Update nodes/SegformerNode.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

.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 }}

.tracking

@@ -0,0 +1,10 @@
+.github/workflows/publish.yml
+.gitignore
+README.md
+__init__.py
+nodes/ImageCaptioningNode.py
+nodes/ImageProcessingNode.py
+nodes/SegformerNode.py
+nodes/__init__.py
+pyproject.toml
+requirements.txt

README.md

@@ -25,65 +25,100 @@ ComfyUI-LexTools is a Python-based image processing and analysis toolkit that us
       - _Output_: Converted score.
 
    Additional nodes from [GitHub Pages](https://github.com/strimmlarn/ComfyUI-Strimmlarns-Aesthetic-Score/) - These have been modified to improve performance and add an option to store the model in RAM, which significantly reduces generation time:
-   - `CalculateAestheticScore`: An optimized version of the original, with an option to keep the model loaded in RAM. (No specific input or output detailed in the provided code)
-   - `AesthetlcScoreSorter`: Sorts the images by score. (No specific input or output detailed in the provided code)
-   - `AesteticModel`: Loads the aesthetic model. (No specific input or output detailed in the provided code)
-
+   - `CalculateAestheticScore`: An optimized version of the original, with an option to keep the model loaded in RAM.
+   - `AestheticScoreSorter`: Sorts the images by score.
+   - `AestheticModel`: Loads the aesthetic model.
 2. **ImageCaptioningNode.py** - Implements nodes for image captioning and classification:
-   - `ImageCaptioningNode`: Provides a caption for the image.
+   - `ImageCaptioningNode`: Provides a caption for the image using BLIP model.
       - _Input_: `image` (IMAGE)
       - _Output_: String caption.
-   - `FoodCategoryNode`: Classifies the food category of an image.
+   - `FoodCategoryClassifierNode`: Classifies food categories in images.
       - _Input_: `image` (IMAGE)
-      - _Output_: String category.
-   - `AgeClassifierNode`: Classifies the age of a person in the image.
+      - _Output_: Top 5 food categories with probabilities.
+   - `AgeClassifierNode`: Classifies the age range in images.
       - _Input_: `image` (IMAGE)
-      - _Output_: String age range.
-   - `ImageClassifierNode`: General image classification.
+      - _Output_: Top 5 age ranges with probabilities.
+   - `ArtOrHumanClassifierNode`: Detects if an image is AI-generated or human-made.
       - _Input_: `image` (IMAGE), `show_on_node` (BOOL)
-      - _Output_: String label, `artificial_prob` (INT), `human_prob` (INT)
-   - `ClassifierNode`: A generic classifier node.
+      - _Output_: Artificial and human probabilities.
+   - `DocumentClassificationNode`: Classifies document types.
       - _Input_: `image` (IMAGE)
-      - _Output_: String label.
+      - _Output_: Document type index and name.
+   - `NSFWClassifierNode`: Classifies content safety levels.
+      - _Input_: `image` (IMAGE), `show_on_node` (BOOL), `threshold` (FLOAT)
+      - _Output_: 
+         - Classification report (STRING)
+         - SFW Score (FLOAT)
+         - NSFW Score (FLOAT)
+         - Is SFW (BOOLEAN)
+         - Is NSFW (BOOLEAN)
+   - `WatermarkDetectionNode`: Detects watermarks in images using EfficientNet.
+      - _Input_: `image` (IMAGE), `show_on_node` (BOOL), `threshold` (FLOAT)
+      - _Output_:
+         - Classification report (STRING)
+         - Clean Score (FLOAT)
+         - Watermark Score (FLOAT)
+         - Is Clean (BOOLEAN)
+         - Has Watermark (BOOLEAN)
 
-3. **SegformerNode.py** - Handles semantic segmentation of images. It includes various nodes such as:
-   - `SegformerNode`: Performs segmentation of the image.
-      - _Input_: `image` (IMAGE), `model_name` (STRING), `show_on_node` (BOOL)
-      - _Output_: Segmented image.
-   - `SegformerNodeMasks`: Provides masks for the segmented images.
-      - _Input_: No specific input detailed in the provided code.
-      - _Output_: Image masks.
-   - `SegformerNodeMergeSegments`: Merges certain segments in the segmented image.
-      - _Input_: `image` (IMAGE), `segments_to_merge` (STRING), `model_name` (STRING), `blur_radius` (INT), `dilation_radius` (INT), `intensity` (INT), `ceiling` (INT), `show_on_node` (BOOL)
-      - _Output_: Image with merged segments.
-   - `SeedIncrementerNode`: Increment the seed used for random processes.
-      - _Input_: `seed` (INT), `increment_at` (INT)
-      - _Output_: Incremented seed.
-   - `StepCfgIncrementNode`: Calculates the step configuration for the process.
-      - _Input_: `seed` (INT), `cfg_start` (INT), `steps_start` (INT), `img_steps` (INT), `max_steps` (INT)
-      - _Output_: Calculated step configuration.
+3. **SegformerNode.py** - Handles semantic segmentation of images:
+   - `SegformerNode`: Performs semantic segmentation with multiple model options.
+      - _Input_: `image` (IMAGE), `model_name` (STRING), `normalize_mask` (BOOL), `binary_mask` (BOOL), `resize_mode` (STRING), `invert_mask` (BOOL), `show_preview` (BOOL), `return_individual_masks` (BOOL), `post_process` (STRING), `post_process_radius` (INT), `segment_groups` (STRING)
+      - _Output_: Segmented image, mask, info, and preview.
+   - `SegformerNodeMasks`: Creates individual segment masks.
+      - _Input_: `image` (IMAGE), `segments_to_merge` (STRING), `model_name` (STRING)
+      - _Output_: Image, mask, and segment info.
+   - `SegformerNodeMergeSegments`: Merges and processes segments with advanced options.
+      - _Input_: `image` (IMAGE), `segments_to_merge_str` (STRING), `model_name` (STRING), `normalize_mask` (BOOL), `binary_mask` (BOOL), `resize_mode` (STRING), `invert_mask` (BOOL), `show_preview` (BOOL), `blur_radius` (INT), `dilation_radius` (INT), `intensity` (FLOAT), `ceiling` (FLOAT)
+      - _Output_: Processed image, mask, info, and preview.
+   - `SeedIncrementerNode`: Manages seed incrementation for workflows.
+      - _Input_: `seed` (INT), `IncrementAt` (INT)
+      - _Output_: Seed string, seed int, subseed string, subseed int.
+   - `StepCfgIncrementNode`: Handles step and configuration increments.
+      - _Input_: `seed` (INT), `cfg_start` (INT), `steps_start` (INT), `image_steps` (INT), `max_steps` (INT)
+      - _Output_: CFG and steps values.
 
 ## Requirements
 
-The project primarily uses the following libraries:
+The project requires the following Python libraries:
 
-- Python
-- Torch
-- Transformers
-- PIL
-- Matplotlib
-- Numpy
-- IO
-- Scipy
+- torch
+- transformers
+- Pillow (PIL)
+- matplotlib
+- numpy
+- scipy
+- huggingface_hub
+- torchvision
 
 ## Installation
 
-To install the necessary libraries, run:
-
+1. Install the required Python packages:
 ```bash
-pip install torch transformers pillow matplotlib numpy scipy
+pip install torch transformers pillow matplotlib numpy scipy huggingface_hub torchvision
 ```
 
+2. Clone this repository into your ComfyUI custom_nodes directory:
+```bash
+cd ComfyUI/custom_nodes
+git clone https://github.com/YourUsername/ComfyUI-LexTools.git
+```
+
+3. Restart ComfyUI to load the new nodes.
+
+## Usage
+
+The nodes will appear in the ComfyUI interface under the "LexTools" category, organized into subcategories:
+- LexTools/ImageProcessing/Segmentation
+- LexTools/ImageProcessing/Classification
+- LexTools/ImageProcessing/Captioning
+- LexTools/Utilities
+
 ## Contributing
-Contributions to this project are welcome. If you find a bug or think of a feature that would benefit the project, please open an issue. If you'd like to contribute code, please open a pull request.
+
+Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
 

__init__.py

@@ -1,5 +1,6 @@
 #from .nodes.SegGPT import segGPTNode
 from .nodes import SegformerNode,ImageCaptioningNode,ImageProcessingNode
+
 NODE_CLASS_MAPPINGS = {
 
     **SegformerNode.NODE_CLASS_MAPPINGS,

nodes/ImageCaptioningNode.py

@@ -4,6 +4,12 @@ from transformers import BlipProcessor,AutoModel, BlipForConditionalGeneration,A
 from PIL import Image
 import numpy as np
 from scipy.ndimage import binary_dilation
+import torchvision.transforms as transforms
+import os
+import requests
+from pathlib import Path
+import folder_paths
+import torchvision.models as models
 
 
 class ImageCaptioningNode:
@@ -127,7 +133,7 @@ class ArtOrHumanClassifierNode:
         return {
             "required": {
                 "image": ("IMAGE",),
-                "show_on_node": ("BOOL", {"default": False}),
+                "show_on_node": ("BOOLEAN", {"default": False}),
             },
         }
     OUTPUT_NODE = True
@@ -152,10 +158,10 @@ class ArtOrHumanClassifierNode:
         proba = outputs.logits.softmax(1)
 
         # Get the probabilities for "artificial" and "human" classes
-        artificial_prob = proba[0][0].item()
-        human_prob = proba[0][1].item()
+        artificial_prob = float(proba[0][0].item())
+        human_prob = float(proba[0][1].item())
 
-        output_ui =  {"text": [artificial_prob]} if show_on_node else {}
+        output_ui =  {"text": [f"Artificial: {artificial_prob:.2%}\nHuman: {human_prob:.2%}"]} if show_on_node else {}
 
         return {"result": (artificial_prob, human_prob), "ui": output_ui}
 
@@ -167,7 +173,7 @@ class DocumentClassificationNode:
     def INPUT_TYPES(cls):
         return {"required": {"image": ("IMAGE", {"default": None})}}
 
-    RETURN_TYPES = ("INT", "STRING")
+    RETURN_TYPES = ("FLOAT", "STRING")
     FUNCTION = "classify"
     CATEGORY = "LexTools/ImageProcessing/Classification"
 
@@ -186,12 +192,230 @@ class DocumentClassificationNode:
         # Perform the classification
         outputs = self.model(**inputs)
         logits = outputs.logits
+        probabilities = torch.softmax(logits, dim=1)
         predicted_class_index = torch.argmax(logits, dim=1).item()
+        confidence_score = float(probabilities[0][predicted_class_index].item())
 
         # Get the class name
         predicted_class_name = self.class_names[predicted_class_index]
 
-        return (predicted_class_index, predicted_class_name)
+        return (confidence_score, predicted_class_name)
+
+class NSFWClassifierNode:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE", {"default": None}),
+                "show_on_node": ("BOOLEAN", {"default": False}),
+                "threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0}),
+            },
+        }
+    OUTPUT_NODE = True
+
+    RETURN_TYPES = ("STRING", "FLOAT", "FLOAT", "BOOLEAN", "BOOLEAN")  # Added boolean outputs
+    RETURN_NAMES = ("Classification", "SFW Score", "NSFW Score", "Is SFW", "Is NSFW")
+    FUNCTION = "classify_nsfw"
+    CATEGORY = "LexTools/ImageProcessing/Classification"
+
+    def __init__(self):
+        self.feature_extractor = AutoFeatureExtractor.from_pretrained("umairrkhn/fine-tuned-nsfw-classification")
+        self.model = AutoModelForImageClassification.from_pretrained("umairrkhn/fine-tuned-nsfw-classification")
+
+    def classify_nsfw(self, image, show_on_node, threshold):
+        try:
+            # Convert the image tensor to numpy array
+            i = 255. * image[0].cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+
+            # Process the image
+            inputs = self.feature_extractor(images=img, return_tensors="pt")
+            outputs = self.model(**inputs)
+            probs = outputs.logits.softmax(1)[0]
+
+            # Get probabilities for each class (model has 2 classes: SFW and NSFW)
+            sfw_prob = float(probs[0].item())  # SFW
+            nsfw_prob = float(probs[1].item())  # NSFW
+
+            # Get the predicted class
+            predicted_class_idx = probs.argmax().item()
+            class_names = ["SFW", "NSFW"]
+            predicted_class = class_names[predicted_class_idx]
+
+            # Determine boolean states using threshold
+            is_sfw = sfw_prob >= threshold
+            is_nsfw = nsfw_prob >= threshold
+
+            # Format the results string
+            results = f"Predicted: {predicted_class}\n"
+            results += f"SFW: {sfw_prob:.2%} ({'Yes' if is_sfw else 'No'})\n"
+            results += f"NSFW: {nsfw_prob:.2%} ({'Yes' if is_nsfw else 'No'})"
+
+            output_ui = {"text": [results]} if show_on_node else {}
+
+            return {"result": (results, sfw_prob, nsfw_prob, is_sfw, is_nsfw), 
+                    "ui": output_ui}
+
+        except Exception as e:
+            print(f"Error in NSFW classification: {str(e)}")
+            return {"result": (str(e), 0.0, 0.0, False, False), 
+                    "ui": {"text": [str(e)]} if show_on_node else {}}
+
+class WatermarkDetectionNode:
+    model = None  # Class-level model instance for caching
+    transform = None  # Class-level transform for caching
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE", {"default": None}),
+                "show_on_node": ("BOOLEAN", {"default": False}),
+                "threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0}),
+            },
+        }
+    OUTPUT_NODE = True
+
+    RETURN_TYPES = ("STRING", "FLOAT", "FLOAT", "BOOLEAN", "BOOLEAN")
+    RETURN_NAMES = ("Classification", "Clean Score", "Watermark Score", "Is Clean", "Has Watermark")
+    FUNCTION = "detect_watermark"
+    CATEGORY = "LexTools/ImageProcessing/Classification"
+
+    def download_model(self):
+        # Create models directory if it doesn't exist
+        models_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "models")
+        os.makedirs(models_dir, exist_ok=True)
+        
+        model_path = os.path.join(models_dir, "watermark_model.pt")
+        
+        # Download the model if it doesn't exist
+        if not os.path.exists(model_path):
+            print("Downloading watermark detection model...")
+            url = "https://huggingface.co/qwertyforce/watermark_detection/resolve/main/model.pt"
+            try:
+                response = requests.get(url, stream=True)
+                response.raise_for_status()
+                
+                with open(model_path, 'wb') as f:
+                    for chunk in response.iter_content(chunk_size=8192):
+                        f.write(chunk)
+                print("Model downloaded successfully")
+            except Exception as e:
+                print(f"Error downloading model: {str(e)}")
+                # Try alternative URL from scenery_watermarks repo
+                url = "https://huggingface.co/qwertyforce/scenery_watermarks/resolve/main/model.pt"
+                print("Trying alternative model source...")
+                response = requests.get(url, stream=True)
+                response.raise_for_status()
+                
+                with open(model_path, 'wb') as f:
+                    for chunk in response.iter_content(chunk_size=8192):
+                        f.write(chunk)
+                print("Model downloaded successfully from alternative source")
+            
+        return model_path
+
+    def __init__(self):
+        if WatermarkDetectionNode.transform is None:
+            # Standard EfficientNet preprocessing
+            WatermarkDetectionNode.transform = transforms.Compose([
+                transforms.Resize(256),
+                transforms.CenterCrop(224),
+                transforms.ToTensor(),
+                transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+            ])
+
+        if WatermarkDetectionNode.model is None:
+            try:
+                # Create a new EfficientNet model
+                base_model = models.efficientnet_b0(pretrained=False)
+                # Modify the classifier for 2 classes
+                base_model.classifier = torch.nn.Sequential(
+                    torch.nn.Dropout(p=0.2, inplace=True),
+                    torch.nn.Linear(in_features=1280, out_features=2, bias=True)
+                )
+                
+                # Download and load the state dict
+                model_path = self.download_model()
+                state_dict = torch.load(model_path, map_location='cpu')
+                
+                # If it's a state dict, try to load it
+                if isinstance(state_dict, dict):
+                    try:
+                        # Try direct loading
+                        base_model.load_state_dict(state_dict)
+                    except:
+                        try:
+                            # Try removing 'module.' prefix
+                            new_state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
+                            base_model.load_state_dict(new_state_dict)
+                        except Exception as e:
+                            print(f"Failed to load state dict: {str(e)}")
+                            # If both attempts fail, just use the base model
+                            pass
+                else:
+                    # If it's already a model, try to extract its state dict
+                    try:
+                        base_model.load_state_dict(state_dict.state_dict())
+                    except:
+                        print("Failed to load model state dict, using base model")
+                
+                WatermarkDetectionNode.model = base_model
+                if torch.cuda.is_available():
+                    WatermarkDetectionNode.model = WatermarkDetectionNode.model.cuda()
+                WatermarkDetectionNode.model.eval()
+                
+            except Exception as e:
+                print(f"Error loading watermark detection model: {str(e)}")
+                raise
+
+    def detect_watermark(self, image, show_on_node, threshold):
+        try:
+            # Convert the image tensor to PIL Image
+            i = 255. * image[0].cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+            
+            # Ensure image is RGB
+            if img.mode != 'RGB':
+                img = img.convert('RGB')
+
+            # Preprocess the image
+            img_tensor = self.transform(img).unsqueeze(0)
+            if torch.cuda.is_available():
+                img_tensor = img_tensor.cuda()
+
+            # Get model predictions
+            with torch.no_grad():
+                outputs = self.model(img_tensor)
+                probs = torch.softmax(outputs, dim=1)[0]
+
+            # Get probabilities for each class
+            clean_prob = float(probs[0].item())  # Clean image
+            watermark_prob = float(probs[1].item())  # Watermarked image
+
+            # Get the predicted class
+            predicted_class_idx = probs.argmax().item()
+            class_names = ["Clean", "Watermarked"]
+            predicted_class = class_names[predicted_class_idx]
+
+            # Determine boolean states using threshold
+            is_clean = clean_prob >= threshold
+            has_watermark = watermark_prob >= threshold
+
+            # Format the results string
+            results = f"Predicted: {predicted_class}\n"
+            results += f"Clean: {clean_prob:.2%} ({'Yes' if is_clean else 'No'})\n"
+            results += f"Watermarked: {watermark_prob:.2%} ({'Yes' if has_watermark else 'No'})"
+
+            output_ui = {"text": [results]} if show_on_node else {}
+
+            return {"result": (results, clean_prob, watermark_prob, is_clean, has_watermark), 
+                    "ui": output_ui}
+
+        except Exception as e:
+            print(f"Error in watermark detection: {str(e)}")
+            return {"result": (str(e), 0.0, 0.0, False, False), 
+                    "ui": {"text": [str(e)]} if show_on_node else {}}
 
 NODE_CLASS_MAPPINGS = {
     "AgeClassifierNode": AgeClassifierNode,
@@ -199,10 +423,14 @@ NODE_CLASS_MAPPINGS = {
     "DocumentClassificationNode": DocumentClassificationNode,
     "ImageCaptioning": ImageCaptioningNode,
     "ArtOrHumanClassifierNode": ArtOrHumanClassifierNode,
+    "NSFWClassifierNode": NSFWClassifierNode,
+    "WatermarkDetectionNode": WatermarkDetectionNode,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "ImageScaleToMin": "Image Scale To Min",
     "ImageCaptioning": "Image Captioning",
     "ArtOrHumanClassifierNode": "Art Or Human Classifier",
+    "NSFWClassifierNode": "NSFW Classifier",
+    "WatermarkDetectionNode": "Watermark Detector",
 }

nodes/ImageProcessingNode.py

@@ -1,19 +1,12 @@
 import hashlib
-import fastapi
-import fastapi
-import torch
-import time
+from fastapi import FastAPI
+import torch, time
 import io
-import cv2
 
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from torchvision import transforms
 import comfy.samplers
-import matplotlib.transforms as mpl_transforms
+from matplotlib import transforms
 from PIL import Image, ImageFilter, ImageEnhance, ImageOps, ImageDraw, ImageChops, ImageFont
 import numpy as np
-from scipy.ndimage import zoom
-
 import comfy.model_management as model_management
 import json
 import uuid
@@ -24,10 +17,8 @@ import torch.nn as nn
 from os.path import join
 import clip
 import folder_paths
-
 # create path to aesthetic model.
-folder_paths.folder_names_and_paths["aesthetic"] = ([os.path.join(
-    folder_paths.models_dir, "aesthetic")], folder_paths.supported_pt_extensions)
+folder_paths.folder_names_and_paths["aesthetic"] = ([os.path.join(folder_paths.models_dir,"aesthetic")], folder_paths.supported_pt_extensions)
 
 
 aspect_ratios = [
@@ -41,34 +32,24 @@ aspect_ratios = [
 MAX_RESOLUTION = 10240  # adjust this value as needed
 
 # Tensor to PIL
-
-
 def tensor2pil(image):
     return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
-
+    
 # PIL to Tensor
-
-
 def pil2tensor(image):
     return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
 
 # PIL Hex
-
-
 def pil2hex(image):
     return hashlib.sha256(np.array(tensor2pil(image)).astype(np.uint16).tobytes()).hexdigest()
 
 # PIL to Mask
-
-
 def pil2mask(image):
     image_np = np.array(image.convert("L")).astype(np.float32) / 255.0
     mask = torch.from_numpy(image_np)
     return 1.0 - mask
-
+    
 # Mask to PIL
-
-
 def mask2pil(mask):
     if mask.ndim > 2:
         mask = mask.squeeze(0)
@@ -77,199 +58,6 @@ def mask2pil(mask):
     return mask_pil
 
 
-def scale_and_print_mask(mask, target_shape=(11, 11)):
-    """
-    Scale a given mask to a target shape and print it rounded to 4 decimal places.
-    """
-    # Calculate scaling factors
-    scale_x = target_shape[0] / mask.shape[0]
-    scale_y = target_shape[1] / mask.shape[1]
-
-    # Rescale the mask
-    scaled_mask = zoom(mask, (scale_x, scale_y))
-
-    # Print the scaled mask, rounded to 4 decimal places
-    for row in scaled_mask:
-        print(", ".join([f"{x:.2f}" for x in row]))
-
-
-def apply_feathering(mask, feathering_distance=10):
-    # Apply Gaussian blur to the mask
-    mask_feathered = mask
-
-    if feathering_distance > 0:
-        # Generate kernel
-        kernel_size = 2 * feathering_distance + 1
-        kernel = cv2.getGaussianKernel(kernel_size, feathering_distance)
-
-        # Convert the mask tensor to a numpy array
-        mask_np = mask.numpy()
-
-        # Apply Gaussian blur to the mask
-        kernel_2d = np.dot(kernel, kernel.T)
-        mask_feathered_np = cv2.filter2D(
-            mask_np, -1, kernel_2d, borderType=cv2.BORDER_CONSTANT)
-
-        # Convert the result back to a PyTorch tensor
-        mask_feathered = torch.tensor(mask_feathered_np, dtype=torch.float32)
-    return mask_feathered
-
-
-def apply_gradient(mask, transition_points, feathering_distance):
-    # Ensure feathering_distance is at least 1
-    gradient = torch.linspace(0, 1, max(feathering_distance, 1))
-    for x, y in transition_points:
-        if x + feathering_distance < mask.shape[0]:
-            mask[x: x + feathering_distance, y] = gradient
-        if x - feathering_distance >= 0:
-            mask[x - feathering_distance: x, y] = gradient[::-1]
-    return mask
-
-
-class ImageAspectPadNode:
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        global aspect_ratios  # Assuming aspect_ratios is a list of aspect ratio strings
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "aspect_ratio": (aspect_ratios, {"default": aspect_ratios[0]}),
-                "invert_ratio": (["true", "false"], {"default": "false"}),
-                "edge_feathering_distance": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
-                "feathering": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
-                "exclude_out_of_bounds": (["true", "false"], {"default": "false"}),
-                "left_padding": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
-                "right_padding": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
-                "top_padding": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
-                "bottom_padding": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
-            },
-            "optional": {
-                "show_on_node": ("INT", {"default": 0}),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE", "MASK")
-    FUNCTION = "expand_image"
-    OUTPUT_NODE = True
-
-    CATEGORY = "LexTools/ImageProcessing/AspectPad"
-
-    def expand_image(self, image, aspect_ratio, invert_ratio, feathering, left_padding, right_padding, top_padding, bottom_padding, show_on_node, exclude_out_of_bounds, edge_feathering_distance):
-        debug_info = {}  # Initialize debug info dictionary
-        debug = True
-        try:
-            # Initial setup
-            d1, d2, d3, d4 = image.size()
-            aspect_ratio = float(aspect_ratio.split(
-                '/')[0]) / float(aspect_ratio.split('/')[1])
-            if invert_ratio == "true":
-                aspect_ratio = 1.0 / aspect_ratio
-
-            # Padding calculations
-            image_aspect_ratio = d3 / d2
-            if image_aspect_ratio > aspect_ratio:
-                pad_height = int(d3 / aspect_ratio) - d2
-                top_padding += pad_height // 2
-                bottom_padding += pad_height - top_padding
-            else:
-                pad_width = int(d2 * aspect_ratio) - d3
-                left_padding += pad_width // 2
-                right_padding += pad_width - left_padding
-
-            # Debug Information
-            debug_info['image_size'] = (d1, d2, d3, d4)
-            debug_info['padding'] = (
-                top_padding, bottom_padding, left_padding, right_padding)
-
-            # Identify the mask boundary
-            boundary_top = top_padding
-            boundary_bottom = top_padding + d2
-            boundary_left = left_padding
-            boundary_right = left_padding + d3
-
-            # Initialize new image and mask
-            new_image = torch.zeros((d1, d2 + top_padding + bottom_padding,
-                                    d3 + left_padding + right_padding, d4), dtype=torch.float32)
-            new_image[:, top_padding:top_padding + d2,
-                      left_padding:left_padding + d3, :] = image
-            mask = torch.ones((d2 + top_padding + bottom_padding,
-                              d3 + left_padding + right_padding), dtype=torch.float32)
-            mask[top_padding:top_padding + d2,
-                 left_padding:left_padding + d3] = 0
-            if debug == True:
-                scale_and_print_mask(mask)
-
-            # Apply edge feathering to the identified boundary within mask
-            transition_points = []
-            for i in range(1, mask.shape[0]):
-                for j in range(mask.shape[1]):
-                    if mask[i, j] != mask[i-1, j]:
-                        transition_points.append((i, j))
-            for i in range(mask.shape[0]):
-                for j in range(1, mask.shape[1]):
-                    if mask[i, j] != mask[i, j-1]:
-                        transition_points.append((i, j))
-            if debug == True:
-                print("Transition Points:", transition_points)  # Debug line
-
-            # Check if "exclude_out_of_bounds" is set to "true"
-            if exclude_out_of_bounds == "true":
-                # Create a mask that excludes the areas touching the bounds
-                inner_mask = torch.zeros_like(mask)
-                inner_mask[boundary_top:boundary_bottom, boundary_left:boundary_right] = 1
-                inner_mask = 1 - inner_mask  # Invert the inner mask
-                
-                # Apply the inner mask to the original mask
-                mask = mask * inner_mask  
-
-                # Filter transition points to only include those within the bounds
-                transition_points = [point for point in transition_points if boundary_top <= point[0] < boundary_bottom and boundary_left <= point[1] < boundary_right]
-
-            # Apply edge feathering to the identified boundary within the new mask
-            if edge_feathering_distance > 0:
-                try:
-                    mask = apply_gradient(mask, transition_points, edge_feathering_distance)
-                except Exception as e:
-                    if debug == True:
-                        print("An error occurred:", str(e))
-            # Apply edge feathering to the identified boundary within mask
-            if debug == True:
-                print("Mask After  Before Feather:")  # Debug line
-                # Assuming this function prints the mask
-                scale_and_print_mask(mask)
-
-            # Apply overall feathering
-            if feathering > 0:
-                try:
-                    mask = apply_feathering(mask, feathering)
-                except Exception as e:
-                    # scale_and_print_mask(mask)
-                    if debug == True:
-                        print("An error occurred:", str(e))
-            if debug == True:
-                print("Mask After  Feather:")  # Debug line
-
-                # Assuming this function prints the mask
-                scale_and_print_mask(mask)
-
-            # Debugging output
-                print("Debug Information:", debug_info)
-
-            output_ui = {}
-            if show_on_node == 1:
-                output_ui = {"ui": {"images": [new_image]}}
-
-            return (new_image, mask, output_ui)
-
-        except Exception as e:
-
-            print("An error occurred:", str(e))
-            if debug == True:
-                print("Debug Information:", debug_info)
-            raise  # Re-raise the caught exception for further handling
-
-
 class ImageRankingNode:
     @classmethod
     def INPUT_TYPES(cls):
@@ -314,63 +102,117 @@ class ImageRankingNode:
             json.dump(data, f)
 
 
-class AutoModelForCausalLMNode:
+class ImageAspectPadNode:
+
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": {
-            "MESSAGE": ("STRING",),
-            "MaxTokens": ("INTEGER",)
-        }}
+        global aspect_ratios  # Assuming aspect_ratios is a list of aspect ratio strings
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "aspect_ratio": (aspect_ratios, {"default": aspect_ratios[0]}),
+                "invert_ratio": (["true", "false"], {"default": "false"}),
+                "feathering": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "left_padding": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "right_padding": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "top_padding": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "bottom_padding": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+            
 
-    RETURN_TYPES = ("STRING")
-    FUNCTION = "caption"
+            },
+            "optional": {
+                    "show_on_node": ("INT", {"default": 0}),
+            }
+        }
 
-    CATEGORY = "LexTools/TextGeneration"
+    RETURN_TYPES = ("IMAGE", "MASK")
+    FUNCTION = "expand_image"
+    OUTPUT_NODE = True
 
-    def __init__(self):
-        self.tokenizer = AutoTokenizer.from_pretrained(
-            "mistralai/Mistral-7B-Instruct-v0.1")
-        self.model = AutoModelForCausalLM.from_pretrained(
-            "mistralai/Mistral-7B-Instruct-v0.1")
+    CATEGORY = "LexTools/ImageProcessing/AspectPad"
 
-    def caption(self, MESSAGE, MaxTokens):
+    def expand_image(self, image, aspect_ratio, invert_ratio, feathering, left_padding, right_padding, top_padding, bottom_padding,show_on_node):
+   
+        d1, d2, d3, d4 = image.size()
+        aspect_ratio = float(aspect_ratio.split('/')[0]) / float(aspect_ratio.split('/')[1])
+        if invert_ratio == "true":
+            aspect_ratio = 1.0 / aspect_ratio
 
-        device = "cuda"  # the device to load the model onto
-        messages = [
-            {"role": "user", "content": MESSAGE},
+        image_aspect_ratio = d3 / d2
+        if image_aspect_ratio > aspect_ratio:
+            pad_height = int(d3 / aspect_ratio) - d2
+            top_padding += pad_height // 2
+            bottom_padding += pad_height - top_padding
+        else:
+            pad_width = int(d2 * aspect_ratio) - d3
+            left_padding += pad_width // 2
+            right_padding += pad_width - left_padding
+        new_image = torch.zeros(
+            (d1, d2 + top_padding + bottom_padding, d3 + left_padding + right_padding, d4),
+            dtype=torch.float32,
+        )
+        new_image[:, top_padding:top_padding + d2, left_padding:left_padding + d3, :] = image
 
-        ]
-        encodeds = self.tokenizer.apply_chat_template(
-            messages, return_tensors="pt")
+        mask = torch.ones(
+            (d2 + top_padding + bottom_padding, d3 + left_padding + right_padding),
+            dtype=torch.float32,
+        )
 
-        model_inputs = encodeds.to(device)
-        self.model.to(device)
+        t = torch.zeros(
+            (d2, d3),
+            dtype=torch.float32
+        )
 
-        generated_ids = self.model.generate(
-            model_inputs, max_new_tokens=MaxTokens, do_sample=True)
-        decoded = self.tokenizer.batch_decode(generated_ids)
+        if feathering > 0 and feathering * 2 < d2 and feathering * 2 < d3:
 
-        return (decoded[0])
+            for i in range(d2):
+                for j in range(d3):
+                    dt = i if top_padding != 0 else d2
+                    db = d2 - i if bottom_padding != 0 else d2
+
+                    dl = j if left_padding != 0 else d3
+                    dr = d3 - j if right_padding != 0 else d3
+
+                    d = min(dt, db, dl, dr)
+
+                    if d >= feathering:
+                        continue
+
+                    v = (feathering - d) / feathering
+
+                    t[i, j] = v * v
+
+        mask[top_padding:top_padding + d2, left_padding:left_padding + d3] = t
+   
+            
+        output_ui = {}
+        if show_on_node ==1:
+            output_ui = {"ui": {"images": [new_image]}}
+
+
+
+        return  (new_image, mask, output_ui)
+
+    
 
 
 class ImageScaleToMin:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"image": ("IMAGE",)},
-                "optional": {"MinScalePix": ("FLOAT", {"default": 512, "min": 0.0, "max": 2056, "step": 1}), }}
+                "optional":{"MinScalePix": ("FLOAT", {"default": 512, "min": 0.0, "max": 2056, "step": 1}),}}
 
     RETURN_TYPES = ("FLOAT",)
     FUNCTION = "calculate_scale"
 
     CATEGORY = "LexTools/ImageProcessing/upscaling"
 
-    def calculate_scale(self, image, MinScalePix):
+    def calculate_scale(self, image,MinScalePix):
         d1, height, width, d4 = image.shape
         min_dim = min(width, height)
         scale = MinScalePix / min_dim
         return (scale,)
-
-
+    
 class ImageFilterByIntScoreNode:
     @classmethod
     def INPUT_TYPES(cls):
@@ -391,70 +233,91 @@ class ImageFilterByIntScoreNode:
         if score < threshold:
             pass
         else:
-            return (image,)
-
-
+            return (image,) 
+    
 class ImageFilterByFloatScoreNode:
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "score": ("FLOAT", {"default": 0.0}),
-                "threshold": ("FLOAT", {"default": 0.0}),
-                "image": ("IMAGE", {"default": None}),
+                "image": ("IMAGE",),
+                "score": ("FLOAT", {"default": 0.0, "min": -100.0, "max": 100.0}),
+                "threshold": ("FLOAT", {"default": 5.0, "min": -100.0, "max": 100.0}),
+                "show_on_node": ("BOOLEAN", {"default": False}),
             },
         }
 
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "filter_image_by_score"
-    CATEGORY = "LexTools/ImageProcessing/Scores"
-
-    def filter_image_by_score(self, score, threshold, image):
-        # If score > threshold, return the image, otherwise return None
-        if score < threshold:
-            pass
-        else:
-            return (image,)
+    RETURN_TYPES = ("IMAGE", "FLOAT")
+    FUNCTION = "filter_image"
+    CATEGORY = "LexTools/ImageProcessing/Filtering"
 
+    def filter_image(self, image, score, threshold, show_on_node):
+        try:
+            if float(score) >= float(threshold):
+                score_text = f"Score {score:.2f} >= Threshold {threshold:.2f}\nImage Passed"
+                output_ui = {"text": [score_text]} if show_on_node else {}
+                return {"result": (image, float(score)), "ui": output_ui}
+            else:
+                score_text = f"Score {score:.2f} < Threshold {threshold:.2f}\nImage Filtered"
+                output_ui = {"text": [score_text]} if show_on_node else {}
+                return {"result": (torch.zeros_like(image), float(score)), "ui": output_ui}
+        except Exception as e:
+            print(f"Error filtering image: {str(e)}")
+            return {"result": (image, 0.0), "ui": {"text": [str(e)]} if show_on_node else {}}
 
 class ImageQualityScoreNode:
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "aesthetic_score": ("INT", {"default": None}),
-                "ai_score_artificial": ("FLOAT", {"default": None}),
-                "ai_score_human": ("FLOAT", {"default": None}),
-                "show_on_node": ("INT", {"default": 0}),
-            },
-            "optional": {
-                "image_score_good": ("FLOAT", {"default": 0}),
-                "image_score_bad": ("FLOAT", {"default": 0}),
-                "weight_good_score": ("FLOAT", {"default": 1}),
-                "weight_aesthetic_score": ("FLOAT", {"default": 1.0}),
-                "weight_bad_score": ("FLOAT", {"default": 1.0}),
-                "weight_AIDetection": ("FLOAT", {"default": 1.0}),
-                "weight_HumanDetection": ("FLOAT", {"default": 1.0}),
-                "MultiplyScoreBy": ("FLOAT", {"default": 100000}),
+                "aesthetic_score": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0}),
+                "image_score_good": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0}),
+                "image_score_bad": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0}),
+                "ai_score_artificial": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0}),
+                "ai_score_human": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0}),
+                "weight_good_score": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}),
+                "weight_aesthetic_score": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}),
+                "weight_bad_score": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}),
+                "weight_AIDetection": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}),
+                "weight_HumanDetection": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}),
+                "MultiplyScoreBy": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}),
+                "show_on_node": ("BOOLEAN", {"default": False}),
             },
         }
-    OUTPUT_NODE = True
 
     RETURN_TYPES = ("FLOAT",)
     FUNCTION = "calculate_score"
-    CATEGORY = "LexTools/ImageProcessing/Scores"
+    CATEGORY = "LexTools/ImageProcessing/Scoring"
 
-    def calculate_score(self, image_score_good, image_score_bad, aesthetic_score, ai_score_artificial, ai_score_human, weight_good_score, weight_aesthetic_score, weight_bad_score, weight_AIDetection, MultiplyScoreBy, show_on_node, weight_HumanDetection):
-        # Define the weights and maximum possible values
-        maxA, maxB, maxC = 3, 3, 1000
-        # Compute the exponential effect of the AI score
-        ai_score_artificial_exp = 10 ** ai_score_artificial
-        # Compute the final score according to the provided formula
-        final_score = ((((((image_score_good + maxA) / (2 * maxA) * weight_good_score) + (aesthetic_score / maxC) * weight_bad_score) / (weight_good_score + weight_bad_score)) -
-                       weight_aesthetic_score * ((image_score_bad + maxB) / (2 * maxB))) * ((weight_HumanDetection * (ai_score_human))-(weight_AIDetection * (ai_score_artificial_exp)))) * MultiplyScoreBy
+    def calculate_score(self, aesthetic_score, image_score_good, image_score_bad, ai_score_artificial, ai_score_human,
+                       weight_good_score, weight_aesthetic_score, weight_bad_score, weight_AIDetection, weight_HumanDetection,
+                       MultiplyScoreBy, show_on_node):
+        try:
+            # Calculate weighted scores
+            weighted_aesthetic = float(aesthetic_score) * weight_aesthetic_score
+            weighted_good = float(image_score_good) * weight_good_score
+            weighted_bad = float(image_score_bad) * weight_bad_score
+            weighted_ai = float(ai_score_artificial) * weight_AIDetection
+            weighted_human = float(ai_score_human) * weight_HumanDetection
 
-        # Prepare the output UI
-        return (final_score, {"ui": {"STRING": [final_score]}})
+            # Calculate total score
+            total_score = (weighted_aesthetic + weighted_good - weighted_bad + weighted_human - weighted_ai) * MultiplyScoreBy
+
+            # Format score for display
+            score_text = f"Score: {total_score:.2f}\n"
+            score_text += f"Aesthetic (w:{weight_aesthetic_score:.1f}): {aesthetic_score:.2f}\n"
+            score_text += f"Good (w:{weight_good_score:.1f}): {image_score_good:.2f}\n"
+            score_text += f"Bad (w:{weight_bad_score:.1f}): {image_score_bad:.2f}\n"
+            score_text += f"AI (w:{weight_AIDetection:.1f}): {ai_score_artificial:.2f}\n"
+            score_text += f"Human (w:{weight_HumanDetection:.1f}): {ai_score_human:.2f}\n"
+            score_text += f"Multiplier: {MultiplyScoreBy:.1f}"
+
+            output_ui = {"text": [score_text]} if show_on_node else {}
+
+            return {"result": (float(total_score),), "ui": output_ui}
+        except Exception as e:
+            print(f"Error calculating score: {str(e)}")
+            return {"result": (0.0,), "ui": {"text": [str(e)]} if show_on_node else {}}
 
 
 #
@@ -468,155 +331,59 @@ class MLP(pl.LightningModule):
         self.ycol = ycol
         self.layers = nn.Sequential(
             nn.Linear(self.input_size, 1024),
-            # nn.ReLU(),
+            #nn.ReLU(),
             nn.Dropout(0.2),
             nn.Linear(1024, 128),
-            # nn.ReLU(),
+            #nn.ReLU(),
             nn.Dropout(0.2),
             nn.Linear(128, 64),
-            # nn.ReLU(),
+            #nn.ReLU(),
             nn.Dropout(0.1),
             nn.Linear(64, 16),
-            # nn.ReLU(),
+            #nn.ReLU(),
             nn.Linear(16, 1)
         )
-
     def forward(self, x):
         return self.layers(x)
-
     def training_step(self, batch, batch_idx):
-        x = batch[self.xcol]
-        y = batch[self.ycol].reshape(-1, 1)
-        x_hat = self.layers(x)
-        loss = F.mse_loss(x_hat, y)
-        return loss
-
+            x = batch[self.xcol]
+            y = batch[self.ycol].reshape(-1, 1)
+            x_hat = self.layers(x)
+            loss = F.mse_loss(x_hat, y)
+            return loss
     def validation_step(self, batch, batch_idx):
         x = batch[self.xcol]
         y = batch[self.ycol].reshape(-1, 1)
-        x_hat = fastapiself.layers(x)
+        x_hat =fastapiself.layers(x)
         loss = fastapi.mse_loss(x_hat, y)
         return loss
-
     def configure_optimizers(self):
         optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
         return optimizer
-
-    def normalized(a, axis=-1, order=2):
-        import numpy as np  # pylint: disable=import-outside-toplevel
-        l2 = np.atleast_1d(np.linalg.norm(a, order, axis))
-        l2[l2 == 0] = 1
-        return a / np.expand_dims(l2, axis)
-
+def normalized(a, axis=-1, order=2):
+    import numpy as np  # pylint: disable=import-outside-toplevel
+    l2 = np.atleast_1d(np.linalg.norm(a, order, axis))
+    l2[l2 == 0] = 1
+    return a / np.expand_dims(l2, axis)
 
 class AesteticModel:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"model_name": (folder_paths.get_filename_list("aesthetic"), )}}
-    RETURN_TYPES = ("AESTHETIC_MODEL",)
-    FUNCTION = "load_model"
-    CATEGORY = "LexTools/ImageProcessing/aestheticscore"
-
-    def load_model(self, model_name):
-        # load model
-        m_path = folder_paths.folder_names_and_paths["aesthetic"][0]
-        m_path2 = os.path.join(m_path[0], model_name)
-        return (m_path2,)
-
-
-class SaturationMatchingNode:
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "working_image": ("IMAGE",),
-                "master_image": ("IMAGE",),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "run"
-    CATEGORY = "LexTools/ImageProcessing/SaturationMatching"
-
-    def __init__(self):
-        self.working_image = None
-        self.master_image = None
-
-    def calculate_saturation(self, tensor_image):
-
-        if len(tensor_image.shape) == 4:
-            # Take the first image from the batch
-            tensor_image = tensor_image[0]
-
-        # Convert tensor to numpy array and then to PIL Image
-        img = (tensor_image * 255).to(torch.uint8).numpy()
-        pil_image = Image.fromarray(img, mode='RGB')
-
-        # Convert to HSV
-        hsv_image = pil_image.convert('HSV')
-        s_channel = np.array(hsv_image)[:, :, 1]
-
-        # Calculate average saturation
-        avg_saturation = np.mean(s_channel)
-        return avg_saturation
-
-    def adjust_saturation(self, tensor_image, target_saturation):
-
-        original_shape = tensor_image.shape
-        if len(tensor_image.shape) == 4:
-            # Take the first image from the batch
-            tensor_image = tensor_image[0]
-
-        # Convert tensor to numpy array and then to PIL Image
-        img = (tensor_image * 255).to(torch.uint8).cpu().numpy()
-        img = np.transpose(img, (1, 2, 0))
-        pil_image = Image.fromarray(img, mode='RGB')
-
-        # Convert to HSV
-        hsv_image = pil_image.convert('HSV')
-        hsv_array = np.array(hsv_image)
-
-        # Calculate current average saturation
-        current_saturation = np.mean(hsv_array[:, :, 1])
-
-        # Calculate adjustment factor
-        factor = target_saturation / current_saturation
-
-        # Adjust saturation
-        hsv_array[:, :, 1] = np.clip(
-            hsv_array[:, :, 1] * factor, 0, 255).astype(np.uint8)
-
-        # Convert back to PIL Image and then to tensor
-        adjusted_hsv_image = Image.fromarray(hsv_array, 'HSV')
-        adjusted_rgb_image = adjusted_hsv_image.convert('RGB')
-        tensor_transform = transforms.ToTensor()
-        adjusted_tensor = tensor_transform(adjusted_rgb_image)
-
-        # Reshape to match the original tensor shape
-        if len(original_shape) == 4:
-            adjusted_tensor = adjusted_tensor.unsqueeze(0)
-
-        return adjusted_tensor
-
-    def run(self, working_image, master_image):
-        self.working_image = working_image
-        self.master_image = master_image
-
-        # Calculate target saturation from master image
-        target_saturation = self.calculate_saturation(self.master_image)
-
-        # Adjust the saturation of the working image
-        adjusted_image = self.adjust_saturation(
-            self.working_image, target_saturation)
-
-        return adjusted_image
+  def __init__(self):
+    pass
+  @classmethod
+  def INPUT_TYPES(s):
+    return { "required": {"model_name": (folder_paths.get_filename_list("aesthetic"), )}}
+  RETURN_TYPES = ("AESTHETIC_MODEL",)
+  FUNCTION = "load_model"
+  CATEGORY = "LexTools/ImageProcessing/aestheticscore"
+  def load_model(self, model_name):
+    #load model
+    m_path = folder_paths.folder_names_and_paths["aesthetic"][0]
+    m_path2 = os.path.join(m_path[0],model_name)
+    return (m_path2,)
 
 
 class CalculateAestheticScore:
-    device = "cuda"
+    device = "cuda" 
     model2 = None
     preprocess = None
     model = None
@@ -632,7 +399,7 @@ class CalculateAestheticScore:
                 "aesthetic_model": ("AESTHETIC_MODEL",),
             },
             "optional": {
-                "keep_in_memory": ("BOOL", {"default": True}),
+                "keep_in_memory": ("BOOLEAN", {"default": True}),
             }
         }
 
@@ -642,18 +409,16 @@ class CalculateAestheticScore:
 
     def execute(self, image, aesthetic_model, keep_in_memory):
         if not self.model2 or not self.preprocess:
-            self.model2, self.preprocess = clip.load(
-                "ViT-L/14", device=self.device)  # RN50x64
+            self.model2, self.preprocess = clip.load("ViT-L/14", device=self.device)  #RN50x64 
 
         m_path2 = aesthetic_model
 
         if not self.model:
-            # CLIP embedding dim is 768 for CLIP ViT L 14
-            self.model = MLP(768)
+            self.model = MLP(768)  # CLIP embedding dim is 768 for CLIP ViT L 14
             s = torch.load(m_path2)
             self.model.load_state_dict(s)
             self.model.to(self.device)
-
+        
         self.model.eval()
 
         tensor_image = image[0]
@@ -668,8 +433,7 @@ class CalculateAestheticScore:
             image_features = self.model2.encode_image(image2)
 
         im_emb_arr = normalized(image_features.cpu().detach().numpy())
-        prediction = self.model(torch.from_numpy(im_emb_arr).to(
-            self.device).type(torch.cuda.FloatTensor))
+        prediction = self.model(torch.from_numpy(im_emb_arr).to(self.device).type(torch.cuda.FloatTensor))
         final_prediction = int(float(prediction[0])*100)
 
         if not keep_in_memory:
@@ -678,11 +442,10 @@ class CalculateAestheticScore:
             self.preprocess = None
 
         return (final_prediction,)
-
-
+    
 class MD5ImageHashNode:
     device = "cuda"
-
+    
     def __init__(self):
         pass
 
@@ -700,7 +463,7 @@ class MD5ImageHashNode:
 
     def execute(self, image):
         tensor_image = image[0]
-
+        
         # Convert the tensor to a PIL image
         img = (tensor_image * 255).to(torch.uint8).cpu().numpy()
         pil_image = Image.fromarray(img, mode='RGB')
@@ -717,33 +480,29 @@ class MD5ImageHashNode:
 
         return (md5_hash,)
 
-
 class AesthetlcScoreSorter:
-    def __init__(self):
-        pass
+  def __init__(self):
     pass
-
-    @classmethod
-    def INPUT_TYPES(s):
+  pass
+  @classmethod
+  def INPUT_TYPES(s):
         return {
-            "required": {
-                "image": ("IMAGE",),
-                "score": ("SCORE",),
-                "image2": ("IMAGE",),
-                "score2": ("SCORE",),
-            }
+          "required":{
+            "image": ("IMAGE",),
+            "score": ("SCORE",),
+            "image2": ("IMAGE",),
+            "score2": ("SCORE",),
+          }
         }
-    RETURN_TYPES = ("IMAGE", "SCORE", "IMAGE", "SCORE",)
-    FUNCTION = "execute"
-    CATEGORY = "LexTools/ImageProcessing/aestheticscore"
-
-    def execute(self, image, score, image2, score2):
-        if score >= score2:
-            return (image, score, image2, score2,)
-        else:
-            return (image2, score2, image, score,)
-
-
+  RETURN_TYPES = ("IMAGE", "SCORE", "IMAGE", "SCORE",)
+  FUNCTION = "execute"
+  CATEGORY = "LexTools/ImageProcessing/aestheticscore"
+  def execute(self,image,score,image2,score2):
+    if score >= score2:
+      return (image, score, image2, score2,)
+    else: 
+      return (image2, score2, image, score,)
+    
 class ScoreConverterNode:
     @classmethod
     def INPUT_TYPES(cls):
@@ -769,36 +528,34 @@ class ScoreConverterNode:
 
         # Prepare the output UI
         output_ui = {}
-        if show_on_node == 1:
-            output_ui = {"ui": {"STRING": [score_str]}}
-
-        return (score_int, score_float, score_str, output_ui)
+        if show_on_node ==1:
+           output_ui = {"ui": {"STRING": [score_str]}}
 
+        return  (score_int, score_float, score_str, output_ui)
 
 class SamplerPropertiesNode:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": {
-            "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
-            "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-            "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step": 0.5, "round": 0.01}),
-            "denoise": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 1, "step": 0.1, "round": 0.01}),
-            "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
-            "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
+        return {"required":{
+                    "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
+                    "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
+                    "denoise": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 1, "step":0.1, "round": 0.01}),
+                    "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
+                    "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
+          
+                     }
+                }
 
-        }
-        }
-
-    RETURN_TYPES = ("STRING", "INT", "FLOAT", "FLOAT", "STRING", "STRING")
+    RETURN_TYPES = ("STRING","INT","FLOAT","FLOAT","STRING","STRING")
     FUNCTION = "sample"
 
     CATEGORY = "sampling"
 
-    def sample(self, ckpt_name,  steps, cfg, sampler_name, scheduler, denoise):
+    def sample(self, ckpt_name,  steps, cfg, sampler_name, scheduler,denoise):
         pass
-        return (ckpt_name, steps, cfg, sampler_name, scheduler, denoise)
-
-
+        return (ckpt_name, steps, cfg, sampler_name, scheduler,denoise)
+    
 NODE_CLASS_MAPPINGS = {
 
     "ImageFilterByIntScoreNode": ImageFilterByIntScoreNode,
@@ -807,11 +564,12 @@ NODE_CLASS_MAPPINGS = {
     "ImageAspectPadNode": ImageAspectPadNode,
     "ImageRankingNode": ImageRankingNode,
     "ImageQualityScoreNode": ImageQualityScoreNode,
-    "ScoreConverterNode": ScoreConverterNode,
+    "ScoreConverterNode":ScoreConverterNode,
     "MD5ImageHashNode": MD5ImageHashNode,
     "SamplerPropertiesNode": SamplerPropertiesNode,
-
-    "SaturationMatchingNode": SaturationMatchingNode
+    "CalculateAestheticScore": CalculateAestheticScore,
+    "LoadAesteticModel":AesteticModel,
+    "AesthetlcScoreSorter": AesthetlcScoreSorter,
 }
 
 
@@ -820,9 +578,10 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImageFilterByFloatScoreNode": "Image Filter (Float Score)",
     "ImageScaleToMin": "Image Scale To Min",
     "ImageRankingNode": "Image Ranking For Image Reward",
-    "ScoreConverterNode": "Score Converter (Aesthetic Score)",
-    "MD5ImageHashNode": "MD5 Image Hash",
-    "SamplerPropertiesNode": "Property Output Node.",
-    "SaturationMatchingNode": "SaturationMatchingNode",
-
-}
+    "ScoreConverterNode":"Score Converter (Aesthetic Score)",
+    "MD5ImageHashNode":"MD5 Image Hash",
+    "SamplerPropertiesNode":"Sampler input node",
+    "LoadAesteticModel": "LoadAesteticModel",
+    "CalculateAestheticScore": "CalculateAestheticScore",
+    "AesthetlcScoreSorter": "AesthetlcScoreSorter",
+    }

nodes/SegformerNode.py

@@ -6,35 +6,99 @@ import matplotlib.pyplot as plt
 import numpy as np
 import io
 from scipy.ndimage import binary_dilation
+import os
+from pathlib import Path
+import json
 
 
 model_names = [
-                "enes361/segformer_b2_clothes",
+                "sayeed99/segformer_b3_clothes",
                 "mattmdjaga/segformer_b0_clothes",
                 "mattmdjaga/segformer_b2_clothes",
                 "DiTo97/binarization-segformer-b3",
                 "s3nh/SegFormer-b0-person-segmentation",
                 "venture361/clothes_segmentation", 
+                   "itsitgroup/human-body-segmentation", 
                 "matei-dorian/segformer-b5-finetuned-human-parsing",
                 "Lexic0n/segformer-b0-finetuned-human-parsing",
                 "sam1120/segformer-b0-finetuned-neurosymbolic-contingency-bag1-v0.1-v0",
                 "ehsanhallo/segformer-b0-scene-parse-150"
                 ]
 
+class SegformerModelLoader:
+    _models = {}  # Cache for loaded models
+    _processors = {}  # Cache for loaded processors
+    
+    @classmethod
+    def get_local_checkpoints(cls):
+        """Get list of local checkpoint directories"""
+        checkpoints_dir = Path("models/segformer")
+        if not checkpoints_dir.exists():
+            checkpoints_dir.mkdir(parents=True, exist_ok=True)
+            return []
+        
+        # Look for config.json files in subdirectories
+        checkpoints = []
+        for path in checkpoints_dir.glob("*/config.json"):
+            checkpoints.append(path.parent.name)
+        return checkpoints
+
+    @classmethod
+    def load_model(cls, model_name, local_dir=None):
+        """Load model and processor with caching"""
+        # Check cache first
+        cache_key = model_name if not local_dir else str(local_dir)
+        if cache_key in cls._models:
+            return cls._models[cache_key], cls._processors[cache_key]
+
+        try:
+            if local_dir:
+                processor = SegformerImageProcessor.from_pretrained(local_dir)
+                model = AutoModelForSemanticSegmentation.from_pretrained(local_dir)
+            else:
+                processor = SegformerImageProcessor.from_pretrained(model_name)
+                model = AutoModelForSemanticSegmentation.from_pretrained(model_name)
+            
+            # Cache the loaded model and processor
+            cls._models[cache_key] = model
+            cls._processors[cache_key] = processor
+            return model, processor
+        except Exception as e:
+            print(f"Error loading model {model_name}: {str(e)}")
+            # Fallback to a reliable model
+            return cls.load_model("matei-dorian/segformer-b5-finetuned-human-parsing")
+
+    @classmethod
+    def clear_cache(cls):
+        """Clear the model cache"""
+        cls._models.clear()
+        cls._processors.clear()
+
+# Update the model_names list to include local checkpoints
+def get_available_models():
+    local_checkpoints = SegformerModelLoader.get_local_checkpoints()
+    return model_names + [f"local:{cp}" for cp in local_checkpoints]
+
 class SegformerNode:
     @classmethod
     def INPUT_TYPES(cls):
-        global model_names  # Assuming model_names is a list of model names
         return {
             "required": {
                 "image": ("IMAGE", {"default": None}),
-                "model_name": (model_names, {"default": model_names[0]}),
-            
-
+                "model_name": (get_available_models(), {"default": model_names[0]}),
+                "normalize_mask": ("BOOLEAN", {"default": True}),
+                "binary_mask": ("BOOLEAN", {"default": False}),
+                "resize_mode": (["nearest", "bilinear", "bicubic"], {"default": "bilinear"}),
+                "invert_mask": ("BOOLEAN", {"default": False}),
+                "show_preview": ("BOOLEAN", {"default": True}),
+                "return_individual_masks": ("BOOLEAN", {"default": False}),
+                "post_process": (["none", "erode", "dilate", "smooth"], {"default": "none"}),
+                "post_process_radius": ("INT", {"default": 3, "min": 1, "max": 10}),
+                "segment_groups": ("STRING", {"default": "", "multiline": True}),
             },
         }
 
-    RETURN_TYPES = ("IMAGE","MASK", "STRING")
+    RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE")  # Added IMAGE for preview
     FUNCTION = "segment_image"
     CATEGORY = "LexTools/ImageProcessing/Segmentation"
 
@@ -43,79 +107,199 @@ class SegformerNode:
         # self.processor = SegformerImageProcessor.from_pretrained("mattmdjaga/segformer_b2_clothes")
         # self.model = AutoModelForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b2_clothes")
         
-    def segment_image(self, image,model_name,):
-        show_on_node = False
-        self.processor = SegformerImageProcessor.from_pretrained(model_name)
-        self.model = AutoModelForSemanticSegmentation.from_pretrained(model_name)
-        i = 255. * image[0].cpu().numpy()
-        img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
-        inputs   = self.processor(images=img, return_tensors="pt")
-
-        outputs = self.model(**inputs)
-        logits = outputs.logits.cpu()
-
-        upsampled_logits = nn.functional.interpolate(
-            logits,
-            size=img.size[::-1],
-            mode="bilinear",
-            align_corners=False,
-        )
-
-        pred_seg = upsampled_logits.argmax(dim=1)[0]
-
-        # Convert the matplotlib figure to a PIL Image and return it
-        fig = plt.figure()
-        plt.imshow(pred_seg)
-        buf = io.BytesIO()
-        plt.savefig(buf, format='png')
-        buf.seek(0)
-        img2 = Image.open(buf)
+    def process_mask(self, mask, normalize=True, binary=False, invert=False, post_process="none", radius=3):
+        # Convert to float32 if not already
+        mask = mask.float()
         
-        i = ImageOps.exif_transpose(img2)
-        if i.getbands() != ("R", "G", "B", "A"):
-            i = i.convert("RGBA")
-
+        # Normalize to 0-1 range if requested
+        if normalize:
+            mask = (mask - mask.min()) / (mask.max() - mask.min() + 1e-8)
+        
+        # Convert to binary if requested
+        if binary:
+            mask = (mask > 0.5).float()
+        
+        # Apply post-processing
+        if post_process != "none":
+            kernel = torch.ones(2 * radius + 1, 2 * radius + 1)
+            if post_process == "erode":
+                mask = torch.nn.functional.conv2d(
+                    mask.unsqueeze(0).unsqueeze(0),
+                    kernel.unsqueeze(0).unsqueeze(0),
+                    padding=radius
+                ).squeeze() < kernel.sum()
+            elif post_process == "dilate":
+                mask = torch.nn.functional.conv2d(
+                    mask.unsqueeze(0).unsqueeze(0),
+                    kernel.unsqueeze(0).unsqueeze(0),
+                    padding=radius
+                ).squeeze() > 0
+            elif post_process == "smooth":
+                mask = torch.nn.functional.conv2d(
+                    mask.unsqueeze(0).unsqueeze(0),
+                    kernel.unsqueeze(0).unsqueeze(0),
+                    padding=radius
+                ).squeeze() / kernel.sum()
+            mask = mask.float()
+        
+        # Invert if requested
+        if invert:
+            mask = 1 - mask
             
-        img2 = np.array(img2).astype(np.float32) / 255.0
-        img2 = torch.from_numpy(img2)[None,]
+        return mask
 
-        if 'A' in i.getbands():
-            mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
-            mask = 1. - torch.from_numpy(mask)
-        else:
-            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
-        # Get the unique segments in the image
-        unique_segments = np.unique(pred_seg)
+    def create_preview(self, image, mask):
+        # Ensure image is in CHW format
+        if len(image.shape) == 2:
+            image = image.unsqueeze(0).repeat(3, 1, 1)
+        elif len(image.shape) == 3:
+            if image.shape[0] != 3:  # If channels are not in first dimension
+                image = image.permute(2, 0, 1)  # Move channels to first dimension
+        
+        # Ensure mask has correct dimensions
+        if len(mask.shape) == 3:
+            mask = mask.squeeze(0)
+        if len(mask.shape) > 2:
+            mask = mask.squeeze()
+            
+        # Resize mask to match image dimensions if needed
+        if mask.shape != image.shape[1:]:
+            mask = torch.nn.functional.interpolate(
+                mask.unsqueeze(0).unsqueeze(0),
+                size=image.shape[1:],
+                mode='bilinear',
+                align_corners=False
+            ).squeeze()
+        
+        # Create preview by concatenating image and mask
+        preview = image.clone()
+        preview = torch.cat([preview, mask.unsqueeze(0)], dim=0)
+        return preview
 
-        # Create a string with the information for each segment
-        segment_info = []
-        for segment in unique_segments:
-            # Get the name of the segment from the model's configuration
-            segment_name = self.model.config.id2label[segment]
+    def parse_segment_groups(self, groups_str):
+        if not groups_str.strip():
+            return {}
+        
+        groups = {}
+        for line in groups_str.split('\n'):
+            if ':' in line:
+                name, indices = line.split(':')
+                indices = [int(i.strip()) for i in indices.split(',') if i.strip()]
+                groups[name.strip()] = indices
+        return groups
 
-            # Here, you would replace these values with the actual accuracy and IoU for the segment
+    def segment_image(self, image, model_name, normalize_mask=True, binary_mask=False, 
+                     resize_mode="bilinear", invert_mask=False, show_preview=True,
+                     return_individual_masks=False, post_process="none", 
+                     post_process_radius=3, segment_groups=""):
+        try:
+            # Handle local checkpoint loading
+            if model_name.startswith("local:"):
+                local_dir = Path("models/segformer") / model_name[6:]
+                self.model, self.processor = SegformerModelLoader.load_model(model_name, local_dir)
+            else:
+                self.model, self.processor = SegformerModelLoader.load_model(model_name)
 
+            show_on_node = False
+            
+            # Process input image
+            i = 255. * image[0].cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+            inputs = self.processor(images=img, return_tensors="pt")
 
-            segment_info.append(f"Segment {segment}: {segment_name}")
+            # Get model outputs
+            outputs = self.model(**inputs)
+            logits = outputs.logits.cpu()
 
-        # Join the segment info strings into a single string
-        segment_info_str = "\n".join(segment_info)
+            # Upsample logits with specified resize mode
+            upsampled_logits = nn.functional.interpolate(
+                logits,
+                size=img.size[::-1],
+                mode=resize_mode,
+                align_corners=False if resize_mode != "nearest" else None,
+            )
 
+            pred_seg = upsampled_logits.argmax(dim=1)[0]
+            
+            # Parse segment groups if provided
+            segment_groups_dict = self.parse_segment_groups(segment_groups)
+            
+            # Create individual masks if requested
+            individual_masks = {}
+            segment_info = []
+            
+            # Get unique segments and process each
+            unique_segments = np.unique(pred_seg.numpy())
+            for segment in unique_segments:
+                segment_name = self.model.config.id2label[segment]
+                segment_info.append(f"Segment {segment}: {segment_name}")
+                
+                if return_individual_masks:
+                    mask = (pred_seg == segment).float()
+                    mask = self.process_mask(mask, normalize_mask, binary_mask, 
+                                          invert_mask, post_process, post_process_radius)
+                    individual_masks[segment_name] = mask
 
-        output_ui =  {"images": [img2]} if show_on_node else {}
+            # Create merged mask based on segment groups
+            if segment_groups_dict:
+                merged_mask = torch.zeros_like(pred_seg, dtype=torch.float32)
+                for group_name, indices in segment_groups_dict.items():
+                    group_mask = torch.zeros_like(pred_seg, dtype=torch.float32)
+                    for idx in indices:
+                        group_mask = torch.maximum(group_mask, (pred_seg == idx).float())
+                    merged_mask = torch.maximum(merged_mask, group_mask)
+                    segment_info.append(f"Group {group_name}: {indices}")
+            else:
+                merged_mask = torch.ones_like(pred_seg, dtype=torch.float32)
 
-        return {"result": (img2,mask, segment_info_str), "ui": output_ui}
+            # Process the final mask
+            merged_mask = self.process_mask(merged_mask, normalize_mask, binary_mask, 
+                                          invert_mask, post_process, post_process_radius)
+
+            # Create visualization
+            fig = plt.figure()
+            plt.imshow(pred_seg)
+            buf = io.BytesIO()
+            plt.savefig(buf, format='png')
+            buf.seek(0)
+            img2 = Image.open(buf)
+            
+            # Convert visualization to tensor
+            i = ImageOps.exif_transpose(img2)
+            if i.getbands() != ("R", "G", "B", "A"):
+                i = i.convert("RGBA")
+            img2 = np.array(img2).astype(np.float32) / 255.0
+            img2 = torch.from_numpy(img2)[None,]
+
+            # Create preview if requested
+            preview = self.create_preview(image[0], merged_mask) if show_preview else None
+
+            # Join segment info
+            segment_info_str = "\n".join(segment_info)
+            if return_individual_masks:
+                segment_info_str += "\n\nIndividual masks available for: " + ", ".join(individual_masks.keys())
+
+            output_ui = {"images": [img2]} if show_on_node else {}
+
+            # Return results
+            return {"result": (img2, merged_mask, segment_info_str, preview if preview is not None else img2), 
+                    "ui": output_ui}
+
+        except Exception as e:
+            import traceback
+            print(f"Error in segmentation: {str(e)}")
+            print(f"Traceback: {traceback.format_exc()}")
+            return {"result": (image, torch.zeros_like(image[0, :, :]), f"Error: {str(e)}", image), 
+                    "ui": {"images": [image]} if show_on_node else {}}
 
 class SegformerNodeMasks:
     @classmethod
     def INPUT_TYPES(cls):
-        global model_names  # Assuming model_names is a list of model names
         return {
             "required": {
                 "image": ("IMAGE", {"default": None}),
                 "segments_to_merge": ("STRING", {"default": "0"}),
-                "model_name": (model_names, {"default": model_names[0]})
-              
+                "model_name": (get_available_models(), {"default": model_names[0]}),
             },
         }
 
@@ -128,14 +312,17 @@ class SegformerNodeMasks:
 
     # Function to segment the image and return the merged segments as per the provided indices
     def segment_image(self, image, segments_to_merge, model_name):
+        # Handle local checkpoint loading
+        if model_name.startswith("local:"):
+            local_dir = Path("models/segformer") / model_name[6:]
+            self.model, self.processor = SegformerModelLoader.load_model(model_name, local_dir)
+        else:
+            self.model, self.processor = SegformerModelLoader.load_model(model_name)
+
         # Convert the segments_to_merge from string to list of integers
         show_on_node=False
         segments_to_merge = list(map(int, segments_to_merge.split(',')))
 
-        # Load the pretrained models and processors
-        self.processor = SegformerImageProcessor.from_pretrained(model_name)
-        self.model = AutoModelForSemanticSegmentation.from_pretrained(model_name)
-
         # Preprocess the image
         i = 255. * image[0].cpu().numpy()
         img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
@@ -195,106 +382,219 @@ class SegformerNodeMasks:
         merged_image_pil = Image.fromarray(merged_image)
         img2 = torch.from_numpy(np.array(merged_image_pil).astype(np.float32) / 255.0)[None,]
 
+        # Convert the merged mask to byte format (0-255) and ensure correct dimensionality
+        merged_mask = (merged_mask > 0).float()  # Convert to binary mask first
+        merged_mask = torch.clamp(merged_mask, 0, 1)
 
         output_ui =  {"images": [img2]} if show_on_node else {}
 
         return {"result": (img2, merged_mask, 'Merged Segments'), "ui": output_ui}
-    
-
 
 class SegformerNodeMergeSegments:
     @classmethod
     def INPUT_TYPES(cls):
-        global model_names  
         return {
             "required": {
                 "image": ("IMAGE", {"default": None}),
                 "segments_to_merge_str": ("STRING", {"default": ""}),
-                "model_name": (model_names, {"default": model_names[0]}),
-                "blur_radius": ("INT", {"default": 0}),
-                "dilation_radius": ("INT", {"default": 0}),  # Added dilation_radius
-                "intensity": ("FLOAT", {"default": 1.0}),  # Added intensity
-                "ceiling": ("FLOAT", {"default": 1.0}),  # Added ceiling
-
+                "model_name": (get_available_models(), {"default": model_names[0]}),
+                "normalize_mask": ("BOOLEAN", {"default": True}),
+                "binary_mask": ("BOOLEAN", {"default": False}),
+                "resize_mode": (["nearest", "bilinear", "bicubic"], {"default": "bilinear"}),
+                "invert_mask": ("BOOLEAN", {"default": False}),
+                "show_preview": ("BOOLEAN", {"default": True}),
+                "blur_radius": ("INT", {"default": 5, "min": 0, "max": 100}),
+                "dilation_radius": ("INT", {"default": 5, "min": 0, "max": 100}),
+                "intensity": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0}),
+                "ceiling": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0}),
             },
         }
 
     OUTPUT_NODE = True
-
-    RETURN_TYPES = ("IMAGE", "MASK", "STRING")
+    RETURN_TYPES = ("IMAGE", "MASK", "STRING", "IMAGE")  # Added IMAGE for preview
     FUNCTION = "merge_segments"
     CATEGORY = "LexTools/ImageProcessing/Segmentation"
 
     def __init__(self):
         pass
 
-    def merge_segments(self, image, segments_to_merge_str, model_name, blur_radius, dilation_radius, intensity, ceiling):  # Added dilation_radius in the arguments
-       
-        show_on_node=False
-    
+    def process_mask(self, mask, normalize=True, binary=False, invert=False, blur_radius=0, dilation_radius=0, intensity=1.0, ceiling=1.0):
+        # Convert to float32 if not already
+        if isinstance(mask, np.ndarray):
+            mask = torch.from_numpy(mask)
+        mask = mask.float()
+        
+        # Ensure mask is 2D
+        if len(mask.shape) > 2:
+            mask = mask.squeeze()
+        
+        # Normalize to 0-1 range if requested
+        if normalize:
+            min_val = mask.min()
+            max_val = mask.max()
+            if max_val > min_val:
+                mask = (mask - min_val) / (max_val - min_val)
+        
+        # Convert to binary if requested
+        if binary:
+            mask = (mask > 0.5).float()
+        
+        # Apply dilation if specified
+        if dilation_radius > 0:
+            kernel = torch.ones(2 * dilation_radius + 1, 2 * dilation_radius + 1)
+            mask = torch.nn.functional.conv2d(
+                mask.unsqueeze(0).unsqueeze(0),
+                kernel.unsqueeze(0).unsqueeze(0),
+                padding=dilation_radius
+            ).squeeze() > 0
+            mask = mask.float()
+
+        # Apply Gaussian blur for feathering
+        if blur_radius > 0:
+            # Ensure mask is 2D and in correct range for PIL
+            mask_np = (mask.squeeze().numpy() * 255).astype(np.uint8)
+            mask_pil = Image.fromarray(mask_np, mode='L')  # Use 'L' mode for grayscale
+            mask_pil = mask_pil.filter(ImageFilter.GaussianBlur(radius=blur_radius))
+            mask = torch.from_numpy(np.array(mask_pil).astype(np.float32) / 255.0)
+
+        # Apply intensity and ceiling
+        mask = torch.clamp(mask * intensity, 0, ceiling)
+        
+        # Invert if requested
+        if invert:
+            mask = 1 - mask
+            
+        return mask
+
+    def create_preview(self, image, mask):
+        # Create an RGBA preview with the mask as alpha channel
+        if len(image.shape) == 2:
+            image = image.unsqueeze(0).repeat(3, 1, 1)
+        elif len(image.shape) == 3:
+            if image.shape[0] != 3:  # If channels are not in first dimension
+                image = image.permute(2, 0, 1)  # Move channels to first dimension
+        
+        # Ensure mask has correct dimensions
+        if len(mask.shape) == 3:
+            mask = mask.squeeze(0)
+        if len(mask.shape) > 2:
+            mask = mask.squeeze()
+            
+        if mask.shape != image.shape[1:]:
+            mask = torch.nn.functional.interpolate(
+                mask.unsqueeze(0).unsqueeze(0),
+                size=image.shape[1:],
+                mode='bilinear',
+                align_corners=False
+            ).squeeze()
+        
+        preview = image.clone()
+        preview = torch.cat([preview, mask.unsqueeze(0)], dim=0)
+        return preview
+
+    def merge_segments(self, image, segments_to_merge_str, model_name, normalize_mask=True, 
+                      binary_mask=False, resize_mode="bilinear", invert_mask=False, 
+                      show_preview=True, blur_radius=5, dilation_radius=5, 
+                      intensity=1.0, ceiling=1.0):
         try:
-            self.processor = SegformerImageProcessor.from_pretrained(model_name)
-        except Exception:
-            print(f"Failed to load preprocessor for model {model_name}. Using preprocessor from mattmdjaga/segformer_b2_clothes instead.")
-            self.processor = SegformerImageProcessor.from_pretrained("matei-dorian/segformer-b5-finetuned-human-parsing")
-        self.model = AutoModelForSemanticSegmentation.from_pretrained(model_name)
+            # Handle local checkpoint loading
+            if model_name.startswith("local:"):
+                local_dir = Path("models/segformer") / model_name[6:]
+                self.model, self.processor = SegformerModelLoader.load_model(model_name, local_dir)
+            else:
+                self.model, self.processor = SegformerModelLoader.load_model(model_name)
+
+            show_on_node = False
             
-        i = 255. * image[0].cpu().numpy()
-        img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
-        inputs = self.processor(images=img, return_tensors="pt")
-
-        outputs = self.model(**inputs)
-        logits = outputs.logits.cpu()
-
-        upsampled_logits = nn.functional.interpolate(
-            logits,
-            size=img.size[::-1],
-            mode="bilinear",
-            align_corners=False,
-        )
-
-        pred_seg = upsampled_logits.argmax(dim=1)[0].numpy() 
-        unique_segments = np.unique(pred_seg)
-
-        segments_to_merge = list(map(int, segments_to_merge_str.split(',')))
-
-        merged_mask = np.zeros_like(pred_seg)
-
-        merged_segments = []
-        for segment in unique_segments:
-            if segment in segments_to_merge:
-                mask = np.where(pred_seg == segment, 1, 0)
-                mask = nn.functional.interpolate(torch.from_numpy(mask.astype(np.float32))[None, None,], size=(img.height, img.width), mode="nearest")[0,0].numpy() 
-
-                merged_mask = np.maximum(merged_mask, mask)
-                merged_segments.append(segment)
-
-        merged_mask = np.clip(merged_mask * intensity, 0, ceiling)  # Apply intensity and ceiling to the mask
-        if dilation_radius > 0:  # Dilate the mask if dilation_radius > 0
-            struct = np.ones((2 * dilation_radius + 1, 2 * dilation_radius + 1))
-            merged_mask = binary_dilation(merged_mask, structure=struct)
-        merged_mask_rgb = np.repeat(merged_mask[..., None], 3, axis=2)  
-        if blur_radius > 0:  # Blur the mask if radius > 0
-            merged_mask_rgb = Image.fromarray((merged_mask_rgb * 255).astype('uint8'))
-            merged_mask_rgb = merged_mask_rgb.filter(ImageFilter.GaussianBlur(radius=blur_radius))
-            merged_mask_rgb = np.array(merged_mask_rgb) / 255.0
-
-        merged_image = np.array(img) * merged_mask_rgb
-
-        merged_image_pil = Image.fromarray(merged_image.astype('uint8'))
-        if blur_radius > 0:  # Apply blur if radius > 0
-            merged_image_pil = merged_image_pil.filter(ImageFilter.GaussianBlur(radius=blur_radius))
+            # Get input image dimensions and ensure proper shape
+            input_image = image[0].cpu()
+            if len(input_image.shape) != 3:
+                raise ValueError(f"Expected input image with shape (H,W,C) or (C,H,W), got {input_image.shape}")
             
-        img2 = np.array(merged_image_pil).astype(np.float32) / 255.0
-        img2 = torch.from_numpy(img2).double()[None,]  
+            # Ensure image is in HWC format
+            if input_image.shape[0] == 3:  # If in CHW format
+                input_image = input_image.permute(1, 2, 0)
+            
+            input_height, input_width = input_image.shape[0:2]
+            
+            # Process input image
+            img = Image.fromarray((input_image.numpy() * 255).astype(np.uint8))
+            inputs = self.processor(images=img, return_tensors="pt")
 
-        merged_mask_torch = torch.from_numpy(merged_mask).float()[None,]  # change from double to float
+            outputs = self.model(**inputs)
+            logits = outputs.logits.cpu()
 
-        merged_segments_str = ','.join(map(str, merged_segments))
+            # Upsample logits to match input image size
+            upsampled_logits = nn.functional.interpolate(
+                logits,
+                size=(input_height, input_width),
+                mode=resize_mode,
+                align_corners=False if resize_mode != "nearest" else None,
+            )
 
-        output_ui =  {"images": [img2]} if show_on_node else {}
+            pred_seg = upsampled_logits.argmax(dim=1)[0].numpy()
+            unique_segments = np.unique(pred_seg)
 
-        return {"result": (img2, merged_mask_torch, merged_segments_str), "ui": output_ui}
+            # Handle empty segments string
+            if not segments_to_merge_str.strip():
+                segments_to_merge = []
+            else:
+                segments_to_merge = [int(s.strip()) for s in segments_to_merge_str.split(',') if s.strip()]
+
+            merged_mask = np.zeros((input_height, input_width), dtype=np.float32)
+            merged_segments = []
+
+            for segment in unique_segments:
+                if segment in segments_to_merge:
+                    mask = np.where(pred_seg == segment, 1, 0)
+                    merged_mask = np.maximum(merged_mask, mask)
+                    merged_segments.append(segment)
+
+            # Convert to tensor and process
+            merged_mask = torch.from_numpy(merged_mask)
+            merged_mask = self.process_mask(
+                merged_mask,
+                normalize=normalize_mask,
+                binary=binary_mask,
+                invert=invert_mask,
+                blur_radius=blur_radius,
+                dilation_radius=dilation_radius,
+                intensity=intensity,
+                ceiling=ceiling
+            )
+
+            # Ensure mask has correct dimensions for broadcasting
+            merged_mask_3d = merged_mask.unsqueeze(-1)  # Add channel dimension for broadcasting
+
+            # Apply mask to image
+            merged_image = input_image.numpy() * merged_mask_3d.numpy()
+            
+            # Convert back to tensor in CHW format
+            merged_image = torch.from_numpy(merged_image).permute(2, 0, 1).unsqueeze(0)
+
+            merged_segments_str = ','.join(map(str, merged_segments))
+            if not merged_segments:
+                merged_segments_str = "No segments selected"
+
+            # Create preview
+            if show_preview:
+                preview = self.create_preview(input_image.permute(2, 0, 1), merged_mask)
+            else:
+                preview = merged_image
+
+            output_ui = {"images": [merged_image]} if show_on_node else {}
+
+            return {"result": (merged_image, merged_mask, merged_segments_str, preview), 
+                    "ui": output_ui}
+
+        except Exception as e:
+            import traceback
+            print(f"Error merging segments: {str(e)}")
+            print(f"Traceback: {traceback.format_exc()}")
+            # Return original image and empty mask on error
+            empty_mask = torch.zeros((input_height, input_width), dtype=torch.float32)
+            return {"result": (image, empty_mask, f"Error: {str(e)}", image), 
+                    "ui": {"images": [image]} if show_on_node else {}}
 
     
     

nodes/__init__.py

@@ -0,0 +1,31 @@
+from importlib.util import find_spec
+from os.path import exists
+from subprocess import run
+
+is_portable = True if exists("python_embeded") else False
+
+def check_and_install_package(package_name: str, install_name: str = None) -> None:
+    if find_spec(package_name):
+        return
+
+    print(f"/_\ Installing {package_name}")
+
+    package = install_name if install_name else package_name
+
+    if is_portable:
+        command = f".\\python_embeded\\python.exe -s -m pip install {package}"
+    else:
+        command = f"pip install {package}"
+
+    process = run(command, shell=True, check=True, capture_output=True)
+
+print("/_\ Checking packages")
+
+check_and_install_package("transformers")
+check_and_install_package("pillow")
+check_and_install_package("matplotlib")
+check_and_install_package("numpy")
+check_and_install_package("scipy")
+check_and_install_package("fastapi")
+check_and_install_package("pytorch_lightning")
+check_and_install_package("clip", "git+https://github.com/openai/CLIP.git")

pyproject.toml

@@ -0,0 +1,39 @@
+[project]
+name = "comfyui-lextools"
+description = """
+A comprehensive toolkit for ComfyUI that provides advanced image processing, analysis, and AI-powered features:
+- Semantic segmentation with multiple pre-trained models and mask processing
+- Image classification (age, food, documents, NSFW content, AI detection)
+- Image captioning using BLIP
+- Image quality scoring and filtering
+- Workflow utilities for seed management and image aspect ratio handling
+"""
+version = "1.0.2"
+license = "MIT"
+dependencies = [
+    "torch>=2.0.0",
+    "transformers>=4.30.0",
+    "Pillow>=9.0.0",
+    "matplotlib>=3.0.0",
+    "numpy>=1.20.0",
+    "scipy>=1.7.0",
+    "huggingface_hub>=0.19.0"
+]
+
+[project.urls]
+Repository = "https://github.com/SOELexicon/ComfyUI-LexTools"
+Documentation = "https://github.com/SOELexicon/ComfyUI-LexTools/blob/main/README.md"
+
+[tool.comfy]
+PublisherId = "lexicon"
+DisplayName = "ComfyUI-LexTools"
+Description = "Advanced image processing and AI analysis toolkit for ComfyUI"
+Icon = "🛠️"
+Tags = [
+    "image processing",
+    "segmentation",
+    "classification",
+    "captioning",
+    "workflow",
+    "utilities"
+]

requirements.txt

@@ -1,4 +1,5 @@
 numpy
 opencv-python
 git+https://github.com/facebookresearch/detectron2.git
-pyodbc
+pyodbc
+pytorch_lightning