0.41

README.md

@@ -1,4 +1,4 @@
-# 🔗 Comfyui : Bjornulf_custom_nodes v0.39 🔗
+# 🔗 Comfyui : Bjornulf_custom_nodes v0.41 🔗
 
 # Coffee : ☕☕☕☕☕ 5/5
 
@@ -33,7 +33,7 @@ huggingface-cli download comfyanonymous/flux_text_encoders clip_l.safetensors --
 huggingface-cli download comfyanonymous/flux_text_encoders t5xxl_fp16.safetensors --local-dir /workspace/ComfyUI/models/clip
 huggingface-cli download black-forest-labs/FLUX.1-dev ae.safetensors --local-dir /workspace/ComfyUI/models/vae
 ```
-To use Flux you can just drag and drop in your browser the .json from my github repo : `workflows/FLUX_dev_troll.json`, direct link : <https://github.com/justUmen/ComfyUI-BjornulfNodes/blob/main/workflows/FLUX_dev_troll.json>.  
+To use Flux you can just drag and drop in your browser comfyui interface the .json from my github repo : `workflows/FLUX_dev_troll.json`, direct link : <https://github.com/justUmen/ComfyUI-BjornulfNodes/blob/main/workflows/FLUX_dev_troll.json>.  
 
 For downloading from civitai (get token here <https://civitai.com/user/account>), just copy/paste the link of checkpoint you want to download and use something like that, with your token in URL :  
 ```
@@ -42,11 +42,46 @@ wget --content-disposition -P /workspace/ComfyUI/models/checkpoints "https://civ
 ```
 If you have any issues with this template from Runpod, please let me know, I'm here to help. 😊   
 
-# Dependencies
+# 🏗 Dependencies (nothing to do for runpod ☁)
+
+## 🪟🐍 Windows : Install dependencies on windows with embedded python (portable version)
+
+First you need to find this python_embedded `python.exe`, then you can right click or shift + right click inside the folder in your file manager to open a terminal there.  
+
+This is where I have it, with the command you need :  
+`H:\ComfyUI_windows_portable\python_embeded> .\python.exe -m pip install pydub ollama`  
+
+When you have to install something you can retake the same code and install the dependency you want :  
+`.\python.exe -m pip install whateveryouwant`  
+
+You can then run comfyui.  
+
+## 🐧🐍 Linux : Install dependencies (without venv, not recommended)
 
 - `pip install ollama` (you can also install ollama if you want :  https://ollama.com/download) - You don't need to really install it if you don't want to use my ollama node. (BUT you need to run `pip install ollama`)
 - `pip install pydub` (for TTS node)
 
+## 🐧🐍 Linux : Install dependencies with python virtual environment (venv)
+
+If you want to use a python virtual environment only for comfyUI, which I recommended, you can do that for example (also pre-install pip) :  
+
+```
+sudo apt-get install python3-venv python3-pip
+python3 -m venv /the/path/you/want/venv/bjornulf_comfyui
+```
+
+Once you have your environment in this new folder, you can activate it with and install dependencies inside :  
+
+```
+source /the/path/you/want/venv/bjornulf_comfyui/bin/activate
+pip install ollama pydub
+```
+
+Then you can start comfyui with this environment (notice that you need to re-activate it each time you want to launch comfyui) :  
+
+```
+cd /where/you/installed/ComfyUI && python main.py
+```
 # Nodes menu
 
 1. [👁 Show (Text, Int, Float)](#1----show-text-int-float)
@@ -64,15 +99,15 @@ If you have any issues with this template from Runpod, please let me know, I'm h
 13. [📏 Resize Exact](#1314------resize-and-save-exact-name-%EF%B8%8F)
 14. [🖼 Save Exact name](#1314------resize-and-save-exact-name-%EF%B8%8F)
 15. [💾 Save Text](#15----save-text)
-16. [🖼💬 Save image for Bjornulf LobeChat](#16----save-image-for-bjornulf-lobechat-for-my-custom-lobe-chat)
+16. [💾🖼💬 Save image for Bjornulf LobeChat](#16-----save-image-for-bjornulf-lobechat-for-my-custom-lobe-chat)
-17. [🖼 Save image as `tmp_api.png` Temporary API](#17----save-image-as-tmp_apipng-temporary-api-%EF%B8%8F)
+17. [💾🖼 Save image as `tmp_api.png` Temporary API](#17-----save-image-as-tmp_apipng-temporary-api-%EF%B8%8F)
-18. [🖼📁 Save image to a chosen folder name](#18----save-image-to-a-chosen-folder-name)
+18. [💾🖼📁 Save image to a chosen folder name](#18-----save-image-to-a-chosen-folder-name)
 19. [🦙 Ollama](#19----ollama)
 20. [📹 Video Ping Pong](#20----video-ping-pong)
 21. [📹 Images to Video](#21----images-to-video)
 22. [🔲 Remove image Transparency (alpha)](#22----remove-image-transparency-alpha)
 23. [🔲 Image to grayscale (black & white)](#23----image-to-grayscale-black--white)
-24. [🖼+🖼 Combine images (Background + Overlay)](#24----combine-images-background--overlay)
+24. [🖼+🖼 Stack two images (Background + Overlay)](#24----combine-images-background--overlay)
 25. [🟩➜▢ Green Screen to Transparency](#25----green-screen-to-transparency)
 26. [🎲 Random line from input](#26----random-line-from-input)
 27. [♻ Loop (All Lines from input)](#27----loop-all-lines-from-input)
@@ -93,7 +128,7 @@ If you have any issues with this template from Runpod, please let me know, I'm h
 42. [♻ Loop (Model+Clip+Vae) - aka Checkpoint / Model](#42----loop-modelclipvae---aka-checkpoint--model)
 43. [📂🖼 Load Images from output folder](#43----load-images-from-output-folder)
 44. [🖼🔍 Select an Image, Pick](#44----select-an-image-pick)
-45. [🔀 If-Else (input == compare_with)](#45----if-else-input--compare_with)
+45. [🔀 If-Else (input / compare_with)](#45----if-else-input--compare_with)
 
 # 📝 Changelog
 
@@ -140,6 +175,7 @@ If you have any issues with this template from Runpod, please let me know, I'm h
 - **v0.38**: New node : If-Else logic. (input == compare_with), examples with different latent space size. +fix some deserialization issues.
 - **v0.39**: Add variables management to Advanced Write Text node.
 - **v0.40**: Add variables management to Loop Advanced Write Text node. Add menu for all nodes to the README.
+- **v0.41**: Two new nodes : image details and combine images. Also ❗ Big changes to the If-Else node. (+many minor changes)
 
 # 📝 Nodes descriptions
 
@@ -272,7 +308,7 @@ Resize an image to exact dimensions. The other node will save the image to the e
 **Description:**  
 Save the given text input to a file. Useful for logging and storing text data.
 
-## 16 - 🖼💬 Save image for Bjornulf LobeChat (❗For my custom [lobe-chat](https://github.com/justUmen/Bjornulf_lobe-chat)❗)
+## 16 - 💾🖼💬 Save image for Bjornulf LobeChat (❗For my custom [lobe-chat](https://github.com/justUmen/Bjornulf_lobe-chat)❗)
 ![Save Bjornulf Lobechat](screenshots/save_bjornulf_lobechat.png)
 
 **Description:**  
@@ -282,13 +318,13 @@ The name will start at `api_00001.png`, then `api_00002.png`, etc...
 It will also create a link to the last generated image at the location `output/BJORNULF_API_LAST_IMAGE.png`.  
 This link will be used by my custom lobe-chat to copy the image inside the lobe-chat project.  
 
-## 17 - 🖼 Save image as `tmp_api.png` Temporary API ⚠️💣
+## 17 - 💾🖼 Save image as `tmp_api.png` Temporary API ⚠️💣
 ![Save Temporary API](screenshots/save_tmp_api.png)
 
 **Description:**  
 Save image for short-term use : ./output/tmp_api.png ⚠️💣  
 
-## 18 - 🖼📁 Save image to a chosen folder name
+## 18 - 💾🖼📁 Save image to a chosen folder name
 ![Save Temporary API](screenshots/save_image_to_folder.png)
 
 **Description:**  
@@ -333,11 +369,11 @@ Convert an image to grayscale (black & white)
 Example : I sometimes use it with Ipadapter to disable color influence.  
 But you can sometimes also want a black and white image... 
 
-## 24 - 🖼+🖼 Combine images (Background + Overlay)
+## 24 - 🖼+🖼 Stack two images (Background + Overlay)
-![Combine Images](screenshots/combine_background_overlay.png)
+![Superpose Images](screenshots/combine_background_overlay.png)
 
 **Description:**  
-Combine two images into a single image : a background and one (or several) transparent overlay. (allow to have a video there, just send all the frames and recombine them after.)  
+Stack two images into a single image : a background and one (or several) transparent overlay. (allow to have a video there, just send all the frames and recombine them after.)  
 Update 0.11 : Add option to move vertically and horizontally. (from -50% to 150%)  
 ❗ Warning : For now, `background` is a static image. (I will allow video there later too.)  
 ⚠️ Warning : If you want to directly load the image with transparency, use my node `🖼 Load Image with Transparency ▢` instead of the `Load Image` node.  
@@ -563,7 +599,6 @@ Loop over all the trios from several checkpoint node.
 **Description:**  
 Quickly select all images from a folder inside the output folder. (Not recursively.)  
 So... As you can see from the screenshot the images are split based on their resolution.  
-It is not a choice I made, it is something that is part of the comfyui environment.  
 It's also not possible to edit dynamically the number of outputs, so I just picked a number : 4.  
 The node will separate the images based on their resolution, so with this node you can have 4 different resolutions per folder. (If you have more than that, maybe you should have another folder...)  
 To avoid error or crash if you have less than 4 resolutions in a folder, the node will just output white tensors. (white square image.)  
@@ -578,6 +613,8 @@ If you are satisfied with this logic, you can then select all these nodes, right
 Here is another example of the same thing but excluding the save folder node :  
 ![pick input](screenshots/bjornulf_save_character_group2.png)
 
+⚠️ If you really want to regroup all the images in one flow, you can use my node 47 `Combine images` to put them all together.  
+
 ### 44 - 🖼🔍 Select an Image, Pick
 
 ![pick input](screenshots/select_image.png)
@@ -589,29 +626,71 @@ Useful in combination with my Load images from folder and preview image nodes.
 You can also of course make a group node, like this one, which is the same as the screenshot above :  
 ![pick input](screenshots/select_image_group.png)
 
-### 45 - 🔀 If-Else (input == compare_with)
+### 45 - 🔀 If-Else (input / compare_with)
+
+![if else](screenshots/if_0.png)
 
-![if else](screenshots/if1.png)
 
 **Description:**  
-If the `input` given is equal to the `compare_with` given in the widget, it will forward `send_if_true`, otherwise it will forward `send_if_false`.  
+If the `input` given is equal to the `compare_with` given in the widget, it will forward `send_if_true`, otherwise it will forward `send_if_false`. (If no `send_if_false` it will return `None`.)  
 You can forward anything, below is an example of forwarding a different size of latent space depending if it's SDXL or not.  
 
-![if else](screenshots/if2.png)
+![if else](screenshots/if_0_1.png)
+
+Here is an example of the node with all outputs displayed with Show text nodes :  
+
+![if else](screenshots/if_1.png)
+
+`send_if_false` is optional, if not connected, it will be replaced by `None`.  
+
+![if else](screenshots/if_2.png)
 
 If-Else are chainables, just connect `output` to `send_if_false`.  
 ⚠️ Always simply test `input` with `compare_with`, and connect the desired value to `send_if_true`. ⚠️  
-Here a simple example with 2 If-Else nodes (choose between 3 different resolutions). ❗ Notice the same write text node is connected to both If-Else nodes input :  
+Here a simple example with 2 If-Else nodes (choose between 3 different resolutions).  
+❗ Notice that the same write text node is connected to both If-Else nodes input :  
 
-![if else](screenshots/if3.png)
+![if else](screenshots/if_3.png)
 
 Let's take a similar example but let's use my Write loop text node to display all 3 types once :  
 
-![if else](screenshots/if4.png)
+![if else](screenshots/if_4.png)
 
-If you understood the previous examples, here is a complete example that will create 3 images, landscape, portrait and normal :  
+If you understood the previous examples, here is a complete example that will create 3 images, landscape, portrait and square :  
 
-![if else](screenshots/if5.png)
+![if else](screenshots/if_5.png)
 
 Workflow is hidden for simplicity, but is very basic, just connect latent to Ksampler, nothing special.)  
-You can also connect the same advanced loop write text node with my save folder node to save the images (landscape/portrait/normal) in separate folders, but you do you...  
+You can also connect the same advanced loop write text node with my save folder node to save the images (landscape/portrait/square) in separate folders, but you do you...  
+
+### 46 - 🖼🔍 Image Details
+
+**Description:**  
+Display the details of an image. (width, height, has_transparency, orientation, type)  
+`RGBA` is considered as having transparency, `RGB` is not.  
+`orientation` can be `landscape`, `portrait` or `square`.  
+
+![image details](screenshots/image_details_1.png)
+
+### 47 - 🖼🔗 Combine Images
+
+**Description:**  
+Combine multiple images (A single image or a list of images.)  
+
+There are two types of logic to "combine images". With "all_in_one" enabled, it will combine all the images into one tensor.  
+Otherwise it will send the images one by one. (check examples below) :  
+
+This is an example of the "all_in_one" option disabled :  
+
+![combine images](screenshots/combine_images_1.png)
+
+But for example, if you want to use my node `select an image, pick`, you need to enable `all_in_one` and the images must all have the same resolution.  
+
+![combine images](screenshots/combine_images_2.png)
+
+You can notice that there is no visible difference when you use `all_in_one` with `preview image` node. (this is why I added the `show text` node, not that show text will make it blue, because it's an image/tensor.)  
+
+When you use `combine image` node, you can actually also send many images at once, it will combine them all.  
+Here is an example with `Load images from folder` node, `Image details` node and `Combine images` node. (Of course it can't have `all_in_one` set to True in this situation because the images have different resolutions) :  
+
+![combine images](screenshots/combine_images_3.png)

__init__.py

@@ -50,6 +50,8 @@ from .load_images_from_folder import LoadImagesFromSelectedFolder
 from .select_image_from_list import SelectImageFromList
 from .random_model_selector import RandomModelSelector
 from .if_else import IfElse
+from .image_details import ImageDetails
+from .combine_images import CombineImages
 
 # from .pass_preview_image import PassPreviewImage
 # from .check_black_image import CheckBlackImage
@@ -59,6 +61,8 @@ from .if_else import IfElse
 NODE_CLASS_MAPPINGS = {
     # "Bjornulf_CustomStringType": CustomStringType,
     "Bjornulf_ollamaLoader": ollamaLoader,
+    "Bjornulf_CombineImages": CombineImages,
+    "Bjornulf_ImageDetails": ImageDetails,
     "Bjornulf_IfElse": IfElse,
     "Bjornulf_RandomModelSelector": RandomModelSelector,
     "Bjornulf_SelectImageFromList": SelectImageFromList,
@@ -149,14 +153,13 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     # "Bjornulf_ShowFloat": "👁 Show (Float)",
     "Bjornulf_ImageMaskCutter": "🖼✂ Cut Image with Mask",
     "Bjornulf_LoadImageWithTransparency": "🖼 Load Image with Transparency ▢",
-    "Bjornulf_CombineBackgroundOverlay": "🖼+🖼 Combine images (Background+Overlay alpha)",
+    "Bjornulf_CombineBackgroundOverlay": "🖼+🖼 Stack two images (Background+Overlay alpha)",
     "Bjornulf_GrayscaleTransform": "🖼➜🔲 Image to grayscale (black & white)",
     "Bjornulf_RemoveTransparency": "▢➜⬛ Remove image Transparency (alpha)",
     "Bjornulf_ResizeImage": "📏 Resize Image",
-    "Bjornulf_SaveImagePath": "🖼 Save Image (exact path, exact name) ⚠️💣",
+    "Bjornulf_SaveImagePath": "💾🖼 Save Image (exact path, exact name) ⚠️💣",
-    "Bjornulf_SaveImageToFolder": "🖼📁 Save Image(s) to a folder",
+    "Bjornulf_SaveImageToFolder": "💾🖼📁 Save Image(s) to a folder",
-    "Bjornulf_SaveTmpImage": "🖼 Save Image (tmp_api.png) ⚠️💣",
+    "Bjornulf_SaveTmpImage": "💾🖼 Save Image (tmp_api.png) ⚠️💣",
-    # "Bjornulf_SaveApiImage": "🖼 Save Image (./output/api_00001.png...)",
     "Bjornulf_SaveText": "💾 Save Text",
     # "Bjornulf_LoadText": "📥 Load Text",
     "Bjornulf_CombineTexts": "🔗 Combine (Texts)",
@@ -169,7 +172,9 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "Bjornulf_PauseResume": "⏸️ Paused. Resume or Stop, Pick 👇",
     "Bjornulf_LoadImagesFromSelectedFolder": "📂🖼 Load Images from output folder",
     "Bjornulf_SelectImageFromList": "🖼🔍 Select an Image, Pick",
-    "Bjornulf_IfElse": "🔀 If-Else (input == compare_with)",
+    "Bjornulf_IfElse": "🔀 If-Else (input / compare_with)",
+    "Bjornulf_ImageDetails": "🖼🔍 Image Details",
+    "Bjornulf_CombineImages": "🖼🔗 Combine Images",
 }
 
 WEB_DIRECTORY = "./web"

combine_images.py

@@ -0,0 +1,86 @@
+import torch
+import numpy as np
+import logging
+
+class CombineImages:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "number_of_images": ("INT", {"default": 2, "min": 1, "max": 50, "step": 1}),
+                "all_in_one": ("BOOLEAN", {"default": False}),
+                "image_1": ("IMAGE",),
+            },
+            "hidden": {
+                **{f"image_{i}": ("IMAGE",) for i in range(2, 51)}
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "all_in_one_images"
+    OUTPUT_NODE = True
+    CATEGORY = "Bjornulf"
+
+    def all_in_one_images(self, number_of_images, all_in_one, ** kwargs):
+        images = [kwargs[f"image_{i}"] for i in range(1, number_of_images + 1) if f"image_{i}" in kwargs]
+        
+        for i, img in enumerate(images):
+            logging.info(f"Image {i+1} shape: {img.shape}, dtype: {img.dtype}, min: {img.min()}, max: {img.max()}")
+        
+        if all_in_one:
+            # Check if all images have the same shape
+            shapes = [img.shape for img in images]
+            if len(set(shapes)) > 1:
+                raise ValueError("All images must have the same resolution to use all_in_one. "
+                                 f"Found different shapes: {shapes}")
+            
+            # Convert images to float32 and scale to 0-1 range if necessary
+            processed_images = []
+            for img in images:
+                if isinstance(img, np.ndarray):
+                    if img.dtype == np.uint8:
+                        img = img.astype(np.float32) / 255.0
+                    elif img.dtype == np.bool_:
+                        img = img.astype(np.float32)
+                elif isinstance(img, torch.Tensor):
+                    if img.dtype == torch.uint8:
+                        img = img.float() / 255.0
+                    elif img.dtype == torch.bool:
+                        img = img.float()
+                
+                # Ensure the image is 3D (height, width, channels)
+                if img.ndim == 4:
+                    img = img.squeeze(0)
+                
+                processed_images.append(img)
+            
+            # Stack all images along a new dimension
+            if isinstance(processed_images[0], np.ndarray):
+                all_in_oned = np.stack(processed_images)
+                all_in_oned = torch.from_numpy(all_in_oned)
+            else:
+                all_in_oned = torch.stack(processed_images)
+            
+            # Ensure the output is in the format expected by the preview node
+            # (batch, height, width, channels)
+            if all_in_oned.ndim == 3:
+                all_in_oned = all_in_oned.unsqueeze(0)
+            if all_in_oned.shape[-1] != 3 and all_in_oned.shape[-1] != 4:
+                all_in_oned = all_in_oned.permute(0, 2, 3, 1)
+            
+            return (all_in_oned,)
+        else:
+            # Return a single tuple containing all images (original behavior)
+            return (images,)
+
+    @classmethod
+    def IS_CHANGED(cls, **kwargs):
+        return float("NaN")
+
+    @classmethod
+    def VALIDATE_INPUTS(cls, ** kwargs):
+        if kwargs['all_in_one']:
+            cls.OUTPUT_IS_LIST = (False,)
+        else:
+            cls.OUTPUT_IS_LIST = (True,)
+        return True

if_else.py

@@ -7,20 +7,97 @@ class IfElse:
     def INPUT_TYPES(cls):
         return {
             "required": {
-                "input": ("STRING", {"forceInput": True, "multiline": False}),
+                "input": (Everything("*"), {"forceInput": True, "multiline": False}),
+                "input_type": ([
+                    "STRING: input EQUAL TO compare_with",
+                    "STRING: input NOT EQUAL TO compare_with",
+                    "BOOLEAN: input IS TRUE",
+                    "NUMBER: input GREATER THAN compare_with",
+                    "NUMBER: input GREATER OR EQUAL TO compare_with",
+                    "NUMBER: input LESS THAN compare_with",
+                    "NUMBER: input LESS OR EQUAL TO compare_with"
+                ], {"default": "STRING: input EQUAL TO compare_with"}),
                 "send_if_true": (Everything("*"),),
-                "send_if_false": (Everything("*"),),
                 "compare_with": ("STRING", {"multiline": False}),
             },
+            "optional": {
+                "send_if_false": (Everything("*"),),
+            }
         }
 
-    RETURN_TYPES = (Everything("*"),"STRING")
+    RETURN_TYPES = (Everything("*"), Everything("*"), "STRING", "STRING", "STRING")
-    RETURN_NAMES = ("output","true_or_false")
+    RETURN_NAMES = ("output", "rejected", "input_type", "true_or_false", "details")
     FUNCTION = "if_else"
     CATEGORY = "Bjornulf"
 
-    def if_else(self, input, send_if_true, send_if_false, compare_with):
+    def if_else(self, input, send_if_true, compare_with, input_type, send_if_false=None):
-        if input == compare_with:
+        result = False
-            return (send_if_true,"True")
+        input_type_str = "STRING"
+        details = f"input: {input}\ncompare_with: {compare_with}\n"
+        error_message = ""
+
+        # Input validation
+        if input_type.startswith("NUMBER:"):
+            try:
+                float(input)
+                float(compare_with)
+            except ValueError:
+                error_message = "If-Else ERROR: For numeric comparisons, both \"input\" and \"compare_with\" must be valid numbers.\n"
+        elif input_type == "BOOLEAN: input IS TRUE":
+            if str(input).lower() not in ("true", "false", "1", "0", "yes", "no", "y", "n", "on", "off"):
+                error_message = "If-Else ERROR: For boolean check, \"input\" must be a recognizable boolean value.\n"
+
+        if error_message:
+            details = error_message + "\n" + details
+            details += "\nContinuing with default string comparison."
+            input_type = "STRING: input EQUAL TO compare_with"
+
+        if input_type == "STRING: input EQUAL TO compare_with":
+            result = str(input) == str(compare_with)
+            details += f"\nCompared strings: '{input}' == '{compare_with}'"
+        elif input_type == "STRING: input NOT EQUAL TO compare_with":
+            result = str(input) != str(compare_with)
+            details += f"\nCompared strings: '{input}' != '{compare_with}'"
+        elif input_type == "BOOLEAN: input IS TRUE":
+            result = str(input).lower() in ("true", "1", "yes", "y", "on")
+            details += f"\nChecked if '{input}' is considered True"
+        else:  # Numeric comparisons
+            try:
+                input_num = float(input)
+                compare_num = float(compare_with)
+                if input_type == "NUMBER: input GREATER THAN compare_with":
+                    result = input_num > compare_num
+                    details += f"\nCompared numbers: {input_num} > {compare_num}"
+                elif input_type == "NUMBER: input GREATER OR EQUAL TO compare_with":
+                    result = input_num >= compare_num
+                    details += f"\nCompared numbers: {input_num} >= {compare_num}"
+                elif input_type == "NUMBER: input LESS THAN compare_with":
+                    result = input_num < compare_num
+                    details += f"\nCompared numbers: {input_num} < {compare_num}"
+                elif input_type == "NUMBER: input LESS OR EQUAL TO compare_with":
+                    result = input_num <= compare_num
+                    details += f"\nCompared numbers: {input_num} <= {compare_num}"
+                input_type_str = "FLOAT" if "." in str(input) else "INT"
+            except ValueError:
+                result = str(input) == str(compare_with)
+                details += f"\nUnexpected error in numeric conversion, compared as strings: '{input}' == '{compare_with}'"
+
+        if result:
+            output = send_if_true
+            rejected = send_if_false if send_if_false is not None else None
         else:
-            return (send_if_false,"False")
+            output = send_if_false if send_if_false is not None else None
+            rejected = send_if_true
+
+
+        result_str = str(result)
+        details += f"\nResult: {result_str}"
+        details += f"\nReturned value to {'output' if result else 'rejected'}"
+        details += f"\n\noutput: {output}"
+        details += f"\nrejected: {rejected}"
+
+        return (output, rejected, input_type_str, result_str, details)
+    
+    @classmethod
+    def IS_CHANGED(cls, input, send_if_true, compare_with, input_type, send_if_false=None):
+        return float("NaN")

image_details.py

@@ -0,0 +1,98 @@
+import torch
+import numpy as np
+from PIL import Image
+import io
+
+class ImageDetails:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image_input": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("INT", "INT", "BOOL", "STRING", "STRING", "STRING")
+    RETURN_NAMES = ("WIDTH", "HEIGHT", "HAS_TRANSPARENCY", "ORIENTATION", "TYPE", "ALL")
+    FUNCTION = "show_image_details"
+    OUTPUT_NODE = True
+    CATEGORY = "Bjornulf"
+
+    def show_image_details(self, image_input):
+        if isinstance(image_input, torch.Tensor):
+            is_tensor = True
+            input_type = "tensor"
+            # Ensure the tensor is on CPU and convert to numpy
+            image_input = image_input.cpu().numpy()
+        elif isinstance(image_input, (bytes, bytearray)):
+            is_tensor = False
+            input_type = "bytes"
+            image_input = [image_input]  # Wrap single bytes object in a list
+        else:
+            is_tensor = False
+            input_type = "bytes"
+        
+        all_widths, all_heights, all_transparencies, all_details, all_orientations = [], [], [], [], []
+
+        if is_tensor:
+            # Handle tensor images
+            if len(image_input.shape) == 5:  # (batch, 1, channels, height, width)
+                image_input = np.squeeze(image_input, axis=1)
+            
+            batch_size = image_input.shape[0]
+            for i in range(batch_size):
+                image = image_input[i]
+                
+                # Ensure the image is in HxWxC format
+                if image.shape[0] == 3 or image.shape[0] == 4:  # If it's in CxHxW format
+                    image = np.transpose(image, (1, 2, 0))  # Change to HxWxC
+                
+                # Normalize to 0-255 range if necessary
+                if image.max() <= 1:
+                    image = (image * 255).astype('uint8')
+                else:
+                    image = image.astype('uint8')
+                
+                pil_image = Image.fromarray(image)
+                self.process_image(pil_image, input_type, all_widths, all_heights, all_transparencies, all_details, all_orientations)
+        else:
+            # Handle bytes-like objects
+            batch_size = len(image_input)
+            for i in range(batch_size):
+                pil_image = Image.open(io.BytesIO(image_input[i]))
+                self.process_image(pil_image, input_type, all_widths, all_heights, all_transparencies, all_details, all_orientations)
+
+        # Combine all details into a single string
+        combined_details = "\n".join(all_details)
+
+        # Return the details of the first image, plus the combined details string
+        return (all_widths[0], all_heights[0], all_transparencies[0], all_orientations[0], 
+                input_type, combined_details)
+
+    def process_image(self, pil_image, input_type, all_widths, all_heights, all_transparencies, all_details, all_orientations):
+        # Get image details
+        width, height = pil_image.size
+        has_transparency = pil_image.mode in ('RGBA', 'LA') or \
+                           (pil_image.mode == 'P' and 'transparency' in pil_image.info)
+
+        # Determine orientation
+        if width > height:
+            orientation = "landscape"
+        elif height > width:
+            orientation = "portrait"
+        else:
+            orientation = "square"
+
+        # Prepare the ALL string
+        details = f"\nType: {input_type}"
+        details += f"\nWidth: {width}"
+        details += f"\nHeight: {height}"
+        details += f"\nLoaded with transparency: {has_transparency}"
+        details += f"\nImage Mode: {pil_image.mode}"
+        details += f"\nOrientation: {orientation}\n"
+
+        all_widths.append(width)
+        all_heights.append(height)
+        all_transparencies.append(has_transparency)
+        all_details.append(details)
+        all_orientations.append(orientation)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "bjornulf_custom_nodes"
 description = "Nodes: Ollama, Text to Speech, Combine Texts, Random Texts, Save image for Bjornulf LobeChat, Text with random Seed, Random line from input, Combine images, Image to grayscale (black & white), Remove image Transparency (alpha), Resize Image, ..."
-version = "0.40"
+version = "0.41"
 license = {file = "LICENSE"}
 
 [project.urls]

random_model_selector.py

@@ -29,28 +29,31 @@ class RandomModelSelector:
     def random_select_model(self, number_of_models, seed, **kwargs):
         random.seed(seed)
 
-        available_models = [kwargs[f"model_{i}"] for i in range(1, number_of_models + 1) if f"model_{i}" in kwargs]
+        # Collect available models from kwargs
+        available_models = [
+            kwargs[f"model_{i}"] for i in range(1, number_of_models + 1) if f"model_{i}" in kwargs and kwargs[f"model_{i}"]
+        ]
         
+        # Raise an error if no models are available
         if not available_models:
             raise ValueError("No models selected")
         
+        # Randomly select a model
         selected_model = random.choice(available_models)
         
-        # Extract just the name of the model (no folders and no extensions)
+        # Get the model name (without folders or extensions)
         model_name = os.path.splitext(os.path.basename(selected_model))[0]
         
-        # Get the full path of the selected model
+        # Get the full path to the selected model
         model_path = get_full_path("checkpoints", selected_model)
         
-        # Get the folder of the selected model (Hopefully people use that to organize their models...)
+        # Get the folder name where the model is located
         model_folder = os.path.basename(os.path.dirname(model_path))
         
-        # Load the model
+        # Load the model using ComfyUI's checkpoint loader
         loaded_objects = comfy.sd.load_checkpoint_guess_config(model_path)
         
         # Unpack only the values we need
-        model = loaded_objects[0]
+        model, clip, vae = loaded_objects[:3]
-        clip = loaded_objects[1]
-        vae = loaded_objects[2]
         
-        return (model, clip, vae, model_path, model_name, model_folder)
+        return model, clip, vae, model_path, model_name, model_folder

web/js/combine_images.js

@@ -0,0 +1,52 @@
+import { app } from "../../../scripts/app.js";
+
+app.registerExtension({
+    name: "Bjornulf.CombineImages",
+    async nodeCreated(node) {
+        if (node.comfyClass === "Bjornulf_CombineImages") {
+            const updateInputs = () => {
+                const numInputsWidget = node.widgets.find(w => w.name === "number_of_images");
+                if (!numInputsWidget) return;
+
+                const numInputs = numInputsWidget.value;
+                
+                // Initialize node.inputs if it doesn't exist
+                if (!node.inputs) {
+                    node.inputs = [];
+                }
+                
+                // Filter existing text inputs
+                const existingInputs = node.inputs.filter(input => input.name.startsWith('image_'));
+                
+                // Determine if we need to add or remove inputs
+                if (existingInputs.length < numInputs) {
+                    // Add new text inputs if not enough existing
+                    for (let i = existingInputs.length + 1; i <= numInputs; i++) {
+                        const inputName = `image_${i}`;
+                        if (!node.inputs.find(input => input.name === inputName)) {
+                            node.addInput(inputName, "IMAGE");
+                        }
+                    }
+                } else {
+                    // Remove excess text inputs if too many
+                    node.inputs = node.inputs.filter(input => !input.name.startsWith('image_') || parseInt(input.name.split('_')[1]) <= numInputs);
+                }
+                
+                node.setSize(node.computeSize());
+            };
+
+            // Move number_of_images to the top initially
+            const numInputsWidget = node.widgets.find(w => w.name === "number_of_images");
+            if (numInputsWidget) {
+                node.widgets = [numInputsWidget, ...node.widgets.filter(w => w !== numInputsWidget)];
+                numInputsWidget.callback = () => {
+                    updateInputs();
+                    app.graph.setDirtyCanvas(true);
+                };
+            }
+
+            // Delay the initial update to ensure node is fully initialized
+            setTimeout(updateInputs, 0);
+        }
+    }
+});

web/js/show_text.js

@@ -47,6 +47,10 @@ app.registerExtension({
                         color = '#0096FF'; // Integer
                     } else if (/^-?\d*\.?\d+$/.test(value)) {
                         color = 'orange'; // Float
+                    } else if (value.startsWith("If-Else ERROR: ")) {
+                        color = 'red'; // If-Else ERROR lines
+                    } else if (value.startsWith("tensor(")) {
+                        color = '#0096FF'; // Lines starting with "tensor("
                     }
                     
                     w.inputEl.style.color = color;

write_text_advanced.py

@@ -11,7 +11,7 @@ class WriteTextAdvanced:
                 "text": ("STRING", {"multiline": True, "lines": 10}),
             },
             "optional": {
-                "variables": ("STRING", {"multiline": True, "lines": 5}),
+                "variables": ("STRING", {"multiline": True, "forceInput": True}),
                 "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
             },
         }