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+extensions/stable-diffusion-webui-intm/images/IMAGE.png filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-simple-manga-maker/SP-MangaEditer/03_images/imgPromptHelper/bottom/00106--rebuild.png filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-simple-manga-maker/SP-MangaEditer/03_images/imgPromptHelper/skin/wrinkles-skin.png filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-simple-manga-maker/SP-MangaEditer/03_images/imgPromptHelper/top-bottom/00067-2139881315.png filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-simple-manga-maker/SP-MangaEditer/font/851CHIKARA-DZUYOKU_kanaA_004/851CHIKARA-DZUYOKU_kanaA_004.ttf filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-simple-manga-maker/SP-MangaEditer/font/851MkPOP_101/851MkPOP_101.ttf filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-simple-manga-maker/SP-MangaEditer/font/ChalkJP_3/Chalk-JP.otf filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-simple-manga-maker/SP-MangaEditer/font/DokiDokiFont2/DokiDokiFantasia.otf filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-simple-manga-maker/SP-MangaEditer/font/DotGothic16/DotGothic16-Regular.ttf filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-simple-manga-maker/SP-MangaEditer/font/Klee_One/KleeOne-Regular.ttf filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-simple-manga-maker/SP-MangaEditer/font/OhisamaFont11/OhisamaFont11.ttf filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-simple-manga-maker/SP-MangaEditer/font/Rampart_One/RampartOne-Regular.ttf filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-simple-manga-maker/SP-MangaEditer/font/Stick/Stick-Regular.ttf filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-simple-manga-maker/SP-MangaEditer/font/Train_One/TrainOne-Regular.ttf filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-size-travel/img/ddim_advance.png filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-size-travel/img/ddim_simple.png filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-size-travel/img/eular_a_advance.png filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-sonar/img/momentum.png filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-text2prompt/pic/pic0.png filter=lfs diff=lfs merge=lfs -text
+extensions/stable-diffusion-webui-two-shot/gradio-3.16.2-py3-none-any.whl filter=lfs diff=lfs merge=lfs -text

extensions/321Prompt/321Prompt/Readme.md

@@ -0,0 +1,8 @@
+This is my attempt to integrate this script as an extension into A1111, but it's not working and I don't have time!
+(I'll let you figure out why, with all my apologies but if this piece of code can help you, so much the better)
+
+Place this directory "321prompt" with this file in it "__init__.py" in the extension directory of A1111.
+Relaunch A1111 and, it's does'nt work !
+
+Hope you do it better !
+Thanks

extensions/321Prompt/321Prompt/__init__.py

@@ -0,0 +1,51 @@
+import gradio as gr
+from modules import scripts
+
+class PromptGeneratorScript(scripts.Script):
+    def title(self):
+        return "Prompt Generator"
+
+    def show(self, is_img2img):
+        return True
+
+    def ui(self, is_img2img):
+        start_phrase1 = gr.Textbox(label="Début de la phrase 1", placeholder="Un [ chat: chien: ")
+        start_value1 = gr.Number(label="Valeur de départ 1", value=0.0)
+        end_value1 = gr.Number(label="Valeur d'arrivée 1", value=1.0)
+        end_phrase1 = gr.Textbox(label="Fin de la phrase 1", placeholder="] dans un jardin.")
+        num_phrases1 = gr.Number(label="Nombre de phrases 1", value=10)
+
+        start_phrase2 = gr.Textbox(label="Début de la phrase 2", placeholder="le temps est [ beau: pluvieux: ", optional=True)
+        start_value2 = gr.Number(label="Valeur de départ 2", value=0.0, optional=True)
+        end_value2 = gr.Number(label="Valeur d'arrivée 2", value=1.0, optional=True)
+        end_phrase2 = gr.Textbox(label="Fin de la phrase 2", placeholder="] sur la montagne.", optional=True)
+        num_phrases2 = gr.Number(label="Nombre de phrases 2", value=10, optional=True)
+
+        generate_button = gr.Button("Générer")
+        output = gr.Textbox(label="Résultats", lines=10, interactive=False)
+
+        def generate_phrases(start_phrase1, start_value1, end_value1, end_phrase1, num_phrases1,
+                             start_phrase2, start_value2, end_value2, end_phrase2, num_phrases2):
+            increment1 = (end_value1 - start_value1) / (num_phrases1 - 1)
+            increment2 = (end_value2 - start_value2) / (num_phrases2 - 1) if start_phrase2 else 0
+
+            combined_results = ""
+            max_phrases = max(num_phrases1, num_phrases2)
+            for i in range(max_phrases):
+                current_value1 = start_value1 + (increment1 * i)
+                current_value2 = start_value2 + (increment2 * i) if start_phrase2 else ''
+                phrase1 = f"{start_phrase1}{current_value1}{end_phrase1}"
+                phrase2 = f", {start_phrase2}{current_value2}{end_phrase2}" if start_phrase2 else ''
+                combined_results += phrase1 + phrase2 + "\n"
+
+            return combined_results
+
+        generate_button.click(fn=generate_phrases, 
+                              inputs=[start_phrase1, start_value1, end_value1, end_phrase1, num_phrases1,
+                                      start_phrase2, start_value2, end_value2, end_phrase2, num_phrases2], 
+                              outputs=output)
+
+        return [start_phrase1, start_value1, end_value1, end_phrase1, num_phrases1,
+                start_phrase2, start_value2, end_value2, end_phrase2, num_phrases2, generate_button, output]
+
+scripts.register_script(PromptGeneratorScript())

extensions/321Prompt/321prompt.php

@@ -0,0 +1,105 @@
+<!DOCTYPE html>
+<html>
+<head>
+    <title>Générateur de Phrases</title>
+    <style>
+        .container {
+            display: flex;
+            flex-wrap: wrap;
+        }
+        .box {
+            flex: 1;
+            min-width: 45%;
+            padding: 10px;
+            box-sizing: border-box;
+        }
+        textarea {
+            width: 100%;
+            height: 200px;
+        }
+    </style>
+</head>
+<body>
+
+<h1>Générateur de Phrases</h1>
+
+<form method="post" action="">
+    <div class="container">
+        <!-- Boîte 1 -->
+        <div class="box">
+            <label for="start_phrase1">Début de la phrase 1 :</label><br>
+            <input type="text" id="start_phrase1" name="start_phrase1" value="<?php echo isset($_POST['start_phrase1']) ? htmlspecialchars($_POST['start_phrase1']) : ''; ?>" required><br><br>
+            
+            <label for="start_value1">Valeur de départ :</label><br>
+            <input type="number" step="0.01" id="start_value1" name="start_value1" value="<?php echo isset($_POST['start_value1']) ? htmlspecialchars($_POST['start_value1']) : ''; ?>" required><br><br>
+            
+            <label for="end_value1">Valeur d'arrivée :</label><br>
+            <input type="number" step="0.01" id="end_value1" name="end_value1" value="<?php echo isset($_POST['end_value1']) ? htmlspecialchars($_POST['end_value1']) : ''; ?>" required><br><br>
+            
+            <label for="end_phrase1">Fin de la phrase 1 :</label><br>
+            <input type="text" id="end_phrase1" name="end_phrase1" value="<?php echo isset($_POST['end_phrase1']) ? htmlspecialchars($_POST['end_phrase1']) : ''; ?>" required><br><br>
+            
+            <label for="num_phrases1">Nombre de phrases :</label><br>
+            <input type="number" id="num_phrases1" name="num_phrases1" value="<?php echo isset($_POST['num_phrases1']) ? htmlspecialchars($_POST['num_phrases1']) : ''; ?>" required><br><br>
+        </div>
+
+        <!-- Boîte 2 -->
+        <div class="box">
+            <label for="start_phrase2">Début de la phrase 2 :</label><br>
+            <input type="text" id="start_phrase2" name="start_phrase2" value="<?php echo isset($_POST['start_phrase2']) ? htmlspecialchars($_POST['start_phrase2']) : ''; ?>"><br><br>
+            
+            <label for="start_value2">Valeur de départ :</label><br>
+            <input type="number" step="0.01" id="start_value2" name="start_value2" value="<?php echo isset($_POST['start_value2']) ? htmlspecialchars($_POST['start_value2']) : ''; ?>"><br><br>
+            
+            <label for="end_value2">Valeur d'arrivée :</label><br>
+            <input type="number" step="0.01" id="end_value2" name="end_value2" value="<?php echo isset($_POST['end_value2']) ? htmlspecialchars($_POST['end_value2']) : ''; ?>"><br><br>
+            
+            <label for="end_phrase2">Fin de la phrase 2 :</label><br>
+            <input type="text" id="end_phrase2" name="end_phrase2" value="<?php echo isset($_POST['end_phrase2']) ? htmlspecialchars($_POST['end_phrase2']) : ''; ?>"><br><br>
+            
+            <label for="num_phrases2">Nombre de phrases :</label><br>
+            <input type="number" id="num_phrases2" name="num_phrases2" value="<?php echo isset($_POST['num_phrases2']) ? htmlspecialchars($_POST['num_phrases2']) : ''; ?>"><br><br>
+        </div>
+    </div>
+    <input type="submit" value="Générer">
+</form>
+
+<?php
+if ($_SERVER["REQUEST_METHOD"] == "POST") {
+    // Récupérer les entrées du formulaire pour la première phrase
+    $startPhrase1 = $_POST['start_phrase1'];
+    $startValue1 = floatval($_POST['start_value1']);
+    $endValue1 = floatval($_POST['end_value1']);
+    $endPhrase1 = $_POST['end_phrase1'];
+    $numPhrases1 = intval($_POST['num_phrases1']);
+    
+    // Récupérer les entrées du formulaire pour la deuxième phrase (si présentes)
+    $startPhrase2 = isset($_POST['start_phrase2']) ? $_POST['start_phrase2'] : '';
+    $startValue2 = isset($_POST['start_value2']) ? floatval($_POST['start_value2']) : 0;
+    $endValue2 = isset($_POST['end_value2']) ? floatval($_POST['end_value2']) : 0;
+    $endPhrase2 = isset($_POST['end_phrase2']) ? $_POST['end_phrase2'] : '';
+    $numPhrases2 = isset($_POST['num_phrases2']) ? intval($_POST['num_phrases2']) : $numPhrases1;
+    
+    // Calculer l'incrément pour les phrases
+    $increment1 = ($endValue1 - $startValue1) / ($numPhrases1 - 1);
+    $increment2 = ($startPhrase2 !== '') ? ($endValue2 - $startValue2) / ($numPhrases2 - 1) : 0;
+    
+    // Générer les phrases combinées
+    $combinedResults = "";
+    $maxPhrases = max($numPhrases1, $numPhrases2);
+    for ($i = 0; $i < $maxPhrases; $i++) {
+        $currentValue1 = $startValue1 + ($increment1 * $i);
+        $currentValue2 = ($startPhrase2 !== '') ? $startValue2 + ($increment2 * $i) : '';
+        $phrase1 = $startPhrase1 . $currentValue1 . $endPhrase1;
+        $phrase2 = ($startPhrase2 !== '') ? ' , ' . $startPhrase2 . $currentValue2 . $endPhrase2 : '';
+        $combinedResults .= $phrase1 . $phrase2 . "\n";
+    }
+    
+    // Afficher les résultats
+    echo "<h2>Résultats :</h2>";
+    echo '<textarea readonly>' . htmlspecialchars($combinedResults) . '</textarea>';
+}
+?>
+
+</body>
+</html>

extensions/321Prompt/LICENSE

@@ -0,0 +1,121 @@
+Creative Commons Legal Code
+
+CC0 1.0 Universal
+
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+    LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN
+    ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS
+    INFORMATION ON AN "AS-IS" BASIS. CREATIVE COMMONS MAKES NO WARRANTIES
+    REGARDING THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS
+    PROVIDED HEREUNDER, AND DISCLAIMS LIABILITY FOR DAMAGES RESULTING FROM
+    THE USE OF THIS DOCUMENT OR THE INFORMATION OR WORKS PROVIDED
+    HEREUNDER.
+
+Statement of Purpose
+
+The laws of most jurisdictions throughout the world automatically confer
+exclusive Copyright and Related Rights (defined below) upon the creator
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+4. Limitations and Disclaimers.
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+    party to this document and has no duty or obligation with respect to
+    this CC0 or use of the Work.

extensions/321Prompt/README.md

@@ -0,0 +1,41 @@
+# 321Prompt
+"321prompt.php" is a php script to generate prompts for A1111. (stable diffusion)
+This involves creating a prompt text file with incremented weights to put in the A1111 interface: "Script/Prompts from file or textbox".
+
+the PHP script works on a classic web server and I tried to make an extension for A1111 but one thing escapes me (it's buggy).
+Do with it what you want!
+
+You will be able to generate a prompt text of this type in 3-4 clicks:
+
+a [cat: dog:0] in the garden , it's [Rainy: sunny: 1 ] Outside
+
+a [cat: dog:0.125] in the garden , it's [Rainy: sunny: 0.875 ] Outside
+
+a [cat: dog:0.25] in the garden , it's [Rainy: sunny: 0.75 ] Outside
+
+a [cat: dog:0.375] in the garden , it's [Rainy: sunny: 0.625 ] Outside
+
+a [cat: dog:0.5] in the garden , it's [Rainy: sunny: 0.5 ] Outside
+
+a [cat: dog:0.625] in the garden , it's [Rainy: sunny: 0.375 ] Outside
+
+a [cat: dog:0.75] in the garden , it's [Rainy: sunny: 0.25 ] Outside
+
+a [cat: dog:0.875] in the garden , it's [Rainy: sunny: 0.125 ] Outside
+
+a [cat: dog:1] in the garden , it's [Rainy: sunny: 0 ] Outside
+
+
+Look at the screenshot (321prompt.png) for understand how to play with this script !
+
+It's work with one sentence (sentence 1, and you do not have to fulfill sentence 2 !)
+But you can fullfill sentence 1 + sentence 2 for more fun ! it's work !
+And you can increase or decrease the weight for sentence 1 or increase or decrease weight for sentence 2.
+Bye !
+
+Demo here: [https://lostcantina.com/321prompt.php](https://lostcantina.com/321prompt.php)
+(sorry, i'm french but you can translate easily with Chrome !)
+
+
+
+

extensions/ABG_extension/.gitignore

@@ -0,0 +1 @@
+scripts/__pycache__

extensions/ABG_extension/README.md

@@ -0,0 +1,32 @@
+<h3 align="center">
+<b>ABG extension</b>
+</h3>
+
+<p align="center">
+	<a href="https://github.com/KutsuyaYuki/ABG_extension/stargazers"><img src="https://img.shields.io/github/stars/KutsuyaYuki/ABG_extension?style=for-the-badge"></a>
+	<a href="https://github.com/KutsuyaYuki/ABG_extension/issues"><img src="https://img.shields.io/github/issues/KutsuyaYuki/ABG_extension?style=for-the-badge"></a>
+	<a href="https://github.com/KutsuyaYuki/ABG_extension/commits/main"><img src="https://img.shields.io/github/last-commit/KutsuyaYuki/ABG_extension?style=for-the-badge"></a>
+</p>
+
+## Installation
+
+ 1. Install extension by going to Extensions tab -> Install from URL -> Paste github URL and click Install.
+ 2. After it's installed, go back to the Installed tab in Extensions and press Apply and restart UI.
+ 3. Installation finished.
+ 4. If the script does not show up or work, please restart the WebUI.
+
+## Usage
+
+### txt2img
+
+ 1. In the bottom of the WebUI in Script, select **ABG Remover**.
+ 2. Select the desired options: **Only save background free pictures** or **Do not auto save**.
+ 3. Generate an image and you will see the result in the output area.
+
+### img2img
+
+ 1. In the bottom of the WebUI in Script, select **ABG Remover**.
+ 2. Select the desired options: **Only save background free pictures** or **Do not auto save**.
+ 3. **IMPORTANT**: Set **Denoising strength** to a low value, like **0.01**
+
+Based on https://huggingface.co/spaces/skytnt/anime-remove-background

extensions/ABG_extension/install.py

@@ -0,0 +1,11 @@
+import launch
+
+for dep in ['onnx', 'onnxruntime', 'numpy']:
+    if not launch.is_installed(dep):
+        launch.run_pip(f"install {dep}", f"{dep} for ABG_extension")
+
+if not launch.is_installed("cv2"):
+    launch.run_pip("install opencv-python", "opencv-python")
+
+if not launch.is_installed("PIL"):
+    launch.run_pip("install Pillow", "Pillow")

extensions/ABG_extension/scripts/app.py

@@ -0,0 +1,183 @@
+import random
+import modules.scripts as scripts
+from modules import images
+from modules.processing import process_images, Processed
+from modules.processing import Processed
+from modules.shared import opts, cmd_opts, state
+
+import gradio as gr
+import huggingface_hub
+import onnxruntime as rt
+import copy
+import numpy as np
+import cv2
+from PIL import Image as im, ImageDraw
+
+
+# Declare Execution Providers
+providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
+
+# Download and host the model
+model_path = huggingface_hub.hf_hub_download(
+    "skytnt/anime-seg", "isnetis.onnx")
+rmbg_model = rt.InferenceSession(model_path, providers=providers)
+
+# Function to get mask
+def get_mask(img, s=1024):
+    # Resize the img to a square shape with dimension s
+    # Convert img pixel values from integers 0-255 to float 0-1
+    img = (img / 255).astype(np.float32)
+    # get the amount of dimensions of img
+    dim = img.shape[2]
+    # Convert the input image to RGB format if it has an alpha channel
+    if dim == 4:
+        img = img[..., :3]
+        dim = 3
+    # Get height and width of the image
+    h, w = h0, w0 = img.shape[:-1]
+    # IF height is greater than width, set h as s and w as s*width/height
+    # ELSE, set w as s and h as s*height/width
+    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
+    # Calculate padding for height and width
+    ph, pw = s - h, s - w
+    # Create a 1024x1024x3 array of 0's
+    img_input = np.zeros([s, s, dim], dtype=np.float32)
+    # Resize the original image to (w,h) and then pad with the calculated ph,pw
+    img_input[ph // 2:ph // 2 + h, pw //
+              2:pw // 2 + w] = cv2.resize(img, (w, h))
+    # Change the axes
+    img_input = np.transpose(img_input, (2, 0, 1))
+    # Add an extra axis (1,0)
+    img_input = img_input[np.newaxis, :]
+    # Run the model to get the mask
+    mask = rmbg_model.run(None, {'img': img_input})[0][0]
+    # Transpose axis
+    mask = np.transpose(mask, (1, 2, 0))
+    # Crop it to the images original dimensions (h0,w0)
+    mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
+    # Resize the mask to original image size (h0,w0)
+    mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
+    return mask
+
+### Function to remove background
+def rmbg_fn(img):
+    # Call get_mask() to get the mask
+    mask = get_mask(img)
+    # Multiply the image and the mask together to get the output image
+    img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
+    # Convert mask value back to int 0-255
+    mask = (mask * 255).astype(np.uint8)
+    # Concatenate the output image and mask
+    img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
+    # Stacking 3 identical copies of the mask for displaying
+    mask = mask.repeat(3, axis=2)
+    return mask, img
+
+
+class Script(scripts.Script):
+    is_txt2img = False
+
+    # Function to set title
+    def title(self):
+        return "ABG Remover"
+
+    def ui(self, is_img2img):
+
+        with gr.Column():
+            only_save_background_free_pictures = gr.Checkbox(
+                label='Only save background free pictures')
+            do_not_auto_save = gr.Checkbox(label='Do not auto save')
+            with gr.Row():
+                custom_background = gr.Checkbox(label='Custom Background')
+                custom_background_color = gr.ColorPicker(
+                    label='Background Color', default='#ff0000')
+            custom_background_random = gr.Checkbox(
+                label='Random Custom Background')
+
+        return [only_save_background_free_pictures, do_not_auto_save, custom_background, custom_background_color, custom_background_random]
+
+    # Function to show the script
+    def show(self, is_img2img):
+        return True
+
+    # Function to run the script
+    def run(self, p, only_save_background_free_pictures, do_not_auto_save, custom_background, custom_background_color, custom_background_random):
+        # If only_save_background_free_pictures is true, set do_not_save_samples to true
+        if only_save_background_free_pictures:
+            p.do_not_save_samples = True
+
+        # Create a process_images object
+        proc = process_images(p)
+
+        has_grid = False
+
+        unwanted_grid_because_of_img_count = len(
+            proc.images) < 2 and opts.grid_only_if_multiple
+        if (opts.return_grid or opts.grid_save) and not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
+            has_grid = True
+
+        # Loop through all the images in proc
+        for i in range(len(proc.images)):
+            # Separate the background from the foreground
+            nmask, nimg = rmbg_fn(np.array(proc.images[i]))
+
+            # Check the number of channels in the nimg array, select only the first 3 or 4 channels
+            num_channels = nimg.shape[2]
+            if num_channels > 4:
+                nimg = nimg[:, :, :4]
+                
+            # Ensure the data type is uint8 and convert the image back to a format that can be saved
+            nimg = nimg.astype(np.uint8)
+            img = im.fromarray(nimg)
+
+            # If only_save_background_free_pictures is true, check if the image has a background
+            if custom_background or custom_background_random:
+                # If custom_background_random is true, set the background color to a random color
+                if custom_background_random:
+                    custom_background_color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
+
+                # Create a new image with the same size as the original image
+                background = im.new('RGBA', img.size, custom_background_color)
+
+                # Draw a colored rectangle onto the new image
+                draw = ImageDraw.Draw(background)
+                draw.rectangle([(0, 0), img.size],
+                               fill=custom_background_color)
+
+                # Merge the two images
+                img = im.alpha_composite(background, img)
+
+            # determine output path
+            outpath = p.outpath_grids if has_grid and i == 0 else p.outpath_samples
+
+            # If we are saving all images, save the mask and the image
+            if not only_save_background_free_pictures:
+                mask = im.fromarray(nmask)
+                # Dot not save the new images if checkbox is checked
+                if not do_not_auto_save:
+                    # Save the new images
+                    images.save_image(
+                        mask, outpath, "mask_", proc.seed + i, proc.prompt, "png", info=proc.info, p=p)
+                    images.save_image(
+                        img, outpath, "img_", proc.seed + i, proc.prompt, "png", info=proc.info, p=p)
+                # Add the images to the proc object
+                proc.images.append(mask)
+                proc.images.append(img)
+            # If we are only saving background-free images, save the image and replace it in the proc object
+            else:
+                proc.images[i] = img
+
+            # Check if automatic saving is enabled
+            if not do_not_auto_save:
+                # Check if the image is the first one and has a grid
+                if has_grid and i == 0:
+                    # Save the image
+                    images.save_image(img, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0],
+                                      opts.grid_format, info=proc.info, short_filename=not opts.grid_extended_filename, p=p)
+                else:
+                    # Save the image
+                    images.save_image(img, outpath, "", proc.seed,
+                                      proc.prompt, "png", info=proc.info, p=p)
+
+        # Return the proc object
+        return proc

extensions/AdverseCleanerExtension/.gitignore

@@ -0,0 +1,2 @@
+**scripts/__pycache__/
+**dist

extensions/AdverseCleanerExtension/LICENSE

@@ -0,0 +1,201 @@
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extensions/AdverseCleanerExtension/README.md

@@ -0,0 +1,8 @@
+# AdverseCleaner Extension for AUTOMATIC1111/stable-diffusion-webui
+This extension provides a simple and easy-to-use way to denoise images using the cv2 bilateral filter and guided filter.
+Original script by: https://github.com/lllyasviel/AdverseCleaner
+## Usage
+Select AdverseCleaner as your active script
+![image](https://user-images.githubusercontent.com/2740936/226215666-17d876fa-b314-4457-9a9f-9bd1d8cf7561.png)
+You will then have access to all the parameters for both filter steps
+![image](https://user-images.githubusercontent.com/2740936/226215696-962efbb5-7ce8-4ea8-a94d-d1c32561796b.png)

extensions/AdverseCleanerExtension/install.py

@@ -0,0 +1,12 @@
+import launch
+
+if not launch.is_installed("opencv-contrib-python"):
+    if launch.is_installed("opencv-python"):
+        launch.run_pip("uninstall opencv-python")    
+        print("Uninstalling opencv-python...")    
+    launch.run_pip("install opencv-contrib-python")
+    print("Installing opencv-contrib-python...")
+
+if not launch.is_installed("numpy"):
+    launch.run_pip("install numpy")
+    print("Installing numpy...")

extensions/AdverseCleanerExtension/scripts/denoise.py

@@ -0,0 +1,74 @@
+
+import gradio as gr
+import numpy as np
+import cv2
+import modules.scripts as scripts
+
+from modules import images
+from modules.shared import opts
+
+from cv2.ximgproc import guidedFilter
+from modules.processing import process_images
+
+
+class Script(scripts.Script):
+    def title(self):
+        return "AdverseCleaner"
+
+    def show(self, is_img2img):
+        return True
+
+    def ui(self, is_img2img):
+        info = gr.Markdown('''
+        ### Bilateral Filter
+        ''')
+        bilateral_steps = gr.Slider(minimum=1, maximum=128, step=1,
+                                    value=64, label="Bilateral Steps")
+        diameter = gr.Slider(minimum=1, maximum=30, step=1,
+                             value=5, label="Diameter")
+        sigma_color = gr.Slider(minimum=1, maximum=30,
+                                step=1, value=8, label="SigmaColor")
+        sigma_space = gr.Slider(minimum=1, maximum=30,
+                                step=1, value=8, label="SigmaSpace")
+        info2 = gr.Markdown('''
+        ### Guided Filter
+        ''')
+        guided_steps = gr.Slider(minimum=1, maximum=64, step=1,
+                                 value=4, label="Guided Steps")
+        radius = gr.Slider(minimum=1, maximum=30, step=1,
+                           value=4, label="Radius")
+        eps = gr.Slider(minimum=1, maximum=30, step=1,
+                        value=16, label="Accuracy")
+        return [info, bilateral_steps, diameter, sigma_color, sigma_space, info2, guided_steps, radius, eps]
+
+    def run(self, p, _, bilateral_steps, diameter, sigma_color, sigma_space, __, guided_steps, radius, eps):
+        from PIL import Image
+        has_grid = False
+
+        proc = process_images(p)
+        unwanted_grid_because_of_img_count = len(
+            proc.images) < 2 and opts.grid_only_if_multiple
+        if (opts.return_grid or opts.grid_save) and not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
+            has_grid = True
+
+
+        def process(im,i):
+            outpath = p.outpath_grids if has_grid and i == 0 else p.outpath_samples
+            img = cv2.cvtColor(
+                np.array(im), cv2.COLOR_RGB2BGR).astype(np.float32)
+            y = img.copy()
+            for _ in range(bilateral_steps):
+                y = cv2.bilateralFilter(y, diameter, sigma_color, sigma_space)
+
+            for _ in range(guided_steps):
+                y = guidedFilter(img, y, radius, eps)
+
+            out_image = Image.fromarray(cv2.cvtColor(
+                y.clip(0, 255).astype(np.uint8), cv2.COLOR_BGR2RGB))
+            images.save_image(out_image, outpath, "img_", proc.seed +
+                              i, proc.prompt, "png", info=proc.info, p=p)
+            return out_image
+
+        for i in range(len(proc.images)):
+            proc.images.append(process(proc.images[i], i))
+        return proc

extensions/Automatic1111-Geeky-Remb/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 GeekyGhost
+
+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.

extensions/Automatic1111-Geeky-Remb/README.md

@@ -0,0 +1,173 @@
+# GeekyRemB: Advanced Background Removal for Automatic1111 Web UI
+
+![download](https://github.com/user-attachments/assets/9a23a8aa-9ab8-4c6a-ae1a-44879b4a696d)
+
+## Overview
+
+GeekyRemB is a powerful extension for Automatic1111 that provides advanced background removal and image/video manipulation capabilities. It is a port of the ComfyUI node, bringing its functionality to the Automatic1111 environment. The extension allows users to remove backgrounds from images and videos, apply various effects, and manipulate foreground elements with precision.
+
+## Key Features
+
+- Background removal for images and videos
+- Support for multiple background removal models
+- Chroma key functionality
+- Foreground manipulation (scaling, rotation, positioning)
+- Various image effects (edge detection, shadow, color adjustments)
+- Mask processing options
+- Custom output dimensions
+- Video background support
+
+## How to Use
+
+1. Install the extension by placing the `geeky_remb.py` file in the `scripts` folder of your Automatic1111 installation.
+2. Restart Automatic1111 or reload the UI.
+3. Navigate to the "GeekyRemB" tab in the Automatic1111 interface.
+4. Choose your input type (Image or Video) and upload your content.
+5. Adjust the settings as needed (described in detail below).
+6. Click the "Run GeekyRemB" button to process your input.
+
+## UI Settings and Their Functions
+
+### Input/Output Settings
+
+- **Input Type**: Choose between "Image" or "Video" as your input source.
+- **Foreground Image/Video**: Upload your image or video file to be processed.
+- **Output Type**: Select whether you want the output to be an "Image" or "Video".
+
+### Foreground Adjustments
+
+- **Scale**: Adjust the size of the foreground element (0.1 to 5.0).
+- **Aspect Ratio**: Modify the aspect ratio of the foreground (0.1 to 10.0).
+- **X Position**: Move the foreground horizontally (-1000 to 1000 pixels).
+- **Y Position**: Move the foreground vertically (-1000 to 1000 pixels).
+- **Rotation**: Rotate the foreground (-360 to 360 degrees).
+- **Opacity**: Adjust the transparency of the foreground (0.0 to 1.0).
+- **Flip Horizontal**: Mirror the foreground horizontally.
+- **Flip Vertical**: Mirror the foreground vertically.
+
+### Background Options
+
+- **Remove Background**: Toggle background removal on/off.
+- **Background Mode**: Choose between "transparent", "color", "image", or "video" backgrounds.
+- **Background Color**: Select a color when "color" mode is chosen.
+- **Background Image**: Upload an image to use as the background.
+- **Background Video**: Upload a video to use as the background.
+
+### Advanced Settings
+
+#### Removal Settings
+
+- **Model**: Select the background removal model (e.g., "u2net", "isnet-general-use").
+- **Output Format**: Choose between "RGBA" (with transparency) or "RGB".
+- **Alpha Matting**: Enable for improved edge detection in complex images.
+- **Alpha Matting Foreground Threshold**: Adjust sensitivity for foreground detection (0-255).
+- **Alpha Matting Background Threshold**: Adjust sensitivity for background detection (0-255).
+- **Post Process Mask**: Apply additional processing to the generated mask.
+
+#### Chroma Key Settings
+
+- **Chroma Key**: Choose a color ("none", "green", "blue", "red") for chroma keying.
+- **Chroma Threshold**: Adjust the sensitivity of the chroma key effect (0-255).
+- **Color Tolerance**: Set the range of colors to be considered part of the chroma key (0-255).
+
+#### Effects
+
+- **Invert Mask**: Invert the generated mask.
+- **Feather Amount**: Soften the edges of the mask (0-100).
+- **Edge Detection**: Apply an edge detection effect.
+- **Edge Thickness**: Adjust the thickness of detected edges (1-10).
+- **Edge Color**: Choose the color for detected edges.
+- **Shadow**: Add a shadow effect to the foreground.
+- **Shadow Blur**: Adjust the blurriness of the shadow (0-20).
+- **Shadow Opacity**: Set the transparency of the shadow (0.0-1.0).
+- **Color Adjustment**: Enable color adjustments for the result.
+- **Brightness**: Adjust the brightness of the result (0.0-2.0).
+- **Contrast**: Adjust the contrast of the result (0.0-2.0).
+- **Saturation**: Adjust the color saturation of the result (0.0-2.0).
+- **Mask Blur**: Apply blur to the mask (0-100).
+- **Mask Expansion**: Expand or contract the mask (-100 to 100).
+
+### Output Settings
+
+- **Image Format**: Choose the output format for images (PNG, JPEG, WEBP).
+- **Video Format**: Select the output format for videos (MP4, AVI, MOV).
+- **Video Quality**: Adjust the quality of the output video (0-100).
+- **Use Custom Dimensions**: Enable to specify custom output dimensions.
+- **Custom Width**: Set a custom width for the output.
+- **Custom Height**: Set a custom height for the output.
+
+## Technical Implementation for Developers
+
+The GeekyRemB extension is implemented as a Python class (`GeekyRemB`) with several key methods:
+
+1. `__init__`: Initializes the class and prepares for background removal sessions.
+
+2. `apply_chroma_key`: Implements chroma key functionality using OpenCV.
+
+3. `process_mask`: Handles mask processing operations like inversion, feathering, and expansion.
+
+4. `remove_background`: The core method that processes images, removing backgrounds and applying effects.
+
+5. `process_frame`: Processes individual video frames.
+
+6. `process_video`: Handles video processing, including background video integration.
+
+The UI is built using Gradio components, with the `on_ui_tabs` function setting up the interface. Key functions include:
+
+- `update_input_type`, `update_output_type`, `update_background_mode`, `update_custom_dimensions`: Dynamic UI updates based on user selections.
+- `process_image` and `process_video`: Wrapper functions for image and video processing.
+- `run_geeky_remb`: The main function that orchestrates the entire process based on user inputs.
+
+The extension uses libraries like `rembg` for background removal, `PIL` for image processing, `cv2` for video handling, and `numpy` for array operations.
+
+Developers can extend the functionality by adding new background removal models, implementing additional effects, or enhancing video processing capabilities. The modular structure allows for easy integration of new features.
+
+## Performance Considerations
+
+- Background removal and video processing can be computationally intensive. Consider implementing progress bars or asynchronous processing for better user experience with large files.
+- The extension currently processes videos frame-by-frame. For longer videos, consider implementing batch processing or multi-threading for improved performance.
+- Memory usage can be high when processing large images or videos. Implement memory management techniques for handling large files.
+
+## Future Enhancements
+
+Potential areas for improvement include:
+- Support for more background removal models
+- Advanced video editing features (e.g., keyframe animation for foreground properties)
+- Integration with other Automatic1111 extensions or workflows
+- GPU acceleration for video processing
+- Real-time preview for adjustments
+
+This extension provides a powerful set of tools for background removal and image/video manipulation, bringing the capabilities of the ComfyUI node to the Automatic1111 environment.
+
+## Troubleshooting
+
+- If the background removal is imperfect, try adjusting the alpha matting thresholds or using a different model.
+- For subjects with similar colors to the background, experiment with the chroma key feature in combination with AI removal.
+- If the resulting image looks unnatural, play with the shadow and color adjustment settings to better integrate the subject with the new background.
+
+## Contributing
+
+We welcome contributions to GeekyRemB! If you have ideas for improvements or new features, feel free to open an issue or submit a pull request.
+
+## License
+
+GeekyRemB is released under the MIT License. Feel free to use, modify, and distribute it as you see fit.
+
+## Acknowledgments
+
+GeekyRemB is built upon the excellent [rembg](https://github.com/danielgatis/rembg) library and integrates seamlessly with the Automatic1111 Stable Diffusion Web UI. We're grateful to the developers of these projects for their fantastic work.
+
+---
+
+<img width="1247" alt="Screenshot 2024-08-08 123752" src="https://github.com/user-attachments/assets/2491ce81-09a7-4d8a-9bdc-58d48f82dfaf">
+
+
+<img width="1235" alt="Screenshot 2024-08-08 124700" src="https://github.com/user-attachments/assets/cd672db7-97fe-4c1b-b8a3-2b50fb152d04">
+
+
+<img width="1238" alt="Screenshot 2024-08-08 123732" src="https://github.com/user-attachments/assets/f7b91764-041e-4ae6-a46a-a81eaa8692c9">
+
+
+
+
+We hope you enjoy using GeekyRemB to create stunning images with ease! If you find it useful, consider starring the repository and sharing it with your friends and colleagues.

extensions/Automatic1111-Geeky-Remb/__init__.py

@@ -0,0 +1,4 @@
+from .geeky_remb import GeekyRemBExtras
+
+def __init__():
+    return [GeekyRemBExtras()]

extensions/Automatic1111-Geeky-Remb/install.py

@@ -0,0 +1,7 @@
+import launch
+
+if not launch.is_installed("rembg"):
+    launch.run_pip("install rembg", "requirement for Geeky RemB")
+
+if not launch.is_installed("opencv-python"):
+    launch.run_pip("install opencv-python", "requirement for Geeky RemB")

extensions/Automatic1111-Geeky-Remb/requirements.txt

@@ -0,0 +1,4 @@
+rembg
+numpy
+opencv-python
+Pillow

extensions/Automatic1111-Geeky-Remb/scripts/geeky-remb.py

@@ -0,0 +1,435 @@
+import os
+import numpy as np
+from rembg import remove, new_session
+from PIL import Image, ImageOps, ImageFilter, ImageEnhance
+import cv2
+from tqdm import tqdm
+import gradio as gr
+from modules import script_callbacks, shared
+import torch
+import tempfile
+
+class GeekyRemB:
+    def __init__(self):
+        self.session = None
+
+    def apply_chroma_key(self, image, color, threshold, color_tolerance=20):
+        hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
+        if color == "green":
+            lower = np.array([40 - color_tolerance, 40, 40])
+            upper = np.array([80 + color_tolerance, 255, 255])
+        elif color == "blue":
+            lower = np.array([90 - color_tolerance, 40, 40])
+            upper = np.array([130 + color_tolerance, 255, 255])
+        elif color == "red":
+            lower = np.array([0, 40, 40])
+            upper = np.array([20 + color_tolerance, 255, 255])
+        else:
+            return np.zeros(image.shape[:2], dtype=np.uint8)
+
+        mask = cv2.inRange(hsv, lower, upper)
+        mask = 255 - cv2.threshold(mask, threshold, 255, cv2.THRESH_BINARY)[1]
+        return mask
+
+    def process_mask(self, mask, invert_mask, feather_amount, mask_blur, mask_expansion):
+        if invert_mask:
+            mask = 255 - mask
+
+        if mask_expansion != 0:
+            kernel = np.ones((abs(mask_expansion), abs(mask_expansion)), np.uint8)
+            if mask_expansion > 0:
+                mask = cv2.dilate(mask, kernel, iterations=1)
+            else:
+                mask = cv2.erode(mask, kernel, iterations=1)
+
+        if feather_amount > 0:
+            mask = cv2.GaussianBlur(mask, (0, 0), sigmaX=feather_amount)
+
+        if mask_blur > 0:
+            mask = cv2.GaussianBlur(mask, (0, 0), sigmaX=mask_blur)
+
+        return mask
+
+    def remove_background(self, image, background_image, model, alpha_matting, alpha_matting_foreground_threshold, 
+                          alpha_matting_background_threshold, post_process_mask, chroma_key, chroma_threshold,
+                          color_tolerance, background_mode, background_color, output_format="RGBA", 
+                          invert_mask=False, feather_amount=0, edge_detection=False, 
+                          edge_thickness=1, edge_color="#FFFFFF", shadow=False, shadow_blur=5, 
+                          shadow_opacity=0.5, color_adjustment=False, brightness=1.0, contrast=1.0, 
+                          saturation=1.0, x_position=0, y_position=0, rotation=0, opacity=1.0, 
+                          flip_horizontal=False, flip_vertical=False, mask_blur=0, mask_expansion=0,
+                          foreground_scale=1.0, foreground_aspect_ratio=None, remove_bg=True,
+                          use_custom_dimensions=False, custom_width=None, custom_height=None,
+                          output_dimension_source="Foreground"):
+        if self.session is None or self.session.model_name != model:
+            self.session = new_session(model)
+
+        bg_color = tuple(int(background_color.lstrip('#')[i:i+2], 16) for i in (0, 2, 4)) + (255,)
+        edge_color = tuple(int(edge_color.lstrip('#')[i:i+2], 16) for i in (0, 2, 4))
+
+        pil_image = image if isinstance(image, Image.Image) else Image.fromarray(np.clip(255. * image[0].cpu().numpy(), 0, 255).astype(np.uint8))
+        original_image = np.array(pil_image)
+
+        if chroma_key != "none":
+            chroma_mask = self.apply_chroma_key(original_image, chroma_key, chroma_threshold, color_tolerance)
+            input_mask = chroma_mask
+        else:
+            input_mask = None
+
+        if remove_bg:
+            removed_bg = remove(
+                pil_image,
+                session=self.session,
+                alpha_matting=alpha_matting,
+                alpha_matting_foreground_threshold=alpha_matting_foreground_threshold,
+                alpha_matting_background_threshold=alpha_matting_background_threshold,
+                post_process_mask=post_process_mask,
+            )
+            rembg_mask = np.array(removed_bg)[:,:,3]
+        else:
+            removed_bg = pil_image.convert("RGBA")
+            rembg_mask = np.full(pil_image.size[::-1], 255, dtype=np.uint8)
+
+        if input_mask is not None:
+            final_mask = cv2.bitwise_and(rembg_mask, input_mask)
+        else:
+            final_mask = rembg_mask
+
+        final_mask = self.process_mask(final_mask, invert_mask, feather_amount, mask_blur, mask_expansion)
+
+        orig_width, orig_height = pil_image.size
+        bg_width, bg_height = background_image.size if background_image else (orig_width, orig_height)
+
+        if use_custom_dimensions and custom_width and custom_height:
+            output_width, output_height = int(custom_width), int(custom_height)
+        elif output_dimension_source == "Background" and background_image:
+            output_width, output_height = bg_width, bg_height
+        else:
+            output_width, output_height = orig_width, orig_height
+
+        new_width = int(orig_width * foreground_scale)
+        if foreground_aspect_ratio is not None:
+            new_height = int(new_width / foreground_aspect_ratio)
+        else:
+            new_height = int(orig_height * foreground_scale)
+
+        fg_image = pil_image.resize((new_width, new_height), Image.LANCZOS)
+        fg_mask = Image.fromarray(final_mask).resize((new_width, new_height), Image.LANCZOS)
+
+        if background_mode == "transparent":
+            result = Image.new("RGBA", (output_width, output_height), (0, 0, 0, 0))
+        elif background_mode == "color":
+            result = Image.new("RGBA", (output_width, output_height), bg_color)
+        else:  # background_mode == "image"
+            if background_image is not None:
+                result = background_image.resize((output_width, output_height), Image.LANCZOS).convert("RGBA")
+            else:
+                result = Image.new("RGBA", (output_width, output_height), (0, 0, 0, 0))
+
+        if flip_horizontal:
+            fg_image = fg_image.transpose(Image.FLIP_LEFT_RIGHT)
+            fg_mask = fg_mask.transpose(Image.FLIP_LEFT_RIGHT)
+        if flip_vertical:
+            fg_image = fg_image.transpose(Image.FLIP_TOP_BOTTOM)
+            fg_mask = fg_mask.transpose(Image.FLIP_TOP_BOTTOM)
+
+        fg_image = fg_image.rotate(rotation, resample=Image.BICUBIC, expand=True)
+        fg_mask = fg_mask.rotate(rotation, resample=Image.BICUBIC, expand=True)
+
+        paste_x = x_position + (output_width - fg_image.width) // 2
+        paste_y = y_position + (output_height - fg_image.height) // 2
+
+        fg_rgba = fg_image.convert("RGBA")
+        fg_with_opacity = Image.new("RGBA", fg_rgba.size, (0, 0, 0, 0))
+        for x in range(fg_rgba.width):
+            for y in range(fg_rgba.height):
+                r, g, b, a = fg_rgba.getpixel((x, y))
+                fg_with_opacity.putpixel((x, y), (r, g, b, int(a * opacity)))
+
+        fg_mask_with_opacity = fg_mask.point(lambda p: int(p * opacity))
+
+        result.paste(fg_with_opacity, (paste_x, paste_y), fg_mask_with_opacity)
+
+        if edge_detection:
+            edge_mask = cv2.Canny(np.array(fg_mask), 100, 200)
+            edge_mask = cv2.dilate(edge_mask, np.ones((edge_thickness, edge_thickness), np.uint8), iterations=1)
+            edge_overlay = Image.new("RGBA", (output_width, output_height), (0, 0, 0, 0))
+            edge_overlay.paste(Image.new("RGB", fg_image.size, edge_color), (paste_x, paste_y), Image.fromarray(edge_mask))
+            result = Image.alpha_composite(result, edge_overlay)
+
+        if shadow:
+            shadow_mask = fg_mask.filter(ImageFilter.GaussianBlur(shadow_blur))
+            shadow_image = Image.new("RGBA", (output_width, output_height), (0, 0, 0, 0))
+            shadow_image.paste((0, 0, 0, int(255 * shadow_opacity)), (paste_x, paste_y), shadow_mask)
+            result = Image.alpha_composite(result, shadow_image.filter(ImageFilter.GaussianBlur(shadow_blur)))
+
+        if color_adjustment:
+            enhancer = ImageEnhance.Brightness(result)
+            result = enhancer.enhance(brightness)
+            enhancer = ImageEnhance.Contrast(result)
+            result = enhancer.enhance(contrast)
+            enhancer = ImageEnhance.Color(result)
+            result = enhancer.enhance(saturation)
+
+        if output_format == "RGB":
+            result = result.convert("RGB")
+
+        return result, fg_mask
+
+    def process_frame(self, frame, *args):
+        pil_frame = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+        processed_frame, _ = self.remove_background(pil_frame, *args)
+        return cv2.cvtColor(np.array(processed_frame), cv2.COLOR_RGB2BGR)
+
+    def process_video(self, input_path, output_path, background_video_path, *args):
+        cap = cv2.VideoCapture(input_path)
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        
+        if background_video_path:
+            bg_cap = cv2.VideoCapture(background_video_path)
+            bg_total_frames = int(bg_cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        
+        for frame_num in tqdm(range(total_frames), desc="Processing video"):
+            ret, frame = cap.read()
+            if not ret:
+                break
+            
+            if background_video_path:
+                bg_frame_num = frame_num % bg_total_frames
+                bg_cap.set(cv2.CAP_PROP_POS_FRAMES, bg_frame_num)
+                bg_ret, bg_frame = bg_cap.read()
+                if bg_ret:
+                    bg_frame_resized = cv2.resize(bg_frame, (width, height))
+                    args = list(args)
+                    args[1] = Image.fromarray(cv2.cvtColor(bg_frame_resized, cv2.COLOR_BGR2RGB))
+                    args = tuple(args)
+            
+            processed_frame = self.process_frame(frame, *args)
+            out.write(processed_frame)
+
+        cap.release()
+        if background_video_path:
+            bg_cap.release()
+        out.release()
+
+        # Convert output video to MP4 container
+        temp_output = output_path + "_temp.mp4"
+        os.rename(output_path, temp_output)
+        os.system(f"ffmpeg -i {temp_output} -c copy {output_path}")
+        os.remove(temp_output)
+
+def on_ui_tabs():
+    with gr.Blocks(analytics_enabled=False) as geeky_remb_tab:
+        gr.Markdown("# GeekyRemB: Background Removal and Image/Video Manipulation")
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                input_type = gr.Radio(["Image", "Video"], label="Input Type", value="Image")
+                foreground_input = gr.Image(label="Foreground Image", type="pil", visible=True)
+                foreground_video = gr.Video(label="Foreground Video", visible=False)
+                
+                with gr.Group():
+                    gr.Markdown("### Foreground Adjustments")
+                    foreground_scale = gr.Slider(label="Scale", minimum=0.1, maximum=5.0, value=1.0, step=0.1)
+                    foreground_aspect_ratio = gr.Slider(label="Aspect Ratio", minimum=0.1, maximum=10.0, value=1.0, step=0.1)
+                    x_position = gr.Slider(label="X Position", minimum=-1000, maximum=1000, value=0, step=1)
+                    y_position = gr.Slider(label="Y Position", minimum=-1000, maximum=1000, value=0, step=1)
+                    rotation = gr.Slider(label="Rotation", minimum=-360, maximum=360, value=0, step=0.1)
+                    opacity = gr.Slider(label="Opacity", minimum=0.0, maximum=1.0, value=1.0, step=0.01)
+                    flip_horizontal = gr.Checkbox(label="Flip Horizontal", value=False)
+                    flip_vertical = gr.Checkbox(label="Flip Vertical", value=False)
+
+            with gr.Column(scale=1):
+                result_type = gr.Radio(["Image", "Video"], label="Output Type", value="Image")
+                result_image = gr.Image(label="Result Image", type="pil", visible=True)
+                result_video = gr.Video(label="Result Video", visible=False)
+                
+                with gr.Group():
+                    gr.Markdown("### Background Options")
+                    remove_background = gr.Checkbox(label="Remove Background", value=True)
+                    background_mode = gr.Radio(label="Background Mode", choices=["transparent", "color", "image", "video"], value="transparent")
+                    background_color = gr.ColorPicker(label="Background Color", value="#000000", visible=False)
+                    background_image = gr.Image(label="Background Image", type="pil", visible=False)
+                    background_video = gr.Video(label="Background Video", visible=False)
+
+        with gr.Accordion("Advanced Settings", open=False):
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("### Removal Settings")
+                    model = gr.Dropdown(label="Model", choices=["u2net", "u2netp", "u2net_human_seg", "u2net_cloth_seg", "silueta", "isnet-general-use", "isnet-anime"], value="u2net")
+                    output_format = gr.Radio(label="Output Format", choices=["RGBA", "RGB"], value="RGBA")
+                    alpha_matting = gr.Checkbox(label="Alpha Matting", value=False)
+                    alpha_matting_foreground_threshold = gr.Slider(label="Alpha Matting Foreground Threshold", minimum=0, maximum=255, value=240, step=1)
+                    alpha_matting_background_threshold = gr.Slider(label="Alpha Matting Background Threshold", minimum=0, maximum=255, value=10, step=1)
+                    post_process_mask = gr.Checkbox(label="Post Process Mask", value=False)
+                
+                with gr.Column():
+                    gr.Markdown("### Chroma Key Settings")
+                    chroma_key = gr.Dropdown(label="Chroma Key", choices=["none", "green", "blue", "red"], value="none")
+                    chroma_threshold = gr.Slider(label="Chroma Threshold", minimum=0, maximum=255, value=30, step=1)
+                    color_tolerance = gr.Slider(label="Color Tolerance", minimum=0, maximum=255, value=20, step=1)
+                
+                with gr.Column():
+                    gr.Markdown("### Effects")
+                    invert_mask = gr.Checkbox(label="Invert Mask", value=False)
+                    feather_amount = gr.Slider(label="Feather Amount", minimum=0, maximum=100, value=0, step=1)
+                    edge_detection = gr.Checkbox(label="Edge Detection", value=False)
+                    edge_thickness = gr.Slider(label="Edge Thickness", minimum=1, maximum=10, value=1, step=1)
+                    edge_color = gr.ColorPicker(label="Edge Color", value="#FFFFFF")
+                    shadow = gr.Checkbox(label="Shadow", value=False)
+                    shadow_blur = gr.Slider(label="Shadow Blur", minimum=0, maximum=20, value=5, step=1)
+                    shadow_opacity = gr.Slider(label="Shadow Opacity", minimum=0.0, maximum=1.0, value=0.5, step=0.1)
+                    color_adjustment = gr.Checkbox(label="Color Adjustment", value=False)
+                    brightness = gr.Slider(label="Brightness", minimum=0.0, maximum=2.0, value=1.0, step=0.1)
+                    contrast = gr.Slider(label="Contrast", minimum=0.0, maximum=2.0, value=1.0, step=0.1)
+                    saturation = gr.Slider(label="Saturation", minimum=0.0, maximum=2.0, value=1.0, step=0.1)
+                    mask_blur = gr.Slider(label="Mask Blur", minimum=0, maximum=100, value=0, step=1)
+                    mask_expansion = gr.Slider(label="Mask Expansion", minimum=-100, maximum=100, value=0, step=1)
+
+            with gr.Row():
+                gr.Markdown("### Output Settings")
+                image_format = gr.Dropdown(label="Image Format", choices=["PNG", "JPEG", "WEBP"], value="PNG")
+                video_format = gr.Dropdown(label="Video Format", choices=["MP4", "AVI", "MOV"], value="MP4")
+                video_quality = gr.Slider(label="Video Quality", minimum=0, maximum=100, value=95, step=1)
+                use_custom_dimensions = gr.Checkbox(label="Use Custom Dimensions", value=False)
+                custom_width = gr.Number(label="Custom Width", value=512, visible=False)
+                custom_height = gr.Number(label="Custom Height", value=512, visible=False)
+                output_dimension_source = gr.Radio(
+                    label="Output Dimension Source",
+                    choices=["Foreground", "Background"],
+                    value="Foreground",
+                    visible=True
+                )
+
+        run_button = gr.Button(label="Run GeekyRemB")
+
+        def update_input_type(choice):
+            return {
+                foreground_input: gr.update(visible=choice == "Image"),
+                foreground_video: gr.update(visible=choice == "Video"),
+            }
+
+        def update_output_type(choice):
+            return {
+                result_image: gr.update(visible=choice == "Image"),
+                result_video: gr.update(visible=choice == "Video"),
+            }
+
+        def update_background_mode(mode):
+            return {
+                background_color: gr.update(visible=mode == "color"),
+                background_image: gr.update(visible=mode == "image"),
+                background_video: gr.update(visible=mode == "video"),
+            }
+
+        def update_custom_dimensions(use_custom):
+            return {
+                custom_width: gr.update(visible=use_custom),
+                custom_height: gr.update(visible=use_custom),
+                output_dimension_source: gr.update(visible=not use_custom)
+            }
+
+        def process_image(image, background_image, *args):
+            geeky_remb = GeekyRemB()
+            result, _ = geeky_remb.remove_background(image, background_image, *args)
+            return result
+
+        def process_video(video_path, background_video_path, *args):
+            geeky_remb = GeekyRemB()
+            with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_file:
+                output_path = temp_file.name
+            geeky_remb.process_video(video_path, output_path, background_video_path, *args)
+            return output_path
+
+        def run_geeky_remb(input_type, foreground_input, foreground_video, result_type, model, output_format, 
+                           alpha_matting, alpha_matting_foreground_threshold, alpha_matting_background_threshold, 
+                           post_process_mask, chroma_key, chroma_threshold, color_tolerance, background_mode, 
+                           background_color, background_image, background_video, invert_mask, feather_amount, 
+                           edge_detection, edge_thickness, edge_color, shadow, shadow_blur, shadow_opacity, 
+                           color_adjustment, brightness, contrast, saturation, x_position, y_position, rotation, 
+                           opacity, flip_horizontal, flip_vertical, mask_blur, mask_expansion, foreground_scale, 
+                           foreground_aspect_ratio, remove_background, image_format, video_format, video_quality,
+                           use_custom_dimensions, custom_width, custom_height, output_dimension_source):
+            
+            args = (model, alpha_matting, alpha_matting_foreground_threshold,
+                    alpha_matting_background_threshold, post_process_mask, chroma_key, chroma_threshold,
+                    color_tolerance, background_mode, background_color, output_format,
+                    invert_mask, feather_amount, edge_detection, edge_thickness, edge_color, shadow, shadow_blur,
+                    shadow_opacity, color_adjustment, brightness, contrast, saturation, x_position,
+                    y_position, rotation, opacity, flip_horizontal, flip_vertical, mask_blur,
+                    mask_expansion, foreground_scale, foreground_aspect_ratio, remove_background,
+                    use_custom_dimensions, custom_width, custom_height, output_dimension_source)
+
+            if input_type == "Image" and result_type == "Image":
+                result = process_image(foreground_input, background_image, *args)
+                if image_format != "PNG":
+                    result = result.convert("RGB")
+                with tempfile.NamedTemporaryFile(delete=False, suffix=f".{image_format.lower()}") as temp_file:
+                    result.save(temp_file.name, format=image_format, quality=95 if image_format == "JPEG" else None)
+                    return temp_file.name, None
+            elif input_type == "Video" and result_type == "Video":
+                output_video = process_video(foreground_video, background_video if background_mode == "video" else None, *args)
+                if video_format != "MP4":
+                    temp_output = output_video + f"_temp.{video_format.lower()}"
+                    os.system(f"ffmpeg -i {output_video} -c:v libx264 -crf {int(20 - (video_quality / 5))} {temp_output}")
+                    os.remove(output_video)
+                    output_video = temp_output
+                return None, output_video
+            elif input_type == "Image" and result_type == "Video":
+                with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_file:
+                    output_path = temp_file.name
+                frame = cv2.cvtColor(np.array(foreground_input), cv2.COLOR_RGB2BGR)
+                height, width = frame.shape[:2]
+                fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+                out = cv2.VideoWriter(output_path, fourcc, 24, (width, height))
+                for _ in range(24 * 5):  # 5 seconds at 24 fps
+                    out.write(frame)
+                out.release()
+                return None, process_video(output_path, background_video if background_mode == "video" else None, *args)
+            elif input_type == "Video" and result_type == "Image":
+                cap = cv2.VideoCapture(foreground_video)
+                ret, frame = cap.read()
+                cap.release()
+                if ret:
+                    pil_frame = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+                    result = process_image(pil_frame, background_image, *args)
+                    if image_format != "PNG":
+                        result = result.convert("RGB")
+                    with tempfile.NamedTemporaryFile(delete=False, suffix=f".{image_format.lower()}") as temp_file:
+                        result.save(temp_file.name, format=image_format, quality=95 if image_format == "JPEG" else None)
+                        return temp_file.name, None
+                else:
+                    return None, None
+
+        input_type.change(update_input_type, inputs=[input_type], outputs=[foreground_input, foreground_video])
+        result_type.change(update_output_type, inputs=[result_type], outputs=[result_image, result_video])
+        background_mode.change(update_background_mode, inputs=[background_mode], outputs=[background_color, background_image, background_video])
+        use_custom_dimensions.change(update_custom_dimensions, inputs=[use_custom_dimensions], outputs=[custom_width, custom_height, output_dimension_source])
+
+        run_button.click(
+            fn=run_geeky_remb,
+            inputs=[
+                input_type, foreground_input, foreground_video, result_type,
+                model, output_format, alpha_matting, alpha_matting_foreground_threshold,
+                alpha_matting_background_threshold, post_process_mask, chroma_key, chroma_threshold,
+                color_tolerance, background_mode, background_color, background_image, background_video,
+                invert_mask, feather_amount, edge_detection, edge_thickness, edge_color,
+                shadow, shadow_blur, shadow_opacity, color_adjustment, brightness, contrast,
+                saturation, x_position, y_position, rotation, opacity, flip_horizontal,
+                flip_vertical, mask_blur, mask_expansion, foreground_scale, foreground_aspect_ratio,
+                remove_background, image_format, video_format, video_quality,
+                use_custom_dimensions, custom_width, custom_height, output_dimension_source
+            ],
+            outputs=[result_image, result_video]
+        )
+
+    return [(geeky_remb_tab, "GeekyRemB", "geeky_remb_tab")]
+
+script_callbacks.on_ui_tabs(on_ui_tabs)

extensions/CFgfade/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>

extensions/CFgfade/README.md

@@ -0,0 +1,66 @@
+# CFG fade #
+### extension for Forge webui for Stable Diffusion ###
+
+---
+## Install ##
+Go to the **Extensions** tab, then **Install from URL**, use the URL for this repository.
+
+---
+#### 25/06/2024 ####
+added/corrected saving of parameters: now they get written to *params.txt* too
+
+### update 15/05/2024 ###
+Now I also patch the main sampling function, to calculate the new CFG there. This means that processing of the uncond can be skipped for low CFG, which is a free performance gain. Speed up depends on proportion of steps that end up with CFG <= 1.0. As far as CFG boost/fade is concerned, results are identical. Heuristic CFG and Reinhard CFG use the uncond for their calculations, but now they have to be skipped on steps when it is not available, so results from them can change. The difference seems minor; I tested an option to force uncond calculation but decided it was unnecessary.
+
+---
+### screenshot of UI ###
+![](screenshot.png "image of extension UI")
+
+---
+## Basic usage ##
+A variety of ways to adjust CFG dynamically. Some are effective.
+Settings used are saved with metadata, and restored from loading through the **PNG Info** tab.
+
+---
+## Advanced / Details ##
+centre conds to mean: a simple calculation to keep channel average centered around zero. Often seems like a free quality win.
+
+CFG 1 until/after step: Sets CFG to 1.0 for early/late steps. Normally seen based on sigmas, but done by step here for consistency with the other settings. This is higher priority than boosting/fading.
+
+CFG boost: a linear scaling increase to weight applied to CFG
+
+CFG fade: a linear scaling decrease to weight applied to CFG. Ranges of boost/fade can overlap.
+
+Heuristic CFG: 0 is disabled. Set higher than actual CFG to increase contrast, details, sharpness; or lower for the opposite. Delaying the start can allow pushing harder, but setting too high will cause burning.
+
+Reinhard Target CFG: 0 is disabled. Uses Reinhard tonemapping to dynamically adjust CFG. Could allow using higher CFGs than normal.
+
+Rescale CFG: 0 is disabled, best seems typically to be 0.5-0.75. Often a free win.
+
+
+---
+## To do? ##
+1. perp_neg? The [Neutral Prompt extension](https://github.com/ljleb/sd-webui-neutral-prompt) is already aiming to cover this
+2. slew limiting: not convinced by this. Seems better overall to limit change using the scheduler, though the effects are different.
+3. different tonemappers
+4. XYZ support
+
+---
+## License ##
+Public domain. Unlicense. Free to a good home.
+All terrible code is my own. Use at your own risk.
+
+---
+## Credits ##
+Thanks to Alex "mcmonkey" Goodwin for the Dynamic Thresholding extension (Forge built-in version). I started this project with zero knowledge, and this source got me started. The first - basic, unreleased - version was essentially hacked out of that extension.
+
+Also thanks to https://github.com/Haoming02. I learned a lot about how to implement this from their work.
+
+rescaleCFG and Reinhard tonemapping based on https://github.com/comfyanonymous/ComfyUI_experiments/
+
+[combating-mean-drift-in-cfg (birchlabs.co.uk)](https://birchlabs.co.uk/machine-learning#combating-mean-drift-in-cfg)
+
+---
+
+
+> Written with [StackEdit](https://stackedit.io/).

extensions/CFgfade/scripts/forge_cfgfade.py

@@ -0,0 +1,317 @@
+import gradio as gr
+
+from modules import scripts
+import modules.shared as shared
+from modules.script_callbacks import on_cfg_denoiser, remove_current_script_callbacks
+import torch, math
+
+#import torchvision.transforms.functional as TF
+
+import ldm_patched.modules.samplers as LDM
+import modules_forge.forge_sampler
+
+#   button to spit weighted cfg to console, better: gradio lineplot for display of weights
+
+
+
+class CFGfadeForge(scripts.Script):
+    weight = 1.0
+    backup_sampling_function = None
+
+    def __init__(self):
+        self.boostStep = 0.0
+        self.highStep = 0.5
+        self.maxScale = 1.0
+        self.fadeStep = 0.5
+        self.zeroStep = 1.0
+        self.minScale = 0.0
+        self.reinhard = 1.0
+        self.rfcgmult = 1.0
+        self.centreMean = False
+        self.heuristic = 0
+        self.hStart = 0.0
+
+    def title(self):
+        return "CFG fade"
+
+    def show(self, is_img2img):
+        # make this extension visible in both txt2img and img2img tab.
+        return scripts.AlwaysVisible
+
+    def ui(self, *args, **kwargs):
+        with gr.Accordion(open=False, label=self.title()):
+            with gr.Row():
+                enabled    = gr.Checkbox(value=False, label='Enable modifications to CFG')
+                cntrMean   = gr.Checkbox(value=False, label='centre conds to mean')
+#                scaleCFGs  = gr.Checkbox(value=False, label='scale hCFG and rCFG')
+            with gr.Row():
+                lowCFG1   = gr.Slider(minimum=0.0, maximum=1.0,  step=0.01, value=0.1, label='CFG 1 until step')
+                maxScale  = gr.Slider(minimum=1.0, maximum=4.0,  step=0.01, value=1.0, label='boost factor')
+            with gr.Row():
+                boostStep = gr.Slider(minimum=0.0, maximum=1.0,  step=0.01, value=0.2, label='CFG boost start step')
+                minScale  = gr.Slider(minimum=0.0, maximum=1.0,  step=0.01, value=1.0, label='fade factor')
+            with gr.Row():
+                highStep  = gr.Slider(minimum=0.0, maximum=1.0,  step=0.01, value=0.4, label='full boost at step')
+                heuristic = gr.Slider(minimum=0.0, maximum=16.0, step=0.5,  value=0,   label='Heuristic CFG')
+            with gr.Row():
+                fadeStep  = gr.Slider(minimum=0.0, maximum=1.0,  step=0.01, value=0.5, label='CFG fade start step')
+                hStart    = gr.Slider(minimum=0.0, maximum=1.0,  step=0.01, value=0.0, label='... start step')
+            with gr.Row():
+                zeroStep  = gr.Slider(minimum=0.0, maximum=1.0,  step=0.01, value=0.7, label='full fade at step')
+                reinhard  = gr.Slider(minimum=0.0, maximum=16.0, step=0.5,  value=0.0, label='Reinhard CFG')
+            with gr.Row():
+                highCFG1  = gr.Slider(minimum=0.0, maximum=1.0,  step=0.01, value=0.8, label='CFG 1 after step')
+                rcfgmult  = gr.Slider(minimum=0.0, maximum=1.0,  step=0.01, value=0.0, label='Rescale CFG')
+
+        self.infotext_fields = [
+            (enabled, lambda d: enabled.update(value=("cfgfade_enabled" in d))),
+            (cntrMean,  "cfgfade_cntrMean"),
+            (boostStep, "cfgfade_boostStep"),
+            (highStep,  "cfgfade_highStep"),
+            (maxScale,  "cfgfade_maxScale"),
+            (fadeStep,  "cfgfade_fadeStep"),
+            (zeroStep,  "cfgfade_zeroStep"),
+            (minScale,  "cfgfade_minScale"),
+            (lowCFG1,   "cfgfade_lowCFG1"),
+            (highCFG1,  "cfgfade_highCFG1"),
+            (reinhard,  "cfgfade_reinhard"),
+            (rcfgmult,  "cfgfade_rcfgmult"),
+            (heuristic, "cfgfade_heuristic"),
+            (hStart,    "cfgfade_hStart"),
+        ]
+
+        return enabled, cntrMean, boostStep, highStep, maxScale, fadeStep, zeroStep, minScale, lowCFG1, highCFG1, reinhard, rcfgmult, heuristic, hStart
+
+#   edited from ldm_patched/modules/samplers to add cond_scaling (initial 3 lines)
+    def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
+            cond_scale *= CFGfadeForge.weight
+            if cond_scale < 1.0:
+                cond_scale = 1.0
+
+            edit_strength = sum((item['strength'] if 'strength' in item else 1) for item in cond)
+
+            if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
+                uncond_ = None
+            else:
+                uncond_ = uncond
+
+            for fn in model_options.get("sampler_pre_cfg_function", []):
+                model, cond, uncond_, x, timestep, model_options = fn(model, cond, uncond_, x, timestep, model_options)
+
+            cond_pred, uncond_pred = LDM.calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options)
+
+            if "sampler_cfg_function" in model_options:
+                args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
+                        "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options}
+                cfg_result = x - model_options["sampler_cfg_function"](args)
+            elif not math.isclose(edit_strength, 1.0):
+                cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale * edit_strength
+            else:
+                cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale
+
+            for fn in model_options.get("sampler_post_cfg_function", []):
+                args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred,
+                        "sigma": timestep, "model_options": model_options, "input": x}
+                cfg_result = fn(args)
+
+            return cfg_result
+
+    def patch(self, model):
+#        sigmin = model.model.model_sampling.sigma(model.model.model_sampling.timestep(model.model.model_sampling.sigma_min))
+#        sigmax = model.model.model_sampling.sigma(model.model.model_sampling.timestep(model.model.model_sampling.sigma_max))
+
+        def sampler_cfgfade(args):
+            cond = args["cond"]
+            cond_scale = args["cond_scale"]
+
+            if cond_scale == 1.0:
+                return cond
+            else:
+                uncond = args["uncond"]
+
+#   sometimes this scaling seems like a win, but only when heuristic/reinhard CFG is too high
+#            if self.scaleCFGs == True:
+#                heuristic = max(1.0, self.heuristic * CFGfadeForge.weight) if (self.heuristic > 0.0) else 0.0
+#                reinhard  = max(1.0, self.reinhard  * CFGfadeForge.weight) if (self.reinhard  > 0.0) else 0.0
+#            else:
+            heuristic = self.heuristic
+            reinhard = self.reinhard
+
+            if self.centreMean == True:     # better after, but value here too?
+                for b in range(len(cond)):
+                    for c in range(4):
+                        cond[b][c] -= cond[b][c].mean()
+                        uncond[b][c] -= uncond[b][c].mean()
+
+#   cond_scale weighting now applied in sampling_function, can avoid processing of uncond for performance increase
+
+            thisStep = shared.state.sampling_step
+            lastStep = shared.state.sampling_steps
+
+#   heuristic scaling, higher hcfg acts to boost contrast/detail/sharpness; low reduces; quantile has effect, but not significant for quality IMO
+            noisePrediction = cond - uncond
+            if heuristic != 0.0 and heuristic != cond_scale and thisStep >= self.hStart * lastStep:
+                base = uncond + cond_scale * noisePrediction
+                heur = uncond + heuristic * noisePrediction
+
+                #   center both on zero
+                baseC = base - base.mean()
+                heurC = heur - heur.mean()
+                del base, heur
+
+                #   calc 99.0% quartiles - doesn't seem to have value as an option
+                baseQ = torch.quantile(baseC.abs(), 0.99)
+                heurQ = torch.quantile(heurC.abs(), 0.99)
+                del baseC, heurC
+
+                if baseQ != 0.0 and heurQ != 0.0:
+                    cond *= (baseQ / heurQ)
+                    uncond *= (baseQ / heurQ)
+#   end: heuristic scaling
+
+                
+#   reinhard tonemap from comfy
+            noisePrediction = cond - uncond
+            if reinhard != 0.0 and reinhard != cond_scale:
+                multiplier = 1.0 / cond_scale * reinhard
+                noise_pred_vector_magnitude = (torch.linalg.vector_norm(noisePrediction, dim=(1)) + 0.0000000001)[:,None]
+                noisePrediction /= noise_pred_vector_magnitude
+
+                mean = torch.mean(noise_pred_vector_magnitude, dim=(1,2,3), keepdim=True)
+                std = torch.std(noise_pred_vector_magnitude, dim=(1,2,3), keepdim=True)
+                top = (std * 3 + mean) * multiplier
+
+                noise_pred_vector_magnitude *= (1.0 / top)
+                new_magnitude = noise_pred_vector_magnitude / (noise_pred_vector_magnitude + 1.0)
+                new_magnitude *= top
+                cond_scale *= new_magnitude
+#   end: reinhard
+
+
+#   rescaleCFG - maybe should be exclusive of other effects, but why restrict?
+            result = uncond + cond_scale * noisePrediction
+            if self.rcfgmult != 0.0:
+                ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True)
+                ro_cfg = torch.std(result, dim=(1,2,3), keepdim=True)
+
+                if ro_pos != 0.0 and ro_cfg != 0.0:
+                    x_rescaled = result * (ro_pos / ro_cfg)
+                    result = torch.lerp (result, x_rescaled, self.rcfgmult)
+                    del x_rescaled                
+#   end: rescaleCFG
+            del noisePrediction
+
+            return result
+
+        m = model.clone()
+        m.set_model_sampler_cfg_function(sampler_cfgfade)
+        return (m, )
+
+
+    def denoiser_callback(self, params):
+        lastStep = params.total_sampling_steps - 1
+        thisStep = params.sampling_step
+        sigma = params.sigma[0]
+
+        lowCFG1   = self.lowCFG1   * lastStep
+        highStep  = self.highStep  * lastStep
+        boostStep = self.boostStep * lastStep
+        highCFG1  = self.highCFG1  * lastStep
+        fadeStep  = self.fadeStep  * lastStep
+        zeroStep  = self.zeroStep  * lastStep
+
+        if thisStep < lowCFG1:
+            boostWeight = 0.0
+        elif thisStep < boostStep:
+            boostWeight = 1.0
+        elif thisStep < highStep:
+            boostWeight = 1.0 + (self.maxScale - 1.0) * ((thisStep - boostStep) / (highStep - boostStep))
+        else:
+            boostWeight = self.maxScale
+
+        if thisStep > highCFG1:
+            fadeWeight = 0.0
+        else:
+            if thisStep < fadeStep:
+                fadeWeight = 1.0
+            elif thisStep < zeroStep:
+                fadeWeight = 1.0 - (thisStep - fadeStep) / (zeroStep  - fadeStep)
+            else:
+                fadeWeight = 0.0
+
+            # at this point, weight is in the range 0.0->1.0
+            fadeWeight *= (1.0 - self.minScale)
+            fadeWeight += self.minScale
+            # now it is minimum->1.0
+
+        CFGfadeForge.weight = boostWeight * fadeWeight
+
+
+    def process_before_every_sampling(self, params, *script_args, **kwargs):
+        enabled = script_args[0]
+        if enabled:
+            unet = params.sd_model.forge_objects.unet
+            unet = CFGfadeForge.patch(self, unet)[0]
+            params.sd_model.forge_objects.unet = unet
+
+        return
+
+    def process(self, params, *script_args, **kwargs):
+        enabled, cntrMean, boostStep, highStep, maxScale, fadeStep, zeroStep, minScale, lowCFG1, highCFG1, reinhard, rcfgmult, heuristic, hStart = script_args
+
+        if not enabled:
+            return
+
+        self.centreMean = cntrMean
+        self.boostStep  = boostStep
+        self.highStep   = highStep
+        self.maxScale   = maxScale
+        self.fadeStep   = fadeStep
+        self.zeroStep   = zeroStep
+        self.minScale   = minScale
+        self.lowCFG1    = lowCFG1
+        self.highCFG1   = highCFG1
+        self.reinhard   = reinhard
+        self.rcfgmult   = rcfgmult
+        self.heuristic  = heuristic
+        self.hStart     = hStart
+
+        # logs, could save boost start/full only if boost factor > 1
+        #       similar for fade
+        params.extra_generation_params.update(dict(
+            cfgfade_enabled   = enabled,
+            cfgfade_cntrMean  = cntrMean,
+            cfgfade_boostStep = boostStep,
+            cfgfade_highStep  = highStep,
+            cfgfade_maxScale  = maxScale,
+            cfgfade_fadeStep  = fadeStep,
+            cfgfade_zeroStep  = zeroStep,
+            cfgfade_minScale  = minScale,
+            cfgfade_lowCFG1   = lowCFG1,
+            cfgfade_highCFG1  = highCFG1,
+            cfgfade_reinhard  = reinhard,
+            cfgfade_rcfgmult  = rcfgmult,
+            cfgfade_heuristic = heuristic,
+            cfgfade_hStart    = hStart,
+        ))
+
+        #   must log the parameters before fixing minScale
+        self.minScale /= self.maxScale
+
+        on_cfg_denoiser(self.denoiser_callback)
+
+        if CFGfadeForge.backup_sampling_function == None:
+            CFGfadeForge.backup_sampling_function = modules_forge.forge_sampler.sampling_function
+
+        modules_forge.forge_sampler.sampling_function = CFGfadeForge.sampling_function
+        return
+
+    def postprocess(self, params, processed, *args):
+        enabled = args[0]
+        if enabled:
+            if CFGfadeForge.backup_sampling_function != None:
+                modules_forge.forge_sampler.sampling_function = CFGfadeForge.backup_sampling_function
+
+        remove_current_script_callbacks()
+        return
+

extensions/CloneCleaner/.gitattributes

@@ -0,0 +1,2 @@
+# Auto detect text files and perform LF normalization
+* text=auto

extensions/CloneCleaner/.gitignore

@@ -0,0 +1,4 @@
+__pycache__/
+*.py[cod]
+*$py.class
+

extensions/CloneCleaner/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 artyfacialintelagent
+
+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.

extensions/CloneCleaner/README.md

@@ -0,0 +1,86 @@
+# CloneCleaner
+
+An extension for Automatic1111 to work around Stable Diffusion's "clone problem". It automatically modifies your prompts with random names, nationalities, hair style and hair color to create more variations in generated people.
+
+# What it does
+
+Many generations of model finetuning and merging have greatly improved the image quality of Stable Diffusion 1.5 when generating humans - but at the cost of overtraining and loss of variability. This manifests as "clones", where batch generations using the same or similar prompts but different random seeds often have identical facial features.
+
+CloneCleaner adds randomized tokens to your prompt that varies the look of a person for every generated image seed. For example, one seed might get "Elsie from Australia, long waist-length updo ginger hair" and the next "Samreen from Bangladesh, medium shoulder-length frizzy coffee-colored hair". This makes every seed quite unique and effectively mitigates the "sameface" problem of many popular but heavily overtrained Stable Diffusion models. So it's basically wildcards, except the tiresome setup work has been done for you and you're ready to go - with lots of easy customization options to control the randomization.
+
+A key detail is that the token randomization seed is (by default) identical to the main image seed - this ensures that the same "person" will be generated again if you modify the prompt or reload the metadata.
+
+# Installation in Automatic1111
+
+Enter this url **manually** in auto1111's extension tab:
+
+https://github.com/artyfacialintelagent/CloneCleaner.git
+
+This first release of CloneCleaner is a public beta and **currently only works for female characters**. Options for male (and indeterminate gender if I can get it to work) coming soon-ish!
+
+# How it works
+
+Prompts are randomized using wildcards, except they're hardcoded in the extension with logic to match ethnicity with typical names and common hair colors for each country, in order to get consistent appearance and quality of the generated images. Main steps:
+
+1. Set the token randomization seed to the main image seed (or optionally a different random seed or a user-specified one).
+2. Select a random REGION among the following: Europe (mainland incl. Russia), Anglosphere (US, Can, Aus, NZ, UK), Latin America, MENA (Middle-East & North Africa), Sub-Saharan Africa, East Asia or South Asia.
+3. Select a random COUNTRY in that region - but note that CloneCleaner only has a sample of countries of each region, the database is not (yet) comprehensive.
+4. Select a random FIRST NAME for people in that country.
+5. Select a random MAIN HAIR COLOR (only black, brown, red, blonde, other), weighted for what is typical in that country. Then select a SPECIFIC HAIR COLOR with more "colorful" (sorry) language for more variability. Color tokens are carefully selected and limited using tricks like reduced attention and prompt editing to minimize "color bleeding" into the rest of your prompt.
+6. Select random HAIR STYLE and HAIR LENGTH.
+7. Assemble the prompt insert: "[FIRST NAME] from [COUNTRY], [HAIR LENGTH] [HAIR STYLE] [SPECIFIC HAIR COLOR] hair". Insert it at beginning or end of the prompt depending on user options
+8. Iterate for remaining images of the batch.
+
+Any of the above token components can be optionally excluded from the prompt randomization. There are also customization options to exclude specific regions, hair lengths and hair color.
+
+# Sample images
+
+The following images are produced using **consecutive seeds**, so they are **NOT CHERRY-PICKED** in any way. But to be fair, not all models work quite as well as these - some models are so overtrained that they just can't be saved. The sample PNGs hosted here on Github should contain full metadata, so you can download and inspect them in the PNGinfo tab, or send them to txt2img to reproduce them.
+
+Right-click and select "Open image in a new tab" to view at 100%, or right-click and select "Save image as" to download.
+
+### Absolute Reality v1
+
+These images were created using the [Absolute Reality model](https://civitai.com/models/81458/absolutereality), from a simple test prompt I made up.
+
+**Absolute Reality v1 (baseline model images)**
+![absolutereality_seeds](https://github.com/artyfacialintelagent/CloneCleaner/assets/137619889/d69ef1e4-6cf6-4401-97bb-bc0eeeef8a2a)
+![absolutereality1](https://github.com/artyfacialintelagent/CloneCleaner/assets/137619889/64f4fb70-0764-470d-a00a-07b8137000f5)
+
+**Absolute Reality v1 + CloneCleaner (default settings)**
+![absolutereality2](https://github.com/artyfacialintelagent/CloneCleaner/assets/137619889/a495ac69-cf17-4be9-814f-389403280c39)
+
+**Absolute Reality v1 + CloneCleaner (East Asia only)**
+![absolutereality3](https://github.com/artyfacialintelagent/CloneCleaner/assets/137619889/9aeeb238-e980-43a8-b5aa-c54baec0a41d)
+
+**Absolute Reality v1 + CloneCleaner (Anglosphere + Europe only, short hair only)**
+![absolutereality4](https://github.com/artyfacialintelagent/CloneCleaner/assets/137619889/f0218f86-9d53-4127-b9d9-5233e4e46776)
+
+### Photon v1
+
+Using the prompt from the sample image hosted on the [Photon model page on Civitai](https://civitai.com/models/84728/photon), slightly modified to make it more SFW for Github.
+
+**Photon v1 (baseline model images)**
+![photon_seeds](https://github.com/artyfacialintelagent/CloneCleaner/assets/137619889/33fad4a4-651b-4806-92cc-3c1a01d58fc9)
+![photon1](https://github.com/artyfacialintelagent/CloneCleaner/assets/137619889/cedea29e-c2c5-4c53-9569-2929db095971)
+
+**Photon v1 + CloneCleaner (default settings)**
+![photon2](https://github.com/artyfacialintelagent/CloneCleaner/assets/137619889/5da77a58-06f2-45c7-8920-95bf25390de9)
+
+**Photon v1 + CloneCleaner (Africa + South Asia only, no blonde or "other" hair color)**
+![photon3](https://github.com/artyfacialintelagent/CloneCleaner/assets/137619889/cf8759c4-9ada-4c6e-be6e-4829e3c34eeb)
+
+**Photon v1 + CloneCleaner (Europe only, reddish hair only)**
+![photon4](https://github.com/artyfacialintelagent/CloneCleaner/assets/137619889/58d1755a-1d0c-4c20-bc98-f00a80816147)
+
+# Some tips
+
+I am very happy with how well this simple prompt modification scheme works. But the best part about CloneCleaner is how it made me completely re-evaluate my opinion of many models, mostly positively. So be sure to retest your models using CloneCleaner - they may yet surprise you!
+
+I recommend using **simple prompts** (< 50 tokens), **simple negatives** (< 30 tokens) and **limited attention weighting** (never > :1.2, except as noted below). An effective minimal negative prompt appears below. Just start with this as a basis and add whatever your image seems to need.
+
+**Negative prompt**: *ugly, asian, underage, 3d render, cartoon, doll, (bad low worst quality:1.3)*
+
+The token "asian" is included to counter the heavy bias towards Korean and Chinese women among most popular models. Asian characters should still appear and look perfectly fine even including this token. Usually the attention setting of the final quality prompt should stick to the range 1.1 - 1.4, but a small number of Asian-oriented models can benefit from high (~1.5) or extreme values (up to 2.0!). Note that this is the exception that proves the rule - in most models such extreme weights would heavily "overcook" your images and destroy both quality and variability.
+
+I rarely use attention weights above 1.0 in my prompts and **never** use attention weights above 1.2 for any other tokens in my prompt other than this general quality negative. In my experience this greatly benefits image consistency and reduces mutations, bad hands and other monstrosities without having to explicitly include these things in your negatives.

extensions/CloneCleaner/prompt_tree.yml

@@ -0,0 +1,286 @@
+hair:
+    length:
+        short: [short, (short-cut:1.2)]
+        medium: ["medium (shoulder-length:1.2)", "medium (chin-length:1.2)"]
+        long: [long, "long (waist-length:1.2)"]
+    style:
+        short: [straight, bangs, combed, "[undercut|punk]", undercut, brushed-back, slicked-back, brush-cut, pixie-cut, semi-buzzcut, buzzcut, mohawk-style, curly, spiky hairstyle, tussled, bob-cut, pigtails, twintails, ponytail]
+        medium: [straight, windblown, bangs, combed, brushed-back, slicked-back, brush-cut, grown-out pixie-cut, wavy, curly, mohawk-style, frizzy, wild, messy, bob-cut, updo, pigtails, twintails, ponytail]
+        long: [straight, windblown, bangs, combed, brushed-back, slicked-back, brush-cut, wavy, curly, frizzy, wild, messy, updo, glorious, pigtails, twintails, ponytail]
+    color:
+        blonde: [platinum-blonde, ash-blonde, champagne-blonde, sandy-blonde, honey-blonde, dirty-blonde, "[blonde:strawberry-blonde:.1]",
+                    "[blonde:vanilla-blonde:.2]", "[blonde:golden-blonde:.2]", "[blonde:reddish-blonde:.2]", "[blonde:rose-gold:.2]"]
+        red: [auburn, ginger, peach, "[auburn:coral:.3]", "[auburn:copper:.3]", "[auburn:reddish:.4]", "[auburn:crimson:.4]", "[auburn:orange:.4]"]
+        brown: [mocca, chestnut, hazelnut, caramel, cinnamon, coffee, chocolate, charcoal, "[honey-brown:.3]", "[golden-brown:.3]",
+                    "[noisette:.3]", "[bronze:.3]", "[mahogany:.2]", "[maroon:.4]", "[burgundy:.4]"]
+        black: ["[black:.3]", "[nearly black:.3]", "[blue-black:.3]", "[violet-black:.3]", "[reddish-black:.3]"]
+        other: ["[blonde:pastel:.2]", "[blonde:gray-blonde:.2]", "[blonde:silver-blonde:.2]", "[blonde:neon:.3]", "[blonde:multicolored:.3]",
+                    "[blonde:rainbow:.3]", "[blonde:pink:.3]", "[blonde:emerald:.3]", "[blonde:mint:.3]", "[blonde:teal:.3]", "[blonde:indigo:.3]",
+                    "[blonde:lilac:.3]", "[blonde:lavender:.3]", "[blonde:violet:.3]"]
+    defaultweight:
+        Europe:             {blonde: 20, red: 15, brown: 35, black: 25, other: 5}
+        Anglosphere:        {blonde: 15, red: 10, brown: 35, black: 35, other: 5}
+        MENA:               {blonde: 5, red: 5, brown: 10, black: 75, other: 5}
+        Sub-Saharan Africa: {blonde: 5, red: 5, brown: 10, black: 75, other: 5}
+        Latin America:      {blonde: 10, red: 5, brown: 30, black: 50, other: 5}
+        East Asia:          {blonde: 10, red: 5, brown: 10, black: 70, other: 5}
+        South Asia:         {blonde: 5, red: 5, brown: 10, black: 75, other: 5}
+country:
+    Europe:
+        Sweden:
+            weight: 3
+            hair: {blonde: 35, red: 15, brown: 30, black: 15, other: 5}
+            names: [Astrid, Freja, Maja, Ebba, Linnea, Agnes, Amelia, Wilma, Elin, Lovisa, Liv, Alva, Märta, Sigrid, Lova, Emelie, Ida, Åsa, Hilma, Frida, Tyra, Siv, Linn]
+        Norway:
+            weight: 2
+            hair: {blonde: 35, red: 15, brown: 30, black: 15, other: 5}
+            names: [Hedda, Tiril, Tone, Agnes, Silje, Vilde, Martine, Stine, Maren, Mari, Synne, Hanne, Sigrid, Ingvild, Lene, Wenche, Maiken, Ragnhild]
+        Denmark:
+            weight: 2
+            hair: {blonde: 35, red: 15, brown: 30, black: 15, other: 5}
+            names: [Nanna, Signe, Amalie, Asta, Nora, Mette, Kirsten, Hanne, Helle, Lene, Lærke, Gry, Laura, Esther, Mathilde, Vera]
+        Finland:
+            weight: 2
+            hair: {blonde: 35, red: 15, brown: 30, black: 15, other: 5}
+            names: [Aino, Helmi, Aada, Onni, Venla, Iida, Eevi, Lilja, Kaarina, Enni, Elli, Pihla, Hannele, Marjatta, Minea, Hilla, Kerttu, Siiri, Veera, Nelli]
+        Scotland:
+            weight: 2
+            hair: {blonde: 20, red: 30, brown: 25, black: 20, other: 5}
+            names: [Isla, Ava, Amelia, Jamie, Sarah, Lucy, Fiona, Evie, Iona, Amy, Eilidh, Bonnie, Sienna, Callie, Skye, Holly, Eilish, Caitlin, Mairi, Moira]
+        Ireland:
+            weight: 2
+            hair: {blonde: 20, red: 30, brown: 25, black: 20, other: 5}
+            names: [Aoife, Niamh, Caoimhe, Maeve, Cian, Fiadh, Orla, Cara, Molly, Shannon, Kelly, Eabha , Roisin, Riley, Kennedy, Sloane, Fiona, Aisling]
+        Netherlands:
+            weight: 3
+            names: [Mila, Yara, Evi, Saar, Lieke, Noor, Lotte, Milou, Nova, Fenna, Isa, Roos, Loïs, Esmee, Fien, Lara, Jasmijn, Kiki, Veerle, Juul, Femke, Philou, Guusje]
+        Belgium:
+            weight: 3
+            names: [Marie, Olivia, Camille, Mila, Alexis, Julie, Sacha, Martine, Nathalie, Monique, Rita, Nicole, Christine, Isabelle]
+        Germany:
+            weight: 4
+            names: [Johanna, Heidi, Elke, Ilse, Ursula, Frieda, Helga, Anja, Eva, Leni, Berta, Eleonor, Frederike, Franka, Henriette, Irma, Luisa, Margarete]
+        Czech Republic:
+            weight: 3
+            names: [Barbora, Agata, Adela, Marketa, Karolina, Aneta, Jana, Tereza, Andrea, Zuzana, Martina, Dominika, Ema]
+        Poland:
+            weight: 4
+            names: [Antonina, Beata, Anastazja, Aniela, Genowefa, Adelajda, Brygida, Alina, Aldona, Estera, Ewelina, Adela, Augustyna, Aneta, Albina, Alfreda]
+        England:
+            weight: 4
+            names: [Ivy, Amelia, Emily, Alice, Hannah, Eleanor, Daisy, Phoebe, Evie, Florence, Ellie, Poppy, Rosie, Anne, Harriet, Jane, Rebecca, Mary, Alice, Elizabeth, Abigail, Holly, Lucy, Nancy]
+        France:
+            weight: 4
+            hair: {blonde: 15, red: 10, brown: 35, black: 35, other: 5}
+            names: [Camille, Chloé, Amélie, Celine, Anais, Dominique, Elise, Genevieve, Colette, Delphine, Jeanne, Sylvie, Margot, Eloise, Cecile, Inès, Lucie, Léonie]
+        Switzerland:
+            weight: 3
+            hair: {blonde: 15, red: 10, brown: 35, black: 35, other: 5}
+            names: [Laura, Alina, Mila, Lena, Lina, Alessia, Chiara, Andrea, Jana, Malia, Elea, Liana, Lara, Lia, Livia, Melina]
+        Austria:
+            weight: 3
+            names: [Johanna, Lena, Alina, Lea, Valentina, Amélie, Sophie, Magdalena, Marlene, Daniela, Hanna, Leni, Lara, Paulina, Frieda, Mathea, Liana]
+        Hungary:
+            weight: 3
+            names: [Lili, Aliz, Bianka, Lilla, Dorina, Dora, Mira, Zita, Emese, Izabella, Liza, Regina, Vivien]
+        Slovakia:
+            weight: 3
+            names: [Ema, Nina, Eliška, Viktória, Natália, Nela, Diana, Tamara, Júlia, Karolína, Michaela, Rebeka, Dominika, Lenka, Betka]
+        Croatia:
+            weight: 3
+            names: [Elena, Nika, Marta, Marija, Andrea, Ines, Adrijana, Mirjana, Lucija, Petra, Rita, Iva, Mila, Leona, Tena, Franka, Katja, Maša, Nikol, Bruna]
+        Serbia:
+            weight: 3
+            names: [Natalija, Ljubica, Milena, Marija, Adrijana, Mirjana, Jovana, Aleksa, Anja, Gordana, Tijana, Marina, Iskra, Petra]
+        Portugal:
+            weight: 3
+            names: [Leonor, Matilde, Beatriz, Carolina, Mariana, Ana, Sofia, Francisca, Inês, Margarida, Benedita, Madalena, Joana, Camila] 
+        Spain:
+            weight: 4
+            hair: {blonde: 10, red: 5, brown: 30, black: 50, other: 5}
+            names: [Ana, Sofia, Isabella, Lucia, Elena, Carmen, Lola, Mariana, Gabriela, Daniela, Adriana, Valeria, Esmeralda, Juana, Natalia]
+        Italy:
+            weight: 4
+            hair: {blonde: 10, red: 5, brown: 30, black: 50, other: 5}
+            names: [Francesca, Chiara, Giulia, Aurora, Bianca, Alessia, Andrea, Caterina, Lucia, Gaia, Isabella, Giovanna, Elisa, Gabriella, Valentina, Viola]
+        Greece:
+            weight: 3
+            hair: {blonde: 10, red: 5, brown: 30, black: 50, other: 5}
+            names: [Eleni, Katerina, Vasiliki, Sophia, Angeliki, Georgia, Dimitra, Konstandina, Paraskevi, Anastasia, Joanna, Danae, Athina, Melina, Ioanna]
+        Romania:
+            weight: 3
+            names: [Antonia, Andreea, Ioana, Alexandra, Luminita, Alina, Bianca, Iolanda, Lavinia, Daniela, Stefana, Doina, Simona, Iulian, Claudia, Angelika]
+        Turkey:
+            weight: 4
+            names: [Elif, Ayşe, Asli, Azra, Aysun, Emel, Dilara, Derya, Afet, Alara, Beyza, Damla, Meryem, Ceren, Nuray, Miray, Ayten, Esra, Eymen, Caria, Esma]
+        Ukraine:
+            weight: 4
+            names: [Aleksandra, Daryna, Olena, Lavra, Ivanna, Anastasia, Bohdana, Maryska, Ionna, Alisa, Nyura, Alina, Aneta, Myroslava, Marynia, Galyna, Nastunye, Nastasiya]
+        Belarus:
+            weight: 3
+            names: [Alina, Elena, Ekaterina, Anya, Faina, Darya, Svetlana, Aleksandra, Natasha, Arina, Alisa, Natalia]
+        Russia:
+            weight: 5
+            names: [Anastasia, Irina, Galina, Anya, Alina, Svetlana, Mila, Ekaterina, Angelina, Oksana, Olga, Nikita, Natasha, Arina, Kira, Sasha, Alisa, Nadia, Alla, Darya]
+    Anglosphere:
+        England:
+            weight: 4
+            names: [Ivy, Amelia, Emily, Alice, Hannah, Eleanor, Daisy, Phoebe, Evie, Florence, Ellie, Poppy, Rosie, Anne, Harriet, Jane, Rebecca, Mary, Alice, Elizabeth, Abigail, Holly, Lucy, Nancy]
+        USA:
+            weight: 5
+            names: [Charlotte, Amelia, Emma, Abigail, Alice, Mia, Eleanor, Sarah, Evelyn, Olivia, Grace, Sophia, Emily, Zuri, Destiny, Amari, Nia, Michelle, Brianna, Ebony,
+                        Eshe, Candice, Tiana, Toni, Jasmine, Simone, Tiffany, Nala, Camila, Sofia, Elena, Valentina, Natalia, Lucia, Alaia, Ximena, Valeria, Catalina]
+        Canada:
+            weight: 3
+            names: [Emily, Amelia, Sarah, Mila, Ellie, Mackenzie, Claire, Lily, Zoey, Evelyn, Avery, Abigail, Madison, Madeline, Everly, Ivy, Hailey, Addison]
+        Australia:
+            weight: 3
+            names: [Charlotte, Amelia, Ava, Sienna, Matilda, Evie, Zara, Mackenzie, Aria, Violet, Florence, Ivy, Elsie, Layla, Frankie, Daisy, Sadie, Stella, Millie]
+        New Zealand:
+            weight: 1
+            names: [Lilly, Lainie, Heide, Emme, Opal, Lacey, Livvie, Adalie, Sidonie, Dunja, Gitta, Kiani, Kenia, Rubia, Ophélie, Zäzilie, Zina, Marli, Meya]
+    MENA:
+        Israel:
+            weight: 2
+            hair: {blonde: 15, red: 10, brown: 35, black: 35, other: 5}
+            names: [Ariel, Leah, Rachel, Esther, Naomi, Talia, Shira, Yael, Lia, Aviva, Moriah, Delilah, Romi]
+        Lebanon:
+            weight: 2
+            names: [Nour, Aya, Alaa, Souad, Reewa, Ilham, Salwa, Sawsane, Fatme, Dina, Elza, Ghofran, Farah]
+        Iran:
+            weight: 4
+            names: [Bahar, Fatemeh, Anahita, Fariba, Parisa, Shirin, Darya, Zahra, Maryam, Yasmin, Rana, Mahsa, Nasrin, Cyra, Haleh]
+        Syria:
+            weight: 2
+            names: [Amena, Reem, Qamar, Haya, Ousa, Rosarita, Aischa, Hala, Uri, Rojda, Layal, Ranim, Souzan]
+        Saudi Arabia:
+            weight: 3
+            names: [Fatima, Jamila, Amira, Hanifa, Farida, Halima, Farah, Malika, Nabila, Habiba, Maha, Karima, Khalida, Maryam, Tahira, Ghada, Nadia, Naila]
+        Egypt:
+            weight: 4
+            names: [Jana, Amunet, Mariam, Salma, Jamila, Aya, Anippe, Halima, Jomana, Isis, Renenutet, Farida, Dalia, Dendera, Eshe, Amal, Yasmin]
+        Algeria:
+            weight: 3
+            names: [Yasmine, Amina, Inčs, Amel, Meriem, Rania, Asma, Nesrine, Mina, Feriel, Anais, Ikram, Camelia, Chiraz, Dounia, Samira]
+        Morocco:
+            weight: 3
+            names: [Amina, Fatima, Salma, Aziza, Faiza, Hajar, Ikram, Farah, Naima, Layla, Adil, Amal, Amira, Jamila, Marwa, Inès, Rania, Zineb]
+    Sub-Saharan Africa:
+        Nigeria:
+            weight: 5
+            names: [Tami, Remi, Sade, Ima, Toya, Tobi, Tomi, Tari, Ajah, Tona, Nneka, Dola, Chioma, Chimere, Zelie, Tonna, Timi, Amarachi, Chiamaka, Adanna, Chinyere]
+        Ethiopia:
+            weight: 4
+            names: [Kayla, Aisha, Amara, Ayana, Zena, Sheba, Amira, Gabra, Jahzara, Makda, Nyala]
+        Congo:
+            weight: 4
+            names: [Belvie, Mado, Rosine, Ruth, Malicia, Exaucée, Keicha, Grasnie, Benie, Chinelvie, Fiavina, Mireille, Aminata, Dayana, Peniel, Taime]
+        Tanzania:
+            weight: 3
+            names: [Mila, Nia, Lulu, Malia, Imani, Asha, Zahra, Aida, Asma, Kali, Lela, Habiba, Rabia, Tisha, Shani, Khadija, Naima]
+        South Africa:
+            weight: 3
+            hair: {blonde: 10, red: 5, brown: 30, black: 50, other: 5}
+            names: [Melokuhle, Omphile, Iminathi, Lisakhanya, Lethabo, Amahle, Lesedi, Rethabile, Christal, Danelle, Alida, Marli, Gerrie, Mariette, Aletta, Leane, Lizanne, Lindie, Elize]
+        Kenya:
+            weight: 3
+            names: [Mila, Imani, Cora, Nia, Kamaria, Nadia, Mumbi, Lulu, Zahra, Lela, Habiba, Shani, Khadija, Naima, Aluna, Kamaria, Zakiya]
+        Uganda:
+            weight: 3
+            names: [Florence, Joan, Sarah, Alice, Tracy, Evelyn, Hanifah, Mariam, Charity, Martha, Angel, Catherine, Patricia, Blessing, Patience, Diana, Ritah]
+        Sudan:
+            weight: 3
+            names: [Roaa, Ruba, Abrar, Felicia, Akong, Doaa, Esraa, Rayan, Nancy, Anila, Kate, Naelia]
+        Angola:
+            weight: 2
+            names: [Ooli, Samara, Hannah, Jazmine, Leila, Hiki, Miranda, Rosine, Cynthia, Nattat, Lara, Christina, Abigail]
+        Mozambique:
+            weight: 2
+            names: [Gaia, Gabrielle, Chelsea, Leila, Hedda, Hyacinta, Xiluva]
+        Ghana:
+            weight: 2
+            names: [Beatrice, Sandra, Priscilla, Abigail, Angela, Esther, Barbara, Alberta, Doris, Linda, Cecilia, Blessing, Maame, Ruth, Patricia, Nancy, Eunice, Janet]
+        Madagascar:
+            weight: 2
+            names: [Miora, Stephanie, Aina, Rotsy, Domoina, Cynthia, Raissa, Erica, Aliciah, Lili, Miantsa, Christelle, Linah, Tsiky, Hanitra]
+        Cameroon:
+            weight: 2
+            names: [Joaddan, Armelle, Sandra, Esther, Daniella, Tracy, Rosy, Mylléna, Jade, Carine, Maeva, Tatiana]
+    Latin America:
+        Brazil:
+            weight: 5
+            names: [Maria, Fernanda, Ana, Gabriela, Aline, Francisca, Beatriz, Julia, Bruna, Antonia, Clara, Helena, Marcia, Flavia, Juliana, Patricia, Vitoria, Bianca]
+        Mexico:
+            weight: 4
+            names: [Juana, Gabriela, Isabella, Elena, Alejandra, Lola, Ana, Lucia, Rosa, Veronica, Daniela, Fernanda, Valeria, Valentina, Carmen, Catalina, Leticia, Francisca, Mariana, Luna]
+        Colombia:
+            weight: 3
+            names: [Isabella, Ana, Daniela, Mariana, Gabriela, Valentina, Luciana, Carmen, Adriana, Guadalupe, Antonia, Natalia, Lucia, Bianca, Clara, Lina, Rosa, Lola]
+        Argentina:
+            weight: 3
+            names: [Camila, Agustina, Paula, Valentina, Ana, Julieta, Micaela, Martina, Daiana, Natalia, Carolina, Noelia, Lucia, Juana]
+        Peru:
+            weight: 3
+            names: [Agata, Alejandra, Antonella, Arsenia, Beatriz, Claudia, Daniela, Dominga, Evita, Guadalupe, Irene, Karmina, Manuela, Maritza, Ofelia, Pilar, Roberta, Teresa, Valentina, Xaviera, Ximena]
+        Venezuela:
+            weight: 3
+            names: [Gioia, Aymar, Consuelo, Mariángela, Gabriela, Vanessa, Selene, Susana, Paola, Federica, Mariam, Claudia, Carolina, Ligia]
+        Chile:
+            weight: 2
+            names: [Isabella, Valentina, Laura, Catalina, Fernanda, Mila, Florencia, Julieta, Gabriela, Isidora, Martina, Josefa, Daniela, Antonia, Constanza, Agustina, Lucia]
+        Ecuador:
+            weight: 2
+            names: [Ana, Gabriela, Maria, Camila, Carla, Elena, Graciela, Lucia, Giselle, Andrea, Daniela, Viviana, Juliana]
+        Guatemala:
+            weight: 2
+            names: [Michelle, Andrea, Esmeralda, Rosa, Luz, Belen, Camila, Isabela, Angelita]
+        Cuba:
+            weight: 1
+            names: [Adelgonda, Agata, Alegria, Alejandra, Angela, Arsenia, Beatriz, Bertalina, Daniela, Dominga, Evita, Georgina, Guadalupe, Havana, Ivelisse, Karmina, Manuela, Maritza, Pabla, Pilar, Roberta, Teresa, Ximena, Yamile]
+    East Asia:
+        China:
+            weight: 5
+            names: [Huan, Ling, Feng, Bao, Jie, Ping, Mei, Liu, Xia, Lian, Chun, Chang, Qing, Xiang, Xue, Qiao, Lei, Caihong, Ying, Jiayi, Lanying]
+        Mongolia:
+            weight: 1
+            names: [Sunjidmaa, Minjinsor, Darimaa, Solongo, Yanjmaa, Ariunzaya, Khongorzul, Gankhuyagiin, Davaademberel, Batkhuyagiin, Batchimeg, Yanjaa, Ankhmaa, Namgar, Nominjin]
+        South-Korea:
+            weight: 2
+            names: [Seo-yeon, Min-seo, Seo-hyeon, Su-bin, Yoo-jin, Min-ji, Seo-yeong, Ji-won, Soo-min, Ye-won, Mi-sun, Hyo-Sonn, Sun-Hee]
+        Taiwan:
+            weight: 1
+            names: [Shu-fen, Shu-hui, Mei-ling, Ya-ting, Mei-hua, Li-hua, Shu-chen, Yi-chun, Tzu-hui, Xian-cha, Ching-min, Chun-yi, Jiau-hua, Ming-zhu, Pei-chien, Pei-yu, Shu-Fang, Xiu-xi]
+        Hong Kong:
+            weight: 1
+            names: [Ellie, Avery, Charlotte, Renee, Cheryl, Evelyn, Hailey, Cynthia, Sylvia, Ally, Hannah, Chloe, Daphne, Aria, Riley, Emily, Mandy, Amber, Catherine, Natalie, Lily, Michelle]
+        Japan:
+            weight: 2
+            names: [Aoi, Akari, Sakura, Ayaka, Akira, Aki, Hinata, Akane, Hikari, Keiko, Kei, Chiyo, Kaede, Ayame, Hanako, Ayako, Akiko, Chika, Ayumi, Yuki]
+        Indonesia:
+            weight: 2
+            names: [Aulia, Budiwati, Bulan, Citra, Gendis, Indri, Kadek, Kemala, Komang, Lestari, Maharani, Melati, Mawar, Mayang, Ningrum, Ningsih, Permata, Pertiwi, Puspita, Putri, Ratih, Sinta, Siti, Wati, Yanti]
+        Philippines:
+            weight: 2
+            names: [Joyce, Cindy, Ruby, Michelle, Serena, Jewel, Nancy, Alodia, Bianca, Chynna, Coleen, Diane, Isabel, Janine, Mariel, Nina, Rachel, Valerie]
+        Vietnam:
+            weight: 2
+            names: [Anh, Bao, Chau, Chinh, Chi, Dao, Duong, Giang, Hoa, Huong, Khanh, Khuyen, Linh, Loan, Minh, Mai, Ngan, Nhung, Nguyet, Phuong, Quyen, Suong, Thanh, Thuy, Trinh, Vinh, Xinh, Xuyen]
+        Thailand:
+            weight: 2
+            names: [Dara, Kamala, Mali, Han, Chariya, Arich, Anchali, Chanthira, Mani, Rune, Kanya, Khajee, Naiyana, Pakpao, Ying, Chalermwan, Chantana, Hansa, Maliwan]
+    South Asia:
+        India:
+            weight: 5
+            names: [Saanvi, Anika, Aditi, Prisha, Aarna, Divya, Parvati, Jiya, Riya, Aaradhya, Myra, Isha, Siya, Anaya, Anaisha, Jaya, Aarya, Ahana, Zara, Gayatri, Aarushi, Aarvi, Adya, Damini]
+        Pakistan:
+            weight: 2
+            names: [Fatima, Inaya, Haniya, Maira, Zainab, Aafreen, Zoya, Neha, Aabish, Aaeedah, Eman, Aaminah, Aalia, Aamaal, Aamira, Aadila, Anabia, Anam, Sana]
+        Bangladesh:
+            weight: 2
+            names: [Barsha, Farhan, Megh, Chandni, Rifah, Hridi, Labiba, Rafia, Disha, Muhaiminul, Abida, Oporajita, Sharmin, Fariha, Nawrin, Samreen, Anamika, Rafsan, Meghbalika, Sanjana, Ranya]
+        Nepal:
+            weight: 1
+            names: [Dhanvi, Ditya, Gamya, Ehani, Chirasmi, Fatehjit, Binsa, Chaha, Bhavisana, Bilhana, Shanoli, Chirashree, Bhavaroopa, Kasmitha, Kopisha, Feba, Feshikha, Chaarumathi, Eenakshi, Chantin, Chimini, Baijanthi]
+        Sri Lanka:
+            weight: 1
+            names: [Hiruni, Tharushi, Sachini, Bhagya, Nethmi, Janani, Sanduni, Thilini, Rashmi, Samadhi, Harshani, Upeksha, Nipuni, Dinithi, Chathu, Senuri, Hansani, Chalani, Shehara, Gayani]

extensions/CloneCleaner/scripts/clonecleaner.py

@@ -0,0 +1,223 @@
+import gradio as gr
+import os
+import random
+import sys
+import yaml
+
+from modules import scripts, script_callbacks, shared, paths
+from modules.processing import Processed
+from modules.ui_components import FormRow, FormColumn, FormGroup, ToolButton
+from modules.ui import random_symbol, reuse_symbol, gr_show
+from modules.generation_parameters_copypaste import parse_generation_parameters
+from pprint import pprint
+
+def read_yaml():
+    promptfile = os.path.join(scripts.basedir(), "prompt_tree.yml")
+    with open(promptfile, "r", encoding="utf8") as stream:
+        prompt_tree = yaml.safe_load(stream)
+        return prompt_tree
+
+def get_last_params(declone_seed, gallery_index):
+    filename = os.path.join(paths.data_path, "params.txt")
+    if os.path.exists(filename):
+        with open(filename, "r", encoding="utf8") as file:
+            prompt = file.read()
+
+    if gallery_index > 0:
+        gallery_index -= 1
+    params = parse_generation_parameters(prompt)
+    if params.get("CC_use_main_seed", "") == "True":
+        return [int(float(params.get("Seed", "-0.0"))) + gallery_index, gr_show(False)]
+    else:
+        return [int(float(params.get("CC_declone_seed", "-0.0"))) + gallery_index, gr_show(False)]
+
+def sorted_difference(a, b):
+    newlist = list(set(a).difference(b))
+    newlist.sort()
+    return newlist
+
+class CloneCleanerScript(scripts.Script):
+    prompt_tree = read_yaml()   # maybe make this an instance property later
+
+    def title(self):
+        return "CloneCleaner"
+
+    # show menu in either txt2img or img2img
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+    
+    def ui(self, is_img2img):
+        with gr.Accordion("CloneCleaner (beta!)", open=True):
+            dummy_component = gr.Label(visible=False)
+            regions = self.prompt_tree["country"].keys()
+            hairlength = self.prompt_tree["hair"]["length"].keys()
+            haircolor = self.prompt_tree["hair"]["color"].keys()
+            with FormRow():
+                with FormColumn(min_width=160):
+                    is_enabled = gr.Checkbox(value=True, label="Enable CloneCleaner")
+                with FormColumn(elem_id="CloneCleaner_gender"):
+                    gender = gr.Radio(["female", "male", "generic"], value="female", label="Male & generic not yet implemented.", elem_classes="ghosted")
+                    gender.style(container=False, item_container=False)
+            with FormRow(elem_id="CloneCleaner_components"):
+                components = ["name", "country", "hair length", "hair style", "hair color"]
+                use_components = gr.CheckboxGroup(components, label="Use declone components", value=components)
+            with FormRow(elem_id="CloneCleaner_midsection"):
+                with FormGroup():
+                    insert_start = gr.Checkbox(value=True, label="Put declone tokens at beginning of prompt")
+                    declone_weight = gr.Slider(minimum=0.0, maximum=2.0, step=0.05, value=1.0, label="Weight of declone tokens", elem_id="CloneCleaner_slider")
+                with FormGroup():
+                    use_main_seed = gr.Checkbox(value=True, label="Use main image seed for decloning")
+                    with FormRow(variant="compact", elem_id="CloneCleaner_seed_row", elem_classes="ghosted"):
+                        declone_seed = gr.Number(label='Declone seed', value=-1, elem_id="CloneCleaner_seed")
+                        declone_seed.style(container=False)
+                        random_seed = ToolButton(random_symbol, elem_id="CloneCleaner_random_seed", label='Random seed')
+                        reuse_seed = ToolButton(reuse_symbol, elem_id="CloneCleaner_reuse_seed", label='Reuse seed')
+            with FormRow(elem_id="CloneCleaner_exclude_row") as exclude_row:
+                exclude_regions = gr.Dropdown(choices=regions, label="Exclude regions", multiselect=True)
+                exclude_hairlength = gr.Dropdown(choices=hairlength, label="Exclude hair lengths", multiselect=True)
+                exclude_haircolor = gr.Dropdown(choices=haircolor, label="Exclude hair colors", multiselect=True)
+        
+        jstoggle = "() => {document.getElementById('CloneCleaner_seed_row').classList.toggle('ghosted')}"
+        jsclickseed = "() => {setRandomSeed('CloneCleaner_seed')}"
+        jsgetgalleryindex = "(x, y) => [x, selected_gallery_index()]"
+        other_jstoggles =   "() => {" + \
+                    "const labels = document.getElementById('CloneCleaner_components').getElementsByTagName('label');" + \
+                    "const excludelabels = document.getElementById('CloneCleaner_exclude_row').getElementsByTagName('label');"  + \
+                    "excludelabels[1].classList.toggle('ghosted', !labels[2].firstChild.checked);" + \
+                    "excludelabels[2].classList.toggle('ghosted', !labels[4].firstChild.checked);" + \
+                    "}"
+        use_main_seed.change(fn=None, _js=jstoggle)
+        random_seed.click(fn=None, _js=jsclickseed, show_progress=False, inputs=[], outputs=[])
+        reuse_seed.click(fn=get_last_params, _js=jsgetgalleryindex, show_progress=False, inputs=[declone_seed, dummy_component], outputs=[declone_seed, dummy_component])
+        use_components.change(fn=None, _js=other_jstoggles)
+        
+        def list_from_params_key(key, params):
+            regionstring = params.get(key, "")
+            regions = regionstring.split(",") if regionstring else []
+            return gr.update(value = regions)
+
+        self.infotext_fields = [
+            (is_enabled, "CloneCleaner enabled"),
+            (gender, "CC_gender"),
+            (insert_start, "CC_insert_start"),
+            (declone_weight, "CC_declone_weight"),
+            (use_main_seed, "CC_use_main_seed"),
+            (declone_seed, "CC_declone_seed"),
+            (exclude_regions, lambda params:list_from_params_key("CC_exclude_regions", params)),
+            (exclude_hairlength, lambda params:list_from_params_key("CC_exclude_hairlength", params)),
+            (exclude_haircolor, lambda params:list_from_params_key("CC_exclude_haircolor", params))
+        ]
+        return [is_enabled, gender, insert_start, declone_weight, use_main_seed, declone_seed, use_components, exclude_regions, exclude_hairlength, exclude_haircolor]
+
+    def process(self, p, is_enabled, gender, insert_start, declone_weight, use_main_seed, declone_seed, use_components, exclude_regions, exclude_hairlength, exclude_haircolor):
+        if not is_enabled:
+            return
+
+        if use_main_seed:
+            declone_seed = p.all_seeds[0]
+        elif declone_seed == -1:
+            declone_seed = int(random.randrange(4294967294))
+        else:
+            declone_seed = int(declone_seed)
+
+        # original_prompt = p.all_prompts[0]
+        # settings = f"gender={gender}, beginning={insert_start}, declone_weight={declone_weight}, main_seed={use_main_seed}, " + \
+        #             f"declone_seed={declone_seed}, exclude_regions={exclude_regions}"
+        p.extra_generation_params["CloneCleaner enabled"] = True
+        p.extra_generation_params["CC_gender"] = gender
+        p.extra_generation_params["CC_insert_start"] = insert_start
+        p.extra_generation_params["CC_declone_weight"] = declone_weight
+        p.extra_generation_params["CC_use_main_seed"] = use_main_seed
+        p.extra_generation_params["CC_declone_seed"] = declone_seed
+        if exclude_regions:
+            p.extra_generation_params["CC_exclude_regions"] = ",".join(exclude_regions)
+        if exclude_hairlength:
+            p.extra_generation_params["CC_exclude_hairlength"] = ",".join(exclude_hairlength)
+        if exclude_haircolor:
+            p.extra_generation_params["CC_exclude_haircolor"] = ",".join(exclude_haircolor)
+
+        countrytree = self.prompt_tree["country"]
+        hairtree = self.prompt_tree["hair"]
+
+        regions = sorted_difference(countrytree.keys(), exclude_regions)
+        hairlengths = sorted_difference(hairtree["length"].keys(), exclude_hairlength)
+        haircolors = sorted_difference(hairtree["color"].keys(), exclude_haircolor)
+
+        use_name = "name" in use_components
+        use_country = "country" in use_components
+        use_length = "hair length" in use_components
+        use_style = "hair style" in use_components
+        use_color = "hair color" in use_components
+
+        for i, prompt in enumerate(p.all_prompts):      # for each image in batch
+            rng = random.Random()
+            seed = p.all_seeds[i] if use_main_seed else declone_seed + i
+            rng.seed(seed)
+
+            region = rng.choice(regions)
+            countries = list(countrytree[region].keys())
+            countryweights = [countrytree[region][cty]["weight"] for cty in countries]
+            country = rng.choices(countries, weights=countryweights)[0]
+
+            countrydata = countrytree[region][country]
+            hairdata = countrydata.get("hair", hairtree["defaultweight"][region])
+            maincolor = rng.choices(haircolors, weights=[hairdata[col] for col in haircolors])[0]
+            color = rng.choice(hairtree["color"][maincolor])
+            mainlength = rng.choice(hairlengths)
+            length = rng.choice(hairtree["length"][mainlength])
+            style = rng.choice(hairtree["style"][mainlength])
+            name = rng.choice(countrydata["names"])
+
+            inserted_prompt = ""
+
+            if use_name or use_country:
+                inserted_prompt += name if use_name else "person"
+                inserted_prompt += " from " + country if use_country else ""
+            
+            if use_length or use_style or use_color:
+                if inserted_prompt:
+                    inserted_prompt += ", "
+                if use_length:
+                    inserted_prompt += length + " "
+                if use_style:
+                    inserted_prompt += style + " "
+                if use_color:
+                    inserted_prompt += color + " "
+                inserted_prompt += "hair"
+
+            if inserted_prompt:
+                if declone_weight != 1:
+                    inserted_prompt = f"({inserted_prompt}:{declone_weight})"
+
+                if insert_start:
+                    p.all_prompts[i] = inserted_prompt + ", " + prompt
+                else:
+                    p.all_prompts[i] = prompt + ", " + inserted_prompt
+    
+    # def postprocess_batch(self, p, *args, **kwargs):
+    #     p.all_prompts[0] = p.prompt    # gets saved in file metadata AND in batch file metadata
+
+    # def process_batch(self, p, *args, **kwargs):
+    #     p.extra_generation_params["CC_TEST"] = "whatever"
+    #     p.all_prompts[0] = p.prompt + " SUFFIX"
+
+    def postprocess(self, p, processed, *args):
+        with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
+            p.all_prompts[0] = p.prompt
+            processed = Processed(p, [], p.seed, "")
+            file.write(processed.infotext(p, 0))
+
+# read with shared.opts.prompt_database_path
+def on_ui_settings():
+    info = shared.OptionInfo("prompt_tree.yml", "CloneCleaner prompt database path", section=("clonecleaner", "CloneCleaner"))
+    shared.opts.add_option("prompt_database_path", info)
+    # shared.opts.add_option("option1", shared.OptionInfo(
+    #     False,
+    #     "option1 description",
+    #     gr.Checkbox,
+    #     {"interactive": True},
+    #     section=('template', "Template"))
+    # )
+
+
+script_callbacks.on_ui_settings(on_ui_settings)

extensions/CloneCleaner/style.css

@@ -0,0 +1,33 @@
+.ghosted {
+    opacity: 0.5 !important;
+    pointer-events: none !important;
+}
+
+/* #CloneCleaner_gender {
+    padding: 0 0 10px 0 !important;
+}
+
+#CloneCleaner_gender > div > label {
+    padding: var(--checkbox-label-padding) 0 !important;
+} */
+
+#CloneCleaner_components {
+    margin-top: -10px;
+}
+
+#CloneCleaner_slider {
+    width: 280px;
+    margin-top: 15px;
+}
+
+#CloneCleaner_seed{
+    min-width: min(80px,100%);
+}
+
+#CloneCleaner_midsection {
+    margin-top: 10px;
+}
+
+#CloneCleaner_exclude_row {
+    margin-top: 10px;
+}

extensions/ComfyUI-AutomaticCFG/.github/workflows/publish.yml

@@ -0,0 +1,21 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@main
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

extensions/ComfyUI-AutomaticCFG/README.md

@@ -0,0 +1,175 @@
+# Abandon this boat and jump on [this one!](https://github.com/Extraltodeus/Skimmed_CFG)
+
+If you liked other functionnalities, I've re-created most in [this](https://github.com/Extraltodeus/pre_cfg_comfy_nodes_for_ComfyUI) repository.
+
+# In short:
+
+My own version "from scratch" of a self-rescaling CFG / anti-burn. It ain't much but it's honest work.
+
+No more burns and 160% faster gens with the warp drive node.
+
+Now includes custom attention modifiers and interesting presets as well as temperature scaling.
+
+Also just tested and it works with pixart sigma.
+
+Works with SD3 for as long as you don't use any boost feature / cutting the uncond (it's the same thing). 20 steps works nicely.
+
+# Note:
+
+The presets are interpreted with eval(). Make sure that you thrust whoever sent a preset to you as it may be used to execute malicious code.
+
+# Update:
+
+- Removed and perfected "Uncond Zero" node and moved it to it's [own repository](https://github.com/Extraltodeus/Uncond-Zero-for-ComfyUI/tree/main)
+- Removed temperature nodes and set a [repository](https://github.com/Extraltodeus/Stable-Diffusion-temperature-settings) for these
+
+# Usage:
+
+![77889aa6-a2f6-48bf-8cde-17c9cbfda5fa](https://github.com/Extraltodeus/ComfyUI-AutomaticCFG/assets/15731540/c725a06c-8966-43de-ab1c-569e2ff5b151)
+
+
+### That's it!
+
+- The "boost" toggle will turn off the negative guidance when the sigmas are near 1. This doubles the inference speed.
+- The negative strength lerp the cond and uncond. Now in normal times the way I do this would burn things to the ground. But since it is initialy an anti-burn it just works. This idea is inspired by the [negative prompt weight](https://github.com/muerrilla/stable-diffusion-NPW) repository.
+- I leave the advanced node for those who are interested. It will not be beneficial to those who do not feel like experimenting.
+
+For 100 steps this is where the sigma are reaching 1:
+
+![image](https://github.com/Extraltodeus/ComfyUI-AutomaticCFG/assets/15731540/525199f1-2857-4027-a96e-105bc4b01860)
+
+Note: the warp drive node improves the speed a lot more. The average speed is 160% the normal one if used with the AYS scheduler (check the workflow images).
+
+There seem to be a slight improvement in quality when using the boost with my other node [CLIP Vector Sculptor text encode](https://github.com/Extraltodeus/Vector_Sculptor_ComfyUI) using the "mean" normalization option.
+
+# Just a note:
+
+Your CFG won't be your CFG anymore. It is turned into a way to guide the CFG/final intensity/brightness/saturation. So don't hesitate to change your habits while trying!
+
+# The rest of the explaination:
+
+While this node is connected, this will turn your sampler's CFG scale into something else.
+This methods works by rescaling the CFG at each step by evaluating the potential average min/max values. Aiming at a desired output intensity (by intensity I mean overall brightness/saturation/sharpness).
+The base intensity has been arbitrarily chosen by me and your sampler's CFG scale will make this target vary.
+I have set the "central" CFG at 8. Meaning that at 4 you will aim at half of the desired range while at 16 it will be doubled. This makes it feel slightly like the usual when you're around the normal values.
+
+The CFG behavior during the sampling being automatically set for each channel makes it behave differently and therefores gives different outputs than the usual.
+From my observations by printing the results while testing, it seems to be going from around 16 at the beginning, to something like 4 near the middle and ends up near ~7. 
+These values might have changed since I've done a thousand tests with different ways but that's to give you an idea, it's just me eyeballing the CLI's output.
+
+I use the upper and lower 25% topk mean value as a reference to have some margin of manoeuver.
+
+It makes the sampling generate overall better quality images. I get much less if not any artifacts anymore and my more creative prompts also tends to give more random, in a good way, different results.
+
+I attribute this more random yet positive behavior to the fact that it seems to be starting high and then since it becomes lower, it self-corrects and improvise, taking advantage of the sampling process a lot more.
+
+It is dead simple to use and made sampling more fun from my perspective :)
+
+You will find it in the model_patches category.
+
+TLDR: set your CFG at 8 to try it. No burned images and artifacts anymore. CFG is also a bit more sensitive because it's a proportion around 8.
+
+Low scale like 4 also gives really nice results since your CFG is not the CFG anymore.
+
+# Updates:
+
+Updated:
+- Up to 28.5% faster generation speed than normal
+- Negative weighting
+
+05.04.24:
+
+- Updated to latest ComfyUI version. If you get an error: update your ComfyUI
+
+15.04.24
+
+- ~~Added "no uncond" node which completely disable the negative and doubles the speed while rescaling the latent space in the post-cfg function up until the sigmas are at 1 (or really, 6.86%). By itself it is not perfect and I'm searching for solutions to improve the final result. It seems to work better with dpmpp3m_sde/exponential if you're not using anything else. If you are using the PAG node then you don't need to care about the sampler but will generate at a normal speed. Result will be simply different (I personally like them).~~ use the warp drive instead
+- To use the [PAG node](https://github.com/pamparamm/sd-perturbed-attention/tree/master) ~~without the complete slow-down (if using the no-uncond node) or at least take advantage of the boost feature:~~
+  ~~- in the "pag_nodes.py" file look for "disable_cfg1_optimization=True"~~
+  ~~- set it to "disable_cfg1_optimization=False".~~ This is not necessary anymore because the dev modified it already :)
+- For the negative lerp function in the other nodes the scale has been divided by two. So if you were using it at 10, set it to 5.
+
+16.04.24
+
+- Added "uncond_start_percentage" as an experimental feature. This allows to start the guidance later as a way to try [Applying Guidance in a Limited Interval Improves
+Sample and Distribution Quality in Diffusion Models](https://arxiv.org/pdf/2404.07724.pdf). A more accurate implementation [can be found here](https://github.com/ericbeyer/guidance_interval) :)
+
+17.04.24
+
+- reworked the advanced node and cleaned up
+- added timing on every options
+- add a post-rescale node which allows to fight deep-frying images a bit more forr some special cases
+- added a tweaked version of the Comfy SAG node with start/end sliders
+- changed start/end sliders, they are related directly to the sigma values and not in percentage anymore. ⚠
+
+01.05.24
+
+- Actually working disabled uncond
+- Added "warp drive" preset to test it out simply.
+
+03.05.24
+
+- Allows unpatch `turn off the negative` by removing or disconnecting the node.
+- added the "Warp drive" node. It uses a new method of my own cooking which uses the previous step to determin a negative. Cutting the generation time by half for approx 3/4 of the steps.
+- added example workflows with 10-12 steps but of course you can do more steps if needed. It is not a goal to do less steps in general but also to show it is compatible.
+
+14.05.24:
+- fix the little mem leak 😀
+- temporarily disabled the timed SAG node because an update broke it.
+- added node: **preset loader**. Can do what the other can and much more like modify the attention mechanisms! Mostly tested on SDXL 😀!
+    - Some presets are slower than others. Just like for the perturbed attention guidance for example. Most are just as fast.
+    - About some of the presets:
+        - For SD 1.5 "crossed conds customized 3" seems amazing!
+        - "Enhanced_details_and_tweaked_attention" works better on creative generations and less on characters.
+        - "Reinforced_style" does not regulates the CFG, gives MUCH MORE importance to your negative prompt, works with 12 steps and is slightly slower.
+        - "The red riding latent" only works with SDXL. It is an almost nonsensical mix of attention tweaks. Best with 12 steps and really nice with creative prompts. Has the tendency to give more red clothings to the characters. Hence the name.
+        - "Excellent_attention" is the default settings for the node described below. Don't delete it or the node won't work. 🙃
+        - "Potato Attention Guidance" is really nice for portraits of happy people...
+        - There are a bunch of others. I've generated examples which you can find in the example grids folder.
+    - Most of these have been tested on SDXL. I have very little idea of the effect on SD 1.5
+    - The presets are .json files and can contain a string which will go through eval(). ⚠
+    - Always check what is inside before running it when it comes from someone else! I hesitated to share a preset which would plan a shutdown in 60 seconds named "actually shut down the computer in one minute" to let you be aware but that would bother more than it would be helfpul.
+- added node: "**Excellent attention**" developped by myself and based on this [astonishingly easy to understand research paper!](https://github.com/Extraltodeus/temp/blob/main/very_science.jpg) But in short:
+   - Just try it. [Do it](https://www.youtube.com/watch?v=ZXsQAXx_ao0).
+   - This node allows to disable the input layer 8 on self and cross attention.
+   - But also to apply a custom modification on cross attention middle layer 0. The "patch_cond" and "patch_uncond" toggles are about this modification.
+   - While the modification is definitely not very ressource costy, the light patch uses less VRAM.
+   - The multiplier influences the cross attention and reinforces prompt-following. But like for real. Works better with the "light patch" toggle ON.
+   - I have ~~only~~ mostly tested it with SDXL.
+   - You can find a grid example of this node's settings in the "grids_example" folder.
+   - For some reason the Juggernaut model does not work with it and I have no idea why.
+- Customizable attention modifiers:
+     - Check the ["attention_modifiers_explainations"](https://github.com/Extraltodeus/ComfyUI-AutomaticCFG/blob/main/workflows/attention_modifiers_explainations.png) in the workflows. 👀 It is basically a tutorial.
+     - Experiment what each layer really do by using what is basically a bruteforcing node! (the Attention modifiers tester node)
+     - This is how you do a [Perturbed Attention Guidance](https://github.com/Extraltodeus/temp/blob/main/PAG.png) for example
+
+
+
+# Examples
+
+### 10 steps with only 2 having the negative enabled. So ~170% faster. 2.5 seconds on a RTX4070
+
+![03640UI_00001_](https://github.com/Extraltodeus/ComfyUI-AutomaticCFG/assets/15731540/673cb47a-095f-4ebb-a186-2f6a49ffd2e1)
+
+### cherry-picked 24 steps uncond fully disabled (these images are also workflows):
+
+
+![03619UI_00001_](https://github.com/Extraltodeus/ComfyUI-AutomaticCFG/assets/15731540/19ee6edc-b039-4472-9ec2-c08ea15dd908)
+
+![03621UI_00001_](https://github.com/Extraltodeus/ComfyUI-AutomaticCFG/assets/15731540/52695e1c-d28e-427f-9109-7ee4e4b3a5f6)
+
+![03604UI_00001_](https://github.com/Extraltodeus/ComfyUI-AutomaticCFG/assets/15731540/ca391b46-f587-43da-98da-a87e4982e4ed)
+
+
+
+# Pro tip:
+
+Did you know that my first activity is to write creative model merging functions?
+
+While the code is too much of a mess to be shared, I do expose and share my models. You can find them in this [gallery](https://github.com/Extraltodeus/shared_models_galleries)! 😁
+
+
+-----
+
+Thanks to ComfyUI for existing and making such things so simple!
+

extensions/ComfyUI-AutomaticCFG/__init__.py

@@ -0,0 +1,27 @@
+from .nodes import *
+# from .experimental_temperature import ExperimentalTemperaturePatchSDXL,ExperimentalTemperaturePatchSD15,CLIPTemperaturePatch,CLIPTemperaturePatchDual
+# from .nodes_sag_custom import * 
+
+NODE_CLASS_MAPPINGS = {
+    "Automatic CFG": simpleDynamicCFG,
+    "Automatic CFG - Negative": simpleDynamicCFGlerpUncond,
+    "Automatic CFG - Warp Drive": simpleDynamicCFGwarpDrive,
+    "Automatic CFG - Preset Loader": presetLoader,
+    "Automatic CFG - Excellent attention": simpleDynamicCFGExcellentattentionPatch,
+    "Automatic CFG - Advanced": advancedDynamicCFG,
+    "Automatic CFG - Post rescale only": postCFGrescaleOnly,
+    "Automatic CFG - Custom attentions": simpleDynamicCFGCustomAttentionPatch,
+    "Automatic CFG - Attention modifiers": attentionModifierParametersNode,
+    "Automatic CFG - Attention modifiers tester": attentionModifierBruteforceParametersNode,
+    "Automatic CFG - Unpatch function": simpleDynamicCFGunpatch,
+    # "Zero Uncond CFG - standalone patch (incompatible with the others)":uncondZeroNode,
+    # "Temperature settings SDXL": ExperimentalTemperaturePatchSDXL,
+    # "Temperature settings SD 1.5": ExperimentalTemperaturePatchSD15,
+    # "Temperature settings CLIP": CLIPTemperaturePatch,
+    # "Temperature separate settings CLIP SDXL": CLIPTemperaturePatchDual,
+    # "SAG delayed activation": SelfAttentionGuidanceCustom,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "Automatic CFG - Unpatch function": "Automatic CFG - Unpatch function(Deprecated)",
+}

extensions/ComfyUI-AutomaticCFG/experimental_temperature.py

@@ -0,0 +1,208 @@
+import torch
+from torch import nn, einsum
+from einops import rearrange, repeat
+import torch.nn.functional as F
+import math
+from comfy import model_management
+import types
+import os
+
+def exists(val):
+    return val is not None
+
+# better than a division by 0 hey
+abs_mean = lambda x: torch.where(torch.isnan(x) | torch.isinf(x), torch.zeros_like(x), x).abs().mean()
+
+class temperature_patcher():
+    def __init__(self, temperature, layer_name="None"):
+        self.temperature = temperature
+        self.layer_name  = layer_name
+    
+    # taken from comfy.ldm.modules
+    def attention_basic_with_temperature(self, q, k, v, extra_options, mask=None, attn_precision=None):
+        if isinstance(extra_options, int):
+            heads = extra_options
+        else:
+            heads = extra_options['n_heads']
+
+        b, _, dim_head = q.shape
+        dim_head //= heads
+        scale = dim_head ** -0.5
+
+        h = heads
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(b, -1, heads, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b * heads, -1, dim_head)
+            .contiguous(),
+            (q, k, v),
+        )
+
+        # force cast to fp32 to avoid overflowing
+        if attn_precision == torch.float32:
+            sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale
+        else:
+            sim = einsum('b i d, b j d -> b i j', q, k) * scale
+
+        del q, k
+
+        if exists(mask):
+            if mask.dtype == torch.bool:
+                mask = rearrange(mask, 'b ... -> b (...)')
+                max_neg_value = -torch.finfo(sim.dtype).max
+                mask = repeat(mask, 'b j -> (b h) () j', h=h)
+                sim.masked_fill_(~mask, max_neg_value)
+            else:
+                if len(mask.shape) == 2:
+                    bs = 1
+                else:
+                    bs = mask.shape[0]
+                mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
+                sim.add_(mask)
+
+        # attention, what we cannot get enough of
+        sim = sim.div(self.temperature if self.temperature > 0 else abs_mean(sim)).softmax(dim=-1)
+
+        out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v)
+        out = (
+            out.unsqueeze(0)
+            .reshape(b, heads, -1, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b, -1, heads * dim_head)
+        )
+        return out
+
+layers_SD15 = {
+    "input":[1,2,4,5,7,8],
+    "middle":[0],
+    "output":[3,4,5,6,7,8,9,10,11],
+}
+
+layers_SDXL = {
+    "input":[4,5,7,8],
+    "middle":[0],
+    "output":[0,1,2,3,4,5],
+}
+
+class ExperimentalTemperaturePatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        required_inputs = {f"{key}_{layer}": ("BOOLEAN", {"default": False}) for key, layers in s.TOGGLES.items() for layer in layers}
+        required_inputs["model"] = ("MODEL",)
+        required_inputs["Temperature"]  = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "round": 0.01})
+        required_inputs["Attention"]    = (["both","self","cross"],)
+        return {"required": required_inputs}
+    
+    TOGGLES = {}
+    RETURN_TYPES = ("MODEL","STRING",)
+    RETURN_NAMES = ("Model","String",)
+    FUNCTION = "patch"
+
+    CATEGORY = "model_patches/Automatic_CFG/Standalone_temperature_patches"
+
+    def patch(self, model, Temperature, Attention, **kwargs):
+        m = model.clone()
+        levels = ["input","middle","output"]
+        parameters_output = {level:[] for level in levels}
+        for key, toggle_enabled in kwargs.items():
+            current_level = key.split("_")[0]
+            if current_level in levels and toggle_enabled:
+                b_number = int(key.split("_")[1])
+                parameters_output[current_level].append(b_number)
+                patcher = temperature_patcher(Temperature,key)
+
+                if Attention in ["both","self"]:
+                    m.set_model_attn1_replace(patcher.attention_basic_with_temperature, current_level, b_number)
+                if Attention in ["both","cross"]:
+                    m.set_model_attn2_replace(patcher.attention_basic_with_temperature, current_level, b_number)
+
+        parameters_as_string = "\n".join(f"{k}: {','.join(map(str, v))}" for k, v in parameters_output.items())
+        parameters_as_string = f"Temperature: {Temperature}\n{parameters_as_string}\nAttention: {Attention}"
+        return (m, parameters_as_string,)
+    
+ExperimentalTemperaturePatchSDXL = type("ExperimentalTemperaturePatch_SDXL", (ExperimentalTemperaturePatch,), {"TOGGLES": layers_SDXL})
+ExperimentalTemperaturePatchSD15 = type("ExperimentalTemperaturePatch_SD15", (ExperimentalTemperaturePatch,), {"TOGGLES": layers_SD15})
+
+class CLIPTemperaturePatch:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": { "clip": ("CLIP",),
+                              "Temperature": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              }}
+    
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "patch"
+    CATEGORY = "model_patches/Automatic_CFG/Standalone_temperature_patches"
+    
+    def patch(self, clip, Temperature):
+        def custom_optimized_attention(device, mask=None, small_input=True):
+            return temperature_patcher(Temperature).attention_basic_with_temperature
+        
+        def new_forward(self, x, mask=None, intermediate_output=None):
+            optimized_attention = custom_optimized_attention(x.device, mask=mask is not None, small_input=True)
+
+            if intermediate_output is not None:
+                if intermediate_output < 0:
+                    intermediate_output = len(self.layers) + intermediate_output
+
+            intermediate = None
+            for i, l in enumerate(self.layers):
+                x = l(x, mask, optimized_attention)
+                if i == intermediate_output:
+                    intermediate = x.clone()
+            return x, intermediate
+
+        m = clip.clone()
+
+        clip_encoder_instance = m.cond_stage_model.clip_l.transformer.text_model.encoder
+        clip_encoder_instance.forward = types.MethodType(new_forward, clip_encoder_instance)
+
+        if getattr(m.cond_stage_model, f"clip_g", None) is not None:
+            clip_encoder_instance_g = m.cond_stage_model.clip_g.transformer.text_model.encoder
+            clip_encoder_instance_g.forward = types.MethodType(new_forward, clip_encoder_instance_g)
+        
+        return (m,)
+
+class CLIPTemperaturePatchDual:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": { "clip": ("CLIP",),
+                              "Temperature": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              "CLIP_Model": (["clip_g","clip_l","both"],),
+                              }}
+    
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "patch"
+    CATEGORY = "model_patches/Automatic_CFG/Standalone_temperature_patches"
+    
+    def patch(self, clip, Temperature, CLIP_Model):
+        def custom_optimized_attention(device, mask=None, small_input=True):
+            return temperature_patcher(Temperature, "CLIP").attention_basic_with_temperature
+        
+        def new_forward(self, x, mask=None, intermediate_output=None):
+            optimized_attention = custom_optimized_attention(x.device, mask=mask is not None, small_input=True)
+
+            if intermediate_output is not None:
+                if intermediate_output < 0:
+                    intermediate_output = len(self.layers) + intermediate_output
+
+            intermediate = None
+            for i, l in enumerate(self.layers):
+                x = l(x, mask, optimized_attention)
+                if i == intermediate_output:
+                    intermediate = x.clone()
+            return x, intermediate
+
+        m = clip.clone()
+
+        if CLIP_Model in ["clip_l","both"]:
+            clip_encoder_instance = m.cond_stage_model.clip_l.transformer.text_model.encoder
+            clip_encoder_instance.forward = types.MethodType(new_forward, clip_encoder_instance)
+
+        if CLIP_Model in ["clip_g","both"]:
+            if getattr(m.cond_stage_model, f"clip_g", None) is not None:
+                clip_encoder_instance_g = m.cond_stage_model.clip_g.transformer.text_model.encoder
+                clip_encoder_instance_g.forward = types.MethodType(new_forward, clip_encoder_instance_g)
+        
+        return (m,)

extensions/ComfyUI-AutomaticCFG/nodes.py

@@ -0,0 +1,1286 @@
+import math
+from copy import deepcopy
+from torch.nn import Upsample
+import comfy.model_management as model_management
+from comfy.model_patcher import set_model_options_patch_replace
+from comfy.ldm.modules.attention import attention_basic, attention_xformers, attention_pytorch, attention_split, attention_sub_quad, optimized_attention_for_device
+from .experimental_temperature import temperature_patcher
+import comfy.samplers
+import comfy.utils
+import numpy as np
+import torch
+import torch.nn.functional as F
+from colorama import Fore, Style
+import json
+import os
+import random
+import base64
+
+original_sampling_function = None
+current_dir = os.path.dirname(os.path.realpath(__file__))
+json_preset_path = os.path.join(current_dir, 'presets')
+attnfunc = optimized_attention_for_device(model_management.get_torch_device())
+check_string   = "UEFUUkVPTi50eHQ="
+support_string = b'CgoKClRoYW5rIHlvdSBmb3IgdXNpbmcgbXkgbm9kZXMhCgpJZiB5b3UgZW5qb3kgaXQsIHBsZWFzZSBjb25zaWRlciBzdXBwb3J0aW5nIG1lIG9uIFBhdHJlb24gdG8ga2VlcCB0aGUgbWFnaWMgZ29pbmchCgpWaXNpdDoKCmh0dHBzOi8vd3d3LnBhdHJlb24uY29tL2V4dHJhbHRvZGV1cwoKCgo='
+
+def support_function():
+    if base64.b64decode(check_string).decode('utf8') not in os.listdir(current_dir):
+        print(base64.b64decode(check_string).decode('utf8'))
+        print(base64.b64decode(support_string).decode('utf8'))
+
+def sampling_function_patched(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None, **kwargs):
+
+    cond_copy   = cond
+    uncond_copy = uncond
+
+    for fn in model_options.get("sampler_patch_model_pre_cfg_function", []):
+        args = {"model": model, "sigma": timestep, "model_options": model_options}
+        model, model_options = fn(args)
+
+    if "sampler_pre_cfg_function" in model_options:
+        uncond, cond, cond_scale = model_options["sampler_pre_cfg_function"](
+            sigma=timestep, uncond=uncond, cond=cond, cond_scale=cond_scale
+        )
+        
+    if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
+        uncond_ = None
+    else:
+        uncond_ = uncond
+
+    conds = [cond, uncond_]
+
+    out = comfy.samplers.calc_cond_batch(model, conds, x, timestep, model_options)
+    cond_pred = out[0]
+    uncond_pred = out[1]
+
+    if "sampler_cfg_function" in model_options:
+        args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
+                "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options, "cond_pos": cond_copy, "cond_neg": uncond_copy}
+        cfg_result = x - model_options["sampler_cfg_function"](args)
+    else:
+        cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale
+
+    for fn in model_options.get("sampler_post_cfg_function", []):
+        args = {"denoised": cfg_result, "cond": cond_copy, "uncond": uncond_copy, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred,
+                "sigma": timestep, "model_options": model_options, "input": x}
+        cfg_result = fn(args)
+        
+    return cfg_result
+
+def monkey_patching_comfy_sampling_function():
+    global original_sampling_function
+
+    if original_sampling_function is None:
+        original_sampling_function = comfy.samplers.sampling_function
+    # Make sure to only patch once
+    if hasattr(comfy.samplers.sampling_function, '_automatic_cfg_decorated'):
+        return
+    comfy.samplers.sampling_function = sampling_function_patched
+    comfy.samplers.sampling_function._automatic_cfg_decorated = True # flag to check monkey patch
+
+def make_sampler_pre_cfg_function(minimum_sigma_to_disable_uncond=0, maximum_sigma_to_enable_uncond=1000000, disabled_cond_start=10000,disabled_cond_end=10000):
+    def sampler_pre_cfg_function(sigma, uncond, cond, cond_scale, **kwargs):
+        if sigma[0] < minimum_sigma_to_disable_uncond or sigma[0] > maximum_sigma_to_enable_uncond:
+            uncond = None
+        if sigma[0] <= disabled_cond_start and sigma[0] > disabled_cond_end:
+            cond = None
+        return uncond, cond, cond_scale
+    return sampler_pre_cfg_function
+
+def get_entropy(tensor):
+    hist = np.histogram(tensor.cpu(), bins=100)[0]
+    hist = hist / hist.sum()
+    hist = hist[hist > 0]
+    return -np.sum(hist * np.log2(hist))
+
+def map_sigma(sigma, sigmax, sigmin):
+    return 1 + ((sigma - sigmax) * (0 - 1)) / (sigmin - sigmax)
+
+def center_latent_mean_values(latent, per_channel, mult):
+    for b in range(len(latent)):
+        if per_channel:
+            for c in range(len(latent[b])):
+                latent[b][c] -= latent[b][c].mean() * mult
+        else:
+            latent[b] -= latent[b].mean() * mult
+    return latent
+
+def get_denoised_ranges(latent, measure="hard", top_k=0.25):
+    chans = []
+    for x in range(len(latent)):
+        max_values = torch.topk(latent[x] - latent[x].mean() if measure == "range" else latent[x], k=int(len(latent[x])*top_k), largest=True).values
+        min_values = torch.topk(latent[x] - latent[x].mean() if measure == "range" else latent[x], k=int(len(latent[x])*top_k), largest=False).values
+        max_val = torch.mean(max_values).item()
+        min_val = abs(torch.mean(min_values).item()) if measure == "soft" else torch.mean(torch.abs(min_values)).item()
+        denoised_range = (max_val + min_val) / 2
+        chans.append(denoised_range**2 if measure == "hard_squared" else denoised_range)
+    return chans
+
+def get_sigmin_sigmax(model):
+    model_sampling = model.model.model_sampling
+    sigmin = model_sampling.sigma(model_sampling.timestep(model_sampling.sigma_min))
+    sigmax = model_sampling.sigma(model_sampling.timestep(model_sampling.sigma_max))
+    return sigmin, sigmax
+
+def gaussian_similarity(x, y, sigma=1.0):
+    diff = (x - y) ** 2
+    return torch.exp(-diff / (2 * sigma ** 2))
+    
+def check_skip(sigma, high_sigma_threshold, low_sigma_threshold):
+    return sigma > high_sigma_threshold or sigma < low_sigma_threshold
+
+def max_abs(tensors):
+    shape = tensors.shape
+    tensors = tensors.reshape(shape[0], -1)
+    tensors_abs = torch.abs(tensors)
+    max_abs_idx = torch.argmax(tensors_abs, dim=0)
+    result = tensors[max_abs_idx, torch.arange(tensors.shape[1])]
+    return result.reshape(shape[1:])
+
+def gaussian_kernel(size: int, sigma: float):
+    x = torch.arange(size) - size // 2
+    gauss = torch.exp(-x**2 / (2 * sigma**2))
+    kernel = gauss / gauss.sum()
+    return kernel.view(1, size) * kernel.view(size, 1)
+
+def blur_tensor(tensor, kernel_size = 9, sigma = 2.0):
+    tensor = tensor.unsqueeze(0)
+    C = tensor.size(1)
+    kernel = gaussian_kernel(kernel_size, sigma)
+    kernel = kernel.expand(C, 1, kernel_size, kernel_size).to(tensor.device).to(dtype=tensor.dtype, device=tensor.device)
+    padding = kernel_size // 2
+    tensor = F.pad(tensor, (padding, padding, padding, padding), mode='reflect')
+    blurred_tensor = F.conv2d(tensor, kernel, groups=C)
+    return blurred_tensor.squeeze(0)
+
+def smallest_distances(tensors):
+    if all(torch.equal(tensors[0], tensor) for tensor in tensors[1:]):
+        return tensors[0]
+    set_device = tensors.device
+    min_val = torch.full(tensors[0].shape, float("inf")).to(set_device)
+    result  = torch.zeros_like(tensors[0])
+    for idx1, t1 in enumerate(tensors):
+        temp_diffs = torch.zeros_like(tensors[0])
+        for idx2, t2 in enumerate(tensors):
+            if idx1 != idx2:
+                temp_diffs += torch.abs(torch.sub(t1, t2))
+        min_val = torch.minimum(min_val, temp_diffs)
+        mask    = torch.eq(min_val,temp_diffs)
+        result[mask] = t1[mask]
+    return result
+
+def rescale(tensor, multiplier=2):
+    batch, seq_length, features = tensor.shape
+    H = W = int(seq_length**0.5)
+    tensor_reshaped = tensor.view(batch, features, H, W)
+    new_H = new_W = int(H * multiplier)
+    resized_tensor = F.interpolate(tensor_reshaped, size=(new_H, new_W), mode='bilinear', align_corners=False)
+    return resized_tensor.view(batch, new_H * new_W, features)
+
+# from  https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475
+def slerp(high, low, val):
+    dims = low.shape
+
+    #flatten to batches
+    low = low.reshape(dims[0], -1)
+    high = high.reshape(dims[0], -1)
+
+    low_norm = low/torch.norm(low, dim=1, keepdim=True)
+    high_norm = high/torch.norm(high, dim=1, keepdim=True)
+
+    # in case we divide by zero
+    low_norm[low_norm != low_norm] = 0.0
+    high_norm[high_norm != high_norm] = 0.0
+
+    omega = torch.acos((low_norm*high_norm).sum(1))
+    so = torch.sin(omega)
+    res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
+    return res.reshape(dims)
+
+normalize_tensor = lambda x: x / x.norm()
+
+def random_swap(tensors, proportion=1):
+    num_tensors = tensors.shape[0]
+    if num_tensors < 2: return tensors[0],0
+    tensor_size = tensors[0].numel()
+    if tensor_size < 100: return tensors[0],0
+
+    true_count = int(tensor_size * proportion)
+    mask = torch.cat((torch.ones(true_count, dtype=torch.bool, device=tensors[0].device), 
+                      torch.zeros(tensor_size - true_count, dtype=torch.bool, device=tensors[0].device)))
+    mask = mask[torch.randperm(tensor_size)].reshape(tensors[0].shape)
+    if num_tensors == 2 and proportion < 1:
+        index_tensor = torch.ones_like(tensors[0], dtype=torch.int64, device=tensors[0].device)
+    else:
+        index_tensor = torch.randint(1 if proportion < 1 else 0, num_tensors, tensors[0].shape, device=tensors[0].device)
+    for i, t in enumerate(tensors):
+        if i == 0: continue
+        merge_mask = index_tensor == i & mask
+        tensors[0][merge_mask] = t[merge_mask]
+    return tensors[0]
+
+def multi_tensor_check_mix(tensors):
+    if tensors[0].numel() < 2 or len(tensors) < 2:
+        return tensors[0]
+    ref_tensor_shape = tensors[0].shape
+    sequence_tensor = torch.arange(tensors[0].numel(), device=tensors[0].device) % len(tensors)
+    reshaped_sequence = sequence_tensor.view(ref_tensor_shape)
+    for i in range(len(tensors)):
+        if i == 0: continue
+        mask = reshaped_sequence == i
+        tensors[0][mask] = tensors[i][mask]
+    return tensors[0]
+
+def sspow(input_tensor, p=2):
+    return input_tensor.abs().pow(p) * input_tensor.sign()
+
+def sspown(input_tensor, p=2):
+    abs_t = input_tensor.abs()
+    abs_t = (abs_t - abs_t.min()) / (abs_t.max() - abs_t.min())
+    return abs_t.pow(p) * input_tensor.sign()
+
+def gradient_merge(tensor1, tensor2, start_value=0, dim=0):
+    if torch.numel(tensor1) <= 1: return tensor1
+    if dim >= tensor1.dim(): dim = 0
+    size = tensor1.size(dim)
+    alpha = torch.linspace(start_value, 1-start_value, steps=size, device=tensor1.device).view([-1 if i == dim else 1 for i in range(tensor1.dim())])
+    return tensor1 * alpha + tensor2 * (1 - alpha)
+
+def save_tensor(input_tensor,name):
+    if "rndnum" in name:
+        rndnum = str(random.randint(100000,999999))
+        name = name.replace("rndnum", rndnum)
+    output_directory = os.path.join(current_dir, 'saved_tensors')
+    os.makedirs(output_directory, exist_ok=True)
+    output_file_path = os.path.join(output_directory, f"{name}.pt")
+    torch.save(input_tensor, output_file_path)
+    return input_tensor
+
+def print_and_return(input_tensor, *args):
+    for what_to_print in args:
+        print(" ",what_to_print)
+    return input_tensor
+
+# Experimental testings
+def normal_attention(q, k, v, mask=None):
+    attention_scores = torch.matmul(q, k.transpose(-2, -1))
+    d_k = k.size(-1)
+    attention_scores = attention_scores / torch.sqrt(torch.tensor(d_k, dtype=torch.float32))
+    if mask is not None:
+        attention_scores = attention_scores.masked_fill(mask == 0, float('-inf'))
+    attention_weights = F.softmax(attention_scores, dim=-1)
+    output = torch.matmul(attention_weights, v)
+    return output
+
+def split_heads(x, n_heads):
+    batch_size, seq_length, hidden_dim = x.size()
+    head_dim = hidden_dim // n_heads
+    x = x.view(batch_size, seq_length, n_heads, head_dim)
+    return x.permute(0, 2, 1, 3)
+
+def combine_heads(x, n_heads):
+    batch_size, n_heads, seq_length, head_dim = x.size()
+    hidden_dim = n_heads * head_dim
+    x = x.permute(0, 2, 1, 3).contiguous()
+    return x.view(batch_size, seq_length, hidden_dim)
+
+def sparsemax(logits):
+    logits_sorted, _ = torch.sort(logits, descending=True, dim=-1)
+    cumulative_sum = torch.cumsum(logits_sorted, dim=-1) - 1
+    rho = (logits_sorted > cumulative_sum / (torch.arange(logits.size(-1)) + 1).to(logits.device)).float()
+    tau = (cumulative_sum / rho.sum(dim=-1, keepdim=True)).gather(dim=-1, index=rho.sum(dim=-1, keepdim=True).long() - 1)
+    return torch.max(torch.zeros_like(logits), logits - tau)
+
+def attnfunc_custom(q, k, v, n_heads, eval_string = ""):
+    q = split_heads(q, n_heads)
+    k = split_heads(k, n_heads)
+    v = split_heads(v, n_heads)
+    
+    d_k = q.size(-1)
+
+    scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(d_k)
+
+    if eval_string == "":
+        attn_weights = F.softmax(scores, dim=-1)
+    else:
+        attn_weights = eval(eval_string)
+
+    output = torch.matmul(attn_weights, v)
+    output = combine_heads(output, n_heads)
+    return output
+
+def min_max_norm(t):
+    return (t - t.min()) / (t.max() - t.min())
+
+class attention_modifier():
+    def __init__(self, self_attn_mod_eval, conds = None):
+        self.self_attn_mod_eval = self_attn_mod_eval
+        self.conds = conds
+
+    def modified_attention(self, q, k, v, extra_options, mask=None):
+        
+        """extra_options contains: {'cond_or_uncond': [1, 0], 'sigmas': tensor([14.6146], device='cuda:0'),
+         'original_shape': [2, 4, 128, 128], 'transformer_index': 4, 'block': ('middle', 0),
+         'block_index': 3, 'n_heads': 20, 'dim_head': 64, 'attn_precision': None}"""
+        
+        if "attnbc" in self.self_attn_mod_eval:
+            attnbc = attention_basic(q, k, v, extra_options['n_heads'], mask)
+        if "normattn" in self.self_attn_mod_eval:
+            normattn = normal_attention(q, k, v, mask)
+        if "attnxf" in self.self_attn_mod_eval:
+            attnxf = attention_xformers(q, k, v, extra_options['n_heads'], mask)
+        if "attnpy" in self.self_attn_mod_eval:
+            attnpy = attention_pytorch(q, k, v, extra_options['n_heads'], mask)
+        if "attnsp" in self.self_attn_mod_eval:
+            attnsp = attention_split(q, k, v, extra_options['n_heads'], mask)
+        if "attnsq" in self.self_attn_mod_eval:
+            attnsq = attention_sub_quad(q, k, v, extra_options['n_heads'], mask)
+        if "attnopt" in self.self_attn_mod_eval:
+            attnopt = attnfunc(q, k, v, extra_options['n_heads'], mask)
+        n_heads = extra_options['n_heads']
+        if self.conds is not None:
+            cond_pos_l = self.conds[0][..., :768].cuda()
+            cond_neg_l = self.conds[1][..., :768].cuda()
+            if self.conds[0].shape[-1] > 768:
+                cond_pos_g = self.conds[0][..., 768:2048].cuda()
+                cond_neg_g = self.conds[1][..., 768:2048].cuda()
+        return eval(self.self_attn_mod_eval)
+
+def experimental_functions(cond_input, method, exp_value, exp_normalize, pcp, psi, sigma, sigmax, attention_modifiers_input, args, model_options_copy, eval_string = ""):
+    """
+    There may or may not be an actual reasoning behind each of these methods.
+    Some like the sine value have interesting properties. Enabled for both cond and uncond preds it somehow make them stronger.
+    Note that there is a "normalize" toggle and it may change greatly the end result since some operation will totaly butcher the values.
+    "theDaRkNeSs" for example without normalizing seems to darken if used for cond/uncond (not with the cond as the uncond or something).
+    Maybe just with the positive. I don't remember. I leave it for now if you want to play around.
+
+    The eval_string can be used to create the uncond replacement.
+    I made it so it's split by semicolons and only the last split is the value in used.
+    What is before is added in an array named "v".
+    pcp is previous cond_pred
+    psi is previous sigma
+    args is the CFG function input arguments with the added cond/unconds (like the actual activation conditionings) named respectively "cond_pos" and "cond_neg"
+
+    So if you write:
+
+    pcp if sigma < 7 else -pcp;
+    print("it works too just don't use the output I guess");
+    v[0] if sigma < 14 else torch.zeros_like(cond);
+    v[-1]*2
+    
+    Well the first line becomes v[0], second v[1] etc.
+    The last one becomes the result.
+    Note that it's just an example, I don't see much interest in that one.
+
+    Using comfy.samplers.calc_cond_batch(args["model"], [args["cond_pos"], None], args["input"], args["timestep"], args["model_options"])[0]
+    can work too.
+
+    This whole mess has for initial goal to attempt to find the best way (or have some bruteforcing fun) to replace the uncond pred for as much as possible.
+    Or simply to try things around :)
+    """
+    if method == "cond_pred":
+        return cond_input
+    default_device = cond_input.device
+    # print()
+    # print(get_entropy(cond))
+    cond = cond_input.clone()
+    cond_norm = cond.norm()
+    if method == "amplify":
+        mask = torch.abs(cond) >= 1
+        cond_copy = cond.clone()
+        cond = torch.pow(torch.abs(cond), ( 1 / exp_value)) * cond.sign()
+        cond[mask] = torch.pow(torch.abs(cond_copy[mask]), exp_value) * cond[mask].sign()
+    elif method == "root":
+        cond = torch.pow(torch.abs(cond), ( 1 / exp_value)) * cond.sign()
+    elif method == "power":
+        cond = torch.pow(torch.abs(cond), exp_value) * cond.sign()
+    elif method == "erf":
+        cond = torch.erf(cond)
+    elif method == "exp_erf":
+        cond = torch.pow(torch.erf(cond), exp_value)
+    elif method == "root_erf":
+        cond = torch.erf(cond)
+        cond = torch.pow(torch.abs(cond), 1 / exp_value ) * cond.sign()
+    elif method == "erf_amplify":
+        cond = torch.erf(cond)
+        mask = torch.abs(cond) >= 1
+        cond_copy = cond.clone()
+        cond = torch.pow(torch.abs(cond), 1 / exp_value ) * cond.sign()
+        cond[mask] = torch.pow(torch.abs(cond_copy[mask]), exp_value) * cond[mask].sign()
+    elif method == "sine":
+        cond = torch.sin(torch.abs(cond)) * cond.sign()
+    elif method == "sine_exp":
+        cond = torch.sin(torch.abs(cond)) * cond.sign()
+        cond = torch.pow(torch.abs(cond), exp_value) * cond.sign()
+    elif method == "sine_exp_diff":
+        cond = torch.sin(torch.abs(cond)) * cond.sign()
+        cond = torch.pow(torch.abs(cond_input), exp_value) * cond.sign() - cond
+    elif method == "sine_exp_diff_to_sine":
+        cond = torch.sin(torch.abs(cond)) * cond.sign()
+        cond = torch.pow(torch.abs(cond), exp_value) * cond.sign() - cond
+    elif method == "sine_root":
+        cond = torch.sin(torch.abs(cond)) * cond.sign()
+        cond = torch.pow(torch.abs(cond), ( 1 / exp_value)) * cond.sign()
+    elif method == "sine_root_diff":
+        cond = torch.sin(torch.abs(cond)) * cond.sign()
+        cond = torch.pow(torch.abs(cond_input), 1 / exp_value) * cond.sign() - cond
+    elif method == "sine_root_diff_to_sine":
+        cond = torch.sin(torch.abs(cond)) * cond.sign()
+        cond = torch.pow(torch.abs(cond), 1 / exp_value) * cond.sign() - cond
+    elif method == "theDaRkNeSs":
+        cond = torch.sin(cond)
+        cond = torch.pow(torch.abs(cond), 1 / exp_value) * cond.sign() - cond
+    elif method == "cosine":
+        cond = torch.cos(torch.abs(cond)) * cond.sign()
+    elif method == "sign":
+        cond = cond.sign()
+    elif method == "zero":
+        cond = torch.zeros_like(cond)
+    elif method in ["attention_modifiers_input_using_cond","attention_modifiers_input_using_uncond","subtract_attention_modifiers_input_using_cond","subtract_attention_modifiers_input_using_uncond"]:
+        cond_to_use = args["cond_pos"] if method in ["attention_modifiers_input_using_cond","subtract_attention_modifiers_input_using_cond"] else args["cond_neg"]
+        tmp_model_options = deepcopy(model_options_copy)
+        for atm in attention_modifiers_input:
+            if sigma <= atm['sigma_start'] and sigma > atm['sigma_end']:
+                block_layers = {"input": atm['unet_block_id_input'], "middle": atm['unet_block_id_middle'], "output": atm['unet_block_id_output']}
+                for unet_block in block_layers:
+                    for unet_block_id in block_layers[unet_block].split(","):
+                        if unet_block_id != "":
+                            unet_block_id = int(unet_block_id)
+                            tmp_model_options = set_model_options_patch_replace(tmp_model_options, attention_modifier(atm['self_attn_mod_eval'], [args["cond_pos"][0]["cross_attn"], args["cond_neg"][0]["cross_attn"]]if "cond" in atm['self_attn_mod_eval'] else None).modified_attention, atm['unet_attn'], unet_block, unet_block_id)
+        
+        cond = comfy.samplers.calc_cond_batch(args["model"], [cond_to_use], args["input"], args["timestep"], tmp_model_options)[0]
+        if method in ["subtract_attention_modifiers_input_using_cond","subtract_attention_modifiers_input_using_uncond"]:
+            cond = cond_input + (cond_input - cond) * exp_value
+
+    elif method == "previous_average":
+        if sigma > (sigmax - 1):
+            cond = torch.zeros_like(cond)
+        else:
+            cond = (pcp / psi * sigma + cond) / 2
+    elif method == "eval":
+        if "condmix" in eval_string:
+            def condmix(args, mult=2):
+                cond_pos_tmp = deepcopy(args["cond_pos"])
+                cond_pos_tmp[0]["cross_attn"] += (args["cond_pos"][0]["cross_attn"] - args["cond_neg"][0]["cross_attn"]*-1) * mult
+                return cond_pos_tmp
+        v = []
+        evals_strings = eval_string.split(";")
+        if len(evals_strings) > 1:
+            for i in range(len(evals_strings[:-1])):
+                v.append(eval(evals_strings[i]))
+        cond = eval(evals_strings[-1])
+    if exp_normalize and torch.all(cond != 0):
+        cond = cond * cond_norm / cond.norm()
+    # print(get_entropy(cond))
+    return cond.to(device=default_device)
+
+class advancedDynamicCFG:
+    def __init__(self):
+        self.last_cfg_ht_one = 8
+        self.previous_cond_pred = None
+        
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                                "model": ("MODEL",),
+
+                                "automatic_cfg" : (["None", "soft", "hard", "hard_squared", "range"], {"default": "hard"},),
+
+                                "skip_uncond" : ("BOOLEAN", {"default": True}),
+                                "fake_uncond_start" : ("BOOLEAN", {"default": False}),
+                                "uncond_sigma_start": ("FLOAT", {"default": 1000, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "uncond_sigma_end":   ("FLOAT", {"default": 1, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+
+                                "lerp_uncond" : ("BOOLEAN", {"default": False}),
+                                "lerp_uncond_strength":    ("FLOAT", {"default": 2, "min": 0.0, "max": 10.0, "step": 0.1, "round": 0.1}),
+                                "lerp_uncond_sigma_start": ("FLOAT", {"default": 1000,  "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "lerp_uncond_sigma_end":   ("FLOAT", {"default": 1, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+
+                                "subtract_latent_mean" : ("BOOLEAN", {"default": False}),
+                                "subtract_latent_mean_sigma_start": ("FLOAT", {"default": 1000,  "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "subtract_latent_mean_sigma_end":   ("FLOAT", {"default": 1, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+
+                                "latent_intensity_rescale"     : ("BOOLEAN", {"default": False}),
+                                "latent_intensity_rescale_method" : (["soft","hard","range"], {"default": "hard"},),
+                                "latent_intensity_rescale_cfg": ("FLOAT", {"default": 8,  "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.1}),
+                                "latent_intensity_rescale_sigma_start": ("FLOAT", {"default": 1000,  "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "latent_intensity_rescale_sigma_end":   ("FLOAT", {"default": 3, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+
+                                "cond_exp": ("BOOLEAN", {"default": False}),
+                                "cond_exp_normalize": ("BOOLEAN", {"default": False}),
+                                "cond_exp_sigma_start": ("FLOAT", {"default": 1000,  "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "cond_exp_sigma_end":   ("FLOAT", {"default": 1,   "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "cond_exp_method": (["amplify", "root", "power", "erf", "erf_amplify", "exp_erf", "root_erf", "sine", "sine_exp", "sine_exp_diff", "sine_exp_diff_to_sine", "sine_root", "sine_root_diff", "sine_root_diff_to_sine", "theDaRkNeSs", "cosine", "sign", "zero", "previous_average", "eval",
+                                                     "attention_modifiers_input_using_cond","attention_modifiers_input_using_uncond",
+                                                     "subtract_attention_modifiers_input_using_cond","subtract_attention_modifiers_input_using_uncond"],),
+                                "cond_exp_value": ("FLOAT", {"default": 2, "min": 0, "max": 100, "step": 0.1, "round": 0.01}),
+                                
+                                "uncond_exp": ("BOOLEAN", {"default": False}),
+                                "uncond_exp_normalize": ("BOOLEAN", {"default": False}),
+                                "uncond_exp_sigma_start": ("FLOAT", {"default": 1000,  "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "uncond_exp_sigma_end":   ("FLOAT", {"default": 1,   "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "uncond_exp_method": (["amplify", "root", "power", "erf", "erf_amplify", "exp_erf", "root_erf", "sine", "sine_exp", "sine_exp_diff", "sine_exp_diff_to_sine", "sine_root", "sine_root_diff", "sine_root_diff_to_sine", "theDaRkNeSs", "cosine", "sign", "zero", "previous_average", "eval",
+                                                       "subtract_attention_modifiers_input_using_cond","subtract_attention_modifiers_input_using_uncond"],),
+                                "uncond_exp_value": ("FLOAT", {"default": 2, "min": 0, "max": 100, "step": 0.1, "round": 0.01}),
+
+                                "fake_uncond_exp": ("BOOLEAN", {"default": False}),
+                                "fake_uncond_exp_normalize": ("BOOLEAN", {"default": False}),
+                                "fake_uncond_exp_method" : (["cond_pred", "previous_average",
+                                                             "amplify", "root", "power", "erf", "erf_amplify", "exp_erf", "root_erf", "sine", "sine_exp", "sine_exp_diff", "sine_exp_diff_to_sine", "sine_root", "sine_root_diff",
+                                                             "sine_root_diff_to_sine", "theDaRkNeSs", "cosine", "sign", "zero", "eval",
+                                                             "subtract_attention_modifiers_input_using_cond","subtract_attention_modifiers_input_using_uncond",
+                                                             "attention_modifiers_input_using_cond","attention_modifiers_input_using_uncond"],),
+                                "fake_uncond_exp_value": ("FLOAT", {"default": 2, "min": 0, "max": 1000, "step": 0.1, "round": 0.01}),
+                                "fake_uncond_multiplier": ("INT", {"default": 1, "min": -1, "max": 1, "step": 1}),
+                                "fake_uncond_sigma_start": ("FLOAT", {"default": 1000,  "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "fake_uncond_sigma_end": ("FLOAT", {"default": 1,  "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "auto_cfg_topk":    ("FLOAT", {"default": 0.25, "min": 0.0, "max": 0.5, "step": 0.05, "round": 0.01}),
+                                "auto_cfg_ref":    ("FLOAT", {"default": 8, "min": 0.0, "max": 100, "step": 0.5, "round": 0.01}),
+                                "attention_modifiers_global_enabled": ("BOOLEAN", {"default": False}),
+                                "disable_cond": ("BOOLEAN", {"default": False}),
+                                "disable_cond_sigma_start": ("FLOAT", {"default": 1000, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "disable_cond_sigma_end":   ("FLOAT", {"default": 0, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "save_as_preset": ("BOOLEAN", {"default": False}),
+                                "preset_name": ("STRING", {"multiline": False}),
+                              },
+                              "optional":{
+                                  "eval_string_cond": ("STRING", {"multiline": True}),
+                                  "eval_string_uncond": ("STRING", {"multiline": True}),
+                                  "eval_string_fake": ("STRING", {"multiline": True}),
+                                  "args_filter": ("STRING", {"multiline": True, "forceInput": True}),
+                                  "attention_modifiers_positive": ("ATTNMOD", {"forceInput": True}),
+                                  "attention_modifiers_negative": ("ATTNMOD", {"forceInput": True}),
+                                  "attention_modifiers_fake_negative": ("ATTNMOD", {"forceInput": True}),
+                                  "attention_modifiers_global": ("ATTNMOD", {"forceInput": True}),
+                              }
+                              }
+    RETURN_TYPES = ("MODEL","STRING",)
+    FUNCTION = "patch"
+
+    CATEGORY = "model_patches/Automatic_CFG"
+
+    def patch(self, model, automatic_cfg = "None",
+              skip_uncond = False, fake_uncond_start = False, uncond_sigma_start = 1000, uncond_sigma_end = 0,
+              lerp_uncond = False, lerp_uncond_strength = 1, lerp_uncond_sigma_start = 1000, lerp_uncond_sigma_end = 1,
+              subtract_latent_mean     = False,   subtract_latent_mean_sigma_start      = 1000, subtract_latent_mean_sigma_end     = 1,
+              latent_intensity_rescale = False,   latent_intensity_rescale_sigma_start  = 1000, latent_intensity_rescale_sigma_end = 1,
+              cond_exp = False, cond_exp_sigma_start  = 1000, cond_exp_sigma_end = 1000, cond_exp_method = "amplify", cond_exp_value = 2, cond_exp_normalize = False,
+              uncond_exp = False, uncond_exp_sigma_start  = 1000, uncond_exp_sigma_end = 1000, uncond_exp_method = "amplify", uncond_exp_value = 2, uncond_exp_normalize = False,
+              fake_uncond_exp = False, fake_uncond_exp_method = "amplify", fake_uncond_exp_value = 2, fake_uncond_exp_normalize = False, fake_uncond_multiplier = 1, fake_uncond_sigma_start = 1000, fake_uncond_sigma_end = 1,
+              latent_intensity_rescale_cfg = 8, latent_intensity_rescale_method = "hard",
+              ignore_pre_cfg_func = False, args_filter = "", auto_cfg_topk = 0.25, auto_cfg_ref = 8,
+              eval_string_cond = "", eval_string_uncond = "", eval_string_fake = "",
+              attention_modifiers_global_enabled = False,
+              attention_modifiers_positive = [], attention_modifiers_negative = [], attention_modifiers_fake_negative = [], attention_modifiers_global = [],
+              disable_cond=False, disable_cond_sigma_start=1000,disable_cond_sigma_end=1000, save_as_preset = False, preset_name = "", **kwargs
+              ):
+        
+        # support_function()
+        model_options_copy = deepcopy(model.model_options)
+        monkey_patching_comfy_sampling_function()
+        if args_filter != "":
+            args_filter = args_filter.split(",")
+        else:
+            args_filter = [k for k, v in locals().items()]
+        not_in_filter = ['self','model','args','args_filter','save_as_preset','preset_name','model_options_copy']
+        if fake_uncond_exp_method != "eval":
+            not_in_filter.append("eval_string")
+
+        if save_as_preset and preset_name != "":
+            preset_parameters = {key: value for key, value in locals().items() if key not in not_in_filter}
+            with open(os.path.join(json_preset_path, preset_name+".json"), 'w', encoding='utf-8') as f:
+                json.dump(preset_parameters, f)
+            print(f"Preset saved with the name: {Fore.GREEN}{preset_name}{Fore.RESET}")
+            print(f"{Fore.RED}Don't forget to turn the save toggle OFF to not overwrite!{Fore.RESET}")
+
+        args_str = '\n'.join(f'{k}: {v}' for k, v in locals().items() if k not in not_in_filter and k in args_filter)
+
+        sigmin, sigmax = get_sigmin_sigmax(model)
+
+        lerp_start, lerp_end          = lerp_uncond_sigma_start, lerp_uncond_sigma_end
+        subtract_start, subtract_end  = subtract_latent_mean_sigma_start, subtract_latent_mean_sigma_end
+        rescale_start, rescale_end    = latent_intensity_rescale_sigma_start, latent_intensity_rescale_sigma_end
+        print(f"Model maximum sigma: {sigmax} / Model minimum sigma: {sigmin}")
+        m = model.clone()
+
+        if skip_uncond or disable_cond:
+            # set model_options sampler_pre_cfg_function
+            m.model_options["sampler_pre_cfg_function"] = make_sampler_pre_cfg_function(uncond_sigma_end if skip_uncond else 0, uncond_sigma_start if skip_uncond else 100000,\
+                                                                                        disable_cond_sigma_start if disable_cond else 100000, disable_cond_sigma_end if disable_cond else 100000)
+            print(f"Sampling function patched. Uncond enabled from {round(uncond_sigma_start,2)} to {round(uncond_sigma_end,2)}")
+        elif not ignore_pre_cfg_func:
+            m.model_options.pop("sampler_pre_cfg_function", None)
+            uncond_sigma_start, uncond_sigma_end = 1000000, 0
+
+        top_k = auto_cfg_topk
+        previous_cond_pred = None
+        previous_sigma = None
+        def automatic_cfg_function(args):
+            nonlocal previous_sigma
+            cond_scale = args["cond_scale"]
+            input_x = args["input"]
+            cond_pred = args["cond_denoised"]
+            uncond_pred = args["uncond_denoised"]
+            sigma = args["sigma"][0]
+            model_options = args["model_options"]
+            if self.previous_cond_pred is None:
+                self.previous_cond_pred = cond_pred.clone().detach().to(device=cond_pred.device)
+            if previous_sigma is None:
+                previous_sigma = sigma.item()
+            reference_cfg = auto_cfg_ref if auto_cfg_ref > 0 else cond_scale
+
+            def fake_uncond_step():
+                return fake_uncond_start and skip_uncond and (sigma > uncond_sigma_start or sigma < uncond_sigma_end) and sigma <= fake_uncond_sigma_start and sigma >= fake_uncond_sigma_end
+
+            if fake_uncond_step():
+                uncond_pred = cond_pred.clone().detach().to(device=cond_pred.device) * fake_uncond_multiplier
+
+            if cond_exp and sigma <= cond_exp_sigma_start and sigma >= cond_exp_sigma_end:
+                cond_pred = experimental_functions(cond_pred, cond_exp_method, cond_exp_value, cond_exp_normalize, self.previous_cond_pred, previous_sigma, sigma.item(), sigmax, attention_modifiers_positive, args, model_options_copy, eval_string_cond)
+            if uncond_exp and sigma <= uncond_exp_sigma_start and sigma >= uncond_exp_sigma_end and not fake_uncond_step():
+                uncond_pred = experimental_functions(uncond_pred, uncond_exp_method, uncond_exp_value, uncond_exp_normalize, self.previous_cond_pred, previous_sigma, sigma.item(), sigmax, attention_modifiers_negative, args, model_options_copy, eval_string_uncond)
+            if fake_uncond_step() and fake_uncond_exp:
+                uncond_pred = experimental_functions(uncond_pred, fake_uncond_exp_method, fake_uncond_exp_value, fake_uncond_exp_normalize, self.previous_cond_pred, previous_sigma, sigma.item(), sigmax, attention_modifiers_fake_negative, args, model_options_copy, eval_string_fake)
+            self.previous_cond_pred = cond_pred.clone().detach().to(device=cond_pred.device)
+
+            if sigma >= sigmax or cond_scale > 1:
+                self.last_cfg_ht_one = cond_scale
+            target_intensity = self.last_cfg_ht_one / 10
+
+            if ((check_skip(sigma, uncond_sigma_start, uncond_sigma_end) and skip_uncond) and not fake_uncond_step()) or cond_scale == 1:
+                return input_x - cond_pred
+
+            if lerp_uncond and not check_skip(sigma, lerp_start, lerp_end) and lerp_uncond_strength != 1:
+                uncond_pred_norm = uncond_pred.norm()
+                uncond_pred = torch.lerp(cond_pred, uncond_pred, lerp_uncond_strength)
+                uncond_pred = uncond_pred * uncond_pred_norm / uncond_pred.norm()
+            cond   = input_x - cond_pred
+            uncond = input_x - uncond_pred
+
+            if automatic_cfg == "None":
+                return uncond + cond_scale * (cond - uncond)
+
+            denoised_tmp = input_x - (uncond + reference_cfg * (cond - uncond))
+
+            for b in range(len(denoised_tmp)):
+                denoised_ranges = get_denoised_ranges(denoised_tmp[b], automatic_cfg, top_k)
+                for c in range(len(denoised_tmp[b])):
+                    fixeds_scale = reference_cfg * target_intensity / denoised_ranges[c]
+                    denoised_tmp[b][c] = uncond[b][c] + fixeds_scale * (cond[b][c] - uncond[b][c])
+
+            return denoised_tmp
+
+        def center_mean_latent_post_cfg(args):
+            denoised = args["denoised"]
+            sigma    = args["sigma"][0]
+            if check_skip(sigma, subtract_start, subtract_end):
+                return denoised
+            denoised = center_latent_mean_values(denoised, False, 1)
+            return denoised
+
+        def rescale_post_cfg(args):
+            denoised   = args["denoised"]
+            sigma      = args["sigma"][0]
+
+            if check_skip(sigma, rescale_start, rescale_end):
+                return denoised
+            target_intensity = latent_intensity_rescale_cfg / 10
+            for b in range(len(denoised)):
+                denoised_ranges = get_denoised_ranges(denoised[b], latent_intensity_rescale_method)
+                for c in range(len(denoised[b])):
+                    scale_correction = target_intensity / denoised_ranges[c]
+                    denoised[b][c]   = denoised[b][c] * scale_correction
+            return denoised
+        
+        tmp_model_options = deepcopy(m.model_options)
+        if attention_modifiers_global_enabled:
+            # print(f"{Fore.GREEN}Sigma timings are ignored for global modifiers.{Fore.RESET}")
+            for atm in attention_modifiers_global:
+                block_layers = {"input": atm['unet_block_id_input'], "middle": atm['unet_block_id_middle'], "output": atm['unet_block_id_output']}
+                for unet_block in block_layers:
+                    for unet_block_id in block_layers[unet_block].split(","):
+                        if unet_block_id != "":
+                            unet_block_id = int(unet_block_id)
+                            tmp_model_options = set_model_options_patch_replace(tmp_model_options, attention_modifier(atm['self_attn_mod_eval']).modified_attention, atm['unet_attn'], unet_block, unet_block_id)
+            m.model_options = tmp_model_options
+
+        if not ignore_pre_cfg_func:
+            m.set_model_sampler_cfg_function(automatic_cfg_function, disable_cfg1_optimization = False)
+        if subtract_latent_mean:
+            m.set_model_sampler_post_cfg_function(center_mean_latent_post_cfg)
+        if latent_intensity_rescale:
+            m.set_model_sampler_post_cfg_function(rescale_post_cfg)
+        return (m, args_str, )
+
+class attentionModifierParametersNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                                "sigma_start": ("FLOAT", {"default": 1000, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "sigma_end":   ("FLOAT", {"default":  0, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "self_attn_mod_eval":   ("STRING", {"multiline": True }, {"default": ""}),
+                                "unet_block_id_input":  ("STRING", {"multiline": False}, {"default": ""}),
+                                "unet_block_id_middle": ("STRING", {"multiline": False}, {"default": ""}),
+                                "unet_block_id_output": ("STRING", {"multiline": False}, {"default": ""}),
+                                "unet_attn": (["attn1","attn2","both"],),
+                              },
+                              "optional":{
+                                  "join_parameters": ("ATTNMOD", {"forceInput": True}),
+                              }}
+    
+    RETURN_TYPES = ("ATTNMOD","STRING",)
+    RETURN_NAMES = ("Attention modifier", "Parameters as string")
+    FUNCTION = "exec"
+    CATEGORY = "model_patches/Automatic_CFG/experimental_attention_modifiers"
+    def exec(self, join_parameters=None, **kwargs):
+        info_string = "\n".join([f"{k}: {v}" for k,v in kwargs.items() if v != ""])
+        if kwargs['unet_attn'] == "both":
+            copy_kwargs = kwargs.copy()
+            kwargs['unet_attn'] = "attn1"
+            copy_kwargs['unet_attn'] = "attn2"
+            out_modifiers = [kwargs, copy_kwargs]
+        else:
+            out_modifiers = [kwargs]
+        return (out_modifiers if join_parameters is None else join_parameters + out_modifiers, info_string, )
+
+class attentionModifierBruteforceParametersNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                                "sigma_start": ("FLOAT", {"default": 1000, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "sigma_end":   ("FLOAT", {"default":  0, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "self_attn_mod_eval":   ("STRING", {"multiline": True , "default": ""}),
+                                "unet_block_id_input":  ("STRING", {"multiline": False, "default": "4,5,7,8"}),
+                                "unet_block_id_middle": ("STRING", {"multiline": False, "default": "0"}),
+                                "unet_block_id_output": ("STRING", {"multiline": False, "default": "0,1,2,3,4,5"}),
+                                "unet_attn": (["attn1","attn2","both"],),
+                              },
+                              "optional":{
+                                  "join_parameters": ("ATTNMOD", {"forceInput": True}),
+                              }}
+    
+    RETURN_TYPES = ("ATTNMOD","STRING",)
+    RETURN_NAMES = ("Attention modifier", "Parameters as string")
+    FUNCTION = "exec"
+    CATEGORY = "model_patches/Automatic_CFG/experimental_attention_modifiers"
+
+    def create_sequence_parameters(self, input_str, middle_str, output_str):
+        input_values  = input_str.split(",")  if input_str  else []
+        middle_values = middle_str.split(",") if middle_str else []
+        output_values = output_str.split(",") if output_str else []
+        result = []
+        result.extend([{"unet_block_id_input": val, "unet_block_id_middle": "", "unet_block_id_output": ""} for val in input_values])
+        result.extend([{"unet_block_id_input": "", "unet_block_id_middle": val, "unet_block_id_output": ""} for val in middle_values])
+        result.extend([{"unet_block_id_input": "", "unet_block_id_middle": "", "unet_block_id_output": val} for val in output_values])
+        return result
+
+    def exec(self, seed, join_parameters=None, **kwargs):
+        sequence_parameters = self.create_sequence_parameters(kwargs['unet_block_id_input'],kwargs['unet_block_id_middle'],kwargs['unet_block_id_output'])
+        lenseq = len(sequence_parameters)
+        current_index = seed % lenseq
+        current_sequence = sequence_parameters[current_index]
+        kwargs["unet_block_id_input"]  = current_sequence["unet_block_id_input"]
+        kwargs["unet_block_id_middle"] = current_sequence["unet_block_id_middle"]
+        kwargs["unet_block_id_output"] = current_sequence["unet_block_id_output"]
+        if current_sequence["unet_block_id_input"] != "":
+            current_block_string = f"unet_block_id_input: {current_sequence['unet_block_id_input']}"
+        elif current_sequence["unet_block_id_middle"] != "":
+            current_block_string = f"unet_block_id_middle: {current_sequence['unet_block_id_middle']}"
+        elif current_sequence["unet_block_id_output"] != "":
+            current_block_string = f"unet_block_id_output: {current_sequence['unet_block_id_output']}"
+        info_string = f"Progress: {current_index+1}/{lenseq}\n{kwargs['self_attn_mod_eval']}\n{kwargs['unet_attn']} {current_block_string}"
+        if kwargs['unet_attn'] == "both":
+            copy_kwargs = kwargs.copy()
+            kwargs['unet_attn'] = "attn1"
+            copy_kwargs['unet_attn'] = "attn2"
+            out_modifiers = [kwargs, copy_kwargs]
+        else:
+            out_modifiers = [kwargs]
+        return (out_modifiers if join_parameters is None else join_parameters + out_modifiers, info_string, )
+    
+class attentionModifierConcatNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                                "parameters_1": ("ATTNMOD", {"forceInput": True}),
+                                "parameters_2": ("ATTNMOD", {"forceInput": True}),
+                              }}
+    
+    RETURN_TYPES = ("ATTNMOD",)
+    FUNCTION = "exec"
+    CATEGORY = "model_patches/Automatic_CFG/experimental_attention_modifiers"
+    def exec(self, parameters_1, parameters_2):
+        output_parms = parameters_1 + parameters_2
+        return (output_parms, )
+
+class simpleDynamicCFG:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                                "model": ("MODEL",),
+                                "hard_mode" : ("BOOLEAN", {"default": True}),
+                                "boost" : ("BOOLEAN", {"default": True}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "model_patches/Automatic_CFG/presets"
+
+    def patch(self, model, hard_mode, boost):
+        advcfg = advancedDynamicCFG()
+        m = advcfg.patch(model,
+                         skip_uncond = boost,
+                         uncond_sigma_start = 1000,  uncond_sigma_end = 1,
+                         automatic_cfg = "hard" if hard_mode else "soft"
+                         )[0]
+        return (m, )
+
+class presetLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        presets_files = [pj.replace(".json","") for pj in os.listdir(json_preset_path) if ".json" in pj and pj not in ["Experimental_temperature.json","do_not_delete.json"]]
+        presets_files = sorted(presets_files, key=str.lower)
+        return {"required": {
+                                "model": ("MODEL",),
+                                "preset" : (presets_files, {"default": "Excellent_attention"}),
+                                "uncond_sigma_end":   ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "use_uncond_sigma_end_from_preset" : ("BOOLEAN", {"default": True}),
+                                "automatic_cfg" : (["From preset","None", "soft", "hard", "hard_squared", "range"],),
+                              },
+                              "optional":{
+                                  "join_global_parameters": ("ATTNMOD", {"forceInput": True}),
+                              }}
+    RETURN_TYPES = ("MODEL", "STRING", "STRING",)
+    RETURN_NAMES = ("Model", "Preset name", "Parameters as string",)
+    FUNCTION = "patch"
+
+    CATEGORY = "model_patches/Automatic_CFG"
+
+    def patch(self, model, preset, uncond_sigma_end, use_uncond_sigma_end_from_preset, automatic_cfg, join_global_parameters=None):
+        with open(os.path.join(json_preset_path, preset+".json"), 'r', encoding='utf-8') as f:
+            preset_args = json.load(f)
+        if not use_uncond_sigma_end_from_preset:
+            preset_args["uncond_sigma_end"] = uncond_sigma_end
+            preset_args["fake_uncond_sigma_end"] = uncond_sigma_end
+            preset_args["fake_uncond_exp_sigma_end"] = uncond_sigma_end
+            preset_args["uncond_exp_sigma_end"] = uncond_sigma_end            
+        
+        if join_global_parameters is not None:
+            preset_args["attention_modifiers_global"] = preset_args["attention_modifiers_global"] + join_global_parameters
+            preset_args["attention_modifiers_global_enabled"] = True
+
+        if automatic_cfg != "From preset":
+            preset_args["automatic_cfg"] = automatic_cfg
+        
+        advcfg = advancedDynamicCFG()
+        m = advcfg.patch(model, **preset_args)[0]
+        info_string = ",\n".join([f"\"{k}\": {v}" for k,v in preset_args.items() if v != ""])
+        print(f"Preset {Fore.GREEN}{preset}{Fore.RESET} loaded successfully!")
+        return (m, preset, info_string,)
+
+class simpleDynamicCFGlerpUncond:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                                "model": ("MODEL",),
+                                "boost" : ("BOOLEAN", {"default": True}),
+                                "negative_strength": ("FLOAT", {"default": 1, "min": 0.0, "max": 5.0, "step": 0.1, "round": 0.1}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "model_patches/Automatic_CFG/presets"
+
+    def patch(self, model, boost, negative_strength):
+        advcfg = advancedDynamicCFG()
+        m = advcfg.patch(model=model,
+                         automatic_cfg="hard", skip_uncond=boost,
+                         uncond_sigma_start = 15, uncond_sigma_end = 1,
+                         lerp_uncond=negative_strength != 1, lerp_uncond_strength=negative_strength,
+                         lerp_uncond_sigma_start = 15, lerp_uncond_sigma_end = 1
+                         )[0]
+        return (m, )
+
+class postCFGrescaleOnly:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                                "model": ("MODEL",),
+                                "subtract_latent_mean" : ("BOOLEAN", {"default": True}),
+                                "subtract_latent_mean_sigma_start": ("FLOAT", {"default": 1000,  "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.1}),
+                                "subtract_latent_mean_sigma_end":   ("FLOAT", {"default": 7.5, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.1}),
+                                "latent_intensity_rescale"     : ("BOOLEAN", {"default": True}),
+                                "latent_intensity_rescale_method" : (["soft","hard","range"], {"default": "hard"},),
+                                "latent_intensity_rescale_cfg" : ("FLOAT", {"default": 8,  "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.1}),
+                                "latent_intensity_rescale_sigma_start": ("FLOAT", {"default": 1000,  "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.1}),
+                                "latent_intensity_rescale_sigma_end":   ("FLOAT", {"default": 5, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.1}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "model_patches/Automatic_CFG/utils"
+
+    def patch(self, model,
+              subtract_latent_mean, subtract_latent_mean_sigma_start, subtract_latent_mean_sigma_end,
+              latent_intensity_rescale, latent_intensity_rescale_method, latent_intensity_rescale_cfg, latent_intensity_rescale_sigma_start, latent_intensity_rescale_sigma_end
+              ):
+        advcfg = advancedDynamicCFG()
+        m = advcfg.patch(model=model, 
+                         subtract_latent_mean = subtract_latent_mean,
+                         subtract_latent_mean_sigma_start = subtract_latent_mean_sigma_start, subtract_latent_mean_sigma_end = subtract_latent_mean_sigma_end,
+                         latent_intensity_rescale = latent_intensity_rescale, latent_intensity_rescale_cfg = latent_intensity_rescale_cfg, latent_intensity_rescale_method = latent_intensity_rescale_method,
+                         latent_intensity_rescale_sigma_start = latent_intensity_rescale_sigma_start, latent_intensity_rescale_sigma_end = latent_intensity_rescale_sigma_end,
+                         ignore_pre_cfg_func = True
+                         )[0]
+        return (m, )
+
+class simpleDynamicCFGHighSpeed:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                                "model": ("MODEL",),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "model_patches/Automatic_CFG/presets"
+
+    def patch(self, model):
+        advcfg = advancedDynamicCFG()
+        m = advcfg.patch(model=model, automatic_cfg = "hard",
+                         skip_uncond = True, uncond_sigma_start = 7.5, uncond_sigma_end = 1)[0]
+        return (m, )
+
+class simpleDynamicCFGwarpDrive:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                                "model": ("MODEL",),
+                                "uncond_sigma_start": ("FLOAT", {"default": 5.5, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "uncond_sigma_end":   ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "fake_uncond_sigma_end": ("FLOAT", {"default": 1.0,  "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "model_patches/Automatic_CFG/presets"
+
+    def patch(self, model, uncond_sigma_start, uncond_sigma_end, fake_uncond_sigma_end):
+        advcfg = advancedDynamicCFG()
+        print(f"                                                {Fore.CYAN}WARP DRIVE MODE ENGAGED!{Style.RESET_ALL}\n    Settings suggestions:\n"
+            f"              {Fore.GREEN}1/1/1:   {Fore.YELLOW}Maaaxxxiiimum speeeeeed.{Style.RESET_ALL} {Fore.RED}Uncond disabled.{Style.RESET_ALL} {Fore.MAGENTA}Fasten your seatbelt!{Style.RESET_ALL}\n"
+            f"              {Fore.GREEN}3/1/1:   {Fore.YELLOW}Risky space-time continuum distortion.{Style.RESET_ALL} {Fore.MAGENTA}Awesome for prompts with a clear subject!{Style.RESET_ALL}\n"
+            f"              {Fore.GREEN}5.5/1/1: {Fore.YELLOW}Frameshift Drive Autopilot: {Fore.GREEN}Engaged.{Style.RESET_ALL} {Fore.MAGENTA}Should work with anything but do it better and faster!{Style.RESET_ALL}")
+
+        m = advcfg.patch(model=model, automatic_cfg = "hard",
+                         skip_uncond = True, uncond_sigma_start = uncond_sigma_start, uncond_sigma_end = uncond_sigma_end,
+                         fake_uncond_sigma_end = fake_uncond_sigma_end, fake_uncond_sigma_start = 1000, fake_uncond_start=True,
+                         fake_uncond_exp=True,fake_uncond_exp_normalize=True,fake_uncond_exp_method="previous_average",
+                         cond_exp = False, cond_exp_sigma_start  = 9, cond_exp_sigma_end = uncond_sigma_start, cond_exp_method = "erf", cond_exp_normalize = True,
+                         )[0]
+        return (m, )
+
+class simpleDynamicCFGunpatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                                "model": ("MODEL",),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "unpatch"
+
+    CATEGORY = "model_patches/Automatic_CFG/utils"
+
+    def unpatch(self, model):
+        m = model.clone()
+        m.model_options.pop("sampler_pre_cfg_function", None)
+        return (m, )
+
+class simpleDynamicCFGExcellentattentionPatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        inputs = {"required": {
+                        "model": ("MODEL",),
+                        "Auto_CFG": ("BOOLEAN", {"default": True}),
+                        "patch_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 1.0, "round": 0.01}),
+                        "patch_cond":   ("BOOLEAN", {"default": True}),
+                        "patch_uncond": ("BOOLEAN", {"default": True}),
+                        "light_patch":  ("BOOLEAN", {"default": False}),
+                        "mute_self_input_layer_8_cond":    ("BOOLEAN", {"default": False}),
+                        "mute_cross_input_layer_8_cond":   ("BOOLEAN", {"default": False}),
+                        "mute_self_input_layer_8_uncond":  ("BOOLEAN", {"default": True}),
+                        "mute_cross_input_layer_8_uncond": ("BOOLEAN", {"default": False}),
+                        "uncond_sigma_end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                        "bypass_layer_8_instead_of_mute": ("BOOLEAN", {"default": False}),
+                        "save_as_preset": ("BOOLEAN", {"default": False}),
+                        "preset_name":    ("STRING",  {"multiline": False}),
+                        },
+                        "optional":{
+                                "attn_mod_for_positive_operation": ("ATTNMOD", {"forceInput": True}),
+                                "attn_mod_for_negative_operation": ("ATTNMOD", {"forceInput": True}),
+                        },
+                    }
+        if "dev_env.txt" in os.listdir(current_dir):
+            inputs['optional'].update({"attn_mod_for_global_operation": ("ATTNMOD", {"forceInput": True})})
+        return inputs
+    
+    RETURN_TYPES = ("MODEL","STRING",)
+    RETURN_NAMES = ("Model", "Parameters as string",)
+    FUNCTION = "patch"
+
+    CATEGORY = "model_patches/Automatic_CFG"
+
+    def patch(self, model, Auto_CFG, patch_multiplier, patch_cond, patch_uncond, light_patch,
+              mute_self_input_layer_8_cond, mute_cross_input_layer_8_cond,
+              mute_self_input_layer_8_uncond, mute_cross_input_layer_8_uncond,
+              uncond_sigma_end,bypass_layer_8_instead_of_mute, save_as_preset, preset_name,
+              attn_mod_for_positive_operation = None, attn_mod_for_negative_operation = None, attn_mod_for_global_operation = None):
+        
+        parameters_as_string = "Excellent attention:\n" + "\n".join([f"{k}: {v}" for k, v in locals().items() if k not in ["self", "model"]])
+        
+        with open(os.path.join(json_preset_path, "Excellent_attention.json"), 'r', encoding='utf-8') as f:
+            patch_parameters = json.load(f)
+
+        attn_patch = {"sigma_start": 1000, "sigma_end": 0,
+                       "self_attn_mod_eval": f"normalize_tensor(q+(q-attention_basic(attnbc, k, v, extra_options['n_heads'])))*attnbc.norm()*{patch_multiplier}",
+                       "unet_block_id_input": "", "unet_block_id_middle": "0", "unet_block_id_output": "", "unet_attn": "attn2"}
+        attn_patch_light = {"sigma_start": 1000, "sigma_end": 0,
+                       "self_attn_mod_eval": f"q*{patch_multiplier}",
+                       "unet_block_id_input": "", "unet_block_id_middle": "0", "unet_block_id_output": "", "unet_attn": "attn2"}
+
+        kill_self_input_8 = {
+            "sigma_start": 1000,
+            "sigma_end": 0,
+            "self_attn_mod_eval": "q" if bypass_layer_8_instead_of_mute else "torch.zeros_like(q)",
+            "unet_block_id_input": "8",
+            "unet_block_id_middle": "",
+            "unet_block_id_output": "",
+            "unet_attn": "attn1"}
+        
+        kill_cross_input_8 = kill_self_input_8.copy()
+        kill_cross_input_8['unet_attn'] = "attn2"
+        
+        attention_modifiers_positive = []
+        attention_modifiers_fake_negative = []
+
+        if patch_cond: attention_modifiers_positive.append(attn_patch) if not light_patch else attention_modifiers_positive.append(attn_patch_light)
+        if mute_self_input_layer_8_cond:  attention_modifiers_positive.append(kill_self_input_8)
+        if mute_cross_input_layer_8_cond: attention_modifiers_positive.append(kill_cross_input_8)
+
+        if patch_uncond: attention_modifiers_fake_negative.append(attn_patch) if not light_patch else attention_modifiers_fake_negative.append(attn_patch_light)
+        if mute_self_input_layer_8_uncond:  attention_modifiers_fake_negative.append(kill_self_input_8)
+        if mute_cross_input_layer_8_uncond: attention_modifiers_fake_negative.append(kill_cross_input_8)
+
+        patch_parameters['attention_modifiers_positive']      = attention_modifiers_positive
+        patch_parameters['attention_modifiers_fake_negative'] = attention_modifiers_fake_negative
+
+        if attn_mod_for_positive_operation is not None:
+            patch_parameters['attention_modifiers_positive'] = patch_parameters['attention_modifiers_positive'] + attn_mod_for_positive_operation
+        if attn_mod_for_negative_operation is not None:
+            patch_parameters['attention_modifiers_fake_negative'] = patch_parameters['attention_modifiers_fake_negative'] + attn_mod_for_negative_operation
+        if attn_mod_for_global_operation is not None:
+            patch_parameters["attention_modifiers_global_enabled"] = True
+            patch_parameters['attention_modifiers_global'] = attn_mod_for_global_operation
+
+        patch_parameters["uncond_sigma_end"]      = uncond_sigma_end
+        patch_parameters["fake_uncond_sigma_end"] = uncond_sigma_end
+        patch_parameters["automatic_cfg"] = "hard" if Auto_CFG else "None"
+
+        if save_as_preset:
+            patch_parameters["save_as_preset"] = save_as_preset
+            patch_parameters["preset_name"] = preset_name
+
+        advcfg = advancedDynamicCFG()
+        m = advcfg.patch(model, **patch_parameters)[0]
+        
+        return (m, parameters_as_string, )
+    
+class simpleDynamicCFGCustomAttentionPatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                                "model": ("MODEL",),
+                                "Auto_CFG": ("BOOLEAN", {"default": True}),
+                                "cond_mode" :   (["replace_by_custom","normal+(normal-custom_cond)*multiplier","normal+(normal-custom_uncond)*multiplier"],),
+                                "uncond_mode" : (["replace_by_custom","normal+(normal-custom_cond)*multiplier","normal+(normal-custom_uncond)*multiplier"],),
+                                "cond_diff_multiplier":   ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.1, "round": 0.01}),
+                                "uncond_diff_multiplier": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.1, "round": 0.01}),
+                                "uncond_sigma_end":       ("FLOAT", {"default": 1.0, "min":    0.0, "max": 10000, "step": 0.1, "round": 0.01}),
+                                "save_as_preset": ("BOOLEAN", {"default": False}),
+                                "preset_name":    ("STRING",  {"multiline": False}),
+                              },
+                              "optional":{
+                                  "attn_mod_for_positive_operation": ("ATTNMOD", {"forceInput": True}),
+                                  "attn_mod_for_negative_operation": ("ATTNMOD", {"forceInput": True}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("Model",)
+    FUNCTION = "patch"
+
+    CATEGORY = "model_patches/Automatic_CFG/experimental_attention_modifiers"
+
+    def patch(self, model, Auto_CFG, cond_mode, uncond_mode, cond_diff_multiplier, uncond_diff_multiplier, uncond_sigma_end, save_as_preset, preset_name,
+              attn_mod_for_positive_operation = [], attn_mod_for_negative_operation = []):
+                
+        with open(os.path.join(json_preset_path, "do_not_delete.json"), 'r', encoding='utf-8') as f:
+            patch_parameters = json.load(f)
+        
+        patch_parameters["cond_exp_value"]   = cond_diff_multiplier
+        patch_parameters["uncond_exp_value"] = uncond_diff_multiplier
+
+        if cond_mode != "replace_by_custom":
+            patch_parameters["disable_cond"] = False
+        if cond_mode == "normal+(normal-custom_cond)*multiplier":
+            patch_parameters["cond_exp_method"] = "subtract_attention_modifiers_input_using_cond"
+        elif cond_mode == "normal+(normal-custom_uncond)*multiplier":
+            patch_parameters["cond_exp_method"] = "subtract_attention_modifiers_input_using_uncond"
+
+        if uncond_mode != "replace_by_custom":
+            patch_parameters["uncond_sigma_start"] = 1000.0
+            patch_parameters["fake_uncond_exp"]    = False
+            patch_parameters["uncond_exp"]         = True
+        
+        if uncond_mode == "normal+(normal-custom_cond)*multiplier":
+            patch_parameters["uncond_exp_method"] = "subtract_attention_modifiers_input_using_cond"
+        elif uncond_mode == "normal+(normal-custom_uncond)*multiplier":
+            patch_parameters["uncond_exp_method"] = "subtract_attention_modifiers_input_using_uncond"
+
+        if cond_mode != "replace_by_custom" and attn_mod_for_positive_operation != []:
+            smallest_sigma = min([float(x['sigma_end']) for x in attn_mod_for_positive_operation])
+            patch_parameters["disable_cond_sigma_end"] = smallest_sigma
+            patch_parameters["cond_exp_sigma_end"]     = smallest_sigma
+
+        if uncond_mode != "replace_by_custom" and attn_mod_for_negative_operation != []:
+            smallest_sigma = min([float(x['sigma_end'])   for x in attn_mod_for_negative_operation])
+            patch_parameters["uncond_exp_sigma_end"] = smallest_sigma
+            patch_parameters["fake_uncond_start"]    = False
+        # else:
+        #     biggest_sigma = max([float(x['sigma_start']) for x in attn_mod_for_negative_operation])
+        #     patch_parameters["fake_uncond_sigma_start"] = biggest_sigma
+
+        patch_parameters["automatic_cfg"] = "hard" if Auto_CFG else "None"
+        patch_parameters['attention_modifiers_positive']      = attn_mod_for_positive_operation
+        patch_parameters['attention_modifiers_negative']      = attn_mod_for_negative_operation
+        patch_parameters['attention_modifiers_fake_negative'] = attn_mod_for_negative_operation
+        patch_parameters["uncond_sigma_end"]      = uncond_sigma_end
+        patch_parameters["fake_uncond_sigma_end"] = uncond_sigma_end
+        patch_parameters["save_as_preset"] = save_as_preset
+        patch_parameters["preset_name"]    = preset_name
+        
+        advcfg = advancedDynamicCFG()
+        m = advcfg.patch(model, **patch_parameters)[0]
+        
+        return (m, )
+
+
+
+
+class attentionModifierSingleLayerBypassNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                                "sigma_start": ("FLOAT", {"default": 1000, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "sigma_end":   ("FLOAT", {"default":  0, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "block_name":  (["input","middle","output"],),
+                                "block_number": ("INT", {"default": 0, "min": 0, "max": 12, "step": 1}),
+                                "unet_attn": (["attn1","attn2","both"],),
+                              },
+                              "optional":{
+                                  "join_parameters": ("ATTNMOD", {"forceInput": True}),
+                              }}
+    
+    RETURN_TYPES = ("ATTNMOD","STRING",)
+    RETURN_NAMES = ("Attention modifier", "Parameters as string")
+    FUNCTION = "exec"
+    CATEGORY = "model_patches/Automatic_CFG/experimental_attention_modifiers"
+
+    def exec(self, sigma_start, sigma_end, block_name, block_number, unet_attn, join_parameters=None):
+        attn_modifier_dict = {
+        "sigma_start": sigma_start, "sigma_end": sigma_end,
+         "self_attn_mod_eval": "q",
+         "unet_block_id_input":  str(block_number) if block_name == "input" else "",
+         "unet_block_id_middle": str(block_number) if block_name == "middle" else "",
+         "unet_block_id_output": str(block_number) if block_name == "output" else "",
+         "unet_attn": f"{unet_attn}"
+         }
+
+        info_string = "\n".join([f"{k}: {v}" for k,v in attn_modifier_dict.items() if v != ""])
+
+        if unet_attn == "both":
+            attn_modifier_dict['unet_attn'] = "attn1"
+            copy_attn_modifier_dict = attn_modifier_dict.copy()
+            copy_attn_modifier_dict['unet_attn'] = "attn2"
+            out_modifiers = [attn_modifier_dict, copy_attn_modifier_dict]
+        else:
+            out_modifiers = [attn_modifier_dict]
+
+        return (out_modifiers if join_parameters is None else join_parameters + out_modifiers, info_string, )
+
+class attentionModifierSingleLayerTemperatureNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                                "sigma_start": ("FLOAT", {"default": 1000, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "sigma_end":   ("FLOAT", {"default":  0, "min": 0.0, "max": 10000.0, "step": 0.1, "round": 0.01}),
+                                "block_name":  (["input","middle","output"],),
+                                "block_number": ("INT", {"default": 0, "min": 0, "max": 12, "step": 1}),
+                                "unet_attn": (["attn1","attn2","both"],),
+                                "temperature": ("FLOAT", {"default":  1, "min": 0.0, "max": 10000.0, "step": 0.01, "round": 0.01}),
+                              },
+                              "optional":{
+                                  "join_parameters": ("ATTNMOD", {"forceInput": True}),
+                              }}
+    
+    RETURN_TYPES = ("ATTNMOD","STRING",)
+    RETURN_NAMES = ("Attention modifier", "Parameters as string")
+    FUNCTION = "exec"
+    CATEGORY = "model_patches/Automatic_CFG/experimental_attention_modifiers"
+
+    def exec(self, sigma_start, sigma_end, block_name, block_number, unet_attn, temperature, join_parameters=None):
+        attn_modifier_dict = {
+        "sigma_start": sigma_start, "sigma_end": sigma_end,
+         "self_attn_mod_eval": f"temperature_patcher({temperature}).attention_basic_with_temperature(q, k, v, extra_options)",
+         "unet_block_id_input":  str(block_number) if block_name == "input" else "",
+         "unet_block_id_middle": str(block_number) if block_name == "middle" else "",
+         "unet_block_id_output": str(block_number) if block_name == "output" else "",
+         "unet_attn": f"{unet_attn}"
+         }
+
+        info_string = "\n".join([f"{k}: {v}" for k,v in attn_modifier_dict.items() if v != ""])
+
+        if unet_attn == "both":
+            attn_modifier_dict['unet_attn'] = "attn1"
+            copy_attn_modifier_dict = attn_modifier_dict.copy()
+            copy_attn_modifier_dict['unet_attn'] = "attn2"
+            out_modifiers = [attn_modifier_dict, copy_attn_modifier_dict]
+        else:
+            out_modifiers = [attn_modifier_dict]
+
+        return (out_modifiers if join_parameters is None else join_parameters + out_modifiers, info_string, )
+
+class uncondZeroNode:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                                "model": ("MODEL",),
+                                "scale": ("FLOAT", {"default": 1.2, "min": 0.0, "max": 10.0, "step": 0.01, "round": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "model_patches/Automatic_CFG"
+
+    def patch(self, model, scale):
+        def custom_patch(args):
+            cond_pred = args["cond_denoised"]
+            input_x = args["input"]
+            if args["sigma"][0] <= 1:
+                return input_x - cond_pred
+            cond   = input_x - cond_pred
+            uncond = input_x - torch.zeros_like(cond)
+            return uncond + scale * (cond - uncond)
+
+        m = model.clone()
+        m.set_model_sampler_cfg_function(custom_patch)
+        return (m, )

extensions/ComfyUI-AutomaticCFG/nodes_sag_custom.py

@@ -0,0 +1,190 @@
+import torch
+from torch import einsum
+import torch.nn.functional as F
+import math
+
+from einops import rearrange, repeat
+import os
+from comfy.ldm.modules.attention import optimized_attention, _ATTN_PRECISION
+import comfy.samplers
+
+# from comfy/ldm/modules/attention.py
+# but modified to return attention scores as well as output
+def attention_basic_with_sim(q, k, v, heads, mask=None):
+    b, _, dim_head = q.shape
+    dim_head //= heads
+    scale = dim_head ** -0.5
+
+    h = heads
+    q, k, v = map(
+        lambda t: t.unsqueeze(3)
+        .reshape(b, -1, heads, dim_head)
+        .permute(0, 2, 1, 3)
+        .reshape(b * heads, -1, dim_head)
+        .contiguous(),
+        (q, k, v),
+    )
+
+    # force cast to fp32 to avoid overflowing
+    if _ATTN_PRECISION =="fp32":
+        sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale
+    else:
+        sim = einsum('b i d, b j d -> b i j', q, k) * scale
+
+    del q, k
+
+    if mask is not None:
+        mask = rearrange(mask, 'b ... -> b (...)')
+        max_neg_value = -torch.finfo(sim.dtype).max
+        mask = repeat(mask, 'b j -> (b h) () j', h=h)
+        sim.masked_fill_(~mask, max_neg_value)
+
+    # attention, what we cannot get enough of
+    sim = sim.softmax(dim=-1)
+
+    out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v)
+    out = (
+        out.unsqueeze(0)
+        .reshape(b, heads, -1, dim_head)
+        .permute(0, 2, 1, 3)
+        .reshape(b, -1, heads * dim_head)
+    )
+    return (out, sim)
+
+def create_blur_map(x0, attn, sigma=3.0, threshold=1.0):
+    # reshape and GAP the attention map
+    _, hw1, hw2 = attn.shape
+    b, _, lh, lw = x0.shape
+    attn = attn.reshape(b, -1, hw1, hw2)
+    # Global Average Pool
+    mask = attn.mean(1, keepdim=False).sum(1, keepdim=False) > threshold
+    ratio = 2**(math.ceil(math.sqrt(lh * lw / hw1)) - 1).bit_length()
+    mid_shape = [math.ceil(lh / ratio), math.ceil(lw / ratio)]
+
+    # Reshape
+    mask = (
+        mask.reshape(b, *mid_shape)
+        .unsqueeze(1)
+        .type(attn.dtype)
+    )
+    # Upsample
+    mask = F.interpolate(mask, (lh, lw))
+
+    blurred = gaussian_blur_2d(x0, kernel_size=9, sigma=sigma)
+    blurred = blurred * mask + x0 * (1 - mask)
+    return blurred
+
+def gaussian_blur_2d(img, kernel_size, sigma):
+    ksize_half = (kernel_size - 1) * 0.5
+
+    x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)
+
+    pdf = torch.exp(-0.5 * (x / sigma).pow(2))
+
+    x_kernel = pdf / pdf.sum()
+    x_kernel = x_kernel.to(device=img.device, dtype=img.dtype)
+
+    kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :])
+    kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1])
+
+    padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2]
+
+    img = F.pad(img, padding, mode="reflect")
+    img = F.conv2d(img, kernel2d, groups=img.shape[-3])
+    return img
+
+def get_denoised_ranges(latent, measure="hard", top_k=0.25):
+    chans = []
+    for x in range(len(latent)):
+        max_values = torch.topk(latent[x] - latent[x].mean() if measure == "range" else latent[x], k=int(len(latent[x])*top_k), largest=True).values
+        min_values = torch.topk(latent[x] - latent[x].mean() if measure == "range" else latent[x], k=int(len(latent[x])*top_k), largest=False).values
+        max_val = torch.mean(max_values).item()
+        min_val = torch.mean(torch.abs(min_values)).item() if (measure == "hard" or measure == "range") else abs(torch.mean(min_values).item())
+        denoised_range = (max_val + min_val) / 2
+        chans.append(denoised_range)
+    return chans
+
+class SelfAttentionGuidanceCustom:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                             "scale": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 100.0, "step": 0.1}),
+                             "blur_sigma": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.1}),
+                             "sigma_start": ("FLOAT", {"default": 15.0, "min": 0.0, "max": 1000.0, "step": 0.1, "round": 0.1}),
+                             "sigma_end": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.1, "round": 0.1}),
+                             "auto_scale" : ("BOOLEAN", {"default": False}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "model_patches"
+
+    def patch(self, model, scale, blur_sigma, sigma_start, sigma_end, auto_scale):
+        m = model.clone()
+        
+        attn_scores = None
+
+        # TODO: make this work properly with chunked batches
+        #       currently, we can only save the attn from one UNet call
+        def attn_and_record(q, k, v, extra_options):
+            nonlocal attn_scores
+            # if uncond, save the attention scores
+            heads = extra_options["n_heads"]
+            cond_or_uncond = extra_options["cond_or_uncond"]
+            b = q.shape[0] // len(cond_or_uncond)
+            if 1 in cond_or_uncond:
+                uncond_index = cond_or_uncond.index(1)
+                # do the entire attention operation, but save the attention scores to attn_scores
+                (out, sim) = attention_basic_with_sim(q, k, v, heads=heads)
+                # when using a higher batch size, I BELIEVE the result batch dimension is [uc1, ... ucn, c1, ... cn]
+                n_slices = heads * b
+                attn_scores = sim[n_slices * uncond_index:n_slices * (uncond_index+1)]
+                return out
+            else:
+                return optimized_attention(q, k, v, heads=heads)
+
+        def post_cfg_function(args):
+            nonlocal attn_scores
+            uncond_attn = attn_scores
+
+            sag_scale = scale
+            sag_sigma = blur_sigma
+            sag_threshold = 1.0
+            model = args["model"]
+            uncond_pred = args["uncond_denoised"]
+            uncond = args["uncond"]
+            cfg_result = args["denoised"]
+            sigma = args["sigma"]
+            model_options = args["model_options"]
+            x = args["input"]
+            if uncond_pred is None or uncond is None or uncond_attn is None:
+                return cfg_result
+            if min(cfg_result.shape[2:]) <= 4: #skip when too small to add padding
+                return cfg_result
+            if sigma[0] > sigma_start or sigma[0] < sigma_end:
+                return cfg_result
+            # create the adversarially blurred image
+            degraded = create_blur_map(uncond_pred, uncond_attn, sag_sigma, sag_threshold)
+            degraded_noised = degraded + x - uncond_pred
+            # call into the UNet
+            (sag, _) = comfy.samplers.calc_cond_batch(model, [uncond, None], degraded_noised, sigma, model_options)
+            # comfy.samplers.calc_cond_uncond_batch(model, uncond, None, degraded_noised, sigma, model_options)
+
+            if auto_scale:
+                denoised_tmp = cfg_result + (degraded - sag) * 8
+                for b in range(len(denoised_tmp)):
+                    denoised_ranges = get_denoised_ranges(denoised_tmp[b])
+                    for c in range(len(denoised_tmp[b])):
+                        fixed_scale = (sag_scale / 10) / denoised_ranges[c]
+                        denoised_tmp[b][c] = cfg_result[b][c] + (degraded[b][c] - sag[b][c]) * fixed_scale
+                return denoised_tmp
+            
+            return cfg_result + (degraded - sag) * sag_scale
+
+        m.set_model_sampler_post_cfg_function(post_cfg_function, disable_cfg1_optimization=False)
+
+        # from diffusers:
+        # unet.mid_block.attentions[0].transformer_blocks[0].attn1.patch
+        m.set_model_attn1_replace(attn_and_record, "middle", 0, 0)
+
+        return (m, )

extensions/ComfyUI-AutomaticCFG/presets/A subtle touch.json

@@ -0,0 +1 @@
+{"lerp_uncond_sigma_start": 1000.0, "lerp_uncond_sigma_end": 1.0, "subtract_latent_mean": false, "subtract_latent_mean_sigma_start": 1000.0, "subtract_latent_mean_sigma_end": 1.0, "latent_intensity_rescale": false, "latent_intensity_rescale_sigma_start": 1000.0, "latent_intensity_rescale_sigma_end": 3.0, "ignore_pre_cfg_func": false, "auto_cfg_topk": 0.25, "attention_modifiers_global_enabled": true, "attention_modifiers_global": [{"sigma_start": 15, "sigma_end": 0, "self_attn_mod_eval": "q.sin()", "unet_block_id_input": "", "unet_block_id_middle": "0", "unet_block_id_output": "", "unet_attn": "attn2"}], "disable_cond": false, "disable_cond_sigma_start": 1000.0, "disable_cond_sigma_end": 0.0, "kwargs": {}, "model_options_copy": {"transformer_options": {}}, "attention_modifiers_fake_negative": [], "attention_modifiers_negative": [], "attention_modifiers_positive": [], "auto_cfg_ref": 8.0, "automatic_cfg": "hard", "cond_exp": false, "cond_exp_method": "attention_modifiers_input_using_cond", "cond_exp_normalize": false, "cond_exp_sigma_end": 0.0, "cond_exp_sigma_start": 1000.0, "cond_exp_value": 1.0, "eval_string_cond": "", "eval_string_fake": "", "eval_string_uncond": "", "fake_uncond_exp": false, "fake_uncond_exp_method": "attention_modifiers_input_using_uncond", "fake_uncond_exp_normalize": false, "fake_uncond_exp_value": 1.0, "fake_uncond_multiplier": 1, "fake_uncond_sigma_end": 1.0, "fake_uncond_sigma_start": 1000.0, "fake_uncond_start": true, "latent_intensity_rescale_cfg": 8.0, "latent_intensity_rescale_method": "hard", "lerp_uncond": false, "lerp_uncond_strength": 2.0, "not_in_filter": ["self", "model", "args", "args_filter", "save_as_preset", "preset_name", "eval_string"], "skip_uncond": true, "uncond_exp": false, "uncond_exp_method": "subtract_attention_modifiers_input_using_uncond", "uncond_exp_normalize": false, "uncond_exp_sigma_end": 0.0, "uncond_exp_sigma_start": 1000.0, "uncond_exp_value": 1.0, "uncond_sigma_end": 1.0, "uncond_sigma_start": 15.0}

extensions/ComfyUI-AutomaticCFG/presets/Crossed conds customized 1.json

@@ -0,0 +1 @@
+{"lerp_uncond_sigma_start": 15.0, "lerp_uncond_sigma_end": 1.0, "subtract_latent_mean": false, "subtract_latent_mean_sigma_start": 15.0, "subtract_latent_mean_sigma_end": 1.0, "latent_intensity_rescale": false, "latent_intensity_rescale_sigma_start": 15.0, "latent_intensity_rescale_sigma_end": 3.0, "ignore_pre_cfg_func": false, "auto_cfg_topk": 0.25, "attention_modifiers_global_enabled": true, "attention_modifiers_global": [], "disable_cond": false, "disable_cond_sigma_start": 1000.0, "disable_cond_sigma_end": 0.0, "kwargs": {}, "attention_modifiers_fake_negative": [{"sigma_start": 1000, "sigma_end": 0, "self_attn_mod_eval": "k", "unet_block_id_input": "7,8", "unet_block_id_middle": "", "unet_block_id_output": "0", "unet_attn": "attn1"}, {"sigma_start": 1000, "sigma_end": 0, "self_attn_mod_eval": "v", "unet_block_id_input": "4", "unet_block_id_middle": "0", "unet_block_id_output": "3", "unet_attn": "attn1"}, {"sigma_start": 1000, "sigma_end": 0, "self_attn_mod_eval": "torch.zeros_like(q)", "unet_block_id_input": "8", "unet_block_id_middle": "", "unet_block_id_output": "", "unet_attn": "attn2"}], "attention_modifiers_negative": [{"sigma_start": 1000, "sigma_end": 0, "self_attn_mod_eval": "k", "unet_block_id_input": "7,8", "unet_block_id_middle": "", "unet_block_id_output": "0", "unet_attn": "attn1"}, {"sigma_start": 1000, "sigma_end": 0, "self_attn_mod_eval": "v", "unet_block_id_input": "4", "unet_block_id_middle": "0", "unet_block_id_output": "3", "unet_attn": "attn1"}, {"sigma_start": 1000, "sigma_end": 0, "self_attn_mod_eval": "torch.zeros_like(q)", "unet_block_id_input": "8", "unet_block_id_middle": "", "unet_block_id_output": "", "unet_attn": "attn2"}], "attention_modifiers_positive": [{"sigma_start": 1000, "sigma_end": 0, "self_attn_mod_eval": "k", "unet_block_id_input": "7,8", "unet_block_id_middle": "", "unet_block_id_output": "0", "unet_attn": "attn1"}, {"sigma_start": 1000, "sigma_end": 0, "self_attn_mod_eval": "v", "unet_block_id_input": "4", "unet_block_id_middle": "0", "unet_block_id_output": "3", "unet_attn": "attn1"}, {"sigma_start": 1000, "sigma_end": 0, "self_attn_mod_eval": "torch.zeros_like(q)", "unet_block_id_input": "8", "unet_block_id_middle": "", "unet_block_id_output": "", "unet_attn": "attn2"}], "auto_cfg_ref": 8.0, "automatic_cfg": "hard", "cond_exp": true, "cond_exp_method": "subtract_attention_modifiers_input_using_uncond", "cond_exp_normalize": false, "cond_exp_sigma_end": 1.0, "cond_exp_sigma_start": 1000.0, "cond_exp_value": 0.3333333333333333, "eval_string_cond": "", "eval_string_fake": "", "eval_string_uncond": "", "fake_uncond_exp": false, "fake_uncond_exp_method": "cond_pred", "fake_uncond_exp_normalize": false, "fake_uncond_exp_value": 1.0, "fake_uncond_multiplier": 1, "fake_uncond_sigma_end": 0.0, "fake_uncond_sigma_start": 1000.0, "fake_uncond_start": false, "latent_intensity_rescale_cfg": 8.0, "latent_intensity_rescale_method": "hard", "lerp_uncond": false, "lerp_uncond_strength": 2.0, "not_in_filter": ["self", "model", "args", "args_filter", "save_as_preset", "preset_name", "model_options_copy", "eval_string"], "skip_uncond": true, "uncond_exp": true, "uncond_exp_method": "subtract_attention_modifiers_input_using_cond", "uncond_exp_normalize": false, "uncond_exp_sigma_end": 1.0, "uncond_exp_sigma_start": 1000.0, "uncond_exp_value": 0.3333333333333333, "uncond_sigma_end": 1.0, "uncond_sigma_start": 150.0}