diff --git a/.gitignore b/.gitignore
index be53a8b..611dce4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,8 +1,10 @@
 __pycache__
 /venv
+*.code-workspace
+.history
 .vscode
 *.ckpt
 *.pth
 types
 models
-jsconfig.json
\ No newline at end of file
+jsconfig.json
diff --git a/intristic_loras/intrinsic_lora_sd15_albedo.safetensors b/intrinsic_loras/intrinsic_lora_sd15_albedo.safetensors
similarity index 100%
rename from intristic_loras/intrinsic_lora_sd15_albedo.safetensors
rename to intrinsic_loras/intrinsic_lora_sd15_albedo.safetensors
diff --git a/intristic_loras/intrinsic_lora_sd15_depth.safetensors b/intrinsic_loras/intrinsic_lora_sd15_depth.safetensors
similarity index 100%
rename from intristic_loras/intrinsic_lora_sd15_depth.safetensors
rename to intrinsic_loras/intrinsic_lora_sd15_depth.safetensors
diff --git a/intristic_loras/intrinsic_lora_sd15_normal.safetensors b/intrinsic_loras/intrinsic_lora_sd15_normal.safetensors
similarity index 100%
rename from intristic_loras/intrinsic_lora_sd15_normal.safetensors
rename to intrinsic_loras/intrinsic_lora_sd15_normal.safetensors
diff --git a/intristic_loras/intrinsic_lora_sd15_shading.safetensors b/intrinsic_loras/intrinsic_lora_sd15_shading.safetensors
similarity index 100%
rename from intristic_loras/intrinsic_lora_sd15_shading.safetensors
rename to intrinsic_loras/intrinsic_lora_sd15_shading.safetensors
diff --git a/intristic_loras/intrinsic_loras.txt b/intrinsic_loras/intrinsic_loras.txt
similarity index 100%
rename from intristic_loras/intrinsic_loras.txt
rename to intrinsic_loras/intrinsic_loras.txt
diff --git a/nodes/image_nodes.py b/nodes/image_nodes.py
index c33967e..4d2a320 100644
--- a/nodes/image_nodes.py
+++ b/nodes/image_nodes.py
@@ -872,10 +872,14 @@ nodes for example.
             mask_adjusted = mask * mask_opacity
             mask_image = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3).clone()
 
-            color_list = list(map(int, mask_color.split(', ')))
-            mask_image[:, :, :, 0] = color_list[0] // 255 # Red channel
-            mask_image[:, :, :, 1] = color_list[1] // 255 # Green channel
-            mask_image[:, :, :, 2] = color_list[2] // 255 # Blue channel
+            if ',' in mask_color:
+                color_list = np.clip([int(channel) for channel in mask_color.split(',')], 0, 255) # RGB format
+            else:
+                mask_color = mask_color.lstrip('#')
+                color_list = [int(mask_color[i:i+2], 16) for i in (0, 2, 4)] # Hex format
+            mask_image[:, :, :, 0] = color_list[0] / 255 # Red channel
+            mask_image[:, :, :, 1] = color_list[1] / 255 # Green channel
+            mask_image[:, :, :, 2] = color_list[2] / 255 # Blue channel
             
             preview, = ImageCompositeMasked.composite(self, image, mask_image, 0, 0, True, mask_adjusted)
         if pass_through:
diff --git a/nodes/intrinsic_lora_nodes.py b/nodes/intrinsic_lora_nodes.py
index 798cf74..c8f1253 100644
--- a/nodes/intrinsic_lora_nodes.py
+++ b/nodes/intrinsic_lora_nodes.py
@@ -7,7 +7,7 @@ import comfy.sample
 from nodes import CLIPTextEncode
 
 script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-folder_paths.add_model_folder_path("intristic_loras", os.path.join(script_directory, "intristic_loras"))
+folder_paths.add_model_folder_path("intrinsic_loras", os.path.join(script_directory, "intrinsic_loras"))
 
 class Intrinsic_lora_sampling:
     def __init__(self):
@@ -16,7 +16,7 @@ class Intrinsic_lora_sampling:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                "lora_name": (folder_paths.get_filename_list("intristic_loras"), ),
+                "lora_name": (folder_paths.get_filename_list("intrinsic_loras"), ),
                 "task": (
                 [   
                     'depth map',
@@ -81,7 +81,7 @@ with this node pack.
 
         #load lora
         model_clone = model.clone()
-        lora_path = folder_paths.get_full_path("intristic_loras", lora_name)        
+        lora_path = folder_paths.get_full_path("intrinsic_loras", lora_name)        
         lora = load_torch_file(lora_path, safe_load=True)
         self.loaded_lora = (lora_path, lora)