[Apply Mask][Added] Option to process images one by one

README.md

@@ -144,6 +144,7 @@ Currently we just have a few nodes used by other nodes I maintain.
   - `blur_size_two` (`INT`): Diameter for the fine gaussian blur
   - `fill_color` (`BOOLEAN`): When enabled the removed image is replaced by a color, the output is an RGB image. Otherwise the removed part becomes transparent and the output is an RGBA image.
   - `color` (`STRING`): A string representing a color to be used when `fill_color` is enabled. Can be an hexadecimal RGB (i.e. `#AABBCC`) or comma separated RGB components. The components can be in the [0-255] or [0-1.0] range.
+  - `batched` (`BOOLEAN`): Process all the images at once. Otherwise do it one at a time.
 - **Output:**
   - `image` (`IMAGE`): The image after applying the mask.
   - `mask` (`MASK`): The input mask

src/nodes/nodes_img.py

@@ -412,8 +412,17 @@ class ApplyMaskAFFCE:
                 "masks": ("MASK",),
                 "blur_size": BLUR_SIZE_OPT,
                 "blur_size_two": BLUR_SIZE_TWO_OPT,
-                "fill_color": ("BOOLEAN", {"default": False}),
+                "fill_color": ("BOOLEAN", {
+                    "default": False,
+                    "tooltip": ("Fill the background using a color.\n"
+                                "Returns an RGB image, otherwise an RGBA.")
+                }),
                 "color": COLOR_OPT,
+                "batched":  ("BOOLEAN", {
+                    "default": True,
+                    "tooltip": ("Process the images at once.\n"
+                                "Faster, needs more memory")
+                }),
             }
         }
 
@@ -427,7 +436,7 @@ class ApplyMaskAFFCE:
     UNIQUE_NAME = "SET_ApplyMaskAFFCE"
     DISPLAY_NAME = "Apply Mask using AFFCE"
 
-    def get_foreground(self, images, masks, blur_size=91, blur_size_two=7, fill_color=False, color=None):
+    def get_foreground(self, images, masks, blur_size=91, blur_size_two=7, fill_color=False, color=None, batched=True):
         out_images = apply_mask(logger, images, masks, model_management.get_torch_device(), blur_size, blur_size_two,
-                                fill_color, color)
+                                fill_color, color, batched)
         return out_images.cpu(), masks.cpu()