0.74

API_civitai.py

@@ -14,68 +14,135 @@ import node_helpers
 import hashlib
 from folder_paths import get_filename_list, get_full_path, models_dir
 import nodes
+from pathlib import Path
+import subprocess
+
+# ======================
+# SHARED UTILITY FUNCTIONS
+# ======================
+
+def get_civitai_base_paths():
+    """Returns common paths for CivitAI integration"""
+    custom_nodes_dir = Path(__file__).parent.parent.parent.parent
+    civitai_base_path = custom_nodes_dir / "ComfyUI" / "custom_nodes" / "Bjornulf_custom_nodes" / "civitai"
+    return custom_nodes_dir, civitai_base_path, civitai_base_path  # Last one is parsed_models_path
+
+def setup_checkpoint_directory(model_type):
+    """Creates and registers checkpoint directory for specific model type"""
+    _, _, parsed_models_path = get_civitai_base_paths()
+    checkpoint_dir = Path(folder_paths.models_dir) / "checkpoints" / "Bjornulf_civitAI" / model_type
+    
+    checkpoint_dir.mkdir(parents=True, exist_ok=True)
+    
+    checkpoint_folders = list(folder_paths.folder_names_and_paths["checkpoints"])
+    if str(checkpoint_dir) not in checkpoint_folders:
+        checkpoint_folders.append(str(checkpoint_dir))
+        folder_paths.folder_names_and_paths["checkpoints"] = tuple(checkpoint_folders)
+    
+    return checkpoint_dir, parsed_models_path
+
+def setup_image_folders(folder_specs, parent_dir=""):
+    """Creates and registers image folders for different model types
+    
+    Args:
+        folder_specs: Dictionary of folder_name -> sub_path
+        parent_dir: Optional subdirectory to place links under in input folder
+    """
+    _, civitai_base_path, _ = get_civitai_base_paths()
+    
+    for folder_name, sub_path in folder_specs.items():
+        full_path = civitai_base_path / sub_path
+        folder_paths.add_model_folder_path(folder_name, str(full_path))
+        create_symlink(full_path, folder_name, parent_dir)
+
+# Code works, tested on linux and 
+def create_symlink(source, target_name, parent_dir=None):
+    """Creates a symlink inside the ComfyUI/input directory on Linux and Windows."""
+    if os.name == 'nt':  # Windows
+        comfyui_input = Path("ComfyUI/input")
+    else:
+        comfyui_input = Path("input")
+    
+    if parent_dir:
+        parent_path = comfyui_input / parent_dir
+        parent_path.mkdir(parents=True, exist_ok=True)
+        target = parent_path / target_name
+    else:
+        target = comfyui_input / target_name
+    
+    if not target.exists():
+        try:
+            if os.name == 'nt':  # Windows
+                base_path = Path(__file__).resolve().parent  # Get script location
+                source = base_path / "ComfyUI" / source  # Ensure it points inside ComfyUI
+                try:
+                    target.symlink_to(source, target_is_directory=source.is_dir())
+                    #print(f"✅ Symlink created: {target} -> {source}")
+                except OSError:
+                    if source.is_dir():
+                        cmd = [
+                            "powershell", "New-Item", "-ItemType", "Junction",
+                            "-Path", str(target), "-Value", str(source)
+                        ]
+                        subprocess.run(cmd, check=True, shell=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+                        #print(f"✅ Junction created: {target} -> {source}")
+                    else:
+                        print(f"❌ Failed to create symlink/junction for {target_name}.")
+            else:
+                target.symlink_to(source, target_is_directory=True)
+                #print(f"✅ Symlink created: {target} -> {source}")
+        except Exception as e:
+            print(f"❌ Failed to create symlink for {target_name}: {e}")
+
+def download_file(url, destination_path, model_name, api_token=None):
+    """Universal downloader with progress tracking"""
+    headers = {'Authorization': f'Bearer {api_token}'} if api_token else {}
+    filename = f"{model_name}.safetensors"
+    file_path = Path(destination_path) / filename
+
+    try:
+        with requests.get(url, headers=headers, stream=True) as response:
+            response.raise_for_status()
+            file_size = int(response.headers.get('content-length', 0))
             
-# Register the new checkpoint folder
-bjornulf_checkpoint_path = os.path.join(folder_paths.models_dir, "checkpoints", "Bjornulf_civitAI")
-os.makedirs(bjornulf_checkpoint_path, exist_ok=True)
+            with open(file_path, 'wb') as f:
+                for chunk in response.iter_content(chunk_size=8192):
+                    if chunk:
+                        f.write(chunk)
+                        # Add progress reporting here if needed
+                        
+        return str(file_path)
+    except Exception as e:
+        raise RuntimeError(f"Download failed: {str(e)}")
 
-# Convert tuple to list, append new path, and convert back to tuple
+# Set up main checkpoint directory
+_, civitai_base_path, parsed_models_path = get_civitai_base_paths()
+bjornulf_checkpoint_path = Path(folder_paths.models_dir) / "checkpoints" / "Bjornulf_civitAI"
+bjornulf_checkpoint_path.mkdir(parents=True, exist_ok=True)
+
+# Register the main checkpoint folder
 checkpoint_folders = list(folder_paths.folder_names_and_paths["checkpoints"])
-checkpoint_folders.append(bjornulf_checkpoint_path)
-folder_paths.folder_names_and_paths["checkpoints"] = tuple(checkpoint_folders)
-
-# Prepare Models
-custom_nodes_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
-civitai_base_path = os.path.join(custom_nodes_dir, "ComfyUI", "custom_nodes", "Bjornulf_custom_nodes", "civitai")
-parsed_models_path = civitai_base_path
+if str(bjornulf_checkpoint_path) not in checkpoint_folders:
+    checkpoint_folders.append(str(bjornulf_checkpoint_path))
+    folder_paths.folder_names_and_paths["checkpoints"] = tuple(checkpoint_folders)
 
 # Define image folders
 image_folders = {
-    "sdxl_1.0": os.path.join(civitai_base_path, "sdxl_1.0"),
-    "sd_1.5": os.path.join(civitai_base_path, "sd_1.5"),
-    "pony": os.path.join(civitai_base_path, "pony"),
-    "flux.1_d": os.path.join(civitai_base_path, "flux.1_d"),
-    "flux.1_s": os.path.join(civitai_base_path, "flux.1_s"),
-    "lora_sdxl_1.0": os.path.join(civitai_base_path, "lora_sdxl_1.0"),
-    "lora_sd_1.5": os.path.join(civitai_base_path, "lora_sd_1.5"),
-    "lora_pony": os.path.join(civitai_base_path, "lora_pony"),
-    "lora_flux.1_d": os.path.join(civitai_base_path, "lora_flux.1_d"),
-    "lora_hunyuan_video": os.path.join(civitai_base_path, "lora_hunyuan_video"),
+    "sdxl_1.0": "sdxl_1.0",
+    "sd_1.5": "sd_1.5",
+    "pony": "pony",
+    "flux.1_d": "flux.1_d",
+    "flux.1_s": "flux.1_s",
+    "lora_sdxl_1.0": "lora_sdxl_1.0",
+    "lora_sd_1.5": "lora_sd_1.5",
+    "lora_pony": "lora_pony",
+    "lora_flux.1_d": "lora_flux.1_d",
+    "lora_hunyuan_video": "lora_hunyuan_video",
+    # "NSFW_lora_hunyuan_video": "NSFW_lora_hunyuan_video"
 }
-    # "NSFW_lora_hunyuan_video": os.path.join(civitai_base_path, "NSFW_lora_hunyuan_video")
 
-# Add folder paths for each image folder
-for folder_name, folder_path in image_folders.items():
-    folder_paths.add_model_folder_path(folder_name, folder_path)
-    
-    # Create target paths in input directory
-    target_path = os.path.join('input', folder_name)
-    
-    # Create link if it doesn't exist
-    if not os.path.exists(target_path):
-        # try:
-        if os.name == 'nt':  # Windows
-            os.system(f'mklink /J "{target_path}" "{folder_path}"')
-        else:  # Unix-like
-            os.symlink(folder_path, target_path)
-        #print(f"Successfully created link from {folder_path} to {target_path}")
-        # except OSError as e:
-        #     print(f"Failed to create link: {e}")
-
-# Prepare Loras
-# lora_images_path = os.path.join(custom_nodes_dir, "ComfyUI", "custom_nodes", "Bjornulf_custom_nodes", "civitai", "lora_images")
-# folder_paths.add_model_folder_path("lora_images", lora_images_path)
-# target_lora_path = os.path.join('input', 'lora_images')
-# # Create link if it doesn't exist
-# if not os.path.exists(target_lora_path):
-#     try:
-#         if os.name == 'nt':  # Windows
-#             os.system(f'mklink /J "{target_lora_path}" "{lora_images_path}"')
-#         else:  # Unix-like
-#             os.symlink(lora_images_path, target_lora_path)
-#         print(f"Successfully created link from {lora_images_path} to {target_lora_path}")
-#     except OSError as e:
-#         print(f"Failed to create link: {e}")
+# Set up image folders using the function, placing links under input/Bjornulf/
+setup_image_folders(image_folders)
 
 def get_civitai():
     import civitai
@@ -85,10 +152,13 @@ def get_civitai():
 # Check if the environment variable exists
 if "CIVITAI_API_TOKEN" not in os.environ:
     os.environ["CIVITAI_API_TOKEN"] = "d5fc336223a367e6b503a14a10569825"
-else:
-    print("CIVITAI_API_TOKEN already exists in the environment.")
+# else:
+#     print("CIVITAI_API_TOKEN already exists in the environment.")
 import civitai
 
+# ======================
+# GENERATE WITH CIVITAI
+# ======================
 class APIGenerateCivitAI:
     @classmethod
     def INPUT_TYPES(cls):
@@ -170,7 +240,6 @@ class APIGenerateCivitAI:
         os.makedirs(self.output_dir, exist_ok=True)
         os.makedirs(self.metadata_dir, exist_ok=True)
         self._interrupt_event = threading.Event()
-        
 
     def get_next_number(self):
         """Get the next available number for file naming"""
@@ -445,6 +514,10 @@ class APIGenerateCivitAIAddLORA:
             print(f"Error adding LORA: {str(e)}")
             return (json.dumps({"additionalNetworks": {}}),)
 
+# ======================
+# MODEL SELECTOR CLASSES
+# ======================
+
 class CivitAIModelSelectorSD15:
     @classmethod
     def INPUT_TYPES(s):
@@ -469,47 +542,6 @@ class CivitAIModelSelectorSD15:
     CATEGORY = "Bjornulf"
  
     def load_model(self, image, civitai_token):
-        def download_file(url, destination_path, model_name, api_token=None):
-            """
-            Download file with proper authentication headers and simple progress bar.
-            """
-            filename = f"{model_name}.safetensors"
-            file_path = os.path.join(destination_path, filename)
-
-            headers = {}
-            if api_token:
-                headers['Authorization'] = f'Bearer {api_token}'
-
-            try:
-                print(f"Downloading from: {url}")
-                response = requests.get(url, headers=headers, stream=True)
-                response.raise_for_status()
-
-                # Get file size if available
-                file_size = int(response.headers.get('content-length', 0))
-                block_size = 8192
-                downloaded = 0
-
-                with open(file_path, 'wb') as f:
-                    for chunk in response.iter_content(chunk_size=block_size):
-                        if chunk:
-                            f.write(chunk)
-                            downloaded += len(chunk)
-                            
-                            # Calculate progress
-                            if file_size > 0:
-                                progress = int(50 * downloaded / file_size)
-                                bars = '=' * progress + '-' * (50 - progress)
-                                percent = (downloaded / file_size) * 100
-                                print(f'\rProgress: [{bars}] {percent:.1f}%', end='')
-
-                print(f"\nFile downloaded successfully to: {file_path}")
-                return file_path
-
-            except requests.exceptions.RequestException as e:
-                print(f"Error downloading file: {e}")
-                raise
-        
         if image == "none":
             raise ValueError("No image selected")
 
@@ -517,8 +549,13 @@ class CivitAIModelSelectorSD15:
         json_path = os.path.join(parsed_models_path, 'parsed_sd_1.5_models.json')
 
         # Load models info
-        with open(json_path, 'r') as f:
-            models_info = json.load(f)
+        try:
+            with open(json_path, 'r', encoding='utf-8') as f:
+                models_info = json.load(f)
+        except UnicodeDecodeError:
+            # Fallback to latin-1 if UTF-8 fails
+            with open(json_path, 'r', encoding='latin-1') as f:
+                models_info = json.load(f)
         
         # Extract model name from image path
         image_name = os.path.basename(image)
@@ -606,47 +643,6 @@ class CivitAIModelSelectorSDXL:
     CATEGORY = "Bjornulf"
  
     def load_model(self, image, civitai_token):
-        def download_file(url, destination_path, model_name, api_token=None):
-            """
-            Download file with proper authentication headers and simple progress bar.
-            """
-            filename = f"{model_name}.safetensors"
-            file_path = os.path.join(destination_path, filename)
-
-            headers = {}
-            if api_token:
-                headers['Authorization'] = f'Bearer {api_token}'
-
-            try:
-                print(f"Downloading from: {url}")
-                response = requests.get(url, headers=headers, stream=True)
-                response.raise_for_status()
-
-                # Get file size if available
-                file_size = int(response.headers.get('content-length', 0))
-                block_size = 8192
-                downloaded = 0
-
-                with open(file_path, 'wb') as f:
-                    for chunk in response.iter_content(chunk_size=block_size):
-                        if chunk:
-                            f.write(chunk)
-                            downloaded += len(chunk)
-                            
-                            # Calculate progress
-                            if file_size > 0:
-                                progress = int(50 * downloaded / file_size)
-                                bars = '=' * progress + '-' * (50 - progress)
-                                percent = (downloaded / file_size) * 100
-                                print(f'\rProgress: [{bars}] {percent:.1f}%', end='')
-
-                print(f"\nFile downloaded successfully to: {file_path}")
-                return file_path
-
-            except requests.exceptions.RequestException as e:
-                print(f"Error downloading file: {e}")
-                raise
-        
         if image == "none":
             raise ValueError("No image selected")
 
@@ -654,8 +650,13 @@ class CivitAIModelSelectorSDXL:
         json_path = os.path.join(parsed_models_path, 'parsed_sdxl_1.0_models.json')
 
         # Load models info
-        with open(json_path, 'r') as f:
-            models_info = json.load(f)
+        try:
+            with open(json_path, 'r', encoding='utf-8') as f:
+                models_info = json.load(f)
+        except UnicodeDecodeError:
+            # Fallback to latin-1 if UTF-8 fails
+            with open(json_path, 'r', encoding='latin-1') as f:
+                models_info = json.load(f)
         
         # Extract model name from image path
         image_name = os.path.basename(image)
@@ -719,6 +720,7 @@ class CivitAIModelSelectorSDXL:
         m.update(image.encode('utf-8'))
         return m.digest().hex()
 
+
 class CivitAIModelSelectorFLUX_D:
     @classmethod
     def INPUT_TYPES(s):
@@ -743,47 +745,6 @@ class CivitAIModelSelectorFLUX_D:
     CATEGORY = "Bjornulf"
  
     def load_model(self, image, civitai_token):
-        def download_file(url, destination_path, model_name, api_token=None):
-            """
-            Download file with proper authentication headers and simple progress bar.
-            """
-            filename = f"{model_name}.safetensors"
-            file_path = os.path.join(destination_path, filename)
-
-            headers = {}
-            if api_token:
-                headers['Authorization'] = f'Bearer {api_token}'
-
-            try:
-                print(f"Downloading from: {url}")
-                response = requests.get(url, headers=headers, stream=True)
-                response.raise_for_status()
-
-                # Get file size if available
-                file_size = int(response.headers.get('content-length', 0))
-                block_size = 8192
-                downloaded = 0
-
-                with open(file_path, 'wb') as f:
-                    for chunk in response.iter_content(chunk_size=block_size):
-                        if chunk:
-                            f.write(chunk)
-                            downloaded += len(chunk)
-                            
-                            # Calculate progress
-                            if file_size > 0:
-                                progress = int(50 * downloaded / file_size)
-                                bars = '=' * progress + '-' * (50 - progress)
-                                percent = (downloaded / file_size) * 100
-                                print(f'\rProgress: [{bars}] {percent:.1f}%', end='')
-
-                print(f"\nFile downloaded successfully to: {file_path}")
-                return file_path
-
-            except requests.exceptions.RequestException as e:
-                print(f"Error downloading file: {e}")
-                raise
-        
         if image == "none":
             raise ValueError("No image selected")
 
@@ -791,8 +752,13 @@ class CivitAIModelSelectorFLUX_D:
         json_path = os.path.join(parsed_models_path, 'parsed_flux.1_d_models.json')
 
         # Load models info
-        with open(json_path, 'r') as f:
-            models_info = json.load(f)
+        try:
+            with open(json_path, 'r', encoding='utf-8') as f:
+                models_info = json.load(f)
+        except UnicodeDecodeError:
+            # Fallback to latin-1 if UTF-8 fails
+            with open(json_path, 'r', encoding='latin-1') as f:
+                models_info = json.load(f)
         
         # Extract model name from image path
         image_name = os.path.basename(image)
@@ -880,47 +846,6 @@ class CivitAIModelSelectorFLUX_S:
     CATEGORY = "Bjornulf"
  
     def load_model(self, image, civitai_token):
-        def download_file(url, destination_path, model_name, api_token=None):
-            """
-            Download file with proper authentication headers and simple progress bar.
-            """
-            filename = f"{model_name}.safetensors"
-            file_path = os.path.join(destination_path, filename)
-
-            headers = {}
-            if api_token:
-                headers['Authorization'] = f'Bearer {api_token}'
-
-            try:
-                print(f"Downloading from: {url}")
-                response = requests.get(url, headers=headers, stream=True)
-                response.raise_for_status()
-
-                # Get file size if available
-                file_size = int(response.headers.get('content-length', 0))
-                block_size = 8192
-                downloaded = 0
-
-                with open(file_path, 'wb') as f:
-                    for chunk in response.iter_content(chunk_size=block_size):
-                        if chunk:
-                            f.write(chunk)
-                            downloaded += len(chunk)
-                            
-                            # Calculate progress
-                            if file_size > 0:
-                                progress = int(50 * downloaded / file_size)
-                                bars = '=' * progress + '-' * (50 - progress)
-                                percent = (downloaded / file_size) * 100
-                                print(f'\rProgress: [{bars}] {percent:.1f}%', end='')
-
-                print(f"\nFile downloaded successfully to: {file_path}")
-                return file_path
-
-            except requests.exceptions.RequestException as e:
-                print(f"Error downloading file: {e}")
-                raise
-        
         if image == "none":
             raise ValueError("No image selected")
 
@@ -928,8 +853,13 @@ class CivitAIModelSelectorFLUX_S:
         json_path = os.path.join(parsed_models_path, 'parsed_flux.1_s_models.json')
 
         # Load models info
-        with open(json_path, 'r') as f:
-            models_info = json.load(f)
+        try:
+            with open(json_path, 'r', encoding='utf-8') as f:
+                models_info = json.load(f)
+        except UnicodeDecodeError:
+            # Fallback to latin-1 if UTF-8 fails
+            with open(json_path, 'r', encoding='latin-1') as f:
+                models_info = json.load(f)
         
         # Extract model name from image path
         image_name = os.path.basename(image)
@@ -1017,47 +947,6 @@ class CivitAIModelSelectorPony:
     CATEGORY = "Bjornulf"
  
     def load_model(self, image, civitai_token):
-        def download_file(url, destination_path, model_name, api_token=None):
-            """
-            Download file with proper authentication headers and simple progress bar.
-            """
-            filename = f"{model_name}.safetensors"
-            file_path = os.path.join(destination_path, filename)
-
-            headers = {}
-            if api_token:
-                headers['Authorization'] = f'Bearer {api_token}'
-
-            try:
-                print(f"Downloading from: {url}")
-                response = requests.get(url, headers=headers, stream=True)
-                response.raise_for_status()
-
-                # Get file size if available
-                file_size = int(response.headers.get('content-length', 0))
-                block_size = 8192
-                downloaded = 0
-
-                with open(file_path, 'wb') as f:
-                    for chunk in response.iter_content(chunk_size=block_size):
-                        if chunk:
-                            f.write(chunk)
-                            downloaded += len(chunk)
-                            
-                            # Calculate progress
-                            if file_size > 0:
-                                progress = int(50 * downloaded / file_size)
-                                bars = '=' * progress + '-' * (50 - progress)
-                                percent = (downloaded / file_size) * 100
-                                print(f'\rProgress: [{bars}] {percent:.1f}%', end='')
-
-                print(f"\nFile downloaded successfully to: {file_path}")
-                return file_path
-
-            except requests.exceptions.RequestException as e:
-                print(f"Error downloading file: {e}")
-                raise
-        
         if image == "none":
             raise ValueError("No image selected")
 
@@ -1065,8 +954,13 @@ class CivitAIModelSelectorPony:
         json_path = os.path.join(parsed_models_path, 'parsed_pony_models.json')
 
         # Load models info
-        with open(json_path, 'r') as f:
-            models_info = json.load(f)
+        try:
+            with open(json_path, 'r', encoding='utf-8') as f:
+                models_info = json.load(f)
+        except UnicodeDecodeError:
+            # Fallback to latin-1 if UTF-8 fails
+            with open(json_path, 'r', encoding='latin-1') as f:
+                models_info = json.load(f)
         
         # Extract model name from image path
         image_name = os.path.basename(image)
@@ -1129,105 +1023,6 @@ class CivitAIModelSelectorPony:
         m.update(image.encode('utf-8'))
         return m.digest().hex()
 
-
-# class CivitAILoraSelector:
-#     @classmethod
-#     def INPUT_TYPES(s):
-#         # Get list of supported image extensions
-#         image_extensions = ('.png', '.jpg', '.jpeg', '.gif', '.webp', '.bmp')
-#         files = [f"lora_images/{f}" for f in folder_paths.get_filename_list("lora_images") 
-#                 if f.lower().endswith(image_extensions)]
-        
-#         if not files:  # If no files found, provide a default option
-#             files = ["none"]
-            
-#         return {"required":
-#                     {"image": (sorted(files), {"image_upload": True})},  # Added image_upload option here
-#                 }
-
-
-#     RETURN_TYPES = ("IMAGE", "STRING")
-#     RETURN_NAMES = ("image", "image_name")
-#     FUNCTION = "load_image"
-#     CATEGORY = "Bjornulf"
-
-#     def load_image(self, image):
-#         if image == "none":
-#             # Return a small blank image if no image is selected
-#             blank_image = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
-#             return (blank_image, "none")
-            
-#         image_path = os.path.join(lora_images_path, image)
-        
-#         if not os.path.exists(image_path):
-#             raise FileNotFoundError(f"Image not found: {image_path}")
-        
-#         # Copy the image to ComfyUI/input directory
-#         input_dir = folder_paths.get_input_directory()
-#         dest_path = os.path.join(input_dir, os.path.basename(image))
-#         try:
-#             shutil.copy2(image_path, dest_path)
-#         except Exception as e:
-#             print(f"Warning: Failed to copy image to input directory: {e}")
-            
-#         img = node_helpers.pillow(Image.open, image_path)
-        
-#         output_images = []
-#         w, h = None, None
-
-#         excluded_formats = ['MPO']
-        
-#         for i in ImageSequence.Iterator(img):
-#             i = node_helpers.pillow(ImageOps.exif_transpose, i)
-
-#             if i.mode == 'I':
-#                 i = i.point(lambda i: i * (1 / 255))
-#             image = i.convert("RGBA")
-
-#             if len(output_images) == 0:
-#                 w = image.size[0]
-#                 h = image.size[1]
-            
-#             if image.size[0] != w or image.size[1] != h:
-#                 continue
-            
-#             image = np.array(image).astype(np.float32) / 255.0
-#             image = torch.from_numpy(image)[None,]
-#             output_images.append(image)
-
-#         if len(output_images) > 1 and img.format not in excluded_formats:
-#             output_image = torch.cat(output_images, dim=0)
-#         else:
-#             output_image = output_images[0]
-
-#         return (output_image, image)
-    
-#     @classmethod
-#     def IS_CHANGED(s, image):
-#         if image == "none":
-#             return ""
-#         # Use the full path for the image
-#         image_path = os.path.join(lora_images_path, image)
-#         if not os.path.exists(image_path):
-#             return ""
-        
-#         # Calculate hash of the image content
-#         m = hashlib.sha256()
-#         with open(image_path, 'rb') as f:
-#             m.update(f.read())
-#         # Include the image name in the hash to ensure updates when selection changes
-#         m.update(image.encode('utf-8'))
-#         return m.digest().hex()
-
-#     @classmethod
-#     def VALIDATE_INPUTS(s, image):
-#         if image == "none":
-#             return True
-#         image_path = os.path.join(lora_images_path, image)
-#         if not os.path.exists(image_path):
-#             return f"Invalid image file: {image}"
-#         return True
-
 class CivitAILoraSelectorSD15:
     @classmethod
     def INPUT_TYPES(s):
@@ -1302,8 +1097,13 @@ class CivitAILoraSelectorSD15:
         json_path = os.path.join(parsed_models_path, 'parsed_lora_sd_1.5_loras.json')
 
         # Load loras info
-        with open(json_path, 'r') as f:
-            loras_info = json.load(f)
+        try:
+            with open(json_path, 'r', encoding='utf-8') as f:
+                models_info = json.load(f)
+        except UnicodeDecodeError:
+            # Fallback to latin-1 if UTF-8 fails
+            with open(json_path, 'r', encoding='latin-1') as f:
+                models_info = json.load(f)
         
         # Extract lora name from image path
         image_name = os.path.basename(image)
@@ -1445,8 +1245,13 @@ class CivitAILoraSelectorSDXL:
         json_path = os.path.join(parsed_models_path, 'parsed_lora_sdxl_1.0_loras.json')
 
         # Load loras info
-        with open(json_path, 'r') as f:
-            loras_info = json.load(f)
+        try:
+            with open(json_path, 'r', encoding='utf-8') as f:
+                models_info = json.load(f)
+        except UnicodeDecodeError:
+            # Fallback to latin-1 if UTF-8 fails
+            with open(json_path, 'r', encoding='latin-1') as f:
+                models_info = json.load(f)
         
         # Extract lora name from image path
         image_name = os.path.basename(image)
@@ -1588,8 +1393,13 @@ class CivitAILoraSelectorPONY:
         json_path = os.path.join(parsed_models_path, 'parsed_lora_pony_loras.json')
 
         # Load loras info
-        with open(json_path, 'r') as f:
-            loras_info = json.load(f)
+        try:
+            with open(json_path, 'r', encoding='utf-8') as f:
+                models_info = json.load(f)
+        except UnicodeDecodeError:
+            # Fallback to latin-1 if UTF-8 fails
+            with open(json_path, 'r', encoding='latin-1') as f:
+                models_info = json.load(f)
         
         # Extract lora name from image path
         image_name = os.path.basename(image)
@@ -1742,8 +1552,13 @@ class CivitAILoraSelectorHunyuan:
         # json_path = nsfw_json_path if os.path.exists(nsfw_json_path) else regular_json_path
         hunYuan = "hunyuan_video"
 
-        with open(json_path, 'r') as f:
-            loras_info = json.load(f)
+        try:
+            with open(json_path, 'r', encoding='utf-8') as f:
+                models_info = json.load(f)
+        except UnicodeDecodeError:
+            # Fallback to latin-1 if UTF-8 fails
+            with open(json_path, 'r', encoding='latin-1') as f:
+                models_info = json.load(f)
         
         image_name = os.path.basename(image)
         lora_info = next((lora for lora in loras_info 

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "bjornulf_custom_nodes"
 description = "133 ComfyUI nodes : Display, manipulate, and edit text, images, videos, loras, generate characters and more. Manage looping operations, generate randomized content, use logical conditions and work with external AI tools, like Ollama or Text To Speech Kokoro, etc..."
-version = "0.73"
+version = "0.74"
 license = {file = "LICENSE"}
 
 [project.urls]