update RemBgUltra and PixelSpread nodes

README.MD

@@ -575,6 +575,7 @@ Node options:
 * detail_range: Edge detail range.
 * black_point: Edge black sampling threshold.
 * white_point: Edge white sampling threshold.
+* process_detail: Set to false here will skip edge processing to save runtime.
 
 ### <a id="table1">PixelSpread</a>
 Pixel expansion preprocessing on the masked edge of an image can effectively improve the edges of image synthesis.
@@ -583,6 +584,7 @@ Pixel expansion preprocessing on the masked edge of an image can effectively imp
 Node options:  
 ![image](image/pixel_spread_node.png)
 * invert_mask: Whether to reverse the mask.
+* mask_grow: Mask expansion amplitude.
 
 
 

README_CN.MD

@@ -561,6 +561,7 @@
 * detail_range: 边缘细节范围。
 * black_point: 边缘黑色采样阈值。
 * white_point: 边缘黑色采样阈值。
+* process_detail: 此处设为False将跳过边缘处理以节省运行时间。
 
 ### <a id="table1">PixelSpread</a>
 对图像的遮罩边缘部分进行像素扩张预处理，可有效改善图像合成的边缘。
@@ -569,6 +570,7 @@
 节点选项说明:  
 ![image](image/pixel_spread_node.png)
 * invert_mask: 是否反转遮罩。
+* mask_grow: 遮罩扩张幅度。
 
 
 ### <a id="table1">MaskGrow</a>

py/pixel_spread.py

@@ -14,6 +14,7 @@ class PixelSpread:
             "required": {
                 "image": ("IMAGE", ),  #
                 "invert_mask": ("BOOLEAN", {"default": False}),  # 反转mask
+                "mask_grow": ("INT", {"default": 0, "min": -999, "max": 999, "step": 1}),
             },
             "optional": {
                 "mask": ("MASK",),  #
@@ -26,15 +27,19 @@ class PixelSpread:
     CATEGORY = '😺dzNodes/LayerMask'
     OUTPUT_NODE = True
 
-    def pixel_spread(self, image, invert_mask, mask=None):
+    def pixel_spread(self, image, invert_mask, mask_grow, mask=None):
         _image = tensor2pil(image)
         if _image.mode == 'RGBA':
             _mask = _image.split()[-1]
+            if mask_grow != 0:
+                _mask = expand_mask(image2mask(_mask), mask_grow, 0)  # 扩张，模糊
         else:
             _mask = Image.new('L', _image.size, 'white')
         if mask is not None:
             if invert_mask:
                 mask = 1 - mask
+            if mask_grow != 0:
+                _mask = expand_mask(mask, mask_grow, 0)  # 扩张，模糊
             _mask = mask2image(mask).convert('L')
         image = pil2tensor(_image.convert('RGB'))
         _mask = _mask.convert('RGB')

py/rembg_ultra.py

@@ -34,6 +34,7 @@ class RemBgUltra:
                 "detail_range": ("INT", {"default": 8, "min": 0, "max": 256, "step": 1}),
                 "black_point": ("FLOAT", {"default": 0.01, "min": 0.01, "max": 0.98, "step": 0.01}),
                 "white_point": ("FLOAT", {"default": 0.99, "min": 0.02, "max": 0.99, "step": 0.01}),
+                "process_detail": ("BOOLEAN", {"default": True}),
             },
             "optional": {
             }
@@ -44,7 +45,7 @@ class RemBgUltra:
     FUNCTION = "rembg_ultra"
     CATEGORY = '😺dzNodes/LayerMask'
   
-    def rembg_ultra(self, image, detail_range, black_point, white_point):
+    def rembg_ultra(self, image, detail_range, black_point, white_point, process_detail):
 
         rmbgmodel = load_model()
         orig_image = tensor2pil(image).convert('RGB')
@@ -64,19 +65,20 @@ class RemBgUltra:
         result = (result-mi)/(ma-mi)
         im_array = (result*255).cpu().data.numpy().astype(np.uint8)
         _mask = Image.fromarray(np.squeeze(im_array)).convert('L')
-        # ultra edge process
-        d = detail_range * 2 + 1
-        i_dup = copy.deepcopy(image.cpu().numpy().astype(np.float64))
-        a_dup = copy.deepcopy(pil2tensor(_mask.convert('RGB')).cpu().numpy().astype(np.float64))
-        for index, img in enumerate(i_dup):
-            trimap = a_dup[index][:,:,0] # convert to single channel
-            if detail_range > 0:
-                trimap = cv2.GaussianBlur(trimap, (d, d), 0)
-            trimap = fix_trimap(trimap, black_point, white_point)
-            alpha = estimate_alpha_cf(img, trimap, laplacian_kwargs={"epsilon": 1e-6},
-                                      cg_kwargs={"maxiter": 500})
-            a_dup[index] = np.stack([alpha, alpha, alpha], axis=-1)  # convert back to rgb
-        _mask = tensor2pil(torch.from_numpy(a_dup.astype(np.float32))) # alpha
+        if process_detail:
+            # ultra edge process
+            d = detail_range * 2 + 1
+            i_dup = copy.deepcopy(image.cpu().numpy().astype(np.float64))
+            a_dup = copy.deepcopy(pil2tensor(_mask.convert('RGB')).cpu().numpy().astype(np.float64))
+            for index, img in enumerate(i_dup):
+                trimap = a_dup[index][:,:,0] # convert to single channel
+                if detail_range > 0:
+                    trimap = cv2.GaussianBlur(trimap, (d, d), 0)
+                trimap = fix_trimap(trimap, black_point, white_point)
+                alpha = estimate_alpha_cf(img, trimap, laplacian_kwargs={"epsilon": 1e-6},
+                                          cg_kwargs={"maxiter": 500})
+                a_dup[index] = np.stack([alpha, alpha, alpha], axis=-1)  # convert back to rgb
+            _mask = tensor2pil(torch.from_numpy(a_dup.astype(np.float32))) # alpha
         ret_image = RGB2RGBA(orig_image, _mask.convert('L'))
         return (pil2tensor(ret_image), image2mask(_mask),)
 

workflow/pixel_spread_example.json

@@ -9,10 +9,10 @@
         490,
         660
       ],
-      "size": {
-        "0": 330.9508972167969,
-        "1": 298.0813903808594
-      },
+      "size": [
+        330.9508972167969,
+        314
+      ],
       "flags": {},
       "order": 0,
       "mode": 0,
@@ -51,7 +51,7 @@
       ],
       "size": {
         "0": 315,
-        "1": 126
+        "1": 150
       },
       "flags": {},
       "order": 1,
@@ -87,34 +87,10 @@
       "widgets_values": [
         10,
         0.01,
-        0.99
+        0.99,
+        true
       ]
     },
-    {
-      "id": 41,
-      "type": "LayerMask: MaskPreview",
-      "pos": [
-        1247,
-        802
-      ],
-      "size": {
-        "0": 309.01806640625,
-        "1": 246
-      },
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "mask",
-          "type": "MASK",
-          "link": 75
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "LayerMask: MaskPreview"
-      }
-    },
     {
       "id": 42,
       "type": "PreviewImage",
@@ -149,7 +125,7 @@
       ],
       "size": {
         "0": 315,
-        "1": 78
+        "1": 102
       },
       "flags": {},
       "order": 3,
@@ -181,8 +157,34 @@
         "Node name for S&R": "LayerMask: PixelSpread"
       },
       "widgets_values": [
-        false
+        false,
+        0
       ]
+    },
+    {
+      "id": 41,
+      "type": "LayerMask: MaskPreview",
+      "pos": [
+        1244,
+        827
+      ],
+      "size": {
+        "0": 309.01806640625,
+        "1": 246
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 75
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LayerMask: MaskPreview"
+      }
     }
   ],
   "links": [

workflow/rembg_ultra_example.json

@@ -452,7 +452,7 @@
       ],
       "size": {
         "0": 315,
-        "1": 78
+        "1": 102
       },
       "flags": {},
       "order": 7,
@@ -484,19 +484,20 @@
         "Node name for S&R": "LayerMask: PixelSpread"
       },
       "widgets_values": [
-        false
+        false,
+        0
       ]
     },
     {
       "id": 29,
       "type": "LayerMask: RemBgUltra",
       "pos": [
-        1124,
-        1484
+        1126,
+        1519
       ],
       "size": {
         "0": 315,
-        "1": 126
+        "1": 150
       },
       "flags": {},
       "order": 4,
@@ -533,7 +534,8 @@
       "widgets_values": [
         10,
         0.01,
-        0.99
+        0.99,
+        true
       ]
     }
   ],