Several furhter helper nodes

README.md

@@ -7,9 +7,13 @@ A comprehensive collection of useful custom nodes for ComfyUI workflows. Designe
 
 ## 🚀 Current Tools
 
-### 📁 Directory Image Tools
+### 📁 Tools
 - **DirectoryImageLoader**: Load images from a directory by index
 - **DirectoryImageInfo**: Get detailed information about images in a directory
+- **Aspect Ratio and Tile size calculator**: Calculates the aspect ratio of an image and determines optimal tile dimensions based on a target megapixel value, upscale factor, and optional padding. Useful for tiled image processing workflows.
+- **BLIP2 Image Caption**: Automatically generates a descriptive caption for an input image using Salesforce’s BLIP image captioning model. Ideal for semantic image analysis or annotation tasks.
+- **Boolean Toggle**: Returns a boolean value from a toggle input. Useful for conditionally controlling workflow branches.
+- **Concatenate and Test if Empty**: Concatenates up to four text inputs and checks whether the result is empty. Outputs both the combined string and a boolean indicating if it's empty.
 
 *More tools coming soon! This is just the beginning of the Skycoder Tools collection.*
 
@@ -33,4 +37,6 @@ A comprehensive collection of useful custom nodes for ComfyUI workflows. Designe
 ```bash
 cd ComfyUI/custom_nodes
 git clone https://github.com/skycoder/comfyui-skycoder-tools.git
-# Restart ComfyUI
+# Restart ComfyUI
+### 🧠 Image Analysis & Utilities
+

__init__.py

@@ -4,15 +4,30 @@ A collection of useful custom nodes for ComfyUI workflows
 """
 
 from .nodes.directory_image_loader import NODE_CLASS_MAPPINGS as DIR_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as DIR_DISPLAY_MAPPINGS
+from .Aspect_Ratio_and_Tile_size_calculator import NODE_CLASS_MAPPINGS as ASPECT_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as ASPECT_DISPLAY_MAPPINGS
+from .blipimagecaptioning import NODE_CLASS_MAPPINGS as BLIP_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as BLIP_DISPLAY_MAPPINGS
+from .BooleanToggleNode import NODE_CLASS_MAPPINGS as TOGGLE_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as TOGGLE_DISPLAY_MAPPINGS
+from .concatenate_and_test_if_empty import NODE_CLASS_MAPPINGS as CONCAT_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS as CONCAT_DISPLAY_MAPPINGS
 
-# Kombiniere alle Node-Mappings (für zukünftige Erweiterungen)
+# Combine all Node Mappings
 NODE_CLASS_MAPPINGS = {}
 NODE_DISPLAY_NAME_MAPPINGS = {}
 
-# Directory Image Loader Nodes
 NODE_CLASS_MAPPINGS.update(DIR_MAPPINGS)
 NODE_DISPLAY_NAME_MAPPINGS.update(DIR_DISPLAY_MAPPINGS)
 
+NODE_CLASS_MAPPINGS.update(ASPECT_MAPPINGS)
+NODE_DISPLAY_NAME_MAPPINGS.update(ASPECT_DISPLAY_MAPPINGS)
+
+NODE_CLASS_MAPPINGS.update(BLIP_MAPPINGS)
+NODE_DISPLAY_NAME_MAPPINGS.update(BLIP_DISPLAY_MAPPINGS)
+
+NODE_CLASS_MAPPINGS.update(TOGGLE_MAPPINGS)
+NODE_DISPLAY_NAME_MAPPINGS.update(TOGGLE_DISPLAY_MAPPINGS)
+
+NODE_CLASS_MAPPINGS.update(CONCAT_MAPPINGS)
+NODE_DISPLAY_NAME_MAPPINGS.update(CONCAT_DISPLAY_MAPPINGS)
+
 # Exportiere alle Node-Mappings für ComfyUI
 __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
 

nodes/Aspect_Ratio_and_Tile_size_calculator.py

@@ -0,0 +1,64 @@
+from PIL import Image
+import numpy as np
+from math import sqrt, floor
+
+class Aspect_Ratio_and_Tile_size_calculator:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "tile_megapixel": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 100.0, "step": 0.01, "label": "Tile Megapixel"}),
+                "upscale_factor": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 100.0, "step": 0.01, "label": "Upscale Factor"}),
+                "tile_padding": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1, "label": "Tile Padding"}),
+            }
+        }
+
+    RETURN_TYPES = ("FLOAT", "INT", "INT", "FLOAT", "INT", "INT", "INT")
+    RETURN_NAMES = ("aspect_ratio", "width", "height", "upscale_factor", "tile_width", "tile_height", "tile_padding")
+    FUNCTION = "get_aspect_ratio"
+    CATEGORY = "Custom"
+
+    def get_aspect_ratio(self, image, tile_megapixel, upscale_factor, tile_padding):
+        # Tensor to PIL conversion
+        def tensor2pil(img):
+            if hasattr(img, "__len__") and not isinstance(img, (bytes, str)) and len(img) > 0:
+                img = img[0]
+            img = img.detach().cpu().numpy()
+            img = (img * 255).astype(np.uint8)
+            if img.shape[0] == 3:
+                img = np.transpose(img, (1, 2, 0))
+            return Image.fromarray(img)
+
+        pil_img = tensor2pil(image)
+        width, height = pil_img.size
+        aspect = width / height if height else 0.0
+
+        # Variablen
+        a = upscale_factor
+        b = tile_megapixel
+        c = width
+        d = height
+        p = tile_padding
+
+        # Berechnung tile_width und tile_height
+        if c > 0 and d > 0 and b > 0 and a > 0:
+            divisor = floor((c * a) / sqrt(b * 1000000 * c / d)) + 1
+            tile_width = (c * a) / divisor if divisor > 0 else c * a
+            tile_height = tile_width * d / c
+        else:
+            tile_width = 0.0
+            tile_height = 0.0
+
+        # Padding addieren
+        tile_width += p
+        tile_height += p
+
+        return (float(aspect), int(width), int(height), float(upscale_factor), int(tile_width), int(tile_height), int(tile_padding))
+
+NODE_CLASS_MAPPINGS = {
+    "Aspect_Ratio_and_Tile_size_calculator": Aspect_Ratio_and_Tile_size_calculator,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "Aspect_Ratio_and_Tile_size_calculator": "Aspect Ratio and Tile size calculator",
+}

nodes/BooleanToggleNode.py

@@ -0,0 +1,23 @@
+class BooleanToggle:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "toggle": ("BOOLEAN", {"default": False, "label": "Boolean Toggle"}),
+            }
+        }
+
+    RETURN_TYPES = ("BOOLEAN",)
+    RETURN_NAMES = ("toggle_value",)
+    FUNCTION = "toggle_func"
+    CATEGORY = "Custom"
+
+    def toggle_func(self, toggle):
+        return (toggle,)
+
+NODE_CLASS_MAPPINGS = {
+    "BooleanToggle": BooleanToggle,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "BooleanToggle": "Boolean Toggle",
+}

nodes/blipimagecaptioning.py

@@ -0,0 +1,50 @@
+import torch
+from transformers import BlipProcessor, BlipForConditionalGeneration
+from PIL import Image
+import folder_paths
+import numpy as np
+
+class BLIP2Captioning:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE", {"tooltip": "Input image to generate caption for."})
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("caption",)
+    FUNCTION = "generate_caption"
+    CATEGORY = "BLIP2"
+
+    def __init__(self):
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model_id = "Salesforce/blip-image-captioning-base"
+
+        # Laden von Processor & Modell
+        self.processor = BlipProcessor.from_pretrained(self.model_id)
+        self.model = BlipForConditionalGeneration.from_pretrained(self.model_id).to(self.device)
+
+    def generate_caption(self, image):
+        # Sicheres Konvertieren: ComfyUI-Tensor → PIL
+        pil_image = tensor2pil(image).convert("RGB")
+
+        # BLIP Processing + Inferenz
+        inputs = self.processor(images=pil_image, return_tensors="pt").to(self.device)
+        out = self.model.generate(**inputs)
+        caption = self.processor.decode(out[0], skip_special_tokens=True)
+
+        return (caption,)
+
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+NODE_CLASS_MAPPINGS = {
+    "BLIP2Captioning": BLIP2Captioning
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "BLIP2Captioning": "BLIP2 Image Caption"
+}
+

nodes/concatenate_and_test_if_empty.py

@@ -0,0 +1,31 @@
+class ConcatenateAndTestIfEmpty:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "optional": {
+                "text1": ("STRING", {"forceInput": True}),
+                "text2": ("STRING", {"forceInput": True}),
+                "text3": ("STRING", {"forceInput": True}),
+                "text4": ("STRING", {"forceInput": True}),
+            }
+        }
+
+    RETURN_TYPES = ("BOOLEAN", "STRING")
+    RETURN_NAMES = ("is_empty", "result")
+    FUNCTION = "concat_and_test"
+    CATEGORY = "Custom"
+
+    def concat_and_test(self, text1=None, text2=None, text3=None, text4=None):
+        result = ""
+        for t in (text1, text2, text3, text4):
+            if t is not None:
+                result += t
+        is_empty = result == ""
+        return (is_empty, result)
+
+NODE_CLASS_MAPPINGS = {
+    "ConcatenateAndTestIfEmpty": ConcatenateAndTestIfEmpty,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ConcatenateAndTestIfEmpty": "Concatenate and Test if Empty",
+}

pyproject.toml

@@ -7,7 +7,7 @@ name = "comfyui-skycoder-tools"
 version = "1.0.0"
 description = "A collection of useful custom nodes for ComfyUI workflows"
 authors = [
-    {name = "Skycoder", email = "skycoder@example.com"}
+    {name = "Skycoder182", email = "skycoder@example.com"}
 ]
 license = {text = "MIT"}
 readme = "README.md"
@@ -31,7 +31,11 @@ dependencies = [
 ]
 
 [project.urls]
-Homepage = "https://github.com/skycoder/comfyui-skycoder-tools"
-Repository = "https://github.com/skycoder/comfyui-skycoder-tools"
-Issues = "https://github.com/skycoder/comfyui-skycoder-tools/issues"
-Discussions = "https://github.com/skycoder/comfyui-skycoder-tools/discussions"
+Homepage = "https://github.com/skycoder182/comfyui-skycoder-tools"
+Repository = "https://github.com/skycoder182/comfyui-skycoder-tools"
+Issues = "https://github.com/skycoder182/comfyui-skycoder-tools/issues"
+Discussions = "https://github.com/skycoder182/comfyui-skycoder-tools/discussions"
+
+[tool.comfy]
+PublisherId = "skycoder"
+DisplayName = "Skycoder Tools"