Remake ColorToMask node

that code was atrocious

nodes.py

@@ -1000,35 +1000,41 @@ Converts chosen RGB value to a mask
                  "green": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
                  "blue": ("INT", {"default": 0,"min": 0, "max": 255, "step": 1}),
                  "threshold": ("INT", {"default": 10,"min": 0, "max": 255, "step": 1}),
+                 "per_batch": ("INT", {"default": 16, "min": 1, "max": 4096, "step": 1}),
         },
     } 
 
-    def clip(self, images, red, green, blue, threshold, invert):
-        color = np.array([red, green, blue])
-        images = 255. * images.cpu().numpy()
-        images = np.clip(images, 0, 255).astype(np.uint8)
-        images = [Image.fromarray(image) for image in images]
-        images = [np.array(image) for image in images]
+    def clip(self, images, red, green, blue, threshold, invert, per_batch):
 
-        black = [0, 0, 0]
-        white = [255, 255, 255]
+        color = torch.tensor([red, green, blue], dtype=torch.uint8)  
+        black = torch.tensor([0, 0, 0], dtype=torch.uint8)
+        white = torch.tensor([255, 255, 255], dtype=torch.uint8)
+        
         if invert:
-             black, white = white, black
+            black, white = white, black
 
-        new_images = []
-        for image in images:
-            new_image = np.full_like(image, black)
+        steps = images.shape[0]
+        pbar = comfy.utils.ProgressBar(steps)
+        tensors_out = []
+        
+        for start_idx in range(0, images.shape[0], per_batch):
 
-            color_distances = np.linalg.norm(image - color, axis=-1)
-            complement_indexes = color_distances <= threshold
+            # Calculate color distances
+            color_distances = torch.norm(images[start_idx:start_idx+per_batch] * 255 - color, dim=-1)
+            
+            # Create a mask based on the threshold
+            mask = color_distances <= threshold
+            
+            # Apply the mask to create new images
+            mask_out = torch.where(mask.unsqueeze(-1), white, black).float()
+            mask_out = mask_out.mean(dim=-1)
 
-            new_image[complement_indexes] = white
-
-            new_images.append(new_image)
-
-        new_images = np.array(new_images).astype(np.float32) / 255.0
-        new_images = torch.from_numpy(new_images).permute(3, 0, 1, 2)
-        return new_images
+            tensors_out.append(mask_out.cpu())
+            batch_count = mask_out.shape[0]
+            pbar.update(batch_count)
+       
+        tensors_out = torch.cat(tensors_out, dim=0)
+        return tensors_out,
       
 class ConditioningMultiCombine:
     @classmethod
@@ -5058,7 +5064,7 @@ Each mask is generated with the specified width and height.
             mask = torch.ones((height, width), dtype=torch.float32) * value
             masks.append(mask)
         masks_out = torch.stack(masks, dim=0)
-        print(masks_out.shape)
+    
         return(masks_out,)