_landmarks is None:
+            return PredictOutput()
+
+        preview = image.copy()
+        masks = []
+
+        for landmarks in pred.multi_face_landmarks:
+            points = np.array([(land.x * w, land.y * h) for land in landmarks.landmark])
+            left_eyes = points[left_idx]
+            right_eyes = points[right_idx]
+            left_outline = get_convexhull(left_eyes)
+            right_outline = get_convexhull(right_eyes)
+
+            mask = Image.new("L", image.size, "black")
+            draw = ImageDraw.Draw(mask)
+            for outline in (left_outline, right_outline):
+                draw.polygon(outline, fill="white")
+            masks.append(mask)
+
+        bboxes = create_bbox_from_mask(masks, image.size)
+        preview = draw_preview(preview, bboxes, masks)
+        return PredictOutput(bboxes=bboxes, masks=masks, preview=preview)
+
+
+def draw_preview(
+    preview: Image.Image, bboxes: list[list[int]], masks: list[Image.Image]
+) -> Image.Image:
+    red = Image.new("RGB", preview.size, "red")
+    for mask in masks:
+        masked = Image.composite(red, preview, mask)
+        preview = Image.blend(preview, masked, 0.25)
+
+    draw = ImageDraw.Draw(preview)
+    for bbox in bboxes:
+        draw.rectangle(bbox, outline="red", width=2)
+
+    return preview

adetailer/adetailer/traceback.py

@@ -0,0 +1,161 @@
+from __future__ import annotations
+
+import io
+import platform
+import sys
+from importlib.metadata import version
+from typing import Any, Callable
+
+from rich.console import Console, Group
+from rich.panel import Panel
+from rich.table import Table
+from rich.traceback import Traceback
+
+from adetailer.__version__ import __version__
+
+
+def processing(*args: Any) -> dict[str, Any]:
+    try:
+        from modules.processing import (
+            StableDiffusionProcessingImg2Img,
+            StableDiffusionProcessingTxt2Img,
+        )
+    except ImportError:
+        return {}
+
+    p = None
+    for arg in args:
+        if isinstance(
+            arg, (StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img)
+        ):
+            p = arg
+            break
+
+    if p is None:
+        return {}
+
+    info = {
+        "prompt": p.prompt,
+        "negative_prompt": p.negative_prompt,
+        "n_iter": p.n_iter,
+        "batch_size": p.batch_size,
+        "width": p.width,
+        "height": p.height,
+        "sampler_name": p.sampler_name,
+        "enable_hr": getattr(p, "enable_hr", False),
+        "hr_upscaler": getattr(p, "hr_upscaler", ""),
+    }
+
+    info.update(sd_models())
+    return info
+
+
+def sd_models() -> dict[str, str]:
+    try:
+        from modules import shared
+
+        opts = shared.opts
+    except Exception:
+        return {}
+
+    return {
+        "checkpoint": getattr(opts, "sd_model_checkpoint", "------"),
+        "vae": getattr(opts, "sd_vae", "------"),
+        "unet": getattr(opts, "sd_unet", "------"),
+    }
+
+
+def ad_args(*args: Any) -> dict[str, Any]:
+    ad_args = [
+        arg
+        for arg in args
+        if isinstance(arg, dict) and arg.get("ad_model", "None") != "None"
+    ]
+    if not ad_args:
+        return {}
+
+    arg0 = ad_args[0]
+    is_api = arg0.get("is_api", True)
+    return {
+        "version": __version__,
+        "ad_model": arg0["ad_model"],
+        "ad_prompt": arg0.get("ad_prompt", ""),
+        "ad_negative_prompt": arg0.get("ad_negative_prompt", ""),
+        "ad_controlnet_model": arg0.get("ad_controlnet_model", "None"),
+        "is_api": type(is_api) is not tuple,
+    }
+
+
+def library_version():
+    libraries = ["torch", "torchvision", "ultralytics", "mediapipe"]
+    d = {}
+    for lib in libraries:
+        try:
+            d[lib] = version(lib)
+        except Exception:
+            d[lib] = "Unknown"
+    return d
+
+
+def sys_info() -> dict[str, Any]:
+    try:
+        import launch
+
+        version = launch.git_tag()
+        commit = launch.commit_hash()
+    except Exception:
+        version = "Unknown (too old or vladmandic)"
+        commit = "Unknown"
+
+    return {
+        "Platform": platform.platform(),
+        "Python": sys.version,
+        "Version": version,
+        "Commit": commit,
+        "Commandline": sys.argv,
+        "Libraries": library_version(),
+    }
+
+
+def get_table(title: str, data: dict[str, Any]) -> Table:
+    table = Table(title=title, highlight=True)
+    table.add_column(" ", justify="right", style="dim")
+    table.add_column("Value")
+    for key, value in data.items():
+        if not isinstance(value, str):
+            value = repr(value)
+        table.add_row(key, value)
+
+    return table
+
+
+def rich_traceback(func: Callable) -> Callable:
+    def wrapper(*args, **kwargs):
+        string = io.StringIO()
+        width = Console().width
+        width = width - 4 if width > 4 else None
+        console = Console(file=string, width=width)
+        try:
+            return func(*args, **kwargs)
+        except Exception as e:
+            tables = [
+                get_table(title, data)
+                for title, data in [
+                    ("System info", sys_info()),
+                    ("Inputs", processing(*args)),
+                    ("ADetailer", ad_args(*args)),
+                ]
+                if data
+            ]
+            tables.append(Traceback())
+
+            console.print(Panel(Group(*tables)))
+            output = "\n" + string.getvalue()
+
+            try:
+                error = e.__class__(output)
+            except Exception:
+                error = RuntimeError(output)
+            raise error from None
+
+    return wrapper

adetailer/adetailer/ui.py

@@ -0,0 +1,558 @@
+from __future__ import annotations
+
+from functools import partial
+from types import SimpleNamespace
+from typing import Any
+
+import gradio as gr
+
+from adetailer import AFTER_DETAILER, __version__
+from adetailer.args import AD_ENABLE, ALL_ARGS, MASK_MERGE_INVERT
+from controlnet_ext import controlnet_exists, get_cn_models
+
+cn_module_choices = [
+    "inpaint_global_harmonious",
+    "inpaint_only",
+    "inpaint_only+lama",
+]
+
+
+class Widgets(SimpleNamespace):
+    def tolist(self):
+        return [getattr(self, attr) for attr in ALL_ARGS.attrs]
+
+
+def gr_interactive(value: bool = True):
+    return gr.update(interactive=value)
+
+
+def ordinal(n: int) -> str:
+    d = {1: "st", 2: "nd", 3: "rd"}
+    return str(n) + ("th" if 11 <= n % 100 <= 13 else d.get(n % 10, "th"))
+
+
+def suffix(n: int, c: str = " ") -> str:
+    return "" if n == 0 else c + ordinal(n + 1)
+
+
+def on_widget_change(state: dict, value: Any, *, attr: str):
+    state[attr] = value
+    return state
+
+
+def on_generate_click(state: dict, *values: Any):
+    for attr, value in zip(ALL_ARGS.attrs, values):
+        state[attr] = value
+    state["is_api"] = ()
+    return state
+
+
+def on_cn_model_update(cn_model: str):
+    if "inpaint" in cn_model:
+        return gr.update(
+            visible=True, choices=cn_module_choices, value=cn_module_choices[0]
+        )
+    return gr.update(visible=False, choices=["None"], value="None")
+
+
+def elem_id(item_id: str, n: int, is_img2img: bool) -> str:
+    tap = "img2img" if is_img2img else "txt2img"
+    suf = suffix(n, "_")
+    return f"script_{tap}_adetailer_{item_id}{suf}"
+
+
+def adui(
+    num_models: int,
+    is_img2img: bool,
+    model_list: list[str],
+    samplers: list[str],
+    t2i_button: gr.Button,
+    i2i_button: gr.Button,
+):
+    states = []
+    infotext_fields = []
+    eid = partial(elem_id, n=0, is_img2img=is_img2img)
+
+    with gr.Accordion(AFTER_DETAILER, open=False, elem_id=eid("ad_main_accordion")):
+        with gr.Row():
+            with gr.Column(scale=6):
+                ad_enable = gr.Checkbox(
+                    label="Enable ADetailer",
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_enable"),
+                )
+
+            with gr.Column(scale=1, min_width=180):
+                gr.Markdown(
+                    f"v{__version__}",
+                    elem_id=eid("ad_version"),
+                )
+
+        infotext_fields.append((ad_enable, AD_ENABLE.name))
+
+        with gr.Group(), gr.Tabs():
+            for n in range(num_models):
+                with gr.Tab(ordinal(n + 1)):
+                    state, infofields = one_ui_group(
+                        n=n,
+                        is_img2img=is_img2img,
+                        model_list=model_list,
+                        samplers=samplers,
+                        t2i_button=t2i_button,
+                        i2i_button=i2i_button,
+                    )
+
+                states.append(state)
+                infotext_fields.extend(infofields)
+
+    # components: [bool, dict, dict, ...]
+    components = [ad_enable, *states]
+    return components, infotext_fields
+
+
+def one_ui_group(
+    n: int,
+    is_img2img: bool,
+    model_list: list[str],
+    samplers: list[str],
+    t2i_button: gr.Button,
+    i2i_button: gr.Button,
+):
+    w = Widgets()
+    state = gr.State({})
+    eid = partial(elem_id, n=n, is_img2img=is_img2img)
+
+    with gr.Row():
+        model_choices = [*model_list, "None"] if n == 0 else ["None", *model_list]
+
+        w.ad_model = gr.Dropdown(
+            label="ADetailer model" + suffix(n),
+            choices=model_choices,
+            value=model_choices[0],
+            visible=True,
+            type="value",
+            elem_id=eid("ad_model"),
+        )
+
+    with gr.Group():
+        with gr.Row(elem_id=eid("ad_toprow_prompt")):
+            w.ad_prompt = gr.Textbox(
+                label="ad_prompt" + suffix(n),
+                show_label=False,
+                lines=3,
+                placeholder="ADetailer prompt"
+                + suffix(n)
+                + "\nIf blank, the main prompt is used.",
+                elem_id=eid("ad_prompt"),
+            )
+
+        with gr.Row(elem_id=eid("ad_toprow_negative_prompt")):
+            w.ad_negative_prompt = gr.Textbox(
+                label="ad_negative_prompt" + suffix(n),
+                show_label=False,
+                lines=2,
+                placeholder="ADetailer negative prompt"
+                + suffix(n)
+                + "\nIf blank, the main negative prompt is used.",
+                elem_id=eid("ad_negative_prompt"),
+            )
+
+    with gr.Group():
+        with gr.Accordion(
+            "Detection", open=False, elem_id=eid("ad_detection_accordion")
+        ):
+            detection(w, n, is_img2img)
+
+        with gr.Accordion(
+            "Mask Preprocessing",
+            open=False,
+            elem_id=eid("ad_mask_preprocessing_accordion"),
+        ):
+            mask_preprocessing(w, n, is_img2img)
+
+        with gr.Accordion(
+            "Inpainting", open=False, elem_id=eid("ad_inpainting_accordion")
+        ):
+            inpainting(w, n, is_img2img, samplers)
+
+    with gr.Group():
+        controlnet(w, n, is_img2img)
+
+    all_inputs = [state, *w.tolist()]
+    target_button = i2i_button if is_img2img else t2i_button
+    target_button.click(
+        fn=on_generate_click, inputs=all_inputs, outputs=state, queue=False
+    )
+
+    infotext_fields = [(getattr(w, attr), name + suffix(n)) for attr, name in ALL_ARGS]
+
+    return state, infotext_fields
+
+
+def detection(w: Widgets, n: int, is_img2img: bool):
+    eid = partial(elem_id, n=n, is_img2img=is_img2img)
+
+    with gr.Row():
+        with gr.Column():
+            w.ad_confidence = gr.Slider(
+                label="Detection model confidence threshold" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.01,
+                value=0.3,
+                visible=True,
+                elem_id=eid("ad_confidence"),
+            )
+
+        with gr.Column(variant="compact"):
+            w.ad_mask_min_ratio = gr.Slider(
+                label="Mask min area ratio" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.001,
+                value=0.0,
+                visible=True,
+                elem_id=eid("ad_mask_min_ratio"),
+            )
+            w.ad_mask_max_ratio = gr.Slider(
+                label="Mask max area ratio" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.001,
+                value=1.0,
+                visible=True,
+                elem_id=eid("ad_mask_max_ratio"),
+            )
+
+
+def mask_preprocessing(w: Widgets, n: int, is_img2img: bool):
+    eid = partial(elem_id, n=n, is_img2img=is_img2img)
+
+    with gr.Group():
+        with gr.Row():
+            with gr.Column(variant="compact"):
+                w.ad_x_offset = gr.Slider(
+                    label="Mask x(→) offset" + suffix(n),
+                    minimum=-200,
+                    maximum=200,
+                    step=1,
+                    value=0,
+                    visible=True,
+                    elem_id=eid("ad_x_offset"),
+                )
+                w.ad_y_offset = gr.Slider(
+                    label="Mask y(↑) offset" + suffix(n),
+                    minimum=-200,
+                    maximum=200,
+                    step=1,
+                    value=0,
+                    visible=True,
+                    elem_id=eid("ad_y_offset"),
+                )
+
+            with gr.Column(variant="compact"):
+                w.ad_dilate_erode = gr.Slider(
+                    label="Mask erosion (-) / dilation (+)" + suffix(n),
+                    minimum=-128,
+                    maximum=128,
+                    step=4,
+                    value=4,
+                    visible=True,
+                    elem_id=eid("ad_dilate_erode"),
+                )
+
+        with gr.Row():
+            w.ad_mask_merge_invert = gr.Radio(
+                label="Mask merge mode" + suffix(n),
+                choices=MASK_MERGE_INVERT,
+                value="None",
+                elem_id=eid("ad_mask_merge_invert"),
+            )
+
+
+def inpainting(w: Widgets, n: int, is_img2img: bool, samplers: list[str]):
+    eid = partial(elem_id, n=n, is_img2img=is_img2img)
+
+    with gr.Group():
+        with gr.Row():
+            w.ad_mask_blur = gr.Slider(
+                label="Inpaint mask blur" + suffix(n),
+                minimum=0,
+                maximum=64,
+                step=1,
+                value=4,
+                visible=True,
+                elem_id=eid("ad_mask_blur"),
+            )
+
+            w.ad_denoising_strength = gr.Slider(
+                label="Inpaint denoising strength" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.01,
+                value=0.4,
+                visible=True,
+                elem_id=eid("ad_denoising_strength"),
+            )
+
+        with gr.Row():
+            with gr.Column(variant="compact"):
+                w.ad_inpaint_only_masked = gr.Checkbox(
+                    label="Inpaint only masked" + suffix(n),
+                    value=True,
+                    visible=True,
+                    elem_id=eid("ad_inpaint_only_masked"),
+                )
+                w.ad_inpaint_only_masked_padding = gr.Slider(
+                    label="Inpaint only masked padding, pixels" + suffix(n),
+                    minimum=0,
+                    maximum=256,
+                    step=4,
+                    value=32,
+                    visible=True,
+                    elem_id=eid("ad_inpaint_only_masked_padding"),
+                )
+
+                w.ad_inpaint_only_masked.change(
+                    gr_interactive,
+                    inputs=w.ad_inpaint_only_masked,
+                    outputs=w.ad_inpaint_only_masked_padding,
+                    queue=False,
+                )
+
+            with gr.Column(variant="compact"):
+                w.ad_use_inpaint_width_height = gr.Checkbox(
+                    label="Use separate width/height" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_inpaint_width_height"),
+                )
+
+                w.ad_inpaint_width = gr.Slider(
+                    label="inpaint width" + suffix(n),
+                    minimum=64,
+                    maximum=2048,
+                    step=4,
+                    value=512,
+                    visible=True,
+                    elem_id=eid("ad_inpaint_width"),
+                )
+
+                w.ad_inpaint_height = gr.Slider(
+                    label="inpaint height" + suffix(n),
+                    minimum=64,
+                    maximum=2048,
+                    step=4,
+                    value=512,
+                    visible=True,
+                    elem_id=eid("ad_inpaint_height"),
+                )
+
+                w.ad_use_inpaint_width_height.change(
+                    lambda value: (gr_interactive(value), gr_interactive(value)),
+                    inputs=w.ad_use_inpaint_width_height,
+                    outputs=[w.ad_inpaint_width, w.ad_inpaint_height],
+                    queue=False,
+                )
+
+        with gr.Row():
+            with gr.Column(variant="compact"):
+                w.ad_use_steps = gr.Checkbox(
+                    label="Use separate steps" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_steps"),
+                )
+
+                w.ad_steps = gr.Slider(
+                    label="ADetailer steps" + suffix(n),
+                    minimum=1,
+                    maximum=150,
+                    step=1,
+                    value=28,
+                    visible=True,
+                    elem_id=eid("ad_steps"),
+                )
+
+                w.ad_use_steps.change(
+                    gr_interactive,
+                    inputs=w.ad_use_steps,
+                    outputs=w.ad_steps,
+                    queue=False,
+                )
+
+            with gr.Column(variant="compact"):
+                w.ad_use_cfg_scale = gr.Checkbox(
+                    label="Use separate CFG scale" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_cfg_scale"),
+                )
+
+                w.ad_cfg_scale = gr.Slider(
+                    label="ADetailer CFG scale" + suffix(n),
+                    minimum=0.0,
+                    maximum=30.0,
+                    step=0.5,
+                    value=7.0,
+                    visible=True,
+                    elem_id=eid("ad_cfg_scale"),
+                )
+
+                w.ad_use_cfg_scale.change(
+                    gr_interactive,
+                    inputs=w.ad_use_cfg_scale,
+                    outputs=w.ad_cfg_scale,
+                    queue=False,
+                )
+
+        with gr.Row():
+            with gr.Column(variant="compact"):
+                w.ad_use_sampler = gr.Checkbox(
+                    label="Use separate sampler" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_sampler"),
+                )
+
+                w.ad_sampler = gr.Dropdown(
+                    label="ADetailer sampler" + suffix(n),
+                    choices=samplers,
+                    value=samplers[0],
+                    visible=True,
+                    elem_id=eid("ad_sampler"),
+                )
+
+                w.ad_use_sampler.change(
+                    gr_interactive,
+                    inputs=w.ad_use_sampler,
+                    outputs=w.ad_sampler,
+                    queue=False,
+                )
+
+            with gr.Column(variant="compact"):
+                w.ad_use_noise_multiplier = gr.Checkbox(
+                    label="Use separate noise multiplier" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_noise_multiplier"),
+                )
+
+                w.ad_noise_multiplier = gr.Slider(
+                    label="Noise multiplier for img2img" + suffix(n),
+                    minimum=0.5,
+                    maximum=1.5,
+                    step=0.01,
+                    value=1.0,
+                    visible=True,
+                    elem_id=eid("ad_noise_multiplier"),
+                )
+
+                w.ad_use_noise_multiplier.change(
+                    gr_interactive,
+                    inputs=w.ad_use_noise_multiplier,
+                    outputs=w.ad_noise_multiplier,
+                    queue=False,
+                )
+
+        with gr.Row():
+            with gr.Column(variant="compact"):
+                w.ad_use_clip_skip = gr.Checkbox(
+                    label="Use separate CLIP skip" + suffix(n),
+                    value=False,
+                    visible=True,
+                    elem_id=eid("ad_use_clip_skip"),
+                )
+
+                w.ad_clip_skip = gr.Slider(
+                    label="ADetailer CLIP skip" + suffix(n),
+                    minimum=1,
+                    maximum=12,
+                    step=1,
+                    value=1,
+                    visible=True,
+                    elem_id=eid("ad_clip_skip"),
+                )
+
+                w.ad_use_clip_skip.change(
+                    gr_interactive,
+                    inputs=w.ad_use_clip_skip,
+                    outputs=w.ad_clip_skip,
+                    queue=False,
+                )
+
+            with gr.Column(variant="compact"):
+                w.ad_restore_face = gr.Checkbox(
+                    label="Restore faces after ADetailer" + suffix(n),
+                    value=False,
+                    elem_id=eid("ad_restore_face"),
+                )
+
+
+def controlnet(w: Widgets, n: int, is_img2img: bool):
+    eid = partial(elem_id, n=n, is_img2img=is_img2img)
+    cn_models = ["None", *get_cn_models()]
+
+    with gr.Row(variant="panel"):
+        with gr.Column(variant="compact"):
+            w.ad_controlnet_model = gr.Dropdown(
+                label="ControlNet model" + suffix(n),
+                choices=cn_models,
+                value="None",
+                visible=True,
+                type="value",
+                interactive=controlnet_exists,
+                elem_id=eid("ad_controlnet_model"),
+            )
+
+            w.ad_controlnet_module = gr.Dropdown(
+                label="ControlNet module" + suffix(n),
+                choices=cn_module_choices,
+                value="inpaint_global_harmonious",
+                visible=False,
+                type="value",
+                interactive=controlnet_exists,
+                elem_id=eid("ad_controlnet_module"),
+            )
+
+            w.ad_controlnet_weight = gr.Slider(
+                label="ControlNet weight" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.01,
+                value=1.0,
+                visible=True,
+                interactive=controlnet_exists,
+                elem_id=eid("ad_controlnet_weight"),
+            )
+
+            w.ad_controlnet_model.change(
+                on_cn_model_update,
+                inputs=w.ad_controlnet_model,
+                outputs=w.ad_controlnet_module,
+                queue=False,
+            )
+
+        with gr.Column(variant="compact"):
+            w.ad_controlnet_guidance_start = gr.Slider(
+                label="ControlNet guidance start" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.01,
+                value=0.0,
+                visible=True,
+                interactive=controlnet_exists,
+                elem_id=eid("ad_controlnet_guidance_start"),
+            )
+
+            w.ad_controlnet_guidance_end = gr.Slider(
+                label="ControlNet guidance end" + suffix(n),
+                minimum=0.0,
+                maximum=1.0,
+                step=0.01,
+                value=1.0,
+                visible=True,
+                interactive=controlnet_exists,
+                elem_id=eid("ad_controlnet_guidance_end"),
+            )

adetailer/adetailer/ultralytics.py

@@ -0,0 +1,50 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import cv2
+from PIL import Image
+from torchvision.transforms.functional import to_pil_image
+from ultralytics import YOLO
+
+from adetailer import PredictOutput
+from adetailer.common import create_mask_from_bbox
+
+
+def ultralytics_predict(
+    model_path: str | Path,
+    image: Image.Image,
+    confidence: float = 0.3,
+    device: str = "",
+) -> PredictOutput:
+    model = YOLO(model_path)
+    pred = model(image, conf=confidence, device=device)
+
+    bboxes = pred[0].boxes.xyxy.cpu().numpy()
+    if bboxes.size == 0:
+        return PredictOutput()
+    bboxes = bboxes.tolist()
+
+    if pred[0].masks is None:
+        masks = create_mask_from_bbox(bboxes, image.size)
+    else:
+        masks = mask_to_pil(pred[0].masks.data, image.size)
+    preview = pred[0].plot()
+    preview = cv2.cvtColor(preview, cv2.COLOR_BGR2RGB)
+    preview = Image.fromarray(preview)
+
+    return PredictOutput(bboxes=bboxes, masks=masks, preview=preview)
+
+
+def mask_to_pil(masks, shape: tuple[int, int]) -> list[Image.Image]:
+    """
+    Parameters
+    ----------
+    masks: torch.Tensor, dtype=torch.float32, shape=(N, H, W).
+        The device can be CUDA, but `to_pil_image` takes care of that.
+
+    shape: tuple[int, int]
+        (width, height) of the original image
+    """
+    n = masks.shape[0]
+    return [to_pil_image(masks[i], mode="L").resize(shape) for i in range(n)]

adetailer/controlnet_ext/__init__.py

@@ -0,0 +1,7 @@
+from .controlnet_ext import ControlNetExt, controlnet_exists, get_cn_models
+
+__all__ = [
+    "ControlNetExt",
+    "controlnet_exists",
+    "get_cn_models",
+]

adetailer/controlnet_ext/controlnet_ext.py

@@ -0,0 +1,140 @@
+from __future__ import annotations
+
+import importlib
+import re
+from functools import lru_cache
+from pathlib import Path
+
+from modules import extensions, sd_models, shared
+from modules.paths import data_path, models_path, script_path
+
+ext_path = Path(data_path, "extensions")
+ext_builtin_path = Path(script_path, "extensions-builtin")
+controlnet_exists = False
+controlnet_path = None
+cn_base_path = ""
+
+for extension in extensions.active():
+    if not extension.enabled:
+        continue
+    # For cases like sd-webui-controlnet-master
+    if "sd-webui-controlnet" in extension.name:
+        controlnet_exists = True
+        controlnet_path = Path(extension.path)
+        cn_base_path = ".".join(controlnet_path.parts[-2:])
+        break
+
+cn_model_module = {
+    "inpaint": "inpaint_global_harmonious",
+    "scribble": "t2ia_sketch_pidi",
+    "lineart": "lineart_coarse",
+    "openpose": "openpose_full",
+    "tile": None,
+}
+cn_model_regex = re.compile("|".join(cn_model_module.keys()))
+
+
+class ControlNetExt:
+    def __init__(self):
+        self.cn_models = ["None"]
+        self.cn_available = False
+        self.external_cn = None
+
+    def init_controlnet(self):
+        import_path = cn_base_path + ".scripts.external_code"
+
+        self.external_cn = importlib.import_module(import_path, "external_code")
+        self.cn_available = True
+        models = self.external_cn.get_models()
+        self.cn_models.extend(m for m in models if cn_model_regex.search(m))
+
+    def update_scripts_args(
+        self,
+        p,
+        model: str,
+        module: str | None,
+        weight: float,
+        guidance_start: float,
+        guidance_end: float,
+    ):
+        if (not self.cn_available) or model == "None":
+            return
+
+        if module is None:
+            for m, v in cn_model_module.items():
+                if m in model:
+                    module = v
+                    break
+
+        cn_units = [
+            self.external_cn.ControlNetUnit(
+                model=model,
+                weight=weight,
+                control_mode=self.external_cn.ControlMode.BALANCED,
+                module=module,
+                guidance_start=guidance_start,
+                guidance_end=guidance_end,
+                pixel_perfect=True,
+            )
+        ]
+
+        self.external_cn.update_cn_script_in_processing(p, cn_units)
+
+
+def get_cn_model_dirs() -> list[Path]:
+    cn_model_dir = Path(models_path, "ControlNet")
+    if controlnet_path is not None:
+        cn_model_dir_old = controlnet_path.joinpath("models")
+    else:
+        cn_model_dir_old = None
+    ext_dir1 = shared.opts.data.get("control_net_models_path", "")
+    ext_dir2 = getattr(shared.cmd_opts, "controlnet_dir", "")
+
+    dirs = [cn_model_dir]
+    for ext_dir in [cn_model_dir_old, ext_dir1, ext_dir2]:
+        if ext_dir:
+            dirs.append(Path(ext_dir))
+
+    return dirs
+
+
+@lru_cache
+def _get_cn_models() -> list[str]:
+    """
+    Since we can't import ControlNet, we use a function that does something like
+    controlnet's `list(global_state.cn_models_names.values())`.
+    """
+    cn_model_exts = (".pt", ".pth", ".ckpt", ".safetensors")
+    dirs = get_cn_model_dirs()
+    name_filter = shared.opts.data.get("control_net_models_name_filter", "")
+    name_filter = name_filter.strip(" ").lower()
+
+    model_paths = []
+
+    for base in dirs:
+        if not base.exists():
+            continue
+
+        for p in base.rglob("*"):
+            if (
+                p.is_file()
+                and p.suffix in cn_model_exts
+                and cn_model_regex.search(p.name)
+            ):
+                if name_filter and name_filter not in p.name.lower():
+                    continue
+                model_paths.append(p)
+    model_paths.sort(key=lambda p: p.name)
+
+    models = []
+    for p in model_paths:
+        model_hash = sd_models.model_hash(p)
+        name = f"{p.stem} [{model_hash}]"
+        models.append(name)
+    return models
+
+
+def get_cn_models() -> list[str]:
+    if controlnet_exists:
+        return _get_cn_models()
+    return []

adetailer/controlnet_ext/restore.py

@@ -0,0 +1,43 @@
+from __future__ import annotations
+
+from contextlib import contextmanager
+
+from modules import img2img, processing, shared
+
+
+class CNHijackRestore:
+    def __init__(self):
+        self.process = hasattr(processing, "__controlnet_original_process_images_inner")
+        self.img2img = hasattr(img2img, "__controlnet_original_process_batch")
+
+    def __enter__(self):
+        if self.process:
+            self.orig_process = processing.process_images_inner
+            processing.process_images_inner = getattr(
+                processing, "__controlnet_original_process_images_inner"
+            )
+        if self.img2img:
+            self.orig_img2img = img2img.process_batch
+            img2img.process_batch = getattr(
+                img2img, "__controlnet_original_process_batch"
+            )
+
+    def __exit__(self, *args, **kwargs):
+        if self.process:
+            processing.process_images_inner = self.orig_process
+        if self.img2img:
+            img2img.process_batch = self.orig_img2img
+
+
+@contextmanager
+def cn_allow_script_control():
+    orig = False
+    if "control_net_allow_script_control" in shared.opts.data:
+        try:
+            orig = shared.opts.data["control_net_allow_script_control"]
+            shared.opts.data["control_net_allow_script_control"] = True
+            yield
+        finally:
+            shared.opts.data["control_net_allow_script_control"] = orig
+    else:
+        yield

adetailer/install.py

@@ -0,0 +1,78 @@
+from __future__ import annotations
+
+import importlib.util
+import subprocess
+import sys
+from importlib.metadata import version  # python >= 3.8
+
+from packaging.version import parse
+
+import_name = {"py-cpuinfo": "cpuinfo", "protobuf": "google.protobuf"}
+
+
+def is_installed(
+    package: str, min_version: str | None = None, max_version: str | None = None
+):
+    name = import_name.get(package, package)
+    try:
+        spec = importlib.util.find_spec(name)
+    except ModuleNotFoundError:
+        return False
+
+    if spec is None:
+        return False
+
+    if not min_version and not max_version:
+        return True
+
+    if not min_version:
+        min_version = "0.0.0"
+    if not max_version:
+        max_version = "99999999.99999999.99999999"
+
+    try:
+        pkg_version = version(package)
+        return parse(min_version) <= parse(pkg_version) <= parse(max_version)
+    except Exception:
+        return False
+
+
+def run_pip(*args):
+    subprocess.run([sys.executable, "-m", "pip", "install", *args])
+
+
+def install():
+    deps = [
+        # requirements
+        ("ultralytics", "8.0.145", None),
+        ("mediapipe", "0.10.2", None),
+        ("rich", "13.4.2", None),
+        # ultralytics
+        ("py-cpuinfo", None, None),
+        # mediapipe
+        ("protobuf", "3.20", "3.9999"),
+    ]
+
+    for pkg, low, high in deps:
+        if not is_installed(pkg, low, high):
+            if low and high:
+                cmd = f"{pkg}>={low},<={high}"
+            elif low:
+                cmd = f"{pkg}>={low}"
+            elif high:
+                cmd = f"{pkg}<={high}"
+            else:
+                cmd = pkg
+
+            run_pip("-U", cmd)
+
+
+try:
+    import launch
+
+    skip_install = launch.args.skip_install
+except Exception:
+    skip_install = False
+
+if not skip_install:
+    install()

adetailer/preload.py

@@ -0,0 +1,9 @@
+import argparse
+
+
+def preload(parser: argparse.ArgumentParser):
+    parser.add_argument(
+        "--ad-no-huggingface",
+        action="store_true",
+        help="Don't use adetailer models from huggingface",
+    )

adetailer/pyproject.toml

@@ -0,0 +1,26 @@
+[project]
+name = "adetailer"
+description = "An object detection and auto-mask extension for stable diffusion webui."
+authors = [
+    {name = "dowon", email = "ks2515@naver.com"},
+]
+requires-python = ">=3.8,<3.12"
+readme = "README.md"
+license = {text = "AGPL-3.0"}
+
+[project.urls]
+repository = "https://github.com/Bing-su/adetailer"
+
+[tool.isort]
+profile = "black"
+known_first_party = ["launch", "modules"]
+
+[tool.ruff]
+select = ["A", "B", "C4", "C90", "E", "EM", "F", "FA", "I001", "ISC", "N", "PIE", "PT", "RET", "RUF", "SIM", "UP", "W"]
+ignore = ["B008", "B905", "E501", "F401", "UP007"]
+
+[tool.ruff.isort]
+known-first-party = ["launch", "modules"]
+
+[tool.ruff.per-file-ignores]
+"sd_webui/*.py" = ["B027", "F403"]

adetailer/scripts/!adetailer.py

@@ -0,0 +1,808 @@
+from __future__ import annotations
+
+import os
+import platform
+import re
+import sys
+import traceback
+from contextlib import contextmanager
+from copy import copy, deepcopy
+from functools import partial
+from pathlib import Path
+from textwrap import dedent
+from typing import Any
+
+import gradio as gr
+import torch
+from rich import print
+
+import modules
+from adetailer import (
+    AFTER_DETAILER,
+    __version__,
+    get_models,
+    mediapipe_predict,
+    ultralytics_predict,
+)
+from adetailer.args import ALL_ARGS, BBOX_SORTBY, ADetailerArgs, EnableChecker
+from adetailer.common import PredictOutput
+from adetailer.mask import filter_by_ratio, mask_preprocess, sort_bboxes
+from adetailer.traceback import rich_traceback
+from adetailer.ui import adui, ordinal, suffix
+from controlnet_ext import ControlNetExt, controlnet_exists, get_cn_models
+from controlnet_ext.restore import (
+    CNHijackRestore,
+    cn_allow_script_control,
+)
+from sd_webui import images, safe, script_callbacks, scripts, shared
+from sd_webui.devices import NansException
+from sd_webui.paths import data_path, models_path
+from sd_webui.processing import (
+    Processed,
+    StableDiffusionProcessingImg2Img,
+    create_infotext,
+    process_images,
+)
+from sd_webui.sd_samplers import all_samplers
+from sd_webui.shared import cmd_opts, opts, state
+
+no_huggingface = getattr(cmd_opts, "ad_no_huggingface", False)
+adetailer_dir = Path(models_path, "adetailer")
+model_mapping = get_models(adetailer_dir, huggingface=not no_huggingface)
+txt2img_submit_button = img2img_submit_button = None
+SCRIPT_DEFAULT = "dynamic_prompting,dynamic_thresholding,wildcard_recursive,wildcards,lora_block_weight"
+
+if (
+    not adetailer_dir.exists()
+    and adetailer_dir.parent.exists()
+    and os.access(adetailer_dir.parent, os.W_OK)
+):
+    adetailer_dir.mkdir()
+
+print(
+    f"[-] ADetailer initialized. version: {__version__}, num models: {len(model_mapping)}"
+)
+
+
+@contextmanager
+def change_torch_load():
+    orig = torch.load
+    try:
+        torch.load = safe.unsafe_torch_load
+        yield
+    finally:
+        torch.load = orig
+
+
+@contextmanager
+def pause_total_tqdm():
+    orig = opts.data.get("multiple_tqdm", True)
+    try:
+        opts.data["multiple_tqdm"] = False
+        yield
+    finally:
+        opts.data["multiple_tqdm"] = orig
+
+
+@contextmanager
+def preseve_prompts(p):
+    all_pt = copy(p.all_prompts)
+    all_ng = copy(p.all_negative_prompts)
+    try:
+        yield
+    finally:
+        p.all_prompts = all_pt
+        p.all_negative_prompts = all_ng
+
+
+class AfterDetailerScript(scripts.Script):
+    def __init__(self):
+        super().__init__()
+        self.ultralytics_device = self.get_ultralytics_device()
+
+        self.controlnet_ext = None
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}(version={__version__})"
+
+    def title(self):
+        return AFTER_DETAILER
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+
+    def ui(self, is_img2img):
+        num_models = opts.data.get("ad_max_models", 2)
+        model_list = list(model_mapping.keys())
+        samplers = [sampler.name for sampler in all_samplers]
+
+        components, infotext_fields = adui(
+            num_models,
+            is_img2img,
+            model_list,
+            samplers,
+            txt2img_submit_button,
+            img2img_submit_button,
+        )
+
+        self.infotext_fields = infotext_fields
+        return components
+
+    def init_controlnet_ext(self) -> None:
+        if self.controlnet_ext is not None:
+            return
+        self.controlnet_ext = ControlNetExt()
+
+        if controlnet_exists:
+            try:
+                self.controlnet_ext.init_controlnet()
+            except ImportError:
+                error = traceback.format_exc()
+                print(
+                    f"[-] ADetailer: ControlNetExt init failed:\n{error}",
+                    file=sys.stderr,
+                )
+
+    def update_controlnet_args(self, p, args: ADetailerArgs) -> None:
+        if self.controlnet_ext is None:
+            self.init_controlnet_ext()
+
+        if (
+            self.controlnet_ext is not None
+            and self.controlnet_ext.cn_available
+            and args.ad_controlnet_model != "None"
+        ):
+            self.controlnet_ext.update_scripts_args(
+                p,
+                model=args.ad_controlnet_model,
+                module=args.ad_controlnet_module,
+                weight=args.ad_controlnet_weight,
+                guidance_start=args.ad_controlnet_guidance_start,
+                guidance_end=args.ad_controlnet_guidance_end,
+            )
+
+    def is_ad_enabled(self, *args_) -> bool:
+        arg_list = [arg for arg in args_ if isinstance(arg, dict)]
+        if not args_ or not arg_list or not isinstance(args_[0], (bool, dict)):
+            message = f"""
+                       [-] ADetailer: Invalid arguments passed to ADetailer.
+                           input: {args_!r}
+                       """
+            raise ValueError(dedent(message))
+        enable = args_[0] if isinstance(args_[0], bool) else True
+        checker = EnableChecker(enable=enable, arg_list=arg_list)
+        return checker.is_enabled()
+
+    def get_args(self, p, *args_) -> list[ADetailerArgs]:
+        """
+        `args_` is at least 1 in length by `is_ad_enabled` immediately above
+        """
+        args = [arg for arg in args_ if isinstance(arg, dict)]
+
+        if not args:
+            message = f"[-] ADetailer: Invalid arguments passed to ADetailer: {args_!r}"
+            raise ValueError(message)
+
+        if hasattr(p, "adetailer_xyz"):
+            args[0].update(p.adetailer_xyz)
+
+        all_inputs = []
+
+        for n, arg_dict in enumerate(args, 1):
+            try:
+                inp = ADetailerArgs(**arg_dict)
+            except ValueError as e:
+                msgs = [
+                    f"[-] ADetailer: ValidationError when validating {ordinal(n)} arguments: {e}\n"
+                ]
+                for attr in ALL_ARGS.attrs:
+                    arg = arg_dict.get(attr)
+                    dtype = type(arg)
+                    arg = "DEFAULT" if arg is None else repr(arg)
+                    msgs.append(f"    {attr}: {arg} ({dtype})")
+                raise ValueError("\n".join(msgs)) from e
+
+            all_inputs.append(inp)
+
+        return all_inputs
+
+    def extra_params(self, arg_list: list[ADetailerArgs]) -> dict:
+        params = {}
+        for n, args in enumerate(arg_list):
+            params.update(args.extra_params(suffix=suffix(n)))
+        params["ADetailer version"] = __version__
+        return params
+
+    @staticmethod
+    def get_ultralytics_device() -> str:
+        if "adetailer" in shared.cmd_opts.use_cpu:
+            return "cpu"
+
+        if platform.system() == "Darwin":
+            return ""
+
+        if any(getattr(cmd_opts, vram, False) for vram in ["lowvram", "medvram"]):
+            return "cpu"
+
+        return ""
+
+    def prompt_blank_replacement(
+        self, all_prompts: list[str], i: int, default: str
+    ) -> str:
+        if not all_prompts:
+            return default
+        if i < len(all_prompts):
+            return all_prompts[i]
+        j = i % len(all_prompts)
+        return all_prompts[j]
+
+    def _get_prompt(
+        self, ad_prompt: str, all_prompts: list[str], i: int, default: str
+    ) -> list[str]:
+        prompts = re.split(r"\s*\[SEP\]\s*", ad_prompt)
+        blank_replacement = self.prompt_blank_replacement(all_prompts, i, default)
+        for n in range(len(prompts)):
+            if not prompts[n]:
+                prompts[n] = blank_replacement
+        return prompts
+
+    def get_prompt(self, p, args: ADetailerArgs) -> tuple[list[str], list[str]]:
+        i = p._ad_idx
+
+        prompt = self._get_prompt(args.ad_prompt, p.all_prompts, i, p.prompt)
+        negative_prompt = self._get_prompt(
+            args.ad_negative_prompt, p.all_negative_prompts, i, p.negative_prompt
+        )
+
+        return prompt, negative_prompt
+
+    def get_seed(self, p) -> tuple[int, int]:
+        i = p._ad_idx
+
+        if not p.all_seeds:
+            seed = p.seed
+        elif i < len(p.all_seeds):
+            seed = p.all_seeds[i]
+        else:
+            j = i % len(p.all_seeds)
+            seed = p.all_seeds[j]
+
+        if not p.all_subseeds:
+            subseed = p.subseed
+        elif i < len(p.all_subseeds):
+            subseed = p.all_subseeds[i]
+        else:
+            j = i % len(p.all_subseeds)
+            subseed = p.all_subseeds[j]
+
+        return seed, subseed
+
+    def get_width_height(self, p, args: ADetailerArgs) -> tuple[int, int]:
+        if args.ad_use_inpaint_width_height:
+            width = args.ad_inpaint_width
+            height = args.ad_inpaint_height
+        else:
+            width = p.width
+            height = p.height
+
+        return width, height
+
+    def get_steps(self, p, args: ADetailerArgs) -> int:
+        if args.ad_use_steps:
+            return args.ad_steps
+        return p.steps
+
+    def get_cfg_scale(self, p, args: ADetailerArgs) -> float:
+        if args.ad_use_cfg_scale:
+            return args.ad_cfg_scale
+        return p.cfg_scale
+
+    def get_sampler(self, p, args: ADetailerArgs) -> str:
+        sampler_name = args.ad_sampler if args.ad_use_sampler else p.sampler_name
+
+        if sampler_name in ["PLMS", "UniPC"]:
+            sampler_name = "Euler"
+        return sampler_name
+
+    def get_override_settings(self, p, args: ADetailerArgs) -> dict[str, Any]:
+        d = {}
+        if args.ad_use_clip_skip:
+            d["CLIP_stop_at_last_layers"] = args.ad_clip_skip
+        return d
+
+    def get_initial_noise_multiplier(self, p, args: ADetailerArgs) -> float | None:
+        if args.ad_use_noise_multiplier:
+            return args.ad_noise_multiplier
+        return None
+
+    @staticmethod
+    def infotext(p) -> str:
+        return create_infotext(
+            p, p.all_prompts, p.all_seeds, p.all_subseeds, None, 0, 0
+        )
+
+    def write_params_txt(self, p) -> None:
+        infotext = self.infotext(p)
+        params_txt = Path(data_path, "params.txt")
+        params_txt.write_text(infotext, encoding="utf-8")
+
+    def script_filter(self, p, args: ADetailerArgs):
+        script_runner = copy(p.scripts)
+        script_args = deepcopy(p.script_args)
+        self.disable_controlnet_units(script_args)
+
+        ad_only_seleted_scripts = opts.data.get("ad_only_seleted_scripts", True)
+        if not ad_only_seleted_scripts:
+            return script_runner, script_args
+
+        ad_script_names = opts.data.get("ad_script_names", SCRIPT_DEFAULT)
+        script_names_set = {
+            name
+            for script_name in ad_script_names.split(",")
+            for name in (script_name, script_name.strip())
+        }
+
+        if args.ad_controlnet_model != "None":
+            script_names_set.add("controlnet")
+
+        filtered_alwayson = []
+        for script_object in script_runner.alwayson_scripts:
+            filepath = script_object.filename
+            filename = Path(filepath).stem
+            if filename in script_names_set:
+                filtered_alwayson.append(script_object)
+
+        script_runner.alwayson_scripts = filtered_alwayson
+        return script_runner, script_args
+
+    def disable_controlnet_units(self, script_args: list[Any]) -> None:
+        for obj in script_args:
+            if "controlnet" in obj.__class__.__name__.lower():
+                if hasattr(obj, "enabled"):
+                    obj.enabled = False
+                if hasattr(obj, "input_mode"):
+                    obj.input_mode = getattr(obj.input_mode, "SIMPLE", "simple")
+
+            elif isinstance(obj, dict) and "module" in obj:
+                obj["enabled"] = False
+
+    def get_i2i_p(self, p, args: ADetailerArgs, image):
+        seed, subseed = self.get_seed(p)
+        width, height = self.get_width_height(p, args)
+        steps = self.get_steps(p, args)
+        cfg_scale = self.get_cfg_scale(p, args)
+        initial_noise_multiplier = self.get_initial_noise_multiplier(p, args)
+        sampler_name = self.get_sampler(p, args)
+        override_settings = self.get_override_settings(p, args)
+
+        i2i = StableDiffusionProcessingImg2Img(
+            init_images=[image],
+            resize_mode=0,
+            denoising_strength=args.ad_denoising_strength,
+            mask=None,
+            mask_blur=args.ad_mask_blur,
+            inpainting_fill=1,
+            inpaint_full_res=args.ad_inpaint_only_masked,
+            inpaint_full_res_padding=args.ad_inpaint_only_masked_padding,
+            inpainting_mask_invert=0,
+            initial_noise_multiplier=initial_noise_multiplier,
+            sd_model=p.sd_model,
+            outpath_samples=p.outpath_samples,
+            outpath_grids=p.outpath_grids,
+            prompt="",  # replace later
+            negative_prompt="",
+            styles=p.styles,
+            seed=seed,
+            subseed=subseed,
+            subseed_strength=p.subseed_strength,
+            seed_resize_from_h=p.seed_resize_from_h,
+            seed_resize_from_w=p.seed_resize_from_w,
+            sampler_name=sampler_name,
+            batch_size=1,
+            n_iter=1,
+            steps=steps,
+            cfg_scale=cfg_scale,
+            width=width,
+            height=height,
+            restore_faces=args.ad_restore_face,
+            tiling=p.tiling,
+            extra_generation_params=p.extra_generation_params,
+            do_not_save_samples=True,
+            do_not_save_grid=True,
+            override_settings=override_settings,
+        )
+
+        i2i.cached_c = [None, None]
+        i2i.cached_uc = [None, None]
+        i2i.scripts, i2i.script_args = self.script_filter(p, args)
+        i2i._disable_adetailer = True
+
+        if args.ad_controlnet_model != "None":
+            self.update_controlnet_args(i2i, args)
+        else:
+            i2i.control_net_enabled = False
+
+        return i2i
+
+    def save_image(self, p, image, *, condition: str, suffix: str) -> None:
+        i = p._ad_idx
+        if p.all_prompts:
+            i %= len(p.all_prompts)
+            save_prompt = p.all_prompts[i]
+        else:
+            save_prompt = p.prompt
+        seed, _ = self.get_seed(p)
+
+        if opts.data.get(condition, False):
+            images.save_image(
+                image=image,
+                path=p.outpath_samples,
+                basename="",
+                seed=seed,
+                prompt=save_prompt,
+                extension=opts.samples_format,
+                info=self.infotext(p),
+                p=p,
+                suffix=suffix,
+            )
+
+    def get_ad_model(self, name: str):
+        if name not in model_mapping:
+            msg = f"[-] ADetailer: Model {name!r} not found. Available models: {list(model_mapping.keys())}"
+            raise ValueError(msg)
+        return model_mapping[name]
+
+    def sort_bboxes(self, pred: PredictOutput) -> PredictOutput:
+        sortby = opts.data.get("ad_bbox_sortby", BBOX_SORTBY[0])
+        sortby_idx = BBOX_SORTBY.index(sortby)
+        return sort_bboxes(pred, sortby_idx)
+
+    def pred_preprocessing(self, pred: PredictOutput, args: ADetailerArgs):
+        pred = filter_by_ratio(
+            pred, low=args.ad_mask_min_ratio, high=args.ad_mask_max_ratio
+        )
+        pred = self.sort_bboxes(pred)
+        return mask_preprocess(
+            pred.masks,
+            kernel=args.ad_dilate_erode,
+            x_offset=args.ad_x_offset,
+            y_offset=args.ad_y_offset,
+            merge_invert=args.ad_mask_merge_invert,
+        )
+
+    @staticmethod
+    def ensure_rgb_image(image: Any):
+        if hasattr(image, "mode") and image.mode != "RGB":
+            image = image.convert("RGB")
+        return image
+
+    @staticmethod
+    def i2i_prompts_replace(
+        i2i, prompts: list[str], negative_prompts: list[str], j: int
+    ) -> None:
+        i1 = min(j, len(prompts) - 1)
+        i2 = min(j, len(negative_prompts) - 1)
+        prompt = prompts[i1]
+        negative_prompt = negative_prompts[i2]
+        i2i.prompt = prompt
+        i2i.negative_prompt = negative_prompt
+
+    @staticmethod
+    def compare_prompt(p, processed, n: int = 0):
+        if p.prompt != processed.all_prompts[0]:
+            print(
+                f"[-] ADetailer: applied {ordinal(n + 1)} ad_prompt: {processed.all_prompts[0]!r}"
+            )
+
+        if p.negative_prompt != processed.all_negative_prompts[0]:
+            print(
+                f"[-] ADetailer: applied {ordinal(n + 1)} ad_negative_prompt: {processed.all_negative_prompts[0]!r}"
+            )
+
+    @staticmethod
+    def need_call_process(p) -> bool:
+        i = p._ad_idx
+        bs = p.batch_size
+        return i % bs == bs - 1
+
+    @staticmethod
+    def need_call_postprocess(p) -> bool:
+        i = p._ad_idx
+        bs = p.batch_size
+        return i % bs == 0
+
+    @rich_traceback
+    def process(self, p, *args_):
+        if getattr(p, "_disable_adetailer", False):
+            return
+
+        if self.is_ad_enabled(*args_):
+            arg_list = self.get_args(p, *args_)
+            extra_params = self.extra_params(arg_list)
+            p.extra_generation_params.update(extra_params)
+
+    def _postprocess_image(self, p, pp, args: ADetailerArgs, *, n: int = 0) -> bool:
+        """
+        Returns
+        -------
+            bool
+
+            `True` if image was processed, `False` otherwise.
+        """
+        if state.interrupted:
+            return False
+
+        i = p._ad_idx
+
+        i2i = self.get_i2i_p(p, args, pp.image)
+        seed, subseed = self.get_seed(p)
+        ad_prompts, ad_negatives = self.get_prompt(p, args)
+
+        is_mediapipe = args.ad_model.lower().startswith("mediapipe")
+
+        kwargs = {}
+        if is_mediapipe:
+            predictor = mediapipe_predict
+            ad_model = args.ad_model
+        else:
+            predictor = ultralytics_predict
+            ad_model = self.get_ad_model(args.ad_model)
+            kwargs["device"] = self.ultralytics_device
+
+        with change_torch_load():
+            pred = predictor(ad_model, pp.image, args.ad_confidence, **kwargs)
+
+        masks = self.pred_preprocessing(pred, args)
+
+        if not masks:
+            print(
+                f"[-] ADetailer: nothing detected on image {i + 1} with {ordinal(n + 1)} settings."
+            )
+            return False
+
+        self.save_image(
+            p,
+            pred.preview,
+            condition="ad_save_previews",
+            suffix="-ad-preview" + suffix(n, "-"),
+        )
+
+        steps = len(masks)
+        processed = None
+        state.job_count += steps
+
+        if is_mediapipe:
+            print(f"mediapipe: {steps} detected.")
+
+        p2 = copy(i2i)
+        for j in range(steps):
+            p2.image_mask = masks[j]
+            p2.init_images[0] = self.ensure_rgb_image(p2.init_images[0])
+            self.i2i_prompts_replace(p2, ad_prompts, ad_negatives, j)
+
+            if re.match(r"^\s*\[SKIP\]\s*$", p2.prompt):
+                continue
+
+            p2.seed = seed + j
+            p2.subseed = subseed + j
+
+            try:
+                processed = process_images(p2)
+            except NansException as e:
+                msg = f"[-] ADetailer: 'NansException' occurred with {ordinal(n + 1)} settings.\n{e}"
+                print(msg, file=sys.stderr)
+                continue
+            finally:
+                p2.close()
+
+            self.compare_prompt(p2, processed, n=n)
+            p2 = copy(i2i)
+            p2.init_images = [processed.images[0]]
+
+        if processed is not None:
+            pp.image = processed.images[0]
+            return True
+
+        return False
+
+    @rich_traceback
+    def postprocess_image(self, p, pp, *args_):
+        if getattr(p, "_disable_adetailer", False):
+            return
+
+        if not self.is_ad_enabled(*args_):
+            return
+
+        p._ad_idx = getattr(p, "_ad_idx", -1) + 1
+        init_image = copy(pp.image)
+        arg_list = self.get_args(p, *args_)
+
+        if p.scripts is not None and self.need_call_postprocess(p):
+            dummy = Processed(p, [], p.seed, "")
+            with preseve_prompts(p):
+                p.scripts.postprocess(copy(p), dummy)
+
+        is_processed = False
+        with CNHijackRestore(), pause_total_tqdm(), cn_allow_script_control():
+            for n, args in enumerate(arg_list):
+                if args.ad_model == "None":
+                    continue
+                is_processed |= self._postprocess_image(p, pp, args, n=n)
+
+        if is_processed:
+            self.save_image(
+                p, init_image, condition="ad_save_images_before", suffix="-ad-before"
+            )
+
+        if p.scripts is not None and self.need_call_process(p):
+            with preseve_prompts(p):
+                p.scripts.process(copy(p))
+
+        try:
+            ia = p._ad_idx
+            lenp = len(p.all_prompts)
+            if ia % lenp == lenp - 1:
+                self.write_params_txt(p)
+        except Exception:
+            pass
+
+
+def on_after_component(component, **_kwargs):
+    global txt2img_submit_button, img2img_submit_button
+    if getattr(component, "elem_id", None) == "txt2img_generate":
+        txt2img_submit_button = component
+        return
+
+    if getattr(component, "elem_id", None) == "img2img_generate":
+        img2img_submit_button = component
+
+
+def on_ui_settings():
+    section = ("ADetailer", AFTER_DETAILER)
+    shared.opts.add_option(
+        "ad_max_models",
+        shared.OptionInfo(
+            default=2,
+            label="Max models",
+            component=gr.Slider,
+            component_args={"minimum": 1, "maximum": 10, "step": 1},
+            section=section,
+        ),
+    )
+
+    shared.opts.add_option(
+        "ad_save_previews",
+        shared.OptionInfo(False, "Save mask previews", section=section),
+    )
+
+    shared.opts.add_option(
+        "ad_save_images_before",
+        shared.OptionInfo(False, "Save images before ADetailer", section=section),
+    )
+
+    shared.opts.add_option(
+        "ad_only_seleted_scripts",
+        shared.OptionInfo(
+            True, "Apply only selected scripts to ADetailer", section=section
+        ),
+    )
+
+    textbox_args = {
+        "placeholder": "comma-separated list of script names",
+        "interactive": True,
+    }
+
+    shared.opts.add_option(
+        "ad_script_names",
+        shared.OptionInfo(
+            default=SCRIPT_DEFAULT,
+            label="Script names to apply to ADetailer (separated by comma)",
+            component=gr.Textbox,
+            component_args=textbox_args,
+            section=section,
+        ),
+    )
+
+    shared.opts.add_option(
+        "ad_bbox_sortby",
+        shared.OptionInfo(
+            default="None",
+            label="Sort bounding boxes by",
+            component=gr.Radio,
+            component_args={"choices": BBOX_SORTBY},
+            section=section,
+        ),
+    )
+
+
+# xyz_grid
+
+
+def make_axis_on_xyz_grid():
+    xyz_grid = None
+    for script in scripts.scripts_data:
+        if script.script_class.__module__ == "xyz_grid.py":
+            xyz_grid = script.module
+            break
+
+    if xyz_grid is None:
+        return
+
+    model_list = ["None", *model_mapping.keys()]
+    samplers = [sampler.name for sampler in all_samplers]
+
+    def set_value(p, x, xs, *, field: str):
+        if not hasattr(p, "adetailer_xyz"):
+            p.adetailer_xyz = {}
+        p.adetailer_xyz[field] = x
+
+    axis = [
+        xyz_grid.AxisOption(
+            "[ADetailer] ADetailer model 1st",
+            str,
+            partial(set_value, field="ad_model"),
+            choices=lambda: model_list,
+        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] ADetailer prompt 1st",
+            str,
+            partial(set_value, field="ad_prompt"),
+        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] ADetailer negative prompt 1st",
+            str,
+            partial(set_value, field="ad_negative_prompt"),
+        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] Mask erosion / dilation 1st",
+            int,
+            partial(set_value, field="ad_dilate_erode"),
+        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] Inpaint denoising strength 1st",
+            float,
+            partial(set_value, field="ad_denoising_strength"),
+        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] Inpaint only masked 1st",
+            str,
+            partial(set_value, field="ad_inpaint_only_masked"),
+            choices=lambda: ["True", "False"],
+        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] Inpaint only masked padding 1st",
+            int,
+            partial(set_value, field="ad_inpaint_only_masked_padding"),
+        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] ADetailer sampler 1st",
+            str,
+            partial(set_value, field="ad_sampler"),
+            choices=lambda: samplers,
+        ),
+        xyz_grid.AxisOption(
+            "[ADetailer] ControlNet model 1st",
+            str,
+            partial(set_value, field="ad_controlnet_model"),
+            choices=lambda: ["None", *get_cn_models()],
+        ),
+    ]
+
+    if not any(x.label.startswith("[ADetailer]") for x in xyz_grid.axis_options):
+        xyz_grid.axis_options.extend(axis)
+
+
+def on_before_ui():
+    try:
+        make_axis_on_xyz_grid()
+    except Exception:
+        error = traceback.format_exc()
+        print(
+            f"[-] ADetailer: xyz_grid error:\n{error}",
+            file=sys.stderr,
+        )
+
+
+script_callbacks.on_ui_settings(on_ui_settings)
+script_callbacks.on_after_component(on_after_component)
+script_callbacks.on_before_ui(on_before_ui)

adetailer/sd_webui/devices.py

@@ -0,0 +1,11 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+
+    class NansException(Exception):  # noqa: N818
+        pass
+
+else:
+    from modules.devices import NansException

adetailer/sd_webui/images.py

@@ -0,0 +1,62 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from PIL import Image, PngImagePlugin
+
+    from sd_webui.processing import StableDiffusionProcessing
+
+    def save_image(
+        image: Image.Image,
+        path: str,
+        basename: str,
+        seed: int | None = None,
+        prompt: str = "",
+        extension: str = "png",
+        info: str | PngImagePlugin.iTXt = "",
+        short_filename: bool = False,
+        no_prompt: bool = False,
+        grid: bool = False,
+        pnginfo_section_name: str = "parameters",
+        p: StableDiffusionProcessing | None = None,
+        existing_info: dict | None = None,
+        forced_filename: str | None = None,
+        suffix: str = "",
+        save_to_dirs: bool = False,
+    ) -> tuple[str, str | None]:
+        """Save an image.
+
+        Args:
+            image (`PIL.Image`):
+                The image to be saved.
+            path (`str`):
+                The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory.
+            basename (`str`):
+                The base filename which will be applied to `filename pattern`.
+            seed, prompt, short_filename,
+            extension (`str`):
+                Image file extension, default is `png`.
+            pngsectionname (`str`):
+                Specify the name of the section which `info` will be saved in.
+            info (`str` or `PngImagePlugin.iTXt`):
+                PNG info chunks.
+            existing_info (`dict`):
+                Additional PNG info. `existing_info == {pngsectionname: info, ...}`
+            no_prompt:
+                TODO I don't know its meaning.
+            p (`StableDiffusionProcessing`)
+            forced_filename (`str`):
+                If specified, `basename` and filename pattern will be ignored.
+            save_to_dirs (bool):
+                If true, the image will be saved into a subdirectory of `path`.
+
+        Returns: (fullfn, txt_fullfn)
+            fullfn (`str`):
+                The full path of the saved imaged.
+            txt_fullfn (`str` or None):
+                If a text file is saved for this image, this will be its full path. Otherwise None.
+        """
+
+else:
+    from modules.images import save_image

adetailer/sd_webui/paths.py

@@ -0,0 +1,14 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import os
+
+    models_path = os.path.join(os.path.dirname(__file__), "1")
+    script_path = os.path.join(os.path.dirname(__file__), "2")
+    data_path = os.path.join(os.path.dirname(__file__), "3")
+    extensions_dir = os.path.join(os.path.dirname(__file__), "4")
+    extensions_builtin_dir = os.path.join(os.path.dirname(__file__), "5")
+else:
+    from modules.paths import data_path, models_path, script_path

adetailer/sd_webui/processing.py

@@ -0,0 +1,179 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from dataclasses import dataclass, field
+    from typing import Any, Callable
+
+    import numpy as np
+    import torch
+    from PIL import Image
+
+    def _image():
+        return Image.new("L", (512, 512))
+
+    @dataclass
+    class StableDiffusionProcessing:
+        sd_model: torch.nn.Module = field(default_factory=lambda: torch.nn.Linear(1, 1))
+        outpath_samples: str = ""
+        outpath_grids: str = ""
+        prompt: str = ""
+        prompt_for_display: str = ""
+        negative_prompt: str = ""
+        styles: list[str] = field(default_factory=list)
+        seed: int = -1
+        subseed: int = -1
+        subseed_strength: float = 0.0
+        seed_resize_from_h: int = -1
+        seed_resize_from_w: int = -1
+        sampler_name: str | None = None
+        batch_size: int = 1
+        n_iter: int = 1
+        steps: int = 50
+        cfg_scale: float = 7.0
+        width: int = 512
+        height: int = 512
+        restore_faces: bool = False
+        tiling: bool = False
+        do_not_save_samples: bool = False
+        do_not_save_grid: bool = False
+        extra_generation_params: dict[str, Any] = field(default_factory=dict)
+        overlay_images: list[Image.Image] = field(default_factory=list)
+        eta: float = 0.0
+        do_not_reload_embeddings: bool = False
+        paste_to: tuple[int | float, ...] = (0, 0, 0, 0)
+        color_corrections: list[np.ndarray] = field(default_factory=list)
+        denoising_strength: float = 0.0
+        sampler_noise_scheduler_override: Callable | None = None
+        ddim_discretize: str = ""
+        s_min_uncond: float = 0.0
+        s_churn: float = 0.0
+        s_tmin: float = 0.0
+        s_tmax: float = 0.0
+        s_noise: float = 0.0
+        override_settings: dict[str, Any] = field(default_factory=dict)
+        override_settings_restore_afterwards: bool = False
+        is_using_inpainting_conditioning: bool = False
+        disable_extra_networks: bool = False
+        scripts: Any = None
+        script_args: list[Any] = field(default_factory=list)
+        all_prompts: list[str] = field(default_factory=list)
+        all_negative_prompts: list[str] = field(default_factory=list)
+        all_seeds: list[int] = field(default_factory=list)
+        all_subseeds: list[int] = field(default_factory=list)
+        iteration: int = 1
+        is_hr_pass: bool = False
+
+        def close(self) -> None:
+            pass
+
+    @dataclass
+    class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
+        sampler: Callable | None = None
+        enable_hr: bool = False
+        denoising_strength: float = 0.75
+        hr_scale: float = 2.0
+        hr_upscaler: str = ""
+        hr_second_pass_steps: int = 0
+        hr_resize_x: int = 0
+        hr_resize_y: int = 0
+        hr_upscale_to_x: int = 0
+        hr_upscale_to_y: int = 0
+        width: int = 512
+        height: int = 512
+        truncate_x: int = 512
+        truncate_y: int = 512
+        applied_old_hires_behavior_to: tuple[int, int] = (512, 512)
+
+    @dataclass
+    class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
+        sampler: Callable | None = None
+        init_images: list[Image.Image] = field(default_factory=list)
+        resize_mode: int = 0
+        denoising_strength: float = 0.75
+        image_cfg_scale: float | None = None
+        init_latent: torch.Tensor | None = None
+        image_mask: Image.Image = field(default_factory=_image)
+        latent_mask: Image.Image = field(default_factory=_image)
+        mask_for_overlay: Image.Image = field(default_factory=_image)
+        mask_blur: int = 4
+        inpainting_fill: int = 0
+        inpaint_full_res: bool = True
+        inpaint_full_res_padding: int = 0
+        inpainting_mask_invert: int | bool = 0
+        initial_noise_multiplier: float = 1.0
+        mask: torch.Tensor | None = None
+        nmask: torch.Tensor | None = None
+        image_conditioning: torch.Tensor | None = None
+
+    @dataclass
+    class Processed:
+        images: list[Image.Image] = field(default_factory=list)
+        prompt: list[str] = field(default_factory=list)
+        negative_prompt: list[str] = field(default_factory=list)
+        seed: list[int] = field(default_factory=list)
+        subseed: list[int] = field(default_factory=list)
+        subseed_strength: float = 0.0
+        info: str = ""
+        comments: str = ""
+        width: int = 512
+        height: int = 512
+        sampler_name: str = ""
+        cfg_scale: float = 7.0
+        image_cfg_scale: float | None = None
+        steps: int = 50
+        batch_size: int = 1
+        restore_faces: bool = False
+        face_restoration_model: str | None = None
+        sd_model_hash: str = ""
+        seed_resize_from_w: int = -1
+        seed_resize_from_h: int = -1
+        denoising_strength: float = 0.0
+        extra_generation_params: dict[str, Any] = field(default_factory=dict)
+        index_of_first_image: int = 0
+        styles: list[str] = field(default_factory=list)
+        job_timestamp: str = ""
+        clip_skip: int = 1
+        eta: float = 0.0
+        ddim_discretize: str = ""
+        s_churn: float = 0.0
+        s_tmin: float = 0.0
+        s_tmax: float = 0.0
+        s_noise: float = 0.0
+        sampler_noise_scheduler_override: Callable | None = None
+        is_using_inpainting_conditioning: bool = False
+        all_prompts: list[str] = field(default_factory=list)
+        all_negative_prompts: list[str] = field(default_factory=list)
+        all_seeds: list[int] = field(default_factory=list)
+        all_subseeds: list[int] = field(default_factory=list)
+        infotexts: list[str] = field(default_factory=list)
+
+    def create_infotext(
+        p: StableDiffusionProcessingTxt2Img | StableDiffusionProcessingImg2Img,
+        all_prompts: list[str],
+        all_seeds: list[int],
+        all_subseeds: list[int],
+        comments: Any,
+        iteration: int = 0,
+        position_in_batch: int = 0,
+        use_main_prompt: bool = False,
+        index: int | None = None,
+        all_negative_prompts: list[str] | None = None,
+    ) -> str:
+        pass
+
+    def process_images(
+        p: StableDiffusionProcessingTxt2Img | StableDiffusionProcessingImg2Img,
+    ) -> Processed:
+        pass
+
+else:
+    from modules.processing import (
+        Processed,
+        StableDiffusionProcessing,
+        StableDiffusionProcessingImg2Img,
+        StableDiffusionProcessingTxt2Img,
+        create_infotext,
+        process_images,
+    )

adetailer/sd_webui/safe.py

@@ -0,0 +1,10 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import torch
+
+    unsafe_torch_load = torch.load
+else:
+    from modules.safe import unsafe_torch_load

adetailer/sd_webui/script_callbacks.py

@@ -0,0 +1,26 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from typing import Callable
+
+    def on_app_started(callback: Callable):
+        pass
+
+    def on_ui_settings(callback: Callable):
+        pass
+
+    def on_after_component(callback: Callable):
+        pass
+
+    def on_before_ui(callback: Callable):
+        pass
+
+else:
+    from modules.script_callbacks import (
+        on_after_component,
+        on_app_started,
+        on_before_ui,
+        on_ui_settings,
+    )

adetailer/sd_webui/scripts.py

@@ -0,0 +1,94 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from abc import ABC, abstractmethod
+    from collections import namedtuple
+    from dataclasses import dataclass
+    from typing import Any
+
+    import gradio as gr
+    from PIL import Image
+
+    from sd_webui.processing import (
+        Processed,
+        StableDiffusionProcessingImg2Img,
+        StableDiffusionProcessingTxt2Img,
+    )
+
+    SDPType = StableDiffusionProcessingImg2Img | StableDiffusionProcessingTxt2Img
+    AlwaysVisible = object()
+
+    @dataclass
+    class PostprocessImageArgs:
+        image: Image.Image
+
+    class Script(ABC):
+        filename: str
+        args_from: int
+        args_to: int
+        alwayson: bool
+
+        is_txt2img: bool
+        is_img2img: bool
+
+        group: gr.Group
+        infotext_fields: list[tuple[str, str]]
+        paste_field_names: list[str]
+
+        @abstractmethod
+        def title(self):
+            raise NotImplementedError
+
+        def ui(self, is_img2img: bool):
+            pass
+
+        def show(self, is_img2img: bool):
+            return True
+
+        def run(self, p: SDPType, *args):
+            pass
+
+        def process(self, p: SDPType, *args):
+            pass
+
+        def before_process_batch(self, p: SDPType, *args, **kwargs):
+            pass
+
+        def process_batch(self, p: SDPType, *args, **kwargs):
+            pass
+
+        def postprocess_batch(self, p: SDPType, *args, **kwargs):
+            pass
+
+        def postprocess_image(self, p: SDPType, pp: PostprocessImageArgs, *args):
+            pass
+
+        def postprocess(self, p: SDPType, processed: Processed, *args):
+            pass
+
+        def before_component(self, component, **kwargs):
+            pass
+
+        def after_component(self, component, **kwargs):
+            pass
+
+        def describe(self):
+            return ""
+
+        def elem_id(self, item_id: Any) -> str:
+            pass
+
+    ScriptClassData = namedtuple(
+        "ScriptClassData", ["script_class", "path", "basedir", "module"]
+    )
+    scripts_data: list[ScriptClassData] = []
+
+else:
+    from modules.scripts import (
+        AlwaysVisible,
+        PostprocessImageArgs,
+        Script,
+        scripts_data,
+    )

adetailer/sd_webui/sd_samplers.py

@@ -0,0 +1,17 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from typing import Any, Callable, NamedTuple
+
+    class SamplerData(NamedTuple):
+        name: str
+        constructor: Callable
+        aliases: list[str]
+        options: dict[str, Any]
+
+    all_samplers: list[SamplerData] = []
+
+else:
+    from modules.sd_samplers import all_samplers

adetailer/sd_webui/shared.py

@@ -0,0 +1,66 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import argparse
+    from dataclasses import dataclass
+    from typing import Any, Callable
+
+    import torch
+    from PIL import Image
+
+    @dataclass
+    class State:
+        skipped: bool = False
+        interrupted: bool = False
+        job: str = ""
+        job_no: int = 0
+        job_count: int = 0
+        processing_has_refined_job_count: bool = False
+        job_timestamp: str = "0"
+        sampling_step: int = 0
+        sampling_steps: int = 0
+        current_latent: torch.Tensor | None = None
+        current_image: Image.Image | None = None
+        current_image_sampling_step: int = 0
+        id_live_preview: int = 0
+        textinfo: str | None = None
+        time_start: float | None = None
+        need_restart: bool = False
+        server_start: float | None = None
+
+    @dataclass
+    class OptionInfo:
+        default: Any = None
+        label: str = ""
+        component: Any = None
+        component_args: Callable[[], dict] | dict[str, Any] | None = None
+        onchange: Callable[[], None] | None = None
+        section: tuple[str, str] | None = None
+        refresh: Callable[[], None] | None = None
+
+    class Option:
+        data_labels: dict[str, OptionInfo]
+
+        def __init__(self):
+            self.data: dict[str, Any] = {}
+
+        def add_option(self, key: str, info: OptionInfo):
+            pass
+
+        def __getattr__(self, item: str):
+            if self.data is not None and item in self.data:
+                return self.data[item]
+
+            if item in self.data_labels:
+                return self.data_labels[item].default
+
+            return super().__getattribute__(item)
+
+    opts = Option()
+    cmd_opts = argparse.Namespace()
+    state = State()
+
+else:
+    from modules.shared import OptionInfo, cmd_opts, opts, state

artists-to-study/.github/FUNDING.yml

@@ -0,0 +1,13 @@
+# These are supported funding model platforms
+
+github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
+patreon: camenduru
+open_collective: # Replace with a single Open Collective username
+ko_fi: camenduru
+tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
+community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
+liberapay: # Replace with a single Liberapay username
+issuehunt: # Replace with a single IssueHunt username
+otechie: # Replace with a single Otechie username
+lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
+custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']

artists-to-study/LICENSE

@@ -0,0 +1,24 @@
+This is free and unencumbered software released into the public domain.
+
+Anyone is free to copy, modify, publish, use, compile, sell, or
+distribute this software, either in source code form or as a compiled
+binary, for any purpose, commercial or non-commercial, and by any
+means.
+
+In jurisdictions that recognize copyright laws, the author or authors
+of this software dedicate any and all copyright interest in the
+software to the public domain. We make this dedication for the benefit
+of the public at large and to the detriment of our heirs and
+successors. We intend this dedication to be an overt act of
+relinquishment in perpetuity of all present and future rights to this
+software under copyright law.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+OTHER DEALINGS IN THE SOFTWARE.
+
+For more information, please refer to <https://unlicense.org>

artists-to-study/README.md

@@ -0,0 +1,15 @@
+🐣 Please follow me for new updates https://twitter.com/camenduru <br />
+🔥 Please join our discord server https://discord.gg/k5BwmmvJJU <br />
+🥳 Please join my patreon community https://patreon.com/camenduru <br />
+
+# Artists To Study
+
+![artists-to-study](https://user-images.githubusercontent.com/54370274/197829512-e7d30d44-2697-4ecd-b9a7-3665217918c7.jpg)
+
+https://artiststostudy.pages.dev addapted to an extension for [web ui](https://github.com/AUTOMATIC1111/stable-diffusion-webui).
+
+To install it, clone the repo into the `extensions` directory and restart the web ui:
+
+`git clone https://github.com/camenduru/stable-diffusion-webui-artists-to-study`
+
+You can add the artist name to the clipboard by clicking on it. (thanks for the idea @gmaciocci)