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README.md

@@ -53,10 +53,10 @@ Support me and my work : ❤️❤️❤️ <https://ko-fi.com/bjornulf> ❤️
 `116.` [📥 Load Text From Path](#116----load-text-from-path)  
 `117.` [📝👈🅰️ Line selector (🎲 or ♻ or ♻📑)](#117---🅰%EF%B8%8F-line-selector--or--or-)  
 `131.` [✒👉 Write Pick Me Chain](#131----write-pick-me-chain)  
-`136.` [🔛📝 Text Switch On/Off](#136)  
+`136.` [🔛📝 Text Switch On/Off](#136----text-switch-onoff)  
-`138.` [📑👈 Select from List](#138)  
+`138.` [📑👈 Select from List](#138----select-from-list)  
-`141.` [🌎✒👉 Global Write Pick Me](#141)  
+`141.` [🌎✒👉 Global Write Pick Me](#141----global-write-pick-me)  
-`142.` [🌎📥 Load Global Pick Me](#142)  
+`142.` [🌎📥 Load Global Pick Me](#142----load-global-pick-me)  
 
 ## 🔥 Text Generator 🔥
 `81.` [🔥📝 Text Generator 📝🔥](#81----text-generator-)  
@@ -113,8 +113,8 @@ Support me and my work : ❤️❤️❤️ <https://ko-fi.com/bjornulf> ❤️
 `48.` [🔀🎲 Text scrambler (🧑 Character)](#48----text-scrambler--character)  
 `55.` [🎲👑 Random Lora Selector](#55----random-lora-selector)  
 `117.` [📝👈🅰️ Line selector (🎲 or ♻ or ♻📑)](#117---🅰%EF%B8%8F-line-selector--or--or-)  
-`139.` [🎲 Random Integer](#139)  
+`139.` [🎲 Random Integer](#139----random-integer)  
-`140.` [🎲 Random Float](#140)  
+`140.` [🎲 Random Float](#140----random-float)  
 
 ## 🖼💾 Save Image / Text 💾🖼
 `16.` [💾🖼💬 Save image for Bjornulf LobeChat](#16----save-image-for-bjornulf-lobechat-for-my-custom-lobe-chat)  
@@ -221,17 +221,17 @@ Support me and my work : ❤️❤️❤️ <https://ko-fi.com/bjornulf> ❤️
 `66.` [🔊➜📝 STT - Speech to Text](#66----stt---speech-to-text)  
 `118.` [🔊 TTS Configuration ⚙](#118----tts-configuration-)  
 `120.` [📝➜🔊 Kokoro - Text to Speech](#120----kokoro---text-to-speech)  
-`134.` [134 - 🔊▶ Play Audio](#134)  
+`134.` [134 - 🔊▶ Play Audio](#134----play-audio)  
 
 ## 💻 General / System 💻
 `34.` [🧹 Free VRAM hack](#34----free-vram-hack)  
-`137.` [🌎🎲 Global Seed Manager](#137)  
+`137.` [🌎🎲 Global Seed Manager](#137----global-seed-manager)  
 
 ## 🧍 Manual user Control 🧍
 `35.` [⏸️ Paused. Resume or Stop, Pick 👇](#35---%EF%B8%8F-paused-resume-or-stop-)  
 `36.` [⏸️ Paused. Select input, Pick 👇](#36---%EF%B8%8F-paused-select-input-pick-one)  
 `117.` [📝👈🅰️ Line selector (🎲 or ♻ or ♻📑)](#117---🅰%EF%B8%8F-line-selector--or--or-)  
-`135.` [🔛✨ Anything Switch On/Off](#135)  
+`135.` [🔛✨ Anything Switch On/Off](#135----anything-switch-onoff)  
 
 ## 🧠 Logic / Conditional Operations 🧠
 `45.` [🔀 If-Else (input / compare_with)](#45----if-else-input--compare_with)  

hugginface_download.py

@@ -1,14 +1,18 @@
 import os
 import folder_paths
 from huggingface_hub import hf_hub_download
+from huggingface_hub.utils import EntryNotFoundError
 
 class HuggingFaceDownloader:
     """Custom node for downloading models from Hugging Face within ComfyUI"""
     
+    # Mapping of model types to their directory names
     MODELS_DIR = {
         "models/vae": "vae",
         "models/unet": "unet",
         "models/clip": "clip",
+        "models/text_encoders": "text_encoders",
+        "models/diffusion_models": "diffusion_models",
         "models/lora": "loras",
         "models/controlnet": "controlnet",
         "models/upscale": "upscale_models",
@@ -37,27 +41,58 @@ class HuggingFaceDownloader:
     def download_model(self, hf_token, repo_id, filename, model_type, custom_path=None):
         download_dir = "Unknown"
         try:
+            # Set the Hugging Face token
             os.environ["HF_TOKEN"] = hf_token
             
+            # Determine the download directory
             if custom_path:
                 download_dir = custom_path
             else:
                 folder_key = self.MODELS_DIR[model_type]
                 download_dir = folder_paths.get_folder_paths(folder_key)[0]
             
+            # Create the download directory if it doesn’t exist
             os.makedirs(download_dir, exist_ok=True)
             
-            hf_hub_download(
+            # Progress callback to show download progress
-                repo_id=repo_id,
+            def progress_callback(downloaded, total):
-                filename=filename,
+                if total > 0:
-                token=hf_token,
+                    progress = (downloaded / total) * 100
-                local_dir=download_dir
+                    print(f"\rDownloading {filename}: {progress:.2f}%", end="")
-            )
+                if downloaded == total:
+                    print("\nDownload complete.")
             
-            return (f"Successfully downloaded {filename} to {download_dir}",)
+            # First attempt: try downloading the file directly from the repository
+            try:
+                hf_hub_download(
+                    repo_id=repo_id,
+                    filename=filename,
+                    subfolder=None,
+                    token=hf_token,
+                    local_dir=download_dir,
+                    callbacks=[progress_callback]
+                )
+                source = "directly"
+            except EntryNotFoundError:
+                # Second attempt: try downloading from split_files/<model_type>
+                subfolder = f"split_files/{self.MODELS_DIR[model_type]}"
+                hf_hub_download(
+                    repo_id=repo_id,
+                    filename=filename,
+                    subfolder=subfolder,
+                    token=hf_token,
+                    local_dir=download_dir,
+                    callbacks=[progress_callback]
+                )
+                source = f"subfolder {subfolder}"
+            
+            # Return success message with the source of the file
+            return (f"Successfully downloaded {filename} from {repo_id} ({source}) to {download_dir}",)
             
         except IndexError:
             return (f"No directory found for model type: {model_type}. Check folder_paths configuration.",)
+        except EntryNotFoundError:
+            return (f"Error: {filename} not found in {repo_id} or {repo_id}/split_files/{self.MODELS_DIR[model_type]}",)
         except Exception as e:
             return (f"Error downloading model: {str(e)}, {filename} to {download_dir}",)
 

image_details.py

@@ -87,6 +87,7 @@ class ImageDetails:
         details = f"\nType: {input_type}"
         details += f"\nWidth: {width}"
         details += f"\nHeight: {height}"
+        details += f"\nNumber of Pixels: {width * height}"
         details += f"\nLoaded with transparency: {has_transparency}"
         details += f"\nImage Mode: {pil_image.mode}"
         details += f"\nOrientation: {orientation}\n"

resize_image.py

@@ -23,6 +23,30 @@ class ResizeImage:
         # Ensure the input image is on CPU and convert to numpy array
         image_np = image.cpu().numpy()
         
+        # Get original dimensions
+        if image_np.ndim == 4:
+            orig_height, orig_width = image_np.shape[1:3]
+        else:
+            orig_height, orig_width = image_np.shape[:2]
+        
+        # Calculate new dimensions maintaining aspect ratio if needed
+        aspect_ratio = orig_width / orig_height
+        
+        if width == 0 and height == 0:
+            # If both are 0, use original dimensions
+            new_width, new_height = orig_width, orig_height
+        elif width == 0:
+            # If width is 0, calculate it based on height
+            new_height = height
+            new_width = int(height * aspect_ratio)
+        elif height == 0:
+            # If height is 0, calculate it based on width
+            new_width = width
+            new_height = int(width / aspect_ratio)
+        else:
+            # Use provided dimensions
+            new_width, new_height = width, height
+
         # Check if the image is in the format [batch, height, width, channel]
         if image_np.ndim == 4:
             # If so, we'll process each image in the batch
@@ -31,7 +55,7 @@ class ResizeImage:
                 # Convert to PIL Image
                 pil_img = Image.fromarray((img * 255).astype(np.uint8))
                 # Resize
-                resized_pil = pil_img.resize((width, height), Image.LANCZOS)
+                resized_pil = pil_img.resize((new_width, new_height), Image.LANCZOS)
                 # Convert back to numpy and normalize
                 resized_np = np.array(resized_pil).astype(np.float32) / 255.0
                 resized_images.append(resized_np)
@@ -45,7 +69,7 @@ class ResizeImage:
             # Convert to PIL Image
             pil_img = Image.fromarray((image_np * 255).astype(np.uint8))
             # Resize
-            resized_pil = pil_img.resize((width, height), Image.LANCZOS)
+            resized_pil = pil_img.resize((new_width, new_height), Image.LANCZOS)
             # Convert back to numpy and normalize
             resized_np = np.array(resized_pil).astype(np.float32) / 255.0
             # Add batch dimension if it was originally present
@@ -56,7 +80,7 @@ class ResizeImage:
 
         # Update metadata if needed
         if extra_pnginfo is not None:
-            extra_pnginfo["resized_width"] = width
+            extra_pnginfo["resized_width"] = new_width
-            extra_pnginfo["resized_height"] = height
+            extra_pnginfo["resized_height"] = new_height
 
         return (resized_tensor, prompt, extra_pnginfo)