diff --git a/maxedoutnodes.py b/maxedoutnodes.py
index ef496dc..c8352cd 100644
--- a/maxedoutnodes.py
+++ b/maxedoutnodes.py
@@ -1,8 +1,9 @@
 from __future__ import annotations
 import torch, math, comfy, os, folder_paths, node_helpers, comfy.model_management, comfy.utils, json, hashlib, re
+import torch.nn.functional as F
 from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict
 import numpy as np
-from PIL import Image, ImageOps, ImageSequence
+from PIL import Image, ImageOps, ImageSequence, ImageFilter
 from comfy_api.latest import io
 
 ########################################################################################################################
@@ -774,6 +775,91 @@ class LatentHalfMasks:
     
 ########################################################################################################################
 
+# Grow Blur Mask MXD (single-image friendly)
+class GrowBlurMaskMXD:
+    DESCRIPTION = """Expand or contract a mask and blur only the expanded ring (core stays solid)."""
+    TITLE = "Grow Blur Mask MXD"
+    CATEGORY = "MXD/Mask"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mask": ("MASK",),
+                "grow_blur": ("INT", {"default": 0, "min": -64, "max": 64, "step": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "run"
+
+    def _normalize_mask(self, mask: torch.Tensor) -> torch.Tensor:
+        if not isinstance(mask, torch.Tensor):
+            raise ValueError("GrowBlurMaskMXD: mask must be a torch.Tensor.")
+
+        if mask.dim() == 2:
+            mask = mask.unsqueeze(0)
+        elif mask.dim() == 4 and mask.shape[-1] == 1:
+            mask = mask.squeeze(-1)
+
+        if mask.dim() != 3:
+            raise ValueError("GrowBlurMaskMXD: mask must have shape (H,W) or (B,H,W).")
+
+        return mask.float().clamp(0.0, 1.0)
+
+    def _dilate(self, mask: torch.Tensor, radius: int) -> torch.Tensor:
+        if radius <= 0:
+            return mask
+        x = mask.unsqueeze(1)
+        k = 2 * radius + 1
+        y = F.max_pool2d(x, kernel_size=k, stride=1, padding=radius)
+        return y.squeeze(1)
+
+    def _erode(self, mask: torch.Tensor, radius: int) -> torch.Tensor:
+        if radius <= 0:
+            return mask
+        x = mask.unsqueeze(1)
+        k = 2 * radius + 1
+        y = 1.0 - F.max_pool2d(1.0 - x, kernel_size=k, stride=1, padding=radius)
+        return y.squeeze(1)
+
+    def _blur_ring(self, ring: torch.Tensor, radius: int, device: torch.device) -> torch.Tensor:
+        if radius <= 0:
+            return ring
+        ring_cpu = ring.detach().cpu().numpy()
+        blurred = []
+        for i in range(ring_cpu.shape[0]):
+            arr = (ring_cpu[i] * 255.0).astype(np.uint8)
+            pil = Image.fromarray(arr, mode="L")
+            pil = pil.filter(ImageFilter.GaussianBlur(radius))
+            out = np.array(pil).astype(np.float32) / 255.0
+            blurred.append(torch.from_numpy(out))
+        blurred_t = torch.stack(blurred, dim=0).to(device)
+        return blurred_t.clamp(0.0, 1.0)
+
+    def run(self, mask, grow_blur):
+        mask = self._normalize_mask(mask)
+        device = mask.device
+
+        if grow_blur == 0:
+            return (mask,)
+
+        radius = abs(int(grow_blur))
+
+        if grow_blur < 0:
+            eroded = self._erode(mask, radius)
+            return (eroded.clamp(0.0, 1.0),)
+
+        core = mask
+        expanded = self._dilate(mask, radius)
+        ring = (expanded - core).clamp(0.0, 1.0)
+        blurred_ring = self._blur_ring(ring, radius, device)
+        blurred_ring = (blurred_ring * expanded).clamp(0.0, 1.0)
+        out = (core + blurred_ring).clamp(0.0, 1.0)
+        return (out,)
+    
+########################################################################################################################
+
 # Get Latent Size
 class GetLatentSizeMXD:
     DESCRIPTION = """Get image width/height from a latent."""
@@ -1327,6 +1413,7 @@ NODE_CLASS_MAPPINGS = {
     "FluxResolutionMatcher": FluxResolutionMatcher,
     "SDXLResolutionMatcher": SDXLResolutionMatcher,
     "LatentHalfMasks": LatentHalfMasks,
+    "Grow Blur Mask MXD": GrowBlurMaskMXD,
     "Get Latent Size": GetLatentSizeMXD,
     "Place Image By Mask": PlaceImageByMask,
     "Crop Image By Mask": CropImageByMask,
@@ -1350,6 +1437,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "FluxResolutionMatcher": "Flux Resolution Matcher MXD",
     "SDXLResolutionMatcher": "SDXL Resolution Matcher MXD",
     "LatentHalfMasks": "Latent to L/R Masks MXD",
+    "Grow Blur Mask MXD": "Grow Blur Mask MXD",
     "Get Latent Size": "Get Latent Size MXD",
     "Place Image By Mask": "Place Image by Mask MXD",
     "Crop Image By Mask": "Crop Image by Mask MXD",
diff --git a/pyproject.toml b/pyproject.toml
index 4e94b36..a820929 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
-name = "maxedout"
+name = "MaxedOut"
 description = "Custom ComfyUI nodes used in Maxed Out workflows (SDXL, Flux, Wan 2.2, etc.)"
-version = "1.6.7"
+version = "1.6.8"
 license = {file = "LICENSE"} 
 # classifiers = [
 #     # For OS-independent nodes (works on all operating systems)