feat(Nodes): Return mask from GenerateVITMatte

nodes.py

@@ -51,6 +51,7 @@ class SAMLoader:
             }
         }
 
+    RELOAD_INST = True
     RETURN_TYPES = ("SAM_MODEL",)
     FUNCTION = "load_model"
 
@@ -86,6 +87,7 @@ class InitSAMPredictor:
             }
         }
 
+    RELOAD_INST = True
     RETURN_TYPES = ("SAM_PREDICTOR",)
     FUNCTION = "init_predictor"
 
@@ -107,6 +109,7 @@ class LoadDINOModel:
             }
         }
 
+    RELOAD_INST = True
     RETURN_TYPES = ("DINO_MODEL",)
     FUNCTION = "load_model"
 
@@ -174,6 +177,7 @@ class DinoBoxes:
             }
         }
 
+    RELOAD_INST = True
     RETURN_TYPES = ("IMAGE", "DINO_BOXES")
     FUNCTION = "get_boxes"
 
@@ -213,6 +217,7 @@ class SAMMaskFromBoxes:
             }
         }
 
+    RELOAD_INST = True
     RETURN_TYPES = ("MASK",)
     FUNCTION = "get_mask"
 
@@ -250,6 +255,7 @@ class MaskToTrimap:
             }
         }
 
+    RELOAD_INST = True
     RETURN_TYPES = ("TRIMAP","MASK")
     FUNCTION = "get_trimap"
 
@@ -301,6 +307,7 @@ class LoadVITMatteModel:
             }
         }
 
+    RELOAD_INST = True
     RETURN_TYPES = ("VIT_MATTE_MODEL",)
     FUNCTION = "load_model"
 
@@ -322,7 +329,8 @@ class GenerateVITMatte:
             }
         }
 
-    RETURN_TYPES = ("IMAGE",)
+    RELOAD_INST = True
+    RETURN_TYPES = ("IMAGE", "MASK")
     FUNCTION = "generate_matte"
 
     CATEGORY = "Matte Anything"
@@ -336,11 +344,9 @@ class GenerateVITMatte:
             # "trimap": torch.from_numpy(trimap).unsqueeze(0).unsqueeze(0),
         }
         alpha = vit_matte_model(input)['phas'].flatten(0, 2)
-        alpha = alpha.detach().cpu().numpy()
+        alpha_np = alpha.detach().cpu().numpy()
         # Converts alpha matte to RGBA image using the image tensor
         image = cv2.cvtColor(image_in, cv2.COLOR_BGR2RGBA)
-        alpha = np.clip(255. * alpha, 0, 255).astype(np.uint8)
-        image[:, :, 3] = alpha
-        image = Image.fromarray(image)
-        image = pil2tensor(image)
-        return (image,)
+        alpha_np = np.clip(255. * alpha_np, 0, 255).astype(np.uint8)
+        image[:, :, 3] = alpha_np
+        return pil2tensor(Image.fromarray(image)), alpha