optimize blockify mask node

nodes/mask_nodes.py

@@ -6,6 +6,7 @@ import scipy.ndimage
 import numpy as np
 from contextlib import nullcontext
 import os
+from tqdm import tqdm
 
 from comfy import model_management
 from comfy.utils import ProgressBar
@@ -17,7 +18,7 @@ import folder_paths
 from ..utility.utility import tensor2pil, pil2tensor
 
 script_directory = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-device = model_management.get_torch_device()
+main_device = model_management.get_torch_device()
 offload_device = model_management.unet_offload_device()
 
 class BatchCLIPSeg:
@@ -1013,7 +1014,7 @@ class GrowMaskWithBlur:
         previous_output = None
         current_expand = expand
         for m in growmask:
-            output = m.unsqueeze(0).unsqueeze(0).to(device)  # Add batch and channel dims for kornia
+            output = m.unsqueeze(0).unsqueeze(0).to(main_device)  # Add batch and channel dims for kornia
             if abs(round(current_expand)) > 0:
                 # Create kernel - kornia expects kernel on same device as input
                 if tapered_corners:
@@ -1586,8 +1587,11 @@ class BlockifyMask:
         return {"required": {
                     "masks": ("MASK",),
                     "block_size": ("INT", {"default": 32, "min": 8, "max": 512, "step": 1, "tooltip": "Size of blocks in pixels (smaller = smaller blocks)"}),
+                },
+                "optional": {
+                    "device": (["cpu", "gpu"], {"default": "cpu", "tooltip": "Device to use for processing"}),
                 }
-                }
+        }
 
     RETURN_TYPES = ("MASK", )
     RETURN_NAMES = ("mask",)
@@ -1595,49 +1599,70 @@ class BlockifyMask:
     CATEGORY = "KJNodes/masking"
     DESCRIPTION = "Creates a block mask by dividing the bounding box of each mask into blocks of the specified size and filling in blocks that contain any part of the original mask."
 
-    def process(self, masks, block_size):
+    def process(self, masks, block_size, device="cpu"):
-        batch_size = masks.shape[0]
+        processing_device = main_device if device == "gpu" else torch.device("cpu")
-        result_masks = []
+        print("mask.dtype:", masks.dtype, "device:", processing_device)
         
-        for i in range(batch_size):
+        masks = masks.to(processing_device)
+        batch_size, height, width = masks.shape
+        
+        result_masks = torch.zeros_like(masks)
+        
+        for i in tqdm(range(batch_size), desc="BlockifyMask batch"):
             mask = masks[i]
             
-            # Find bounding box using tensor operations
+            # Find bounding box efficiently
-            nonzero_coords = torch.nonzero(mask, as_tuple=True)
+            mask_bool = mask > 0
-            if len(nonzero_coords[0]) == 0:  # Empty mask
+            if not mask_bool.any():
-                result_masks.append(mask)
                 continue
                 
-            y_coords, x_coords = nonzero_coords
+            y_indices = torch.nonzero(mask_bool.any(dim=1), as_tuple=True)[0]
-            y_min, y_max = y_coords.min(), y_coords.max()
+            x_indices = torch.nonzero(mask_bool.any(dim=0), as_tuple=True)[0]
-            x_min, x_max = x_coords.min(), x_coords.max()
+            
+            if len(y_indices) == 0 or len(x_indices) == 0:
+                continue
+                
+            y_min, y_max = y_indices[0], y_indices[-1]
+            x_min, x_max = x_indices[0], x_indices[-1]
             
             bbox_width = x_max - x_min + 1
             bbox_height = y_max - y_min + 1
             
-            # Calculate number of blocks that fit
+            # Calculate block grid
             w_divisions = max(1, bbox_width // block_size)
             h_divisions = max(1, bbox_height // block_size)
             
-            # Calculate actual block sizes (might be slightly larger than block_size)
             w_slice = bbox_width // w_divisions
             h_slice = bbox_height // h_divisions
             
-            # Create output mask (copy of input)
+            # Create coordinate grids only for bbox region
-            output_mask = mask.clone()
+            y_coords = torch.arange(y_min, y_max + 1, device=processing_device).view(-1, 1)
+            x_coords = torch.arange(x_min, x_max + 1, device=processing_device).view(1, -1)
             
-            # Process grid cells
+            # Calculate block indices for bbox region
-            for w_start in range(x_min, x_max + 1, w_slice):
+            w_block_indices = (x_coords - x_min) // w_slice
-                w_end = min(w_start + w_slice, x_max + 1)
+            h_block_indices = (y_coords - y_min) // h_slice
-                for h_start in range(y_min, y_max + 1, h_slice):
-                    h_end = min(h_start + h_slice, y_max + 1)
-                    
-                    # Check if this cell contains any mask content
-                    cell_region = mask[h_start:h_end, w_start:w_end]
-                    if cell_region.sum() > 0:
-                        # Fill the entire cell
-                        output_mask[h_start:h_end, w_start:w_end] = 1.0
             
-            result_masks.append(output_mask)
+            # Clamp to valid range
+            w_block_indices = w_block_indices.clamp(0, w_divisions - 1)
+            h_block_indices = h_block_indices.clamp(0, h_divisions - 1)
+            
+            # Create unique block IDs by combining h and w indices
+            block_ids = h_block_indices * w_divisions + w_block_indices
+            
+            # Get mask region within bbox
+            mask_region = mask[y_min:y_max+1, x_min:x_max+1]
+            
+            # Find which blocks have content using scatter_add
+            max_blocks = h_divisions * w_divisions
+            block_content = torch.zeros(max_blocks, device=processing_device)
+            block_content.scatter_add_(0, block_ids.flatten(), mask_region.flatten())
+            
+            # Create result for blocks that have content
+            has_content = block_content > 0
+            block_mask = has_content[block_ids]
+            
+            # Fill the result
+            result_masks[i, y_min:y_max+1, x_min:x_max+1] = block_mask.float()
         
-        return torch.stack(result_masks, dim=0),
+        return (result_masks.clamp(0, 1),)