diff --git a/Diffusion360_nodes_diffusers.py b/Diffusion360_nodes_diffusers.py
index c30d4c9..c176820 100644
--- a/Diffusion360_nodes_diffusers.py
+++ b/Diffusion360_nodes_diffusers.py
@@ -25,21 +25,24 @@ class InputText:
 class InputImage:
     @classmethod
     def INPUT_TYPES(s):
-        files = ["i2p-image.jpg"]
+        input_dir = folder_paths.get_input_directory()
+        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
         return {"required":
                     {"image": (sorted(files), {"image_upload": True})},
                 }
 
-    CATEGORY = "Diffusion360/diffusers"
+    CATEGORY = "image"
 
     RETURN_TYPES = ("IMAGE", )
     FUNCTION = "load_image"
 
     def load_image(self, image):
-        image_path = os.path.join('custom_nodes', 'Diffusion360_ComfyUI', 'data', image)
+        image_path = folder_paths.get_annotated_filepath(image)
+
         img = node_helpers.pillow(Image.open, image_path)
 
         output_images = []
+        output_masks = []
         w, h = None, None
 
         excluded_formats = ['MPO']
@@ -60,12 +63,20 @@ class InputImage:
 
             image = np.array(image).astype(np.float32) / 255.0
             image = torch.from_numpy(image)[None,]
+            if 'A' in i.getbands():
+                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)
+            else:
+                mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
             output_images.append(image)
+            output_masks.append(mask.unsqueeze(0))
 
         if len(output_images) > 1 and img.format not in excluded_formats:
             output_image = torch.cat(output_images, dim=0)
+            output_mask = torch.cat(output_masks, dim=0)
         else:
             output_image = output_images[0]
+            output_mask = output_masks[0]
 
         return (output_image, )
 
@@ -197,6 +208,6 @@ class Diffusion360LoaderImage2Pano:
     def load_models(self, model_path, model_root):
         # pipe = Image2360PanoramaImagePipeline(os.path.join('models', 'diffusers', model_path), torch_dtype=torch.float16)
         pipe = Image2360PanoramaImagePipeline(os.path.join(model_root, model_path), torch_dtype=torch.float16)
-        mask_path = os.path.join('custom_nodes', 'Diffusion360_ComfyUI', 'data', 'i2p-mask.jpg')
+        mask_path = os.path.join(os.path.dirname(__file__), 'data', 'i2p-mask.jpg')
         mask = load_image(mask_path)
         return (pipe, mask)
diff --git a/workflows/Image2Pano_load_image.json b/workflows/Image2Pano_load_image.json
index 16b874d..c2cbe32 100644
--- a/workflows/Image2Pano_load_image.json
+++ b/workflows/Image2Pano_load_image.json
@@ -84,7 +84,7 @@
         "Node name for S&R": "Diffusion360SamplerImage2Pano"
       },
       "widgets_values": [
-        21172,
+        46120,
         "randomize",
         20,
         7.5,
@@ -158,7 +158,7 @@
       ],
       "size": {
         "0": 315,
-        "1": 78
+        "1": 102
       },
       "flags": {},
       "order": 1,
@@ -187,7 +187,8 @@
         "Node name for S&R": "Diffusion360LoaderImage2Pano"
       },
       "widgets_values": [
-        "diffusion360"
+        "diffusion360",
+        "/workspace/codes/ComfyUI/models/diffusers"
       ]
     },
     {
@@ -229,10 +230,10 @@
         993,
         673
       ],
-      "size": [
-        315,
-        294.00000953674316
-      ],
+      "size": {
+        "0": 315,
+        "1": 294
+      },
       "flags": {},
       "order": 3,
       "mode": 0,